End-user Computing: Concepts, Methodologies, Tools and Applications

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End-User Computing: Concepts, Methodologies, Tools, and Applications Steve Clarke University of Hull, UK

Volume I

Information Science reference Hershey • New York

Acquisitions Editor: Development Editor: Senior Managing Editor: Managing Editor: Typesetter: Cover Design: Printed at:

Kristin Klinger Kristin Roth Jennifer Neidig Sara Reed Michael Brehm, Jeff Ash, Carole Coulson, Elizabeth Duke, Sara Reed, Jamie Snavely, Sean Woznicki Lisa Tosheff Yurchak Printing Inc.

Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Suite 200 Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com/reference and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanonline.com Library of Congress Cataloging-in-Publication Data Library of Congress Cataloging-in-Publication Data End-user computing : concepts, methodologies, tools, and applications / Steve Clarke, editor. p. cm. Summary: "This collection compiles the most authoritative research in this area, . It provides libraries with definitive studies covering all of the salient issues of the field, it gives researchers, managers, and other professionals the knowledge and tools they need to properly understand the role of end-user computing in the modern organization"--Provided by publisher. Includes bibliographical references and index. ISBN-13: 978-1-59904-945-8 (hardcover) ISBN-13: 978-1-59904-946-5 (e-book) 1. End-user computing. I. Clarke, Steve, 1950QA76.9.E53E44 2008 004.01'9--dc22 2007041257 Copyright © 2008 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library.

Editor-in-Chief

Mehdi Khosrow-Pour, DBA Editor-in-Chief Contemporary Research in Information Science and Technology, Book Series

Associate Editors Steve Clarke University of Hull, UK Murray E. Jennex San Diego State University, USA Annie Becker Florida Institute of Technology USA Ari-Veikko Anttiroiko University of Tampere, Finland

Editorial Advisory Board Sherif Kamel American University in Cairo, Egypt In Lee Western Illinois University, USA Jerzy Kisielnicki Warsaw University, Poland Keng Siau University of Nebraska-Lincoln, USA Amar Gupta Arizona University, USA Craig van Slyke University of Central Florida, USA John Wang Montclair State University, USA Vishanth Weerakkody Brunel University, UK

Additional Research Collections found in the “Contemporary Research in Information Science and Technology” Book Series Data Mining and Warehousing: Concepts, Methodologies, Tools, and Applications John Wang, Montclair University, USA • 6-volume set • ISBN 978-1-59904-951-9 Electronic Commerce: Concepts, Methodologies, Tools, and Applications S. Ann Becker, Florida Institute of Technology, USA • 4-volume set • ISBN 978-1-59904-943-4 Electronic Government: Concepts, Methodologies, Tools, and Applications Ari-Veikko Anttiroiko, University of Tampere, Finland • 6-volume set • ISBN 978-1-59904-947-2 End-User Computing: Concepts, Methodologies, Tools, and Applications Steve Clarke, University of Hull, UK • 4-volume set • ISBN 978-1-59904-945-8 Global Information Technologies: Concepts, Methodologies, Tools, and Applications Felix Tan, Auckland University of Technology, New Zealand • 6-volume set • ISBN 978-1-59904-939-7 Information Communication Technologies: Concepts, Methodologies, Tools, and Applications Craig Van Slyke, University of Central Florida, USA • 6-volume set • ISBN 978-1-59904-949-6 Information Security and Ethics: Concepts, Methodologies, Tools, and Applications Hamid Nemati, The University of North Carolina at Greensboro, USA • 6-volume set • ISBN 978-1-59904-937-3 Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications Vijayan Sugumaran, Oakland University, USA • 4-volume set • ISBN 978-1-59904-941-0 Knowledge Management: Concepts, Methodologies, Tools, and Applications Murray E. Jennex, San Diego State University, USA • 6-volume set • ISBN 978-1-59904-933-5 Multimedia Technologies: Concepts, Methodologies, Tools, and Applications Syad Mahbubur Rahman, Minnesota State University, USA • 3-volume set • ISBN 978-1-59904-953-3 Online and Distance Learning: Concepts, Methodologies, Tools, and Applications Lawrence Tomei, Robert Morris University, USA • 6-volume set • ISBN 978-1-59904-935-9 Virtual Technologies: Concepts, Methodologies, Tools, and Applications Jerzy Kisielnicki, Warsaw University, Poland • 3-volume set • ISBN 978-1-59904-955-7

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List of Contributors

Aasheim, Cheryl / Georgia Southern University, USA.................................................................... 1132 Abdel-Wahab, Ahmed Gad / University of Mansoura, Egypt........................................................ 1886 Adam, Alison / University of Salford, UK........................................................................................ 1152 Adams, Carlisle / University of Ottawa, Canada............................................................................. 1167 Ågerfalk, Pär J. / University of Limerick, Örebro University, Ireland, Sweden.............................. 2252 Agnew, Palmer W. / State University of New York at Binghamton, USA............................................. 53 Alcock, Carole / University of South Australia, Australia................................................................ 1022 Alexander, Ian F. / Scenario Plus, London, UK................................................................................. 317 Almeida, Hyggo / Federal University of Campina Grande, Brazil.................................................. 1006 Armani, Jacopo / Università della Svizzera italiana, Switzerland..................................................... 678 Aroyo, Lora / Eindhoven University of Technology, The Netherlands............................................. 1449 Åsand, Hege-René Hansen / University of Oslo, Norway............................................................... 1793 Avery, Gayle C. / Maquarie University, Australia............................................................................ 1864 Aytes, Kregg / Idaho State University, USA...................................................................................... 1690 Bajgoric, Nijaz / Sarajevo University, Bosnia-Herzegovina............................................................ 2401 Baker, Ellen / University of Technology, Australia........................................................................... 1864 Baloh, Peter / University of Ljubljana, Slovenia.............................................................................. 2157 Barbieri, Katerine / University of Ottawa, Canada......................................................................... 1167 Barker, Sandra / University of South Australia, Australia......................................................... 374, 382 Barkhi, Reza / Virginia Polytechnic Institute & State University, USA........................................... 1427 Barrett, Michael / University of Cambridge, UK............................................................................. 2200 Basford, Jay / The University of Queensland, Australia.................................................................. 2096 Beckwith, Laura / Oregon State University, USA................................................................................ 19 Bekkering, Ernst / Mississippi State University, USA......................................................................... 81 Bhattacherjee, Anol / University of South Florida, USA................................................................. 2124 Boswell, Katherine / Middle Tennessee State University, USA........................................................ 1427 Botsis, Taxiarchis / Athens Medical School, Greece........................................................................ 1282 Botturi, Luca / Università della Svizzera italiana, Switzerland......................................................... 678 Boudreau, Marie-Claude / The University of Georgia, USA............................................................ 947 Bowen, Paul L. / The University of Queensland, Australia.............................................................. 2096 Braun, Gerald / Xavier University, USA.......................................................................................... 1552 Breda, Ana M. / University of Aveiro, Portugal................................................................................. 562 Brescia, William / University of Arkansas, USA.............................................................................. 1346 Broer, Wim / Police Education and Knowledge Center, The Netherlands....................................... 1938 Brown, Bradley R. / University of Waterloo, Canada..................................................................... 1375

Browne, Glenn J. / Texas Tech University, USA............................................................................... 1595 Brusa, Jorge / Texas A&M International University, USA............................................................... 1531 Burgess, Lois / University of Wollongong, Australia........................................................................ 1022 Burnett, Margaret / Oregon State University, USA............................................................................. 19 Burns, Beryl / University of Salford, UK.......................................................................................... 1330 Butler, Tom / University College Cork, Ireland................................................................................ 2178 Bychkov, Yury / University of Victoria, Canada................................................................................ 658 Cappleman, Sam / Hewlett-Packard Ltd, UK.................................................................................. 2200 Cartelli, Antonio / University of Cassino, Italy.................................................................................... 71 Castillo, Gladys / University of Aveiro, Portugal............................................................................... 562 Caulkins, Jonathan P. / Carnegie Mellon University Heinz School of Public Policy and Management & Qatar Campus, USA............................................................................................ 856 Chalmers, Patricia A. / Air Force Research Laboratory, USA........................................................ 1734 Chan, Gloria WW / City University of Hong Kong, Hong Kong.................................................... 2038 Chatterjea, Kalyani / Nanyang Technological University, Singapore............................................... 594 Chen, Charlie / Appalachian State University, USA.............................................................. 1763, 1922 Chen, Jason C.H. / Gonzaga University, USA..................................................................................... 35 Cheung, Christy MK / City University of Hong Kong, Hong Kong................................................ 2038 Chiang, Kuan-Pin / Long Island University, USA............................................................................. 509 Choi, Sejung Marina / University of Texas at Austin, USA..................................................... 509, 2056 Clark, Carol / Middle Tennessee State University, USA................................................................... 2274 Cline, Tammy / University of Arkansas, USA................................................................................... 1346 Coakes, Elayne / University of Westminster, UK.................................................................................. 78 Compeau, Deborah / University of Western Ontario, Canada........................................................ 1469 Connolly, Terry / University of Arizona, USA.................................................................................. 1690 Cook, David P. / Old Dominion University, USA............................................................................. 1749 Cooper, Joan / Flinders University, Australia.................................................................................. 1022 Costabile, M. F. / Universita di Bari, Italy....................................................................................... 1823 Crawford, John / University of Technology, Australia.................................................................... 1864 Cristea, A. / Eindhoven University of Technology, The Netherlands................................................ 1489 Cummings, Elizabeth / University of Tasmania, Australia............................................................. 1053 Dahlem, David / University of Victoria, Canada................................................................................ 658 Darroch, Fiona / University of Southern Queensland, Australia..................................................... 1291 Davis, Kimberly / Mississippi State University, USA........................................................................... 81 Dawson, David B. / University of West Florida, USA........................................................................ 451 Day, John / Ohio University, USA....................................................................................................... 897 De Bra, Paul / Eindhoven University of Technology, The Netherlands............................................ 1449 de Laat, Maarten / University of Nijmegen and Utrecht University, The Netherlands................................................................................................................................. 1938 de Oliveira, José Palazzo M. / Instituto de Informática, Brazil........................................................ 544 de Souza Dias, Donaldo / Federal University of Rio de Janeiro, Brazil.......................................... 1014 de Vaujany, François-Xavier / Jean Monnet Université, France...................................................... 745 Dholakia, Ruby Roy / University of Rhode Island, USA................................................................... 509 Dicheva, Darina / Winston-Salem State University, USA................................................................. 1449 Dittrich, Yvonne / IT-University of Copenhagen, Denmark............................................................... 623 Doyle, D. John / Cleveland Clinic Foundation, USA......................................................................... 509

Drennan, Judy / University of Technology, Australia............................................................................ 1 Duggan, Evan W. / University of Alabama, USA............................................................................. 1304 Economides, Anastasios A. / University of Macedonia, Greece...................................................... 1265 Edenius, Mats / Stockholm School of Economics, Sweden............................................................... 1118 Ehlers, Cindy / University at Buffalo, USA.......................................................................................... 27 Emurian, Henry H. / University of Maryland, Baltimore County, USA............................................ 771 Eriksson, Jeanette / Blekinge Institute of Technology, Sweden.......................................................... 623 Esfandiari, Babak / Carleton University, Canada................................................................... 809, 1717 Faiola, Anthony / Indiana University-Perdue University, Indianapolis, USA................................. 1718 Fan, Jing Ping / Brunel University, UK............................................................................................ 1392 Farmer, Rod / The University of Melbourne, Australia........................................................................... Ferneley, Elaine H. / University of Salford, UK................................................................................. 309 Fogli, D. / Università di Brescia, Italy.............................................................................................. 1823 Fowler, Jeremy J. / La Trobe University, Australia............................................................................ 195 Frasson, Claude / University of Montreal, Canada......................................................................... 2376 Fuangvut, Tharitpong / Dhurakij Pundit University, Thailand........................................................ 611 Gama, João / University of Porto, Portugal....................................................................................... 562 Garland, Virginia E. / The University of New Hampshire, USA..................................................... 1040 Gauss, Boris / Center of Human-Machine-Systems, Technische Universität, Berlin....................... 1508 George, Susan E. / University of South Australia, Australia............................................................ 2299 Gogoulou, Agoritsa / University of Athens, Greece........................................................................... 525 Goh, John / Monash University, Australia........................................................................................... 52 Goldkuhl, Göran / Linköping University and Jönköping International Business School, Sweden................................................................................................................... 2252 Goldsmith, Ronald E. / Florida State University, USA..................................................................... 141 Gogoulou, Agoritsa / University of Athens, Greece........................................................................... 525 Gouli, Evangelia / University of Athens, Greece................................................................................ 525 Green, Gina / Baylor University, USA................................................................................................ 897 Grigoriadou, Maria / University of Athens, Greece.................................................................. 525, 579 Gruba, Paul / The University of Melbourne, Australia.................................................................... 1392 Guthrie, Ruth A. / California State Polytechnic University, Pomona, USA.................................... 1523 Hallin, Anette / Royal Institute of Technology (KTH), Sweden........................................................ 2341 Hasan, Bassam / The University of Toledo, USA...................................................................... 840, 1074 Hasan, Helen / University of Wollongong, Australia.......................................................................... 611 Hazari, Sunil / University of West Georgia, USA............................................................................... 108 Heavin, Ciara / University College Cork, Ireland............................................................................ 2178 Heilman, George E. / Winston-Salem State University, USA........................................................... 1537 Higgins, Chris / University of Western Ontario, Canada................................................................. 1469 Hoic-Bozic, Natasa / University of Rijeka, Croatia............................................................................ 699 Holt, Robert W. / Gonzaga University, USA........................................................................................ 35 Hong, Ji-Young / University of Texas at Austin, USA...................................................................... 1607 Hong, Soongoo / Dong-A University, Korea....................................................................................... 488 Horan, Pat / La Trobe University, Australia....................................................................................... 195 Hornik, Steven / University of Central Florida, USA...................................................................... 1247 Houben, Geert-Jan / Eindhoven University of Technology, The Netherlands................................. 1449 Hwang, Gwo-Jen / National University of Tainan, Taiwan............................................................. 1901 Iossifides, Athanassios C. / COSMOTE S.A., Greece...................................................................... 2305

Jacobson, Michael J. / Nanyang Technological University, Singapore........................................... 2356 Jahnke, Jens / University of Victoria, Canada................................................................................... 658 James, Tabitha / Virginia Polytechnic Institute & State University, USA........................................ 1427 Jaspers, M.W.M. / University of Amsterdam, The Netherlands......................................................... 438 Jawahar, I. M. / Illinois State University, USA................................................................................. 2266 Jennex, Murray E. / San Diego State University, USA...................................................................... 296 Johnson, Richard D. / University of South Florida, USA................................................................ 1247 Jones, Mary C. / University of North Texas, USA............................................................................ 1948 Jones, Nory B. / University of Maine, USA........................................................................................ 929 Jørgensen, Håvard D. / Computas, Norway...................................................................................... 715 Joseph, Jimmie L. / The University of Texas at El Paso, USA......................................................... 1749 Joshi, Somya / National Technical University of Athens, Greece..................................................... 2200 Kaltenbach, Marc / University of Montreal, Canada...................................................................... 2376 Kanellis, Panagiotis / Athens University of Economics and Business, Greece.................................. 637 Katerattanakul, Pairin / Western Michigan University, USA............................................................ 488 Kaupins, Gundars / Boise State University, USA............................................................................ 1968 Kawasme, Luay / University of Victoria, Canada.............................................................................. 658 Kellerman, Anne S. / State University of New York at Binghamton, USA........................................... 43 King, Kathleen P. / Fordham University, USA................................................................................. 1915 Kiyokawa, Kiyoshi / Osaka University, Japan.................................................................................. 270 Klisc, Chris / Murdoch University, Australia..................................................................................... 789 Knight, Michael B. / Appalachian State University, USA................................................................ 2284 Kochtanek, Thomas R. / University of Missouri, USA...................................................................... 929 Korba, Larry / National Research Council Canada, Canada......................................................... 1193 Kotzé, Paula / University of South Africa, South Africa..................................................................... 289 Král, Jaroslav / Charles University, Czech Republic......................................................................... 462 Krogstie, John / SINTEF and NTNU, USA......................................................................................... 715 Lanzilotti, R. / Universita di Bari, Italy........................................................................................... 1823 Larsen, Tor J. / Norwegian School of Management, Norway.......................................................... 2011 Lawson-Body, Assion / University of North Dakota.......................................................................... 986 Lau, Adela / The Hong Kong Polytechnic University, Hong Kong.................................................. 1812 Lau, Linda K. / Longwood University, USA....................................................................................... 189 Lauria, Eitel J. M. / Marist College, USA....................................................................................... 1579 Layne Morrison, Erica / IBM Global Services, USA........................................................................ 856 Lee, Chean / Methodscience.com, Australia..................................................................................... 1566 Lee, Wei-Na / University of Texas at Austin, USA.................................................................. 1607, 2056 Lee, Sang M. / University of Nebraska - Lincoln, USA.............................................................. 389, 488 Lepouras, George / University of Peloponnese, Greece.................................................................... 731 Li, Dahui / University of Minnesota Duluth, USA............................................................................ 1595 Li, Zhao / Nanyang Technological University, Singapore.................................................................. 211 Light, Ben / University of Salford, UK............................................................................................. 1330 Lim, Ee Peng / Nanyang Technological University, Singapore.......................................................... 211 Limayem, Moez / HEC Lausanne, Lausanne University of Switzerland, Switzerland.................... 2038 Liu, Liping / University of Akron, USA.................................................................................. 1088, 1101 Liu, Zehua / Nanyang Technological University, Singapore.............................................................. 211 Logan, Joseph / AstraZeneca Pharmaceuticals, USA...................................................................... 1843

Lou, Hao / Ohio University, USA........................................................................................................ 897 Louvros, Spiros / COSMOTE S.A., Greece...................................................................................... 2308 Lowyck, Joost / K.U.Leuven, Belgium................................................................................................ 397 Lundevall, Kristina / The City of Stockholm, Sweden..................................................................... 2341 Ma, Qingxiong / Central Missouri State University, USA...................................................... 1088, 1101 Macredie, Robert D. / Brunel University, UK................................................................................. 1778 Magagula, Cisco M. / University of Swaziland, Swaziland.............................................................. 2394 Magoulas, George D. / University of London, UK............................................................................. 180 Mao, Ji-Ye / City University of Hong Kong, Hong Kong................................................................. 1375 Markellou, Penelope / University of Patras, Greece....................................................................... 1543 Masterson, Michael J. / United States Air Force, USA..................................................................... 905 Matei, Sorin Adam / Purdue University, USA.................................................................................. 1717 McCloskey, Donna Weaver / Widener University, USA.................................................................. 1620 McGill, Tanya / Murdoch University, Australia................................................................................. 789 Minch, Robert / Boise State University, USA................................................................................... 1968 Mitchell, Stella / IBM T. J. Watson Research, USA............................................................................ 996 Monday, Ann / University of South Australia, Australia.................................................................... 374 Mørch, Anders I. / University of Oslo, Norway............................................................................... 1793 Mornar, Vedran / University of Zagreb, Croatia............................................................................... 699 Mullany, Michael J. / Northland Polytechnic, New Zealand........................................................... 1742 Mussio, P. / Università di Milano, Italy............................................................................................ 1823 Mutch, Alistair / Nottingham Trent University, UK......................................................................... 2217 Mykytyn, Peter P. / Southern Illinois University, USA.................................................................... 1322 Ng, Wee Keong / Nanyang Technological University, Singapore...................................................... 211 Northrup, Pam T. / University of West Florida, USA........................................................................ 451 Nuredini, Jasmina / Monash University, Australia.......................................................................... 2229 O’Donovan, Finbarr / University College Cork, Ireland................................................................ 2178 Olfman, Lorne / Claremont Graduate University, USA................................................................... 1922 Oliveira, Loreno / Federal University of Campina Grande, Brazil................................................. 1006 Pagani, Margherita / I-Lab Centre for Research on the Digital Economy, Bocconi University, Italy.................................................................................................................... 2329 Panko, Raymond R. / University of Hawai`i, USA............................................................................ 875 Papanikolaou, Kyparisia A. / University of Athens, Greece..................................................... 525, 579 Paper, David / Utah State University, USA......................................................................................... 965 Paramythis, Alexandros / Institute of Computer Science, Foundation for Research and Technology - Hellas, Greece......................................................................................... 148 Paul, Ray J. / Brunel University, UK.................................................................................................. 637 Pavlovski, Christopher J. / IBM Corporation, Australia.................................................................. 996 Pearson, J. Michael / Southern Illinois University at Carbondale, USA......................................... 2287 Pearson, Will / Sheffield Hallam University, UK................................................................................ 419 Peffers, Ken / University of Nevada, USA.......................................................................................... 427 Perkusich, Angelo / Federal University of Campina Grande, Brazil.............................................. 1006 Piccinno, A. / Universita di Bari, Italy.............................................................................................. 1823 Pirim, Taner / Mississippi Center for Supercomputing Research, USA........................................... 1427 Pollach, Irene / Vienna University of Economics and Business Administration, Austria................. 1667 Polovina, Simon / Sheffield Hallam University, UK........................................................................... 419

Previte, Josephine / The University of Queensland, Australia............................................................... 1 Price, R. Leon / University of Oklahoma, USA................................................................................ 1948 Prybutok, Victor / University of North Texas, USA......................................................................... 1652 Quaddus, Mohammed / Curtin University of Technology, Australia................................................ 351 Rainer, Jr., R. Kelly / Auburn University, USA.................................................................................. 905 Ramos, Mila / International Rice Research Institute, Philippines..................................................... 404 Rasmussen, Karen L. / University of West Florida, USA.................................................................. 451 Razek, Mohammed A. / El-Azhar University, Cairo, Egypt............................................................ 2376 Reithel, Brian / University of Mississippi, USA............................................................................... 1427 Reynolds, Rodney A. / Azusa Pacific University, USA (on leave from Pepperdine University)........................................................................................................................ 894 Rigou, Maria / University of Patras, Greece.................................................................................... 1543 Rohde, Fiona H. / The University of Queensland, Australia............................................................ 2096 Rotrold, Glenda / University of North Dakota, USA......................................................................... 986 Rotrold, Justin / Techwise Solutions, LLC, USA................................................................................ 986 Roupas, Chrysostomos / University of Macedonia, Greece............................................................ 1265 Ryan, Damian / South Eastern Sydney and Illawarra Area Health Service (SESIAHS), Australia......................................................................................................................... 1022 Ryan, Terry / Claremont Graduate University, USA........................................................................ 1922 Sanford, Clive C. / Lviv Polytechnic National University, Ukraine................................................. 2124 Santos, Antonio / Universidad de las Americas-Puebla, Mexico............................................................. Sargent, Jason / University of Wollongong, Australia..................................................................... 1022 Sarkis, Joseph / Clark University, USA............................................................................................ 1420 Schipani, Danilo / Valdani Vicari & Associati, Italy........................................................................ 2329 Seligman, Larry / The University of Georgia, USA........................................................................... 947 Sena, Mark P. / Xavier University, USA........................................................................................... 1552 Serenko, Alexander / Lakehead University, Canada............................................................... 918, 2029 Shanks, Graeme / Monash University, Australia............................................................................. 2229 Shata, Osama / Specializing Engineering Office, Egypt.................................................................... 125 Shaw, R. S. / Tamkang University, Taiwan........................................................................................ 1763 Shayo, Conrad / California State University, San Bernardino, USA............................................... 1523 Sherman, Cherie Ann / Ramapo College of New Jersey, USA.......................................................... 477 Signoret, Françoise Dushinka Brailovsky / Instituto Tecnológico Autónomo de México, Mexico............................................................................................................................. 2090 Simon, Ed / XMLsec Inc., Canada.................................................................................................... 1220 Simon, Steven John / Mercer University, USA.................................................................................. 965 Singh, Shawren / University of South Africa, South Africa................................................................ 289 Sirmakessis, Spiros / University of Patras, Greece.......................................................................... 1543 Song, Ronggong / National Research Council Canada, Canada..................................................... 1193 Sørebø, Øystein / Buskerud University College, Norway................................................................ 2011 Sorte, Shraddha / Oregon State University, USA............................................................................... 192 Souto, Maria Aparecida M. / Instituto de Informática, Brazil.......................................................... 544 Specht, Marcus / Fraunhofer FIT-ICON, Denmark........................................................................... 254 Spedding, Paul / University of Salford, UK...................................................................................... 1152 Spitler, Valerie K. / University of North Florida, USA.................................................................... 1986 Stahl, Bernd Carsten / De Montfort University, UK....................................................................... 2140

Stephanidis, Constantine / Institute of Computer Science, Foundation for Research and Technology - Hellas, Greece, & University of Crete, Greece....................................................... 148 Stewart, Craig / University of Nottingham, Jubilee Campus, UK................................................... 1489 Still, Brian / Texas Tech University, USA............................................................................................ 239 Sugianto, Ly Fie / Monash University, Australia............................................................................... 823 Sullivan, Gillian / Griffith University, Australia..................................................................................... 1 Sun, D. Bruce / California State University at Long Beach, USA........................................................ 35 Sun, Heshan / Syracuse University, USA.......................................................................................... 1065 Sundarraj, R.P. / University of Waterloo, USA................................................................................ 1420 Syrigos, Konstantinos / Athens Medical School, Greece................................................................. 1282 Taniar, David / Monash University, Australia...................................................................................... 52 Thachenkary, Cherian S. / Georgia State University, USA............................................................. 1304 Thompson, Ron / Wake Forest University, USA............................................................................... 1469 Tojib, Dewi Rooslani / Monash University, Australia........................................................................ 823 Toleman, Mark / University of Southern Queensland, Australia..................................................... 1291 Truman, Gregory E. / Babson College, USA................................................................................... 2073 Tsai, Wen-Ling / Chung Hua University, Taiwan............................................................................. 1901 Tseng, Judy C.R. / Chung Hua University, Taiwan.......................................................................... 1901 Tsui, Eric / The Hong Kong Polytechnic University, Hong Kong.................................................... 1812 Turel, Ofir / Lakehead University, Canada....................................................................................... 2029 Turner, Paul / University of Tasmania, Australia............................................................................. 1053 Tuunanen, Tuure / Helsinki School Economics, Finland................................................................... 427 Tysick, Cynthia / University at Buffalo, USA....................................................................................... 27 Urbas, Leon / Center of Human-Machine-Systems, Technische Universität, Berlin........................ 1508 Utsi, Steven / K.U.Leuven, Belgium.................................................................................................... 397 Van Slyke, Craig / University of Central Florida, USA..................................................................... 897 Vassilakis, Costa / University of Peloponnese, Greece...................................................................... 731 Verdin, Regina / PPGIE/UFRGS, Brazil............................................................................................ 544 Wakefield, Robin L. / Hankamer School of Business, Baylor University, USA............................... 1637 Walsham, Geoff / University of Cambridge, UK.............................................................................. 2200 Wang, Ye Diana / University of Maryland, Baltimore County, USA.................................................. 771 Warkentin, Merrill / Mississippi State University, USA...................................................................... 81 Warren, M. J. / Deakin University, Australia................................................................................... 1708 Weber, Ron / Monash University, Australia..................................................................................... 2229 Weibelzahl, Stephan / Fraunhofer IESE, Germany........................................................................... 168 Weidemann, Timothy / Fairweather Consulting, USA...................................................................... 856 Weiss, Michael / Carleton University, Canada................................................................................... 809 Westin, Stu / University of Rhode Island, USA................................................................................... 509 Wetherbe, James C. / Texas Tech University, USA.......................................................................... 1595 White, Nancy / Full Circle Associates, USA........................................................................................ 98 Whitten, Dwayne / Mays School of Business, Texas A&M University, USA................................... 1637 Williams, Susan Rebstock / Georgia Southern University, USA..................................................... 1132 Willis, Robert / Lakehead University, Canada................................................................................. 2029 Wu, Po-Han / National University of Tainan, Taiwan...................................................................... 1901 Wu, Yu / University of Central Florida, USA................................................................................... 1247 Xiao, Xue / Syracuse University, USA.............................................................................................. 1065

Xu, Jun / Southern Cross University, Australia.................................................................................. 351 Yee, George / National Research Council Canada, Canada............................................................ 1193 Young, Randall / University of North Texas, USA............................................................................ 1652 Yu, Calvin / The Hong Kong Polytechnic University, Hong Kong................................................... 1812 Žemlicka, Michal / Charles University, Czech Republic................................................................... 462 Zhang, Lixuan / College of Charleston, USA.................................................................................. 1652

Contents by Volume

Volume I Section 1. Fundamental Concepts and Theories This section serves as a foundation for this exhaustive reference tool by addressing crucial theories essential to the understanding of end-user computing. Chapters found within these pages provide an excellent framework in which to position end-user computing within the field of information science and technology. Insight regarding the critical incorporation of end-user computing and security and ethics is addressed, while crucial stumbling blocks of end-user development are explored. With over 20 chapters comprising this foundational section, the reader can learn and chose from a compendium of expert research on the elemental theories underscoring the end-user computing discipline. Chapter 1.1. Privacy, Risk Perception, and Expert Online Behavior: An Exploratory Study of Household End Users / Judy Drennan, Gillian Sullivan Mort, and Josephine Previte................................................................................................................................... 1 Chapter 1.2. Gender and End-User Computing / Laura Beckwith, Margaret Burnett, and Shraddha Sorte............................................................................................................................. 19 Chapter 1.3. Gender and the Internet User / Cynthia Tysick and Cindy Ehlers................................... 27 Chapter 1.4. Organization and Management Issues in End User Computing / Jason C.H. Chen, Robert W. Holt, and D. Bruce Sun.......................................................................... 35 Chapter 1.5. Fundamentals of Multimedia / Palmer W. Agnew and Anne S. Kellerman..................... 43 Chapter 1.6. Mobile User Data Mining and its Applications / John Goh and David Taniar ............. 52

Chapter 1.7. ICT, CoLs, CoPs, and Virtual Communities / Antonio Cartelli...................................... 71 Chapter 1.8. A Comparison of the Features of Some CoP Software / Elayne Coakes........................ 78 Chapter 1.9. Introducing the Check-Off Password System (COPS): An Advancement in User Authentication Methods and Information Security / Merrill Warkentin, Kimberly Davis, and Ernst Bekkering................................................................................................. 81 Chapter 1.10. Click Connect and Coalesce for NGOs: Exploring the Intersection Between Online Networks, CoPs, and Events / Nancy White............................................................. 98 Chapter 1.11. Perceptions of End-Users on the Requirements in Personal Firewall Software: An Exploratory Study / Sunil Hazari................................................................................ 108 Chapter 1.12. E-Services Privacy: Needs, Approaches, Challenges, Models, and Dimensions / Osama Shata................................................................................................................ 125 Chapter 1.13. Online Consumer Behavior / Ronald E. Goldsmith.................................................... 141 Chapter 1.14. A Generic Adaptation Framework for Web-Based Hypermedia Systems / Alexandros Paramythis and Constantine Stephanidis....................................................................... 148 Chapter 1.15. Problems and Pitfalls in the Evaluation of Adaptive Systems / Stephan Weibelzahl............................................................................................................................ 168 Chapter 1.16. User Modeling in Information Portals / George D. Magoulas.................................... 180 Chapter 1.17. A Successful ERP Implementation Plan: Issues and Challenges / Linda K. Lau...................................................................................................................................... 189 Chapter 1.18. Are Information Systems’ Success and Failure Factors Related? An Exploratory Study / Jeremy J. Fowler and Pat Horan................................................................ 195 Chapter 1.19. Web Information Extraction via Web Views / Wee Keong Ng, Zehua Liu, Zhao Li, and Ee Peng Lim................................................................................................................. 211 Chapter 1.20. An Open Source Primer / Brian Still........................................................................... 239 Chapter 1.21. Contextualized Learning: Supporting Learning in Context / Marcus Specht.................................................................................................................................... 254 Chapter 1.22. An Introduction to Head Mounted Displays for Augmented Reality / Kiyoshi Kiyokawa.............................................................................................................................. 270

Section 2. Development and Design Methodologies This section provides in-depth coverage of conceptual architecture frameworks to provide the reader with a comprehensive understanding of the emerging technological developments within the field of end-user computing. Research fundamentals imperative to the understanding of developmental processes within end-user computing are offered. From broad examinations to specific discussions on end-user tools, the research found within this section spans the discipline while offering detailed, specific discussions. From basic designs to abstract development, these chapters serve to expand the reaches of development and design technologies within the user community. This section includes over 35 contributions from researchers throughout the world on the topic of end-user computing. Chapter 2.1. Development Methodologies and Users / Shawren Singh and Paula Kotzé........................................................................................................................................ 289 Chapter 2.2. End-User System Development: Lessons from a Case Study of IT Usage in an Engineering Organization / Murray E. Jennex......................................................................... 296 Chapter 2.3. Covert End User Development: A Study of Success / Elaine H. Ferneley............................................................................................................................. 309 Chapter 2.4. A Taxonomy of Stakeholders: Human Roles in System Development / Ian F. Alexander................................................................................................................................. 317 Chapter 2.5. Exploring the Factors Influencing End Users’ Acceptance of Knowledge Management Systems: Development of a Research Model of Adoption and Continued Use / Jun Xu and Mohammed Quaddus.................................................................... 351 Chapter 2.6. Developing Graduate Qualities Through Information Systems and Information Technology Literacy Skills / Ann Monday and Sandra Barker..................................... 374 Chapter 2.7. Business Graduates as End-User Developers: Understanding Information Literacy Skills Required / Sandra Barker.......................................................................................... 382 Chapter 2.8. Information Literacy for Telecenter Users in Low-Income Regional Mexican Communities / Antonio Santos............................................................................................ 389 Chapter 2.9. Digital Literacy and the Position of the End-User / Steven Utsi and Joost Lowyck...................................................................................................................................... 397 Chapter 2.10. Sharing Digital Knowledge with End-Users: Case Study of the International Rise Research Institute Library and Documentation Service in the Philippines / Mila Ramos........................................................................................................................................ 404 Chapter 2.11. Communication + Dynamic Interface = Better User Experience / Simon Polovina and Will Pearson..................................................................................................... 419

Chapter 2.12. Meeting the Demands of Wide Audience End Users / Ken Peffers and Tuure Tuunanen.................................................................................................................................. 427 Chapter 2.13. The Think Aloud Method and User Interface Design / M.W.M. Jaspers . ............................................................................................................................... 438 Chapter 2.14. Ergonomic User Interface Design in Computerized Medical Equipment / D. John Doyle.................................................................................................................................... 445 Chapter 2.15. Designing and Reusing Learning Objects to Streamline WBI Development / Pam T. Northrup, Karen L. Rasmussen, and David B. Dawson........................................................ 451 Chapter 2.16. Architecture, Specification, and Design of Service-Oriented Systems / Jaroslav Král and Michal Žemlička................................................................................................... 462 Chapter 2.17. Web Systems Design, Litigation, and Online Consumer Behavior / Cherie Ann Sherman.......................................................................................................................... 477 Chapter 2.18. Framework for User Perception of Effective E-Tail Web Sites / Sang M. Lee, Pairin Katerattanakul, and Soongoo Hong................................................................. 488 Chapter 2.19. E-Search: A Conceptual Framework of Online Consumer Behavior / Kuan-Pin Chiang, Ruby Roy Dholakia, and Stu Westin.................................................................... 509 Chapter 2.20. An Adaptive Feedback Framework to Support Reflection, Guiding, and Tutoring / Evangelia Gouli, Agoritsa Gogoulou, Kyparisia A. Papanikolaou, and Maria Grigoriadou............................................................................................................................ 525 Chapter 2.21. Modeling Learner’s Cognitive Abilities in the Context of a Web-Based Learning Environment / Maria Aparecida M. Souto, Regina Verdin, and José Palazzo M. de Oliveira.............................................................................................................. 544 Chapter 2.22. An Adaptive Predictive Model for Student Modeling / Gladys Castillo, João Gama, and Ana M. Breda.......................................................................................................... 562 Chapter 2.23. Building an Instructional Framework to Support Learner Control in Adaptive Educational Systems / Kyparisia A. Papanikolaou and Maria Grigoriadou.................... 579 Volume II Chapter 2.24. Asynchronous Learning Using a Hybrid Learning Package: A Teacher Development Strategy in Geography / Kalyani Chatterjea............................................................... 594 Chapter 2.25. Accommodating End-Users’ Online Activities with a Campus Portal / Tharitpong Fuangvut and Helen Hasan............................................................................................ 611

Chapter 2.26. Combining Tailoring and Evolutionary Software Development for Rapidly Changing Business Systems / Jeanette Eriksson and Yvonne Dittrich................................ 623 Chapter 2.27. User Behaving Badly: Phenomena and Paradoxes from an Investigation into Information Systems Misfit / Panagiotis Kanellis and Ray J. Paul .......................................... 637 Chapter 2.28. Semantic Composition of Web Portal Components / Jens Jahnke, Yury Bychkov, David Dahlem, and Luay Kawasme........................................................................... 658 Chapter 2.29. Bridging the Gap with MAID: A Method for Adaptive Instructional Design / Jacopo Armani and Luca Botturi....................................................................................................... 678 Chapter 2.30. Authoring of Adaptive Hypermedia Courseware Using AHyCO System / Natasa Hoic-Bozic and Vedran Mornar............................................................................................ 699 Chapter 2.31. Interactive Models for Virtual Enterprises / Håvard D. Jørgensen and John Krogstie..................................................................................................................................... 715 Chapter 2.32. Adaptive Virtual Reality Museums on the Web / George Lepouras and Costas Vassilakis . ............................................................................................................................. 731 Chapter 2.33. Modeling Sociotechnical Change in IS with a Quantitative Longitudinal Approach: The PPR Method / François-Xavier de Vaujany.............................................................. 745 Chapter 2.34. Trust in E-Commerce: Consideration of Interface Design Factors / Ye Diana Wang and Henry H. Emurian ............................................................................................ 771 Chapter 2.35. End-User Perceptions of the Benefits and Risks of End-User Web Development / Tanya McGill and Chris Klisc................................................................................... 789 Chapter 2.36. Modeling Method for Assessing Privacy Technologies / Michael Weiss and Babak Esfandiari............................................................................................................................... 809 Chapter 2.37. The Development and Empirical Validation of the B2E Portal User Satisfaction (B2EPUS) Scale / Dewi Rooslani Tojib and Ly Fie Sugianto........................................ 823 Chapter 2.38. Effectiveness of Computer Training: The Role of Multilevel Computer Self Efficacy / Bassam Hasan . ......................................................................................................... 840 Chapter 2.39. Spreadsheet Errors and Decision Making: Evidence from Field Interviews / Jonathan P. Caulkins, Erica Layne Morrison, and Timothy Weidemann ......................................... 856 Chapter 2.40. Two Experiments in Reducing Overconfidence in Spreadsheet Development / Raymond R. Panko.................................................................................................... 875

Section 3. Tools and Technologies This section presents an extensive coverage of various tools and technologies available in the field of end-user computing that practitioners and academicians alike can utilize to develop different techniques. These chapters enlighten readers about fundamental research on the many methods used to facilitate and enhance the integration of this worldwide phenomenon by exploring end-user trust and automatically generated program code—an increasingly pertinent research arena. It is through these rigorously researched chapters that the reader is provided with countless examples of the up-and-coming tools and technologies emerging from the field of end-user computing. With more than 35 chapters, this section offers a broad treatment of some of the many tools and technologies within the end user community. Chapter 3.1. Measurment of End-User Computing Satisfaction / Rodney A. Reynolds.................... 894 Chapter 3.2. User Perceptions and Groupware Use / Gina Green, John Day, Hao Lou, and Craig Van Slyke........................................................................................................................... 897 Chapter 3.3. A Multitrait-Multimethod Analysis of the End User Computing Satisfaction and Computer Self-Efficacy Instruments / Michael J. Masterson and R. Kelly Rainer, Jr................ 905 Chapter 3.4. Importance of Interface Agent Characteristics from End-User Perspective / Alexander Serenko............................................................................................................................. 918 Chapter 3.5. Success Factors in the Implementation of a Collaborative Technology, and Resulting Productivity Improvements in a Small Business: An Exploratory Study / Nory B. Jones and Thomas R. Kochtanek . ....................................................................................... 929 Chapter 3.6. Quality of Use of a Complex Technology: A Learning-Based Model / Marie-Claude Boudreau and Larry Seligman................................................................................... 947 Chapter 3.7. User Acceptance of Voice Recognition Technology: An Empirical Extension of the Technology Acceptance Model / Steven John Simon and David Paper................. 965 Chapter 3.8. The Decision Making Process of Integrating Wireless Technology into Organizations / Assion Lawson-Body, Justin Rotvold, and Glenda Rotvold..................................... 986 Chapter 3.9. Mobility and Multimodal User Interfaces / Christopher J. Pavlovski and Stella Mitchell.................................................................................................................................... 996 Chapter 3.10. Mobile Users in Smart Spaces / Loreno Oliveira, Hyggo Almeida, and Angelo Perkusich............................................................................................................................. 1006 Chapter 3.11. Motivation for Using Microcomputers / Donaldo de Souza Dias............................. 1014 Chapter 3.12. PDAs as Mobile-Based Health Information Deployment Platforms for Ambulatory Care: Clinician-Centric End-User Considerations / Jason Sargent, Carole Alcock, Lois Burgess, Joan Cooper, and Damian Ryan....................................................... 1022

Chapter 3.13. Digital Literacy and the Use of Wireless Portable Computers, Planners, and Cell Phones for K-12 Education / Virginia E. Garland............................................................ 1040 Chapter 3.14. Considerations for Deploying Web and Mobile Technologies to Support the Building of Patient Self-Efficacy and Self-Management of Chronic Illness / Elizabeth Cummings and Paul Turner............................................................................................. 1055 Chapter 3.15. User Acceptance of Virtual Technologies / Heshan Sun and Xue Xiao.................... 1065 Chapter 3.16. Examining the Effects of Computer Self-Efficacy and System Complexity on Technology Acceptance / Bassam Hasan................................................................................... 1074 Chapter 3.17. The Technology Acceptance Model: A Meta-Analysis of Empirical Findings / Qingxiong Ma and Liping Liu........................................................................................ 1088 Chapter 3.18. The Role of Internet Self-Efficacy in the Acceptance of Web-Based Electronic Medical Records / Qingxiong Ma and Liping Liu.......................................................... 1101 Chapter 3.19. The Function of Representation in a “Smart Home Context” / Mats Edenius.................................................................................................................................... 1118 Chapter 3.20. Information Technology in the Practice of Law Enforcement / Susan Rebstock Williams and Cheryl Aasheim................................................................................ 1132 Chapter 3.21. Trusting Computers Through Trusting Humans: Software Verification in a Saftey-Critical Information System / Alison Adam and Paul Spedding................................... 1152 Chapter 3.22. Privacy Enforcement in E-Services Environments / Carlisle Adams and Katerine Barbieri............................................................................................................................. 1167 Volume III Chapter 3.23. Privacy Management Architecture for E-Services / Larry Korba, Ronggong Song, and George Yee..................................................................................................... 1193 Chapter 3.24. Protecting Privacy Using XML, XACML, and SAML / Ed Simon.......................... 1220 Chapter 3.25. When Technology Does Not Support Learning: Conflicts Between Epistemological Beliefs and Technology Support in Virtual Learning Environments / Steven Hornik, Richard D. Johnson, and Yu Wu.............................................................................. 1247 Chapter 3.26. Evaluation of Computer Adaptive Testing Systems / Anastasios A. Economides and Chrysostomos Roupas.................................................................... 1265

Chapter 3.27. Implementation of a Computerized System in an Oncology Unit / Taxiarchis Botsis and Konstantinos Syrigos.................................................................................... 1282 Section 4. Utilization and Application This section discusses a variety of applications and opportunities available that can be considered by practitioners in developing viable and effective end-user programs and processes. This section includes more than 20 chapters, some of which examine the implementation of two learning management systems (LMS) in a university environment. Further chapters propose an integrative model. Also considered in this section is the role that application development plays in the eventual success of the application for the user. Contributions included in this section provide excellent coverage of today’s user community and how research into these technologies is impacting the social fabric of our present-day global village. Chapter 4.1. Lessons in Implementing a Learning System in a University: The Academic User Perspective / Fiona Darroch and Mark Toleman.................................................................... 1291 Chapter 4.2. Supporting the JAD Facilitator with the Nominal Group Technique / Evan W. Duggan and Cherian S. Thachenkary .............................................................................. 1304 Chapter 4.3. Educating Our Students in Computer Application Concepts: A Case for Problem-Based Learning / Peter P. Mykytyn................................................................................... 1322 Chapter 4.4. Users as Developers: A Field Study of Call Centre Knowledge Work / Beryl Burns and Ben Light............................................................................................................... 1330 Chapter 4.5. Online Calculator Training in Mathematics and Technology / William Brescia and Tammy Cline................................................................................................... 1346 Chapter 4.6. The Effectiveness of Online Task Support vs. Instructor-Led Training / Ji-Ye Mao and Bradley R. Brown..................................................................................................... 1375 Chapter 4.7. Understanding the Nature of Task Analysis in Web Design / Rod Farmer and Paul Gruba............................................................................................................................... 1392 Chapter 4.8. Implementation Management of an E-Commerce-Enabled Enterprise Information System / Joseph Sarkis and R.P. Sundarraj................................................................. 1420 Chapter 4.9. Determining the Intention to Use Biometric Devices: An Application and Extension of the Technology Acceptance Model / Tabitha James, Taner Pirim, Katherine Boswell, Brian Reithel, and Reza Barkhi........................................................................ 1427 Chapter 4.10. Adaptation Engineering in Adaptive Concept-Based System / Geert-Jan Houben, Lora Aroyo, Paul De Bra, and Darina Dicheva.............................................. 1449

Chapter 4.11. Intentions to Use Information Technologies: An Integrative Model / Ron Thompson, Deborah Compeau, and Chris Higgins................................................................. 1469 Chapter 4.12. Authoring of Adaptive Hypermedia / A. Cristea and Craig Stewart........................ 1489 Chapter 4.13. Adaptable Navigation in a SCORM Compliant Learning Module / Boris Gauss and Leon Urbas........................................................................................................... 1508 Chapter 4.14. End-User Computing Success Measurement / Conrad Shayo and Ruth A. Guthrie................................................................................................................................ 1523 Chapter 4.15. Validating the End-User Computing Satisfaction Survey Instrument in Mexico / George E. Heilman and Jorge Brusa........................................................................... 1531 Section 5. Organizational and Social Implications This section includes a wide range of research pertaining to the social and organizational impact of end-user computing around the world. Chapters introducing this section present the overall consumer purchase decision cycle and investigates the issues that affect Web users, from selecting a specific eshop to the delivery of the product and the overall assessment of the shopping experience. Additional chapters included in this section present a case study following the activities of super users and local developers during the adoption of a new business application by an accounting firm. Also investigating a concern within the field of end-user computing is research exploring cognitive problem-solving style and its impact on user resistance, based on the premise that the greater the cognitive difference between users and developers, the greater the user resistance is likely to be. With over 25 chapters, the discussions presented in this section offer research into the integration of end-user computing, as well as implementation of user considerations for all organizations. Chapter 5.1. A Closer Look to the Online Consumer Behavior / Penelope Markellou, Maria Rigou, and Spiros Sirmakessis.............................................................................................. 1543 Chapter 5.2. An Examination of Consumer Behavior on eBay Motors / Mark P. Sena and Gerald Braun................................................................................................................................... 1552 Chapter 5.3. Mobile CRM: Reaching, Acquiring, and Retaining Mobility Consumers / Chean Lee........................................................................................................................................ 1566 Chapter 5.4. Exploring the Behavioral Dimension of Client/Server Technology Implementation: An Empirical Investigation / Eitel J. M. Lauría................................................... 1579 Chapter 5.5. Online Consumers’ Switching Behavior: A Buyer-Seller Relationship Perspective / Dahui Li, Glenn J. Browne, and James C. Wetherbe................................................. 1595

Chapter 5.6. Consumer Complaint Behavior in the Online Environment / Ji-Young Hong and Wei-Na Lee....................................................................................................... 1607 Chapter 5.7. The Importance of Ease of Use, Usefulness, and Trust to Online Consumers: An Examination of the Technology Acceptance Model with Older Customers / Donna Weaver McCloskey............................................................................................................... 1620 Chapter 5.8. Examining User Perceptions of Third Party Organizations Credibility and Trust in an E-Retailer / Robin L. Wakefield and Dwayne Whitten............................................ 1637 Chapter 5.9. Inhibitors of Two Illegal Behaviors: Hacking and Shoplifting / Lixuan Zhang, Randall Young, and Victor Prybutok....................................................................... 1652 Chapter 5.10. Privacy Statements as a Means of Uncertainty Reduction in WWW Interactions / Irene Pollach . ........................................................................................................... 1667 Chapter 5.11. Computer Security and Risky Computing Practices: A Rational Choice Perspective / Kregg Aytes and Terry Connolly.................................................................... 1690 Chapter 5.12. Hackers and Cyber Terrorists / M. J. Warren............................................................ 1708 Chapter 5.13. Cultural Cognitive Style and the Web: Toward a Theory and Practice of Web Design for International Users / Anthony Faiola and Sorin Adam Matei............................... 1717 Chapter 5.14. The Effect of Usability Guidelines on Web Site User Emotions / Patricia A. Chalmers....................................................................................................................... 1734 Chapter 5.15. Relating Cognitive Problem-Solving Style to User Resistance / Michael J. Mullany.......................................................................................................................... 1742 Chapter 5.16. Information Imbalance in Medical Decision Making: Upsetting the Balance / Jimmie L. Joseph and David P. Cook......................................................................... 1749 Chapter 5.17. Online Synchronous vs. Asynchronous Software Training Through the Behavioral Modeling Approach: A Longitudinal Field Experiment / Charlie C. Chen and R. S. Shaw...................................................................................................... 1763 Chapter 5.18. Gender Differences and Hypermedia Navigation: Principles for Adaptive Hypermedia Learning Systems / Jing Ping Fan and Robert D. Macredie...................... 1778 Chapter 5.19. Super Users and Local Developers: The Organization of End-User Development in an Accounting Company / Hege-René Hansen Åsand and Anders I. Mørch............................................................................................................................... 1793

Volume IV Chapter 5.20. A Two-Tier Approach to Elicit Enterprise Portal User Requirements / Eric Tsui, Calvin Yu, and Adela Lau................................................................................................ 1812 Section 6. Managerial Impact This section presents contemporary coverage of the social implications of end-user computing, more specifically related to the corporate and managerial utilization of information sharing technologies and applications, and how these technologies can be facilitated within organizations. Core ideas such as training and continuing education of human resources in modern organizations are discussed through these chapters. Issues such as how a certain methodology is applied to a project for the development of an interactive system in the medical domain. Equally as crucial, chapters within this section explore the attitude of information workers toward the concept of telecommuting and to examine the relationships between such attitude and workers’ expectations of their productivity and job satisfaction. Also in this section is an investigation of the theoretical proposition that personal IT innovativeness will positively impact the use of novel computer technologies. The research model used in this chapter includes the individual traits of age, gender, experience with IT, and educational level. Chapter 6.1. Supporting Work Practice Through End-User Development Environments / M. F. Costabile, D. Fogli, R. Lanzilotti, P. Mussio, and A. Piccinno.............................................. 1823 Chapter 6.2. Managing and Practicing OD in an IT Environment: A Structured Approach to Developing IT Project Teams / Joseph Logan............................................................................. 1843 Chapter 6.3. Home Alone: The Role of Technology in Telecommuting / Ellen Baker, Gayle C. Avery and John Crawford................................................................................................. 1864 Chapter 6.4. Employees’ Attitudes Toward Telecommuting: An Empirical Investigation in the Egyptian Governornate of Dakahlia / Ahmed Gad Abdel-Wahab.......................................... 1886 Chapter 6.5. An Efficient and Effective Approach to Developing Engineering E-Training Courses / Judy C.R. Tseng, Wen-Ling Tsai, Gwo-Jen Hwang, and Po-Han Wu....................................................................................................................................... 1901 Chapter 6.6. The Transformation Model / Kathleen P. King........................................................... 1915 Chapter 6.7. Online Behavior Modeling: An Effective and Affordable Software Training Method / Charlie Chen, Terry Ryan, and Lorne Olfman.................................................. 1922 Chapter 6.8. CoPs for Cops: Managing and Creating Knowledge through Networked Expertise / Maarten de Laat and Wim Broer................................................................ 1938

Chapter 6.9. Organizational Knowledge Sharing in ERP Implementation: Lessons from Industry / Mary C. Jones and R. Leon Price............................................................. 1948 Chapter 6.10. Legal and Ethical Implications of Employee Location Monitoring / Gundars Kaupins and Robert Minch............................................................................................... 1968 Chapter 6.11. Learning to Use IT in the Workplace: Mechanisms and Masters / Valerie K. Spitler.............................................................................................................................. 1986 Chapter 6.12. Impact of Personal Innovativeness on the Use of the Internet Among Employees at Work / Tor J. Larsen and Øystein Sørebø................................................................. 2011 Chapter 6.13. Contractual Obligations between Mobile Service Providers and Users / Robert Willis, Alexander Serenko, and Ofir Turel . ......................................................................... 2029 Section 7. Critical Issues This section contains over 10 chapters addressing issues such as online consumer behavior, Web users, virtual reality users, user ethics, and Web services to name a few. Within the chapters, the reader is presented with an in-depth analysis of the most current and relevant issues within this growing field of study. An exhaustive review of prior theoretical literature and an integrative model of online consumer behavior are discussed. Crucial questions are addressed and alternatives offered such as is responsibility for information assurance and privacy a problem of individual ethics? Also theorized in this section is the data structure that yields the lowest weighted average complexity for a representative sample of information requests of end users. Chapter 7.1. A Critical Review of Online Consumer Behavior: Empirical Research / Christy MK Cheung, Gloria WW Chan, and Moez Limayem.......................................................... 2038 Chapter 7.2. Classifying Web Users: A Cultural Value-Based Approach / Wei-Na Lee and Sejung Marina Choi.............................................................................................. 2056 Chapter 7.3. Software Use Through Monadic and Dyadic Procedure: User-Friendly or Not-So-Friendly? / Gregory E. Truman........................................................................................... 2073 Chapter 7.4. Virtual Reality User Acceptance / Françoise Dushinka Brailovsky Signoret............. 2090 Chapter 7.5. Ex Ante Evaluations of Alternate Data Structures for End User Queries: Theory and Experimental Test / Paul L. Bowen, Fiona H. Rohde, and Jay Basford...................................................................................................................................... 2096 Chapter 7.6. An Empirical Study of the Effects of Training Sequences on Database Training Tasks and User Outcomes / Clive C. Sanford and Anol Bhattacherjee............................. 2124

Chapter 7.7. Responsibility for Information Assurance and Privacy: A Problem of Individual Ethics? / Bernd Carsten Stahl......................................................................................... 2140 Chapter 7.8. The Role of Fit in Knowledge Management Systems: Tentative Propositions of the KMS Design / Peter Baloh.................................................................................................... 2157 Chapter 7.9. A Theoretical Model and Framework for Understanding Knowledge Management System Implementation / Tom Butler, Ciara Heavin, and Finbarr O’Donovan......................................................................................................................... 2178 Chapter 7.10. Balancing Local Knowledge Within Global Organisations Through Computer-Based Systems: An Activity Theory Approach / Somya Joshi, Michael Barrett, Geoff Walsham, and Sam Cappleman.................................................................. 2200 Chapter 7.11. Concerns with “Mutual Constitution”: A Critical Realist Commentary / Alistair Mutch.................................................................................................................................. 2217 Chapter 7.12. Evaluating Conceptual Modeling Practices: Composites, Things, Properties / Graeme Shanks, Jasmina Nuredini, and Ron Weber.................................................... 2229 Chapter 7.13. IT Artefacts as Socio-Pragmatic Instruments: Reconciling the Pragmatic, Semiotic, and Technical / Göran Goldkuhl and Pär J. Ågerfalk..................................................... 2252 Section 8. Emerging Trends This section highlights research potential within the field of end-user computing technologies while exploring uncharted areas of study for the advancement of the discipline. Introducing this section are chapters that set the stage for future research directions and topical suggestions for continued debate. Discussions regarding Web searches as well as hypermedia are offered. Another debate which currently finds itself at the forefront of research is the development of virtual communities while focusing upon virtual religion and its impact on humanity. Found in these chapters concluding this exhaustive multi-volume set are areas of emerging trends and suggestions for future research within this rapidly expanding discipline. Chapter 8.1. The Past, Present, and Future of End-User Performance / I. M. Jawahar.................. 2266 Chapter 8.2. End User Computing Ergonomics: Facts or Fads? / Carol Clark............................... 2274 Chapter 8.3. The Changing Demographics: The Diminishing Role of Age and Gender in Computer Usage / Michael B. Knight and J. Michael Pearson................................................... 2284 Chapter 8.4. Believe It or Not: Virtual Religion in the 21st Century / Susan E. George................ 2299

Chapter 8.5. Next Generation Cellular Network Planning: Transmission Issues and Proposals / Spiros Louvros and Athanassios C. Iossifides........................................................ 2308 Chapter 8.6. Motivations and Barriers to the Adoption of 3G Mobile Multimedia Services: An End User Perspective in the Italian Market / Margherita Pagani and Danilo Schipani............................................................................................................................... 2329 Chapter 8.7. mCity: User Focused Development of Mobile Services Within the City of Stockholm / Anette Hallin and Kristina Lundevall..................................................................... 2341 Chapter 8.8. From Non-Adaptive to Adaptive Educational Hypermedia: Theory, Research, and Methodological Issues / Michael J. Jacobson.......................................................... 2354 Chapter 8.9. Dominant Meanings Approach Towards Individualized Web Search for Learning Environments / Mohammed A. Razek, Claude Frasson, and Marc Kaltenbach.............. 2376 Chapter 8.10. Forging Partnerships to Provide Computer Literacy in Swaziland / Cisco M. Magagula.......................................................................................................................... 2394 Chapter 8.11. Toward Always-On Enterprise Information Systems / Nijaz Bajgoric..................... 2401

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Preface

Bridging the gap among information technology professionals, researchers, academicians and the user, end-user computing has become a mainstream focus within present and previous years. Now more than ever, the information technology landscape is growing with emerging research and new discoveries to expand to all points of the globe. Profoundly traversing all facets of compound societies, end-user computing implicates and impacts not only information science, political science, healthcare systems, international relations, sociology and more, but branches out to the every-day user as well. During this period of time, numerous researchers and academicians have developed a variety of techniques, methodologies, and measurement tools that have allowed them to develop, deliver and at the same time evaluate the effectiveness of several areas of end-user computing. The explosion of these technologies and methodologies have created an abundance of new, state-of-the-art literature related to all aspects of this expanding discipline, allowing researchers and practicing educators to learn about the latest discoveries in the field of end-user computing. Due to rapid technological changes that are continually taking place, it is a constant challenge for researchers and experts in this discipline to stay abreast of the far-reaching effects of end-user computing and to be able to develop and deliver more innovative methodologies and techniques, utilizing new technological innovation. In order to provide the most comprehensive, in-depth, and recent coverage of all issues related to this global phenomenon, as well as to offer a single reference source on all conceptual, methodological, technical and managerial issues, as well as the opportunities, future challenges and emerging trends related to end-user computing, Information Science Reference is pleased to offer a four-volume reference collection on this rapidly growing discipline, in order to empower students, researchers, academicians, and practitioners with a comprehensive understanding of the most critical areas within this field of study. Entitled End-User Computing: Concepts, Methodologies, Tools, and Applications, this collection is organized in eight distinct sections, providing the most wide-ranging coverage of topics such as: (1) Fundamental Concepts and Theories; (2) Development and Design Methodologies; (3) Tools and Technologies; (4) Utilization and Application; (5) Organizational and Social Implications; (6) Managerial Impact; (7) Critical Issues; and (8) Emerging Trends. The following provides a summary of what is covered in each section of this multi-volume reference collection: Section 1, Fundamental Concepts and Theories, serves as a foundation for this extensive reference tool by addressing crucial theories essential to the understanding of end-user computing. Chapters such as, Privacy, Risk Perception, and Expert Online Behavior: An Exploratory Study of Household End Users by J. Drennan, G. Sullivan, and J. Previte, as well as Gender and End-User Computing by Laura Beckwith, Margaret Burnett, and Shraddha Sorte provide an excellent framework in which to position end-user computing within the field of information science and technology. Perceptions of End-Users on the Requirements in Personal Firewall Software: An Exploratory Study, by Sunil Hazari, offers excellent

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insight into the critical incorporation of information security into end-user computing, while chapters such as, An Open Source Primer by Brian Still address some of the basic, yet principle stumbling blocks of issues within end-user computing. With over 20 chapters comprising this foundational section, the reader can learn and chose from a compendium of expert research on the elemental theories underscoring the end-user computing discipline. Section 2, Development and Design Methodologies, provides in-depth coverage of conceptual architecture frameworks to provide the reader with a comprehensive understanding of the emerging technological developments within the field of end-user computing. Development Methodologies and Users by Shawren Singh and Paula Kotzé offers research fundamentals imperative to the understanding of research and developmental processes within end-user computing. From broad examinations to specific discussions on end users such as, Elaine H. Ferneley’s, Covert End User Development: A Study of Success, the research found within this section spans the discipline while offering detailed, specific discussions. From basic designs to abstract development, chapters such as Framework for User Perception of Effective E-Tail Web Sites by Sang M. Lee, Pairin Katerattanakul, and Soongoo Hong, and End-User Perceptions of the Benefits and Risks of End-User Web Development, by Tanya McGill and Chris Klisc, serve to expand the reaches of development and design methodologies within the end-user computing community. This section includes over 35 contributions from researchers throughout the world on the topic of end-users within the information science and technology field. Section 3, Tools and Technologies, presents an extensive coverage of various tools and technologies available in the field of end-user computing that practitioners and academicians alike can utilize to develop different techniques. Chapters, such as Jason Sargent, Carole Alcock, Lois Burgess, Joan Cooper, and Damian Ryan’s, PDAs as Mobile-Based Health Information Deployment Platforms for Ambulatory Care: Clinician-Centric End-User Considerations, enlighten readers about clinician-centric end-user acceptance toward the adoption of personal digital assistants as mobile-based health information deployment platforms within ambulatory care service settings, whereas chapters like, Trusting Computers Through Trusting Humans: Software Verification in a Safety-Critical Information System by Alison Adam and Paul Spedding, explore trusting automatically generated program code—an increasingly pertinent research arena. It is through these rigorously researched chapters that the reader is provided with countless examples of the up-and-coming tools and technologies emerging from end-user information technology. With more than 25 chapters, this section offers a broad treatment of some of the many tools and technologies within the end-user community. Section 4, Utilization and Application, discusses a variety of applications and opportunities available that can be considered by practitioners in developing viable and effective end-user programs and processes. This section includes more than 15 chapters, such as Lessons in Implementing a Learning System in a University: The Academic User Perspective by Fiona Darroch and Mark Toleman, which examines the implementation of two learning management systems (LMS) in a university environment. Additional chapters, such as Ron Thompson, Deborah Compeau, and Chris Higgins’ Intentions to Use Information Technologies: An Integrative Model, propose an integrative model explaining intentions to use in information technology in order to obtain a clearer picture of how intentions are formed. Also considered in this section is the potential use of biometrics across a wide variety of organizational contexts as outlined in Determinig the Intention to Use Biometric Devices: An Application and Extension of the Technology Acceptance Model, by Tabitha James, Taner Pirim, Katherine Boswell, Brian Reithel, and Reza Barkhi. Contributions included in this section provide excellent coverage of today’s end-user community and how research into information technology is impacting the social fabric of our presentday global village. Section 5, Organizational and Social Implications, includes a wide range of research pertaining to the social and organizational impact of end-user computing in information technologies around the world.

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Introducing this section is Penelope Markellou, Maria Rigou, and Spiros Sirmakessis’ chapter entitled, A Closer Look to the Online Consumer Behavior. This chapter presents the overall consumer purchase decision cycle and investigates the issues that affect Web users, from selecting a specific e-shop to the delivery of the product and the overall assessment of the shopping experience. Additional chapters included in this section, such as Super Users and Local Developers: The Organization of End-User Development in an Accounting Company by Hege-René Hansen Åsand and Anders I. Mørch, present a case study following the activities of super users and local developers during the adoption of a new business application by an accounting firm in Scandinavia. Also investigating a concern within the field of enduser computing is Michael J. Mullany’s, Relating Cognitive Problem-Solving Style to User Resistance, which explores cognitive problem-solving style and its impact on user resistance, based on the premise that the greater the cognitive difference between users and developers, the greater the user resistance is likely to be. The 20 chapters in this section offer insight on the integration of end-user technologies and computational access for all. Section 6, Managerial Impact, presents contemporary coverage of the social implications of end-user computing, more specifically related to the corporate and managerial utilization of information technologies and applications, and how these technologies can be facilitated within organizations. Core ideas such as training and continuing education of human resources in modern organizations are discussed through these chapters. Supporting Work Practice through End-User Development Environments, by M. F. Costabile, D. Fogli, R. Lanzilotti, P. Mussio, and A. Piccinno, illustrates how a certain methodology is applied to a project for the development of an interactive system in the medical domain. Equally as crucial, chapters, such as Employees’ Attitudes Toward Telecommuting: An Empirical Investigation in the Egyptian Governornate of Dakahlia by Ahmed Gad Abdel-Wahab, explore the attitude of Egyptian information workers toward the concept of telecommuting and to examine the relationships between such attitude and workers’ expectations of their productivity and job satisfaction. Also found within this section is a chapter by Tor J. Larsen and Øystein Sørebø, titled Impact of Personal Innovativeness on the Use of the Internet Among Employees at Work. This chapter investigates the theoretical proposition that personal IT innovativeness will positively impact the use of novel computer technologies. The research model used within this chapter includes the individual traits of age, gender, experience with IT, and educational level. Section 7, Critical Issues, contains over 10 chapters addressing issues such as online consumer behavior, Web users, virtual reality users, user ethics, and Web services to name a few. Within these chapters, the reader is presented with an in-depth analysis of the most current and relevant issues within this growing field of study. Christy MK. Cheung, Gloria WW. Chan, and Moez Limayem’s A Critical Review of Online Consumer Behavior: Empirical Research provides an exhaustive review of prior theoretical literature and provides an integrative model of online consumer behavior, while Ex Ante Evaluations of Alternative Data Structures for End User Queries: Theory and Experimental Test, by Paul L. Bowen, Fiona H. Rohde, and Jay Basford, theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. Crucial questions are addressed such as that presented in Bernd Carsten Stahl’s chapter, Responsibility for Information Assurance and Privacy: A Problem of Individual Ethics?, which explores the question of whether individual responsibility is a useful construct to address ethical issues of this security and privacy. The concluding section of this authoritative reference tool, Emerging Trends, highlights research potential within the field of end-user computing, while exploring uncharted areas of study for the advancement of the discipline. Introducing this section is a chapter titled, The Past, Present, and Future of End-User Performance by I. M. Jawahar, which sets the stage for future research directions and topical

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suggestions for continued debate. Providing an alternative view of end-user computing is the chapter, Believe It or Not: Virtual Religion in the 21st Century by Susan E. George. This chapter considers the development of virtual communities while focusing upon virtual religion and its impact on humanity. Another debate which currently finds itself at the forefront of research within this field is presented by Nijaz Bajgoric’s research, Toward Always-On Enterprise Information Systems, which presents a framework for the implementation of continuous computing technologies for improving business continuity. The framework is presented within a systemic view of developing an “always-on” enterprise information system. Found in these chapters concluding this exhaustive multi-volume set are areas of emerging trends and suggestions for future research within this rapidly expanding discipline. Although the primary organization of the contents in this multi-volume set is based on its eight sections, offering a progression of coverage of the important concepts, methodologies, technologies, applications, social issues, and emerging trends, the reader can also identify specific contents by utilizing the extensive indexing system listed at the end of each volume. Furthermore to ensure that the scholar, researcher, and educator have access to the entire contents of this multi-volume set as well as additional coverage that could not be included in the print version of this publication, the publisher will provide unlimited multi-user electronic access to the online aggregated database of this collection for the life of the edition, free of charge when a library purchases a print copy. This aggregated database provides far more contents than what can be included in the print version in addition to continual updates. This unlimited access, coupled with the continuous updates to the database ensures that the most current research is accessible knowledge seekers. End-user computing as a discipline has witnessed fundamental changes during the past two decades, allowing information seekers around the globe to have access to information which two decades ago, was inaccessible. In addition to this transformation, many traditional organizations and business enterprises have taken advantage of the technologies offered by the development of end-user technologies in order to expand and augment their existing programs and practices. This has allowed practitioners and researchers to serve their customers, employees, and stakeholders more effectively and efficiently in the modern virtual world. With continued technological innovations in information and communication technology and with on-going discovery and research into newer and more innovative techniques and applications, the end-user computing discipline will continue to witness an explosion of information within this rapidly growing field. The diverse and comprehensive coverage of end-user technologies in this four-volume authoritative publication will contribute to a better understanding of all topics, research, and discoveries in this developing, significant field of study. Furthermore, the contributions included in this multi-volume collection series will be instrumental in the expansion of the body of knowledge in this enormous field, resulting in a greater understanding of the fundamentals while fueling the research initiatives in emerging fields. We at Information Science Reference, along with the editor of this collection and the publisher, hope that this multi-volume collection will become instrumental in the expansion of the discipline and will promote the continued growth of end-user computing.

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Introductory Chapter

Contemporary Research in End-User Computing Steve Clarke University of Hull, UK

Introduction Before I look at end-user computing in some detail, I find it interesting to consider how relatively recently computing has been part of our daily lives. On May 6, 1949, the “Leo” (Lyons Electronic Office) Computer was activated for the first time, and November 1951 marked the first business application to be run on any computer anywhere in the world—by J. Lyons & Co in London. Leo 1 was the first computer able to run the world’s first routine office computer job—calculating the payroll. Leo 1 filled a vast office, and consisted of seven thousand valves: all this for a computer that could do only three things at once and could remember only a thousand combinations of digits. From these early beginnings, the involvement of “end users” in the computing process was not exactly a rapid development. From the 1950s to the early 1980s, the end user was at most someone linked to a mini- or mainframe computer via a terminal, and was typically someone with specific computer operations skills and training. The end-user revolution finally took hold in the mid- to late 1980s, with the growth of the microcomputer as a business tool. Even then, in the early years, user operations were often limited to word processing and spreadsheets. Consequently, it is only from the 1990s onward that serious consideration has been given to the place of the end user in computing. To throw more light on this development, I present here an essentially personal view of it, but one which I hope will have resonance for many readers. This begins next with a perspective on the concepts and theories which might be seen to inform the domain.

Fundamental Concepts and Theories in End-User Computing End-user computing is a domain which brings together the theory and practice of organisations, computing, and human activity. It is therefore reasonable to propose that any approach to the domain which privileges only technology or human activity will be deficient. What is required is a method which combines both activities in an organisational setting. To address these issues, I want to start by “unpacking” the concept of “end-user computing” (EUC) in organisations, with the aim of furthering the debate toward grounding the domain. To begin with, this

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is undertaken by looking at possible interpretations of “organisations”, “computing”, and “end users”. These interpretations are then taken forward to propose a common ground which can be used to inform, philosophically, theoretically, and pragmatically, the EUC domain.

Organisations The beginnings of organisational studies are often traced to the work of Frederick Taylor (1947) on scientific management at the beginning of the 20th century. Major subsequent developments have been administrative management theory (Fayol, 1949), where the management process is defined (to forecast and plan, to organise, to command, to co-ordinate and control), and bureaucracy theory (Weber, 1947). These theories adhere to the rational model, which views organisations mechanistically, seeing the attainment of maximum efficiency as achievable by putting together the parts in an effective way under the control of management. Hierarchy, authority and rational decision making are fundamental to this. In the 1920s, in a movement exemplified by the Hawthorne experiments, the human relations model began to gain ground, based on social structures of people at work and motivation. This model pointed to the benefits of a more democratic, employee-centered management. More recently, the system’s model has viewed organisations systemically as open systems responding to environmental changes (Katz & Kahn, 1978). Broadly, the system’s model recommends that if an organisation is not functioning properly the sub-systems should be examined to see that they are meeting organisational needs, and the organisation examined to see that it is well adjusted to its environment.

Computing Early approaches to computing were firmly rooted in the functionalist traditions of the natural sciences. This history has given rise to a problem-solving, reductionist focus which is still in evidence today, but which leads to tensions when the system to be developed is more process- or user-based. The need to address user requirements is seldom disputed by computer systems developers, but is typically achieved by including a user-analysis stage within an existing problem-solving approach, relying on the system’s development life cycle (SDLC) as the primary method. The period since the 1970s has seen the growth of soft approaches to computer systems development (see, for example, Clarke, 2004a, 2004b; Clarke & Lehaney, 2002; Clarke, Lehaney, et al., 2004). These soft methods treat computer systems as human activity systems, and concentrate on interpretation of the problem situation from the viewpoint of those involved in and affected by the system. The need for such methods is supported by a study conducted by the British Computer Society’s special interest group, which looked at organisational aspects of information technology (OASIG, 1996), and concluded that up to 90% of information technology investments do not meet the performance goals set for them, listing the technology led nature of the process, and the lack of attention to human and organisational factors as key issues in this lack of success.

EUC and End Users The issues for end users flow quite naturally as a consequence of the variety of approaches taken to organisations and computing. The organisational tensions between scientific management and a more human-centered or systems approach, are echoed in the hard-soft debate within the computing world. A number of methodologies (e.g., Multiview: Avison & Wood-Harper, 1991; Watson & Wood-Harper, 1995) have been developed to address this problem in computing, focusing on a more human-centered

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approach. However, such methodologies are not the purpose of this section, which instead aims to ground our thinking in the domain. The means for achieving this are drawn from social theory, from which functionalist, interpretivist, and critical views can be applied to offer some insights.

Social Theory and User Issues The foregoing analysis has offered a largely pragmatic perspective on the domain of EUC. In subsequent sections, these findings are viewed from a theoretical standpoint. A synthesis of the pragmatic and theoretical positions is then undertaken to determine a grounding for EUC.

EUC: A Functionalist View The functionalist perspective sees EUC as primarily a technological domain. Computer systems in organisations are viewed as independent of human activity, and their design and development undertaken largely as a technical exercise. The incorporation of user requirements into EUC is achieved by including a user-analysis stage within the existing problem-solving approach (Wetherbe & Vitalari, 1994, p. 211). Advice on how to undertake this user analysis is often addressed only weakly, reinforcing the functionalist, problem-means-solution view of organisational end-user computing.

EUC: An Interpretivist View The interpretivist, or human-centered approach to EUC, has focused on the use of so-called soft methods. Soft systems methodology (Checkland, 1994), for example, incorporates human factors into the development process by taking a more holistic, systemic view: “Focusing on one aspect or even several aspects of a situation is unsystemic, and at best systematic. The systematic nature of IT clashes with the systemic nature of IS... coping with this tension between systematic and systemic natures is a challenge which has to be taken up by the IS profession” (Angell, 1990). Stowell (Stowell & West, 1994) takes a similar position, differentiating the idea of an information system from that of a computer system, two terms which he argues are too often viewed as synonymous. The information system is seen as a more systemic whole in which the computer system may play a part. End-user computing needs to be driven by interpretivism, and not, at the technical development stage, “engulfed by functionalism” (Stowell & West, 1994). These, and other contributors to the soft school (e.g., Bennetts, Mills, et al., 2000; Mumford, 1983; Mumford & Henshall, 1978), effectively indicate a need to combine hard (technical) and soft (social) aspects within a single intervention. Recent thinking (Clarke, 2004) has sought to develop EUC beyond this hard-soft debate by pursuing the critical strand in management science.

EUC: A Critical View The idea of a “critical” view is drawn directly from social theory, in which critical social theory (CSoT) might be perceived as a natural progression from earlier functionalist and interpretivist theories. A prime aim of CSoT might be seen as allowing human participants the freedom to contribute within a given domain: in terms of EUC, its relevance to the possible enhancement of human interaction in a domain which is often characterised as technological seems clear. One way to try to perceive the possible benefits of a critical approach is by raising a critique of hard and soft thinking. Jackson’s (1990) analysis of Checkland’s views on hard systems thinking categorises

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it as being “…guided by functionalist assumptions. The world is seen as made up of systems which can be studied ‘objectively’ and which have clearly identifiable purposes.” These functionalist roots of hard systems thinking severely limit its domain of applicability, but equally soft systems thinking suffers from its own limitations: The recommendations of soft systems thinking remain ‘regulative’ because no attempt is made to ensure that the conditions for ‘genuine’ debate are provided. The kind of open participative debate which is essential for the success of the soft systems approach, and is the only justification for the results obtained, is impossible to obtain in problem situations in which there is conflict between interest groups, each of which is able to mobilise differential power resources. (Jackson, 1990) An early recognition of the theoretical validity of a critical approach in computing and information systems was by Hirschheim and Klein (1989), who saw neither the functionalist nor the interpretivist approach as adequate. Hirschheim and Klein viewed functionalism as the “orthodox approach to systems development”, and characterised it as means and ends dominated but with little discussion about the ends, since these are taken as given: “There is one reality that is measurable and essentially the same for everyone …the role of the developer is to design information systems that model this reality.” But the ends can seldom be assumed to be agreed, and in modelling reality the question of whose reality becomes paramount. While interpretivism offers an alternative to functionalism, in so far as it does not accept there to be an objective reality but only socially-constructed reality, its relativist stance makes it “ … completely uncritical of the potential dysfunctional side effects of using particular tools and techniques for organisational end-user computing” (Hirschheim & Klein, 1989): different end-user computing outcomes are simply viewed as the result of different socially-constructed realities. Through critical social theory there is the possibility of moving to a critically reflective, radical position. This critical social stream has been taken up by critical thinkers in the domain of management science, under the banner of critical systems thinking, and it is from here that a way forward for EUC is to be found.

EUC: What Can Be Learned from Management Science The problem faced by EUC has been explored extensively over the last 30 years or so in management science (for a summary, see Jackson, 2000). A number of approaches relevant to EUC have emerged from this work, the key examples of which are summarised in the following: •

•

Ormerod (Mingers & Gill, 1997, pp. 29-58), through the presentation of a number of case studies, presents a compelling argument for an essentially pragmatic approach, taking available methodologies and mixing them in a contingent fashion. Perhaps the most comprehensive attempt at applying the ideas from CSoT to organisations was undertaken through total systems intervention (TSI: Flood, 1995), where “complementarism” was promoted as a way forward, enabling methodologies from different paradigms to be used together in a single intervention, applied to the same problem situation (for an example of TSI in use, see Clarke & Lehaney, 2000). TSI, drawing on Habermas (1971), sees the functionalist view of organisations as an insufficient basis, serving only the technical interest. What is needed in addition is social science, to service the practical (hermeneutic) interest in achieving communication and consensus, together with critical science to deal with issues of power and domination, serving the emancipatory interest.

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• •

•

•

•

Midgley and Ulrich (Midgley, 1997; Ulrich, 1983) focus on boundary critique, which they apply to critically challenge what should or should not be considered part of any system. Flood and Romm (1996) promote diversity management and triple-loop learning as an improved way to deal with mixing methods. In essence, triple-loop learning is seen as a way to manage the diversity of methodologies and theories available, in addressing the diversity of issues to be found in organisational intervention. Jackson (2000) promotes critical pluralism. Pluralism in the use of methodology is advocated: “... to make the best use of the methodologies, methods, models, and techniques…to tackle diverse and difficult problem situations while … ensuring their continual improvement through research” (p. 382). This pluralism must: encourage flexibility in the use of methodologies, enabling practitioners to decompose approaches and tailor them, within a critical framework; encourage paradigm diversity: using methodologies from different paradigms in the same intervention (for a critique of a range of methodologies, see Clarke, Lehaney, et al., 1998). Nicholls et al. (Nicholls, Clarke, et al., 2001) and Mingers (Mingers & Gill, 1997) similarly promote the use of a mix of methodologies: variously termed mixed-mode modelling, or multi-paradigm multi-methodology. The Nicholls text contains a number of approaches to this, with a summary of issues drawn from critical management science. Finally, Taket and White (1996) offer pragmatic pluralism as a means of mixing methods from an essentially postmodernist perspective. Their approach is strongly grounded theoretically, with the suggestion that guidelines, examples, stories, and metaphors are of more value than prescribed frameworks for action. The approach is expressly holistic, and sees pluralism as a means of addressing diversity. Pluralism, they argue, should be applied to the roles of the interventionist, modes of representation, and the nature of the client. It is explicit that work with the disempowered should be seen as fundamental within any intervention context.

In this brief section, I have tried to give a flavour of the current thinking which is being applied to the domain of management systems. Fundamental within this is a striving to combine a number of different approaches within a critical framework, to address problem contexts in which technological and human factors are mixed. EUC seems clearly to fit within this definition, and it therefore appears profitable to pursue approaches to the domain, which are underpinned explicitly by critical social theory. More specifically, much attention has been devoted recently to developments in CSoT which focus on theories of language, and it is a strand of this which is pursued in the next section.

The Future: A Critical Approach to EUC The ability to communicate by use of language is something that human beings bring to the world by nature of their existence: that is to say, it is not developed empirically, but is a priori. To the extent that any theoretical position can be grounded on such an a priori ability, then such a position may be seen as fundamental to us as communicative human actors. But what is it that we seek to communicate? Are we concerned with the properties of an objective world (mapping to a requirement in EUC to control through technology); or is our world essentially more subjective (privileging in EUC a more humancentered, critical approach)? Critical theory points to knowledge as not reducible (as is so often seen in scientific or pseudo-scientific study) to the properties of an objective world, suggesting it can be defined both objectively and according to the a priori concepts that the knowing subject brings to the act of perception. This knowing

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subject, being social, mediates all knowledge through social action and experience: subject and object are linked in the acts of cognition and social interaction, and the so-called subjective and objective perspectives may be represented as just a convenient tool for understanding, which has been accorded too much primacy as a form of reality. Now, all human endeavour becomes mediated through subjective understanding, and the hard-soft debate becomes less problematic. What we are left with is: 1. Accepting all human actions as mediated through subjective understanding leads to the possibility of a basis for EUC in the universal characteristics of language. 2. The dichotomy between subject and object has gone, and with it, the difficulties of combining technological and human-centered analysis within a single problem context. 3. EUC development is recast as an entirely communicative issue, and the ways in which technology may further enable human interaction, all within a framework of human intercommunication, becomes the goal. 4. The difficulty which now arises is essentially a practical one, of how to incorporate these ideas into EUC practice. Habermas’ theories of communicative action (Habermas, 1976, 1984) present a universal theory of language which suggests that all language is oriented toward three fundamental validity claims: truth, rightness, and sincerity. What is most compelling about this theory, however, is that all three validity claims are communicatively mediated. This viewpoint is most radically seen in respect of the truth claim, where it is proposed that such a claim results not from the content of descriptive statements, but from the Wittgenstinian approach casting them as arising in language games which are linked to culture: truth claims are socially contextual. “Truth” can therefore be assessed by reference to communication: truth is what statements, when true, state! Rightness is about norms of behaviour, which are culturally relevant, and are therefore to be determined by reference to that which is acceptable to those involved and affected in the system of concern as a cultural group. Finally, sincerity is about the speaker’s internal world: his/her internal subjectivity.

Some Conceptual and Theoretical Conclusions These ideas help to provide an approach to EUC which is theoretically grounded, and closer to that which is experienced in action. As a domain which needs to address organisational, technological, and human-centered problem contexts, EUC has much to gain from the application of ideas from critical social theory. Earlier in this chapter I briefly described approaches drawn from management systems which could be applied to EUC, for which a number of examples of application are to be found (see, for example, Clarke & Drake, 2002; Clarke & Greaves, 2002). In the future, the research team with which I am working will be focusing on communicative action theory, developing guidelines toward a methodology for EUC based on communicative competence.

End-User Computing Development and Design Methodologies Drawing on the background outlined previously, clearly the methodologies used to undertake EUC development must address both technological and human-centered issues. I begin this section by outlining three possible approaches to this.

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A Methodology to Address Both Functionalist and Human-Centered Issues Approaches traditionally taken to EUC development—when the target system is computer-based—privilege, I would argue, functionalist ahead of human-centered issues. Included in this are systems analysis, systems engineering, the various systems development life cycle methods, and newer approaches such as joint application design and rapid application design. Their unsuitability in EUC stems from their fundamental reliance on the need for a system design effort at the center of the development, causing concentration on technical issues of design ahead of interpretivistic issues needing a debating forum for their resolution. Two of the most suitable information systems methodologies which combine technical and humancentered issues are client-led design (Stowell & West, 1994) and ETHICS (Mumford, Hirschheim, et al., 1985), while interactive planning (Ackoff, 1981), though not specifically an information systems methodology, also aims to address both functionalist and interpretivist issues. Two key factors which need to be borne in mind at this stage are: 1.

2.

Typically EUC designers will lack detailed knowledge of the problem context in advance of undertaking any analysis, and this renders choice of methodology difficult if not impossible. Each of these three methodologies, for example, has strengths and weaknesses vis a vis the others, making a choice of the most applicable methodology at this stage seem at best premature. In a similar vein, no one methodology is able to address all of the issues within a given problem context, so a mix of methodologies would seem always to be indicated.

A Mix of Methodologies In the past, common approaches considered under this heading include the “front-ending” of a functionalist methodology with a soft or interpretivist one, and, arguably the most widely accepted approach to mixing methodologies in information systems, Multiview (Wood-Harper, Antill, et al., 1985). The objective of Multiview is to create a framework which attempts to take account of the different points of view of all the people involved in using a computer system, based on the assertion that at any stage of information systems development the approach is contingent on the circumstances met at that stage. Multiview accepts the view that no one methodology can be seen to work in all cases, and that the methodology to be chosen cannot be decided in advance of the problem situation being known. Methodological mixing offers a viable approach in many EUC developments, but there are situations in which the lack of detailed knowledge of the problem context surfaces again: even if methodologies are to be mixed, which methodologies should be chosen? In addition there are theoretical objections to a contingency-based approach to methodological mixing. The realist ontological stance of hard methodologies is seen by many to be incompatible with the nominalist ontology of soft methodologies, giving rise to practical difficulties in moving between a highly participative problem structuring approach to a more objective problem solving one.

Methodologies from Critical Management Science: A “Critical Complementarist” Approach The relativist stance of interpretivistic approaches renders them “completely uncritical of the potential dysfunctional side effects of using particular tools and techniques for information systems development.

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Different products of systems development are simply viewed as the result of different socially constructed realities” (Hirschheim & Klein, 1989). This lack of critique can be a key issue within some EUC developments. Critical theory has been assessed by Hirschheim and Klein (1989) as “hypothetical … in that it has been constructed from theory”. While they see theoretical value in such an approach, they question the potential for achieving this in practical terms: “… while theoretically strong, it is difficult to see how [critical theory] actually works in practice. [It] is normative without providing clear detail on how it could be implemented” (Hirschheim & Klein, 1989). The way out of this dilemma is to be found by drawing on developments in critical management science. Critical systems thinking (CST) offers an approach seen to be capable of resolving both the theoretical and practical difficulties. To summarise, EUC development may therefore be approached from three key perspectives, all of which combine human-centered and technological aspects of the development. 1. 2. 3.

Front-end a “hard” computer systems development methodology with a soft method. Use a methodology which at its heart has the objective of combining hard and soft approaches, probably in a contingent fashion. Approach EUC development from a critical perspective; Examples of this abound in the literature (see, for example, Bentley, Clarke, et al., 2004; Clarke, 2004; Clarke & Drake, 2002; Clarke & Lehaney, 2002).

Finally, to conclude this section, some further examples of EUC development are outlined next. In a recent special issue of the Journal of Organisational and End User Computing, Mehandjiev, Suttcliffe, and Wood-Harper (2006) sought to address the “Technology Interaction Aspects of EndUser Development”. End-user development (EUD), they argue, aims to empower users who are not professional programmers to develop or modify software in a variety of contexts such as work, home, or leisure activities. In all these contexts, we are surrounded by increasingly sophisticated software technology, and we interact with this technology in a number of nontrivial ways. Initial optimistic visions of end-user development, they suggest, drew parallels between programmers and chauffeurs, suggesting the user friendliness of software will soon increase to a degree where we would be able to “drive”, or develop our applications, without the help of specialists. But where has this led us a quarter of a century later? Mehandjiev et al. suggest that we have indeed achieved some success in narrow areas such as cognitive design of EUD languages and visual interactive interfaces, and AI tools providing automatic inference of user intentions. But a number of organisational and behavioral studies of end-user development practices and risks have made us realise that the complexity of the problem lies in addressing the interactions between all these components, and in the provision of appropriate support and environment to foster EUD activities. At the same time, the elements of EUD solutions are developed, like the components of a modern aircraft, all over the scientific “world,” distributed within separate communities aligned with diverse academic disciplines such as cognitive psychology, knowledge engineering, and organisational behavior. Software engineers build tools, cognitive psychologists produce increasingly sophisticated problem-solving models, management and social scientists publish interesting results from their studies of end-user computing practices and factors, yet papers and systems bridging these elements are few and far between. Mehandjiev et al. see bringing solution components together in a holistic approach to EUD as an important milestone to realising the vision of end-user development. The articles in the special issue gave a selective flavour of the ideas current in the domain, and are summarised next. Åsand and Mørch use activity theory as a conceptual analysis framework to analyse real-world

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tailoring practices in a sophisticated organisational context, explored through a case study where a complex business application is implemented in an accounting company. The organisational context embeds formally defined roles of end users, super users, and application coordinators, and the article offers interesting findings regarding the relationships and interactions between these roles, concluding that the role of super users fills an important niche in supporting organisation-wide EUD. The analysis in the article results in two critical success factors when implementing complex generic applications in distributed organisations: institutionalisation of user tailoring practices and grounding the tailoring activities within the context of work. The recommendations of this article would clearly benefit other organisations in their EUD efforts. McGill and Klisc are concerned with organisational issues of supporting EUD, with a focus on approaches to alleviating risks of EUD. Their work compares perceptions and opinions regarding risk management within two contexts: end-user development of Web pages and end-user development of spreadsheets. They use a questionnaire-based survey to gather information regarding practices and perceptions of Web page development among end users. The importance of the Web page development context comes from the external nature of the Web pages as development artifacts, which means that consequences of end-user development are much wider than the conventional EUD activities such as spreadsheet development, and mistakes can affect core business processes involving customers and suppliers. The survey targets end users who are known to have developed spreadsheets, probing the extent to which they undertake Web page development, and using their experience of EUD in both contexts. One interesting finding is that training is perceived to be the most important approach to risk reduction, despite the lack of such training among the survey sample of end-user developers. Costabile et al. analyse the richness of working practices, representations, and tacit knowledge found in professional communities, and explore the interactions between these and software tools at the levels of use, design, and meta-design. They propose the SSW (software shaping workshops) methodology, which focuses on enabling the participation of end users in the development of their software environments to ensure the environment is tuned to their needs. The tools used in everyday work of users are gathered in environments called application workshops, while the tools necessary to design and customise those are gathered in system workshops, and so forth. Jahnke et al. propose a novel approach to enabling end-user development of Web portals. Web portals have recently gained importance in information rich and agile domains such as health care. As the size and complexity of portal-based content delivery applications increases, current component-based technologies are no longer suitable because of their significant cognitive overload for end-user developers in terms of type checking, debugging, and complex metaphors. The core innovation in this research is the use of the semantic-based composition model, achieving integration of ontologies and componentbased technology to simplify end-user development in this particular context. The proposed approach is implemented in a tool and evaluated using an application scenario. What I have tried to present in this section is a conceptual analysis of EUC development, followed by some of the methods currently in use. The key issue which emerges from all studies of EUC development is the need for a user focus. It is not sufficient simply to adopt existing functionalist computer systems design methods and insert “user analysis” at different stages. EUC design must begin with the end user, and must retain users at the core of its processes throughout.

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End-User Computing Tools and Technologies A list and analysis of all the tools and technologies now available and being developed for end users would fill the whole of this volume. However, the dominant usage may be seen as taking the forms of: 1. 2. 3.

Applications run stand-alone by the user. Word processing, spreadsheets, databases, and presentation software are prime in this respect. Networked applications. New technology developments.

It is not possible to cover this range within such a short article, but I would first like to highlight some of the issues through two examples of techniques: spreadsheet usage and computer conferencing. Through an interesting perspective on spreadsheet usage, Troutt (2005) argues that electronic spreadsheet models and modeling (ESMs) have applications to virtually every functional area of business and to the standard topics of management science, while more specialised spreadsheet application research includes budgeting, simulation, simulation optimisation, and production planning. Solver engines are now available for such applications as simulation optimisation, and further growth in all of these and other areas continues. The conclusion to be drawn from this is that spreadsheets are no longer simply an electronic column of figures. Their use is limited only by the imagination of users and developers, to the extent that the boundaries between applications such as spreadsheets and word processors are becoming blurred. In an interesting perspective on computer conferencing, Foroughi et al. (Foroughi, Perkins, et al., 2005) argue that, in order for available new technologies to be used most effectively, more investigation is needed into the impact of various media on decision making, such as that in negotiation. In particular, negotiators need to have a means of choosing the most appropriate communication medium, based on the amount of richness inherent in the medium. Their research examines the effectiveness of a computerised negotiation support system (NSS) in supporting bargaining carried out in a dispersed, but synchronous setting. A key finding is that computer conferencing might prove to be more advantageous in dispersed bargaining involving multiple members on each bargaining side. Whereas several bargainers speaking in an audio conference might have difficulty in distinguishing each other’s voices, the written text of computer conferencing, which identifies the inputs of bargainers, might help to keep bargainers’ inputs straight. The written text would also provide bargainers with an ongoing log of what had transpired thus far in the negotiation.

New Technology Developments Few have benefited so much from new technology developments as the end user. As described earlier, this can be seen as having begun with the distribution of computer power to end users from the 1970s onward. But the growth is now becoming exponential. In the early 1980s, the key development arena was networking allied to micro-computing, and now, while the domain of development often remains the same, we have moved on to embrace computing within the mobile phone revolution. Ever-increasing processing power, memories, display, and print technologies and so on enhance this development, many examples of which are to be found within these volumes.

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Organisational and Social Implications of End-User Computing Conceptual issues with regard to organisational and social implications of EUC were the subject of the earlier theoretical section. Here I want to look at some specific contributions to the domain drawn from the last three years’ contributions to The Journal of Organisational and End User Computing (JOEUC). In “End User Computing Ergonomics: Facts or Fads?”, Clark (2006) discusses how, until recent years, the end user computing ergonomic focus has primarily been on stationary computer use. With an increasing number of end users working outside the traditional office, mobile computing devices have grown in importance. From an in-depth review of the current and emerging issues, Clark concludes that end users need to be actively involved in creating and maintaining an ergonomically correct computer work environment, and that managers need to be proactive in this regard. End-user ergonomic issues can have a dramatic effect on the work environment, but this can be influenced, and sometimes determined, with intentional efforts on the part of both end users and managers. Thompson et al. (Thompson, Compeau, et al., 2006) propose an integrative model which explains intentions to use information technology. The primary objective is to obtain a clearer picture of how intentions are formed. The conceptual model was tested using questionnaires, and the outcome was a call for the inclusion of less instrumental beliefs (e.g., personal innovativeness with IT) as influences on technology adoption, and an understanding of the complexity of the mechanisms through which general beliefs such as personal innovativeness and self-efficacy influence adoption intention. Research by Yellen (2006) reiterates the importance but failure of end-user involvement in the learning process, and posits that the learning which is done in a distance learning mode—as opposed to the traditional lesson delivery method—might be suited to insure that the end user is more fully involved in learning to use the systems and software (S&S). Yellen argues that the delivery of S&S to an organisation must not omit the human component of an information system. The current situation, according to Yellen, is that end-user training on the use of S&S is inadequate, and the best that one can hope for is that small incremental improvements in the way instruction is delivered will be made. Perhaps after a period of time in which end users become more sophisticated and S&S become more user friendly, this problem will solve itself. Yellen’s view is that distance learning has the potential to mostly solve the problem of end-user training. The advantages for the university learner using this type of delivery system are well documented. If organisations, wishing their employees to be better trained on the use of S&S, pursue this avenue then it is hoped that the problems will be at least better addressed. Ling and Ding (2006) examine the moderating role of gender in the context of IT service, and propose that expertise, relational selling behavior, perceived network quality, and service recovery indirectly influence a customer’s loyalty through mediation of relationship quality, with gender moderating each model path. Results indicate that the influences of perceived network quality on relationship quality and of relationship quality on loyalty are stronger for males than females, while relational selling behavior influences relationship quality more for females than for males. Information security, argues Hazari (2005), is usually considered a technical discipline with much attention being focused on topics such as encryption, hacking, break-ins, and credit card theft. Security products, such as anti-virus programs and personal firewall software, are now available for end users to install on their computers to protect against threats endemic to networked computers. But the behavioral aspects of maintaining enterprise security have received little attention from researchers and practitioners. The study raises a number of issues for managers, focusing on user rather than technical factors. Mao and Brown (2005) investigate the effectiveness of online task support relative to instructor-led training, and explore the underlying cognitive process in terms of the development of mental models.

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Analysis shows that users of online task support tended to outperform instructor-trained individuals on high-level tasks, whereas the performance difference on low-level tasks was not significant. The cognitive processes underlying the difference are also noteworthy. Task support users were more likely to develop conceptual mental models as opposed to procedural ones, which accounted for their better high-level performance. Mental model completeness was also found to be closely associated with performance on both low and high-level tasks. Westin (2005) looks at adaptive technology in relation to the Internet, and considers some of the issues surrounding accessibility to Web systems and services by individuals with imperfect abilities. Through this study, Westin provides insights into the special design requirements for Web content, and presents a business rationale for embracing adaptive technology. Through an analysis of Global Energy PLC’s (GE) structural changes during the 1990s, precipitated by the deregulation of the electricity industry in the UK, Kanellis and Paul (2005) consider the disruptive effect on the organisation’s enterprise information systems, which were found unable to adapt to the new and constantly emerging organisational realities. GE’s experiences illustrate the vulnerability of information systems in turbulent environments, provide for a rich description of the causes of misfit due to contextual change, and establish the ability of a system to flex and adapt as a dependent success variable. In addition, the idiographic details of this interpretive field study raise interesting questions about a number of assumptions we hold regarding the development of information systems and the means by which flexibility can be attained. Spitler (2005) looks at “fluency” with IT, and concludes that, while training is one mechanism to build fluency, other mechanisms also play a role. To learn to use the IT of their jobs, workers in the study relied not only on formal training, but also on on-the-job learning through experimentation; reading books, manuals, and online help; and social interaction with their peers. The research identified different types of “master users” who were indispensable for this learning to take place. The findings of this study suggest that managers and researchers interested in training users also devote attention to these other mechanisms for learning, especially the “master user” phenomenon. Horton and Dewar (2005) ask how people can be assisted in learning from practice, as a basis for configuring information technology (IT) in organisations. Three patterns are presented that have been derived from a longitudinal empirical study that has focused on practices surrounding IT configuration. They argue that Alexanderian patterns offer a valuable means of learning from past experience, and that learning from experience is an important dimension of deciding “what needs to be done” in configuring IT with organisational context. Through an examination of Internet use among employees, Larsen and Sorebo (2005) investigate the theoretical proposition that personal IT innovativeness will positively impact the use of novel computer technologies. The results indicate that users perceive structural differences across various types of Internet use areas, and that personal IT innovativeness is the best predictor of organisationally relevant use of the Internet. Ma and Liu (2005), through an application of the technology acceptance model, propose that perceived ease of use and perceived usefulness predict the acceptance of information technology, with a strong correlation between usefulness and acceptance. Jones and Price (2004) examine organisational knowledge sharing in enterprise resource planning (ERP) implementation, arguing that ERP requires end users to have more divergent knowledge than is required in the use of traditional systems. They must understand how their tasks fit into the overall process, and how their process fits with other organisational processes. Knowledge sharing among organisational members, they argue, is a critical piece of ERP implementation.

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This range of approaches to issues related to the organisational and social implications of EUC give some idea of the way in which such problems are being addressed. Underlying all of them is a requirement to privilege human aspects ahead of technological or organisational factors.

Managerial Impact of End-User Computing Managers, of course, need to pay attention to all of the EUC issues highlighted in these volumes. However, in this section, I want to review some specific managerial concerns drawn from recent research. Few topics can be of such importance to managers as information security, and the growth of EUC, putting power in the hands of the user, has exacerbated problems in this regard. In an ongoing eight-year research study which was motivated by evidence that information security predominantly focuses on technical issues within an environment that clearly affects and is affected by people, Drake and Clarke (2001), construct and test an evaluative model which explicitly focuses on both the technical and human aspects of the domain. The outcome is a theoretically and empirically informed perspective on information security which casts it as predominantly technical and functionalist. The research then moves on to the question of how the management of information security may be changed to address this issue. This results in the development of an action framework for information security management explicitly based on critical social theory, together with clear guidance on implementing and testing the framework. Information assurance, similarly, is an issue of increasing relevance to managers. Schou and Trimmer (2004) argue that it contains all the elements of information security (confidentiality) but also includes elements of availability and integrity. To defend information and data, there are three fundamental countermeasure categories: technology; operations; awareness, training, and education. Technology includes everything from operating systems to routers, switches, and electronic intrusion detection systems. No matter how well designed these technical countermeasures are, they are ineffective if they are not supported by well-designed operational plans, policies, and goals. However, in the final analysis, all of this fails if one does not have end users who are aware of information assurance issues, trained to operate systems appropriately. From an end-user perspective, education awareness and training are important countermeasures. In the U.S., the federal government took training the end user seriously enough to demand that all federal employees using sensitive systems receive annual training. The Computer Security Act (PL 100-235) also delineated the responsibility for information assurance standards between the National Institute of Standards and Technology (NIST) and the National Security Agency (NSA). Computer training is a further key issue for managers. In a recent study, Hasan (2006) develops and tests a model of the impact of multi-level computer self efficacy (CSE) on effectiveness of computer training. The results revealed that general CSE had positive effects on far-transfer learning and perceived ease of use, whereas application-specific CSE demonstrated positive effects on near-transfer learning and perceived ease of use. They also showed that general and application-specific CSE had negative effects on computer anxiety. IT ethical behavior is a subject which would hardly have merited discussion as recently as 20 to 30 years ago, but which managers in the 21st century ignore at their peril. From a survey of the literature regarding IT ethical behavior models, Cronan and Douglas (2006) propose a comprehensive IT ethical behavioral framework, which suggests that ethical behavioral intention is influenced by an individual’s attitude (which, in turn, is influenced by a variety of other factors such as perceived importance of the issue, consequences of the action, and beliefs), as well as other elements drawn from the theory of planned behavior and theory of reasoned action, equity theory, the environment, control, norms, and individual characteristics. This proposed model provides a fundamental basis for additional research

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that should foster a better understanding regarding ethical/unethical behavior and determinants of that behavior. Results from further research in ethical behavior will provide a better understanding of unethical behavior and inappropriate acts allowing organisations to develop realistic training programs for IT professionals, users, and managers as well as incorporate effective deterrent and preventive measures that can curb the rising tide of undesired misuse and unethical behavior in the IT arena. Support of distributed groups is high on the agenda in many organisational settings. Kim (2006) argues that the presence of a group leader is found to make a significant difference in objective decision quality and satisfaction with the decision process. Morris and Marshall (2004) look at perceptions of control in explaining human behavior and motivation. Five factors were seen to represent a user’s perceptions of control when working with an interactive information system: (1) timeframe; (2) feedback signal; (3) feedback duration; (4) strategy; and (5) metaphor knowledge. Computer task performance is an essential driver of end-user productivity. Recent research by Yi and Im (2004) indicates that computer self-efficacy (CSE) is an important determinant of computer task performance. Employing CSE and PG, the present research develops and validates a theoretical model that predicts individual computer task performance. The model was tested using PLS on data from an intensive software (Microsoft Excel) training program, in which 41 MBA students participated. Results largely support the theorised relationships of the proposed model and provide important insights on how individual motivational beliefs influence computer skill acquisition and task performance. Implications are drawn for future research and practice. User resistance is a common occurrence when new information systems are implemented within health care organisations (Adams, Berner, et al., 2004). Individuals responsible for overseeing implementation of these systems in the health care environment may encounter more resistance than trainers in other environments. It is important to be aware of methods to reduce resistance in end users. Proper training of end users is an important strategy for minimising resistance. This article reviews the literature on the reasons for user resistance to health care information systems and the implications of this literature for designing training programs. The other principles for reducing resistance (communication, user involvement, strategic use of consultants) are illustrated with a case study involving training clinical managers on business applications. Individuals responsible for health care information system implementations should recognise that end-user resistance can lead to system failure and should employ these best practices when embarking on new implementations. Practitioners and academics often assume that investments in technology will lead to productivity improvements. While the literature provides many examples of performance improvements resulting from adoption of different technologies, there is little evidence demonstrating specific, generalisable factors that contribute to these improvements. Furthermore, investment in technology does not guarantee effective implementation. In a qualitative study, Jones and Kochtanek (2004) examined the relationship between four classes of potential success factors on the adoption of a collaborative technology and whether they were related to performance improvements in a small service company. Users of a newly adopted collaborative technology were interviewed to explore which factors contributed to their initial adoption and subsequent effective use of this technology. Results showed a qualitative link to several performance improvements including time-saving and improved decision making. These results are discussed in terms of generalisability as well as suggestions for future research. In terms of managerial impact, the range of issues is clearly vast. However, if I had to make one fundamental recommendation to managers regarding end users, it would be to look at all of the issues

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from an end-user perspective. This is clearly indicated in all the earlier-mentioned examples, and you may be forgiven for thinking that as a matter of course managers realise this and act on it—sadly, all too often they do not.

Critical Issues in End-User Computing Critical issues in EUC are again a vast domain. However, some light can be cast on this by looking at the concepts which any analysis of EUC surfaces as critical to its success. Drawing on recent research, as outlined in the foregoing sections, we can classify, not so much what we should be regarding as critical, but, rather, the approaches to the domain which are critical to our success in dealing with it. • • • •

EUC is a domain consisting of organisational, technical, and human factors. The end user is more fundamental to success in the domain than the computing. To address the end user, it is essential to privilege user perceptions ahead of technical or organisational considerations. Addressing user perceptions means explicit use of interpretive techniques and, for a deeper understanding, the application of specific social theory.

Once these approaches are clear, we are able to progress through EUC in the firm knowledge that, whatever topic we may be addressing, we have a good chance of success. You will find it interesting to look at the critical issues in these volumes; when you do so, trying to relate them to these four bullet points will help you to classify the concepts covered.

Emerging Trends in End-User Computing A Classification of the EUC Literature Through a thorough review of EUC Research Issues and Trends as published in key journals (19902000), Downey (2004) argues that EUC research can be logically divided into three dimensions: the end user, the technology, and the organisation. Of the articles reviewed, the end-user dimension included 203 total articles (42.4%), while both the technology and organisational dimensions had 138 total articles (28.8%). The following analysis gives a summary of the findings from Downey’s paper.

End-User Dimension Articles that focused on the end-user dimension reflected over 11 different themes (Table 1):

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Table 1. End-user dimension themes (from Downey, 2004) Theme

Number of Occurrences

%

Usage

91

22

Acceptance/Diffusion

62

15

Satisfaction

57

14

Training

45

11

Skills

43

10

Attitudes

33

8

Self-Efficacy

17

4

Anxiety

13

3

Task

9

2

Norms

6

1

Other

41

10

Total

417

100

Technology Dimension Table 2 lists the themes/variables in the technology dimension. Table 2. Technology-dimension themes (from Downey, 2004) Theme

Number of Occurrences

%

Group Impact

76

54

Individual Impact

42

30

System/Info Impact

17

12

Organisational Impact

4

3

Other

1

1

Total

140

100

These articles focused on the impact (to the end user) of the object technology. Most of the variables pertaining to this dimension considered the impact of the technology on either the decision process and/or group processes.

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Organisational Dimension Table 3 provides the themes for EUC articles that focused on the organisation (with an end-user variable). Table 3. Organisational-dimension themes (from Downey, 2004) Theme

Number of Occurrences

%

Project/Applic Development

55

37

EUC Support

40

27

Strategy

30

20

Acceptance/Diffusion

18

12

Other

5

4

Total

148

100

The most important finding in this study is the consistency of EUC research across all 11 years. EUC remains an important topic for researchers, one that has not diminished during this time. There was no significant difference in the number of articles in any of the three dimensions for any of the 11 years. This provides some compelling evidence of the importance of EUC for both researchers and practitioners. Across all three dimensions, for each individual theme, none showed a significant decline. Usage, satisfaction, training, attitudes, group and individual impact studies, and organisational studies were persistent through these 11 years. There were, however, some emerging trends that should be noted. The first is the upward trend in articles concerning the end-user dimension. From 1990 through 1994, all three dimensions were equally represented in research. But beginning in 1995, there were more enduser dimension articles than either of the other two. That disparity remained until the last year of the study (2000), when all three dimensions were again close to each other in number of articles: end-user dimension—16 articles; technology—14 articles; and organisation—nine articles. It remains to be seen whether the 2000 data are an anomaly for the end-user dimension or whether the gap is actually closing between the three. The second developing trend involves individual themes. Technology acceptance articles show a slow steady rise from 1996-2000. Group impact articles (technology dimension) and strategy articles (organisation dimension) also show increases. Perhaps the most significant increase is in the number of self-efficacy articles. There were six articles in 2000, significantly higher than in other years. In observing the trend in self-efficacy, 15 of 17 articles on self-efficacy were published in the last six years. The final developing trend involves articles dealing with the Internet or WWW-based studies. Of the 13 total in this study, all were submitted in the last five years. This is clearly an important and developing theme in the EUC research and practitioner community. The themes of EUC, including the end user, technology, and the organisation, remain important and pervasive. EUC research remains predominantly empirical in nature; almost 90% of the articles during this timeframe were empirical. The upward trend in the end-user dimension suggests that many unanswered questions remain in how and why individuals respond to and use technology. The persistence of usage and satisfaction, along with the rise in technology acceptance and computer self-efficacy, clearly indicate

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that the research focus on the individual remains. The Internet brings a new dimension to individuals and organisations, and is moving to the vanguard in EUC research. New technology in the future will continue to provide both researchers and practitioners with opportunities. But the critical link between the technology and the user remains one of the most viable interests in EUC.

Conceptual and Theoretical Trends I discussed earlier my own recommendation to look beyond the hard/soft debate toward critical social theory as a foundation for EUC issues. Before leaving this topic, I would just like to briefly refer to some alternative theoretical perspectives which you might find appealing. •

• •

There is a considerable body of work on socio-technical theory, and its relevance to EUC, which may be perceived at one level as a blend of social and technical issues within an organisational framework, should not be under estimated. For those interested in social theory, there are many approaches which may be used to complement the key underpinning already developed from critical social theory. Finally, cognitive psychology is a domain insufficiently addressed in looking at EUC issues, although some reference is made to approaches based on psychology both within this chapter and elsewhere in these volumes.

Overall, a wide range of theoretical and conceptual ideas is to be found in this publication, and dare I suggest that, as EUC moves increasingly toward a human-centered practice, theoretical grounding will develop accordingly.

CONCLUSION What I have tried to present here is a flavour of the contemporary research in end-user computing, with some ideas of what is most interesting researchers and practitioners today, and where the domain seems to be heading in the future. The overwhelming conclusion to be drawn from studies so far is that there is real danger in privileging “computing” ahead of the “end user”. It is not sufficient to continue, in end-user environments, with approaches favoured in computer systems development. This simply puts the computer at the centre, and treats the user as at best secondary and at worst passive in the development process. For end-user developments, users are not in some way incidental to the approach—they are a fundamental element which lies at the core of the issues to be addressed. For those who are involved in business systems design, this often proves not to be a problem. Many who have been in management development roles find that placing the user at the heart of the process is only what they have always done. Nevertheless, it can still prove problematic when individuals undertaking these tasks come into contact with computer systems designers. I have been on both sides of the fence in my business and academic careers, and it is my considered view that those who most need to adapt to these changing conditions are the computer analysts and programmers. End-user design frequently takes them out of their instrumental, pseudo-scientific comfort zone, but they must adapt—professional end-user development depends on this. These volumes will offer you a number of perspectives on EUC. I hope you enjoy reading them as much as I have, and I hope that they provide you with some food for thought and, perhaps, more than a little helpful advice.

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Cronan, T. P., & Douglas, D. E. (2006). Information technology ethical behavior: Toward a comprehensive ethical behavior model. Journal of Organizational End User Computing, 18(1), i-x. Downey, J. P. (2004). Toward a comprehensive framework: EUC issues and trends (1990-2000). Journal of Organizational End User Computing, 16(4), 1-16. Drake, P., & Clarke, S. A. (2001). Information security: A technical or human domain? Managing Information Technology in a Global Environment, Toronto, Canada. Hershey, PA: Idea Group Publishing. Fayol, H. (1949). General and industrial management. London: Pitman. Flood, R. L. (1995). Total systems intervention (TSI): A reconstitution. Journal of the Operational Research Society, 46, 174-191. Flood, R. L., & Romm, N. R. A. (1996). Diversity management: Triple loop learning. Chichester: Wiley. Foroughi, A., Perkins, W. C., et al. (2005). A comparison of audio-conferencing and computer conferencing in a dispersed negotiation setting: Efficiency matters! Journal of Organizational End User Computing, 17(3), 1-26. Habermas, J. (1971). Knowledge and human interests. Boston: Beacon Press. Habermas, J. (1976). On systematically distorted communication. Inquiry, 13, 205-218. Habermas, J. (1984). The theory of communicative action. Cambridge, UK: Polity Press. Hasan, B. (2006). Effectiveness of computer training: The role of multilevel computer self-efficacy. Journal of Organizational End User Computing, 18(1), 50-68. Hazari, S. (2005). Perceptions of end-users on the requirements in personal firewall software: An exploratory study. Journal of Organizational End User Computing, 17(3), 49-65. Hirschheim, R., & Klein, H. K. (1989). Four paradigms of information systems development. Communications of the ACM, 32(10), 1199-1216. Horton, K. S., & Dewar, R. G. (2005). Learning from patterns during information technology configuration. Journal of Organizational End User Computing, 17(2), 26-42. Jackson, M. C. (1990). Beyond a system of systems methodologies. Journal of the Operational Research Society, 41(8), 657-668. Jackson, M. C. (2000). Systems approaches to management. New York: Kluwer/Plenum. Jones, M. C., & Price, R. L. (2004). Organizational knowledge sharing in ERP implementation: Lessons from industry. Journal of Organizational End User Computing, 16(1), 21-41. Jones, N. B., & Kochtanek, T. R. (2004). Success factors in the implementation of a collaborative technology and resulting productivity improvements in a small business: An exploratory study. Journal of Organizational End User Computing, 16(1), 1-20. Kanellis, P., & Paul, R. J. (2005). Users behaving badly: Phenomena and paradoxes from an investigation into information systems misfit. Journal of Organizational End User Computing, 17(2), 64-91. Katz, D., & Kahn, R. L. (1978). The social psychology of organisations. New York: Wiley.

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Kim, Y. (2006). Supporting distributed groups with group support systems: A study of the effect of group leaders and communication modes on group performance. Journal of Organizational End User Computing, 18(2), 20-37. Larsen, T. J., & Sorebo, O. (2005). Impact of personal innovativeness on the use of the Internet among employees at work. Journal of Organizational End User Computing, 17(2), 43-63. Ling, C.-P., & Ding, D. G. (2006). Evaluating group differences in gender during the formation of relationship quality and loyalty in ISP service. Journal of Organizational End User Computing, 18(2), 38-62. Ma, Q., & Liu, L. (2005). The role of Internet self-efficacy in the acceptance of Web-based electronic medical records. Journal of Organizational End User Computing, 17(1), 38-57. Mao, J.-Y., & Brown, B. R. (2005). The effectiveness of online task support vs. instructor-led training. Journal of Organizational End User Computing, 17(3), 27-46. Mehandjiev, N., Suttcliffe, A., & Wood-Harper, A. T. (2006). Technology interaction aspects of end-user development. Journal of Organizational End User Computing, 18(4), i-iv. Midgley, G. (1997). Dealing with coercion: Critical systems heuristics and beyond. Systems Practice, 10(1), 37-57. Mingers, J., & Gill, A. (Eds.) (1997). Multi methodology. Chichester: Wiley. Morris, S. A., & Marshall, T. E. (2004). Perceived control in information systems. Journal of Organizational End User Computing, 16(2), 38-56. Mumford, E. (1983). Participative systems design. The Computer Journal, 27(3), 283. Mumford, E., & Henshall, D. (1978). A participative approach to computer systems design. London: Associated Business Press. Mumford, E., Hirschheim, R., et al. (Eds.) (1985). Research methods in information systems. Amsterdam: Elsevier. Nicholls, M. G., Clarke, S.. et al. (Eds.) (2001). Mixed-mode modelling: Mixing methodologies for organisational intervention. In Applied optimization. Dordrecht, Netherlands: Kluwer Academic. OASIG. (1996). Why do IT projects so often fail? OR Newsletter, 309, 12-16. Schou, C. D., & Trimmer, K. J. (2004). Information assurance and security. Journal of Organizational End User Computing, 16(3), i-vii. Spitler, V. K. (2005). Learning to use IT in the workplace: Mechanisms and masters. Journal of Organizational End User Computing, 17(2), 1-25. Stowell, F. A., & West, D. (1994). “Soft” systems thinking and information systems: A framework for client-led design. Information Systems Journal, 4(2), 117-127. Taket, A., & White, L. (1996). Pragmatic pluralism: An explication. Systems Practice, 9(6), 571-586. Taylor, F. W. (1947). The principles of scientific management. New York: Harper and Row. Thompson, R., Compeau, D., et al. (2006). Intentions to use information technologies: An integrative model. Journal of Organizational End User Computing, 18(3), 25-46.

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Troutt, M. D. (2005). Spreadsheet usage, some new challenges and opportunities. Journal of Organizational End User Computing, 17(1), i-v. Ulrich, W. (1983). Critical heuristics of social planning: A new approach to practical philosophy. Berne: Haupt. Watson, H., & Wood-Harper, T. (1995). Methodology as metaphor: The practical basis for multiview methodology. Information Systems, 5, 225-231. Weber, M. (1947). The theory of social and economic organization. London: The Free Press, Collier Macmillan. Westin, S. (2005). Cutting curbs on the information highway: Embracing adaptive technology to broaden the Web. Journal of Organizational End User Computing, 17(3), i-x. Wetherbe, J. C., & Vitalari, N. P. (1994). Systems analysis and design: Best practices. St. Paul, MN: West. Wood-Harper, A. T., Antill, L., et al. (1985). Information systems definition: The multiview approach. London: Blackwell. Yellen, R. E. (2006). A new look at learning for the organization. Journal of Organizational End User Computing, 18(3), i-iv. Yi, M. Y., & Im, K. S. (2004). Predicting computer task performance: Personal goal and self-efficacy. Journal of Organizational End User Computing, 16(2), 1-19.

Section 1

Fundamental Concepts and Theories This section serves as a foundation for this exhaustive reference tool by addressing crucial theories essential to the understanding of electronic commerce. Chapters found within these pages provide an excellent framework in which to position electronic commerce within the field of information science and technology. Insight regarding the critical incorporation of global measures into electronic commerce is addressed, while crucial stumbling blocks of this field are explored. With over 15 chapters comprising this foundational section, the reader can learn and chose from a compendium of expert research on the elemental theories underscoring the electronic commerce discipline.

Index

Chapter 1.1

Privacy, Risk Perception, and Expert Online Behavior: An Exploratory Study of Household End Users Judy Drennan Queensland University of Technology, Australia Gillian Sullivan Mort Griffith University, Australia Josephine Previte The University of Queensland, Australia

ABSTRACT Advances in online technologies have raised new concerns about privacy. A sample of expert household end users was surveyed concerning privacy, risk perceptions, and online behavior intentions. A new e-privacy typology consisting of privacyaware, privacy-suspicious, and privacy-active types was developed from a principal component factor analysis. Results suggest the presence of a privacy hierarchy of effects where awareness leads to suspicion, which subsequently leads to active behavior. An important finding was that privacy-active behavior that was hypothesized to increase the likelihood of online subscription and purchasing was not found to be significant.

A further finding was that perceived risk had a strong negative influence on the extent to which respondents participated in online subscription and purchasing. Based on these results, a number of implications for managers and directions for future research are discussed.

Introduction The number of Internet users has continued to grow, with a worldwide population of 934 million as of the final quarter of 2002 (Nua Internet Surveys, 2003). In addition, household users of the Internet are increasing rapidly with 136.6 million Americans and 8.79 million Australians

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Privacy, Risk Perception, and Expert Online Behavior

having online access at home (Greenspan, 2004). As this burgeoning number of household end users of the Internet embarks on new activities online, the issue of privacy and security becomes a major concern for consumers (Milne & Rohm, 2000; Sheehan & Hoy, 2000), governments, and consumer organizations (Consumer Reports Org, 2002; Federal Trade Commission, 1996, 2000a, 2000b; Office of the Federal Privacy Commissioner, 2001a). As a result, specific calls have emerged for end-user research on security and privacy to be extended to household end users (Troutt, 2002). Businesses also recognize privacy as an important positioning tool with, for example, the ISP EarthLink positioning itself on privacy in its competition against the dominant company AOL (Sweat, 2001). Thus, as more users move to the online environment and become more expert in that environment, privacy in the electronic domain (e-privacy) needs specific research attention (Perri 6, 2002). Given the growing number of competent experienced Internet users, e-privacy issues need to be reframed and investigated in the context of their online expertise. This article focuses on the expert household end user, defined as highly competent experienced Internet users who consistently spend time online, are likely to have subscribed to commercial and/or government Web sites, to have purchased online, and to have Internet access via a home computer. The article proceeds as follows. First, privacy conceptualizations and typologies are examined. Second, theoretical approaches to consumers’ online privacy and risk perceptions are addressed, together with the argument that privacy issues from the perspective of the expert online household user need to be considered. Third, the methodology is explained, and results of the two studies undertaken are provided. Finally, findings are discussed, management implications are drawn, and future research directions are identified.

Conceptualization of Privacy and Typologies The protection of privacy has received growing attention in the literature (Buchholz & Rosenthal, 2002; Charters, 2002; Cook & Coupey, 1998; Hoy & Phelps, 2003; Milne & Rohm, 2000; Miyazaki & Fernandez, 2001) in conjunction with the advances in technology and its applications to the Internet (Sappington & Silk, 2003). There are a number of conceptualizations of privacy, but fundamentally, privacy has been viewed as the right to be left alone (Warren & Brandeis, 1890), manifesting in the definition that other people, groups, or entities should not intrude on an individual’s seclusion or solitude (McCloskey, 1980). For many people, there is now an expectation of privacy as a basic consumer right (Goodwin, 1991). However, privacy is not enshrined in constitutional rights nor is it grounded as essential to the operation of a democracy, as free speech is held to be essential in countries like the United States of America. Privacy is, thus, a weak right (Charters, 2002) that may be overridden easily by other legislative rights. Privacy and anonymity also are associated for many with personal freedom and liberty. Specifically, privacy is considered to exist when consumers are able to control their personal information (McCloskey, 1980) or restrict the use of their personal information (Culnan, 1995; Nowak & Phelps, 1995). Some futurists, for example George Orwell in 1984 (Orwell, 1951), foreshadowed the interest of the state in observing the citizen. More recently, it is the motivation of business in monitoring and maintaining surveillance of customers, which is considered likely to undermine anonymity, privacy, and, thus, perhaps freedom, that has received the most attention (Retsky, 2001). Implicit in the conceptualization of privacy as the ability of individuals to restrict information is the recognition that there may emerge a community consensus regarding which type of personal information is not for public consumption (Charters, 2002).

Privacy, Risk Perception, and Expert Online Behavior

It has been accepted (Westin for Federal Trade Commission, 1996) that consumers fall into three basic types with regard to privacy in the general sense: the privacy fundamentalists, who always tend to choose privacy controls over consumer benefits; the privacy unconcerned, who tend to forgo most privacy claims in exchange for service benefits; and the privacy pragmatists, who weigh the benefits of various consumer opportunities and services against the degree of personal information sought. Recently, the Westin typology has been tested and extended to apply specifically to the online consumer. The study (Sheenhan, 2002) found that online consumers in the U.S. are better represented by a four-part typology consisting of the Unconcerned, the Circumspect, the Wary, and the Alarmed Internet users. This four-part typology for online consumers was established as a result of the identification of the high percentage of pragmatists (81%) compared to the Westin study (50%). Pragmatists thus were divided into the Circumspect and the Wary groups. Fundamentalist were renamed Alarmed Internet users. The Unconcerned Internet users rarely complain about privacy breaches and when registering at Web sites rarely provide inaccurate information. The next group, the Circumspect Internet users, have minimal concerns about privacy overall, although there are some situations that may cause them to have higher levels of concern about privacy. In addition, they give incomplete information quite often when registering for Web sites. The third group, the Wary Internet users, have a moderate level of concern with most online situations. They experience higher than average concern for Internet privacy, including clandestine data collection practices. They occasionally complain about privacy breaches and are likely to provide incomplete information when they sporadically register for Web sites. The final group, the Alarmed Internet users, are most likely to complain about privacy breaches, rarely register

for Web sites, and when they do, they are likely to provide incomplete and inaccurate data. Sheehan and Hoy (2000) also conducted a survey using 15 privacy scenarios. Results from this study indicate that privacy dimensions first are related to control over and collection of information. The other two privacy dimensions refer to privacy within a short-term transactional relationship and an established long-term relationship. The typologies of Sheehan and Hoy (2000) and Sheehan (2002) relate to early types of technology use, such as e-mail, and construct the end user in a passive role where he or she only becomes aware of privacy issues after a breach has occurred. The studies are descriptive and do not link privacy types overtly to consequent online behaviors. Moreover, the typologies do not focus on the issue of expert end users’ privacy concerns.

Theoretical Approaches to Consumers’ Online Privacy, Risk Perception, and the Expert Online Consumer A recent literature review has criticized privacy typologies such as Sheehan (2002) based on the conventional segmentation of fundamentalist, unconcerned, and pragmatist on a number of grounds (Perri 6, 2002). These include criticisms that the typology bears no relationship to risk in other consumption practices and has no underlying theoretical rationale to explain the privacy types. Moreover, at a practical level, criticism has been raised that the most common type, the pragmatic type, is too vague and likely to lead to business complacency. A new way of understanding privacy risk perception has been proposed (Perri 6, 2002) based on neo-Durkheimian institutionalist theory, using the social group as the unit of analysis. Perri 6 (2002) did not undertake empirical research to test the validity of the typology, and, owing to unfamiliar theoretical framework in neo-

Privacy, Risk Perception, and Expert Online Behavior

Durkheimian institutionalist theory (rather than the more usual individual psychology paradigm), it has limited practical utility for online privacy research. However, the conceptualization appears to offer some ability to understand how consumers may move in their privacy risk perception and offers marketers some insight into how privacy communication may be framed. Consistent with Perri 6 (2002) and others (Ho & Ng, 1994; Miyazaki & Fernandez, 2001; Hoy & Phelps, 2003) we argue that perceived risk is fundamental to understanding consumer concerns about privacy online and that the relationship among privacy, risk, and online purchase intentions is central to enhancing our understanding the behavior of expert online household end users. The issue of perceived risk in consumer purchase has been addressed by a large number of studies over the years (see, for example, Mitchell, 1999). Perceived risk can be defined as an expectation of loss (Stone, 1987) or “consumer’s subjective belief of suffering a loss in pursuit of a desired outcome” (Pavlou, 2003, p. 109). Viewed in this way, risk is strongly negatively correlated with intentions and behavior (Stone, 1987). Pavlou (2003) suggests that behavioral uncertainty is created as a result of Web retailers misrepresenting products, leaking private information, providing misleading advertising, using false identities, and denouncing warranties. Specifically, consumers may perceive risks in terms of monetary losses (economic risk), the purchase of unsafe products and services (personal risk), imperfect monitoring of products (seller performance risk), and disclosure of private information (privacy risks). Environmental uncertainty is also an important issue, leading consumers to fear theft of personal information online. Consumer intentions to transact business online are thus “contingent upon beliefs about Web retailers that are partly determined by behavioral and environmental factors,” and therefore, perceived risk is likely to negatively influence consumer’s intentions to

undertake transactions on Internet sites (Pavlou, 2003). Risk has been studied in a number of contexts such as food technology (Frewer, 1994), banking (Ho & Ng, 1994), and retail patronage mode (e.g., mail order, catalogue, and in-home shopping) (Festerand, 1986; Schiffman, Schus, & Winer, 1976). Different types of perceived risk have been identified, including functional, physical, financial, social, and psychological risk (Kaplan, Szybillo, & Jacoby, 1974). Saythe (1999) studied the risk referred to as “the security and reliability of transactions over the Internet,” a type of physical risk, and found that this type of risk was a significant barrier to diffusion of Internet banking. Bahatnagar, Misra, and Rao (2000) examined financial and product risk in purchasing on the Web, where financial risk is related to the possibility of credit card fraud. Product risk was not as important as financial risk in predicting the likelihood of online purchases. Perceived risk generally produces wariness or risk aversion and leads to a variety of risk handling behaviors, which include buying well-known major brands, brand loyal behavior, seeking information, wider search, increased use of use of word-of-mouth information sources, a preference for congruent rather than incongruent products in a product category, or avoiding purchase altogether (Campbell & Goodstein, 2001; Dowling & Staelin, 1994; Roselius, 1971). Concerns about privacy were not found to affect online purchasing rates directly; neither was concern about online retailer fraud, such as nondelivery of goods or misrepresentation of goods (Miyazaki & Fernandez, 2001). Only the general perceived risk of online purchase and what was termed in the study “system security issues,” such as unauthorized access to personal and credit card information, were found to directly affect rates of online purchase. Empirical research examining privacy concerns and experienced online consumers is begin-

Privacy, Risk Perception, and Expert Online Behavior

ning to emerge. For example, Graeff and Harmon (2002) undertook a study of U.S. consumers and asked about their privacy concerns, use, and familiarity with loyalty cards and online purchase behavior. Those with online purchase experience were significantly more likely than non-purchasers to consider that customers should be informed and have a say in such information-gathering and selling practices. In addition, Koyuncu and Lien (2002) found that those with more online experience in a private and secure home environment were more likely to purchase on the Internet. While there has been some attention to the experienced online consumer and privacy concerns, no research to date has empirically developed a typology of experienced “expert” household end users nor sought to relate these typologies to online behavior. Expert end users are of particular interest because they are becoming the dominant group online. We are rapidly exiting the era when most online household end users were novices with low levels of knowledge and experience in the online environment. More often, a purposeful motivation has replaced random surfing of the Internet (Rodgers & Sheldon, 2002). Experts are an important population to sample in order to answer questions about consumer privacy protection strategies online, because they are better able to distinguish between relevant and irrelevant information and have more differentiated and organized knowledge (Larkin, McDermott, Simon, & Simon, 1980). This research addresses the need to develop an e-privacy typology of expert household end users and relate these privacy types to online behavior. Perceived risk is used as a central theoretical foundation. Informed by typologies developed in earlier research (Perry 6, 2002; Sheehan, 2002; Sheehan & Hoy, 2000), we integrate government privacy guidelines (www.dcita.gov.au) for end users to ascertain the dimensions of privacy concerns. The next section presents the method undertaken for two studies: Study 1 empirically

derives a typology of e-privacy dimensions for expert household end users; Study 2 tests the causal relationships among these derived e-privacy dimensions, perceived risk, and online subscription and purchasing behaviors.

Methodology The Sample A sample of 76 expert household end users was recruited for the present study by surveying an Internet marketing class of university e-commerce students who all had access to the Internet via their home computers. There was a 91% response rate to the survey. Sixty-three percent of respondents were male, and 37% were female. Ninety-four percent were in the age range of 18 to 25 years old, while 6% were over 25 years old. A convenience sample of college students was considered appropriate for the current study, because demographically, they share characteristic of the stereotypical user—young, university/college-educated males. Importantly, representation of women in the sample corresponds to the changing gender trend in user statistics over time that indicates a growing population of educated women online (Rainie & Kohut, 2000). It is argued, therefore, that university or college students are representative of a dominant cohort of online users for the following reasons: the tertiary student group is the most connected segment of the population in the United States, with 93% of American college students regularly using the Internet (Nua Internet Surveys, 2002); and a similar trend is evident in the Australian population (Australian Bureau of Statistics, 2004). In addition, their visits to online shopping sites are growing dramatically (Nua Internet Surveys, 2002). Though no specific data were available for Australia, it has further been predicted that U.S. and European teenagers are likely to spend $10.6 billion U.S. online by 2005

Privacy, Risk Perception, and Expert Online Behavior

(Nua Internet Surveys, 2002). Thus, a sample drawn from university students is appropriate, as it is drawn from an active and rapidly growing segment of experienced and frequent users of the Internet. Additionally, this sample was considered to be illustrative of expert household end users, as it is descriptive of experiential behavior of Internet users based on the following criteria. First, survey respondents were second-year ecommerce students who had completed courses in introductory e-commerce business studies and Web-based design and development subjects. Therefore, they meet the criteria of competence and experience. Second, as e-commerce students, they were required by their studies to spend extensive time online (approximately 20 hours per week). They thus meet the criterion of consistent time spent online. Third, expert consumers also are more likely to have subscribed to a Web site and purchased online. Approximately 54% of the sample population had purchased goods or services over the Internet. This is well above the population average for online purchasing, where only 10% had done so (Australian Bureau of Statistics, 2001). In addition, 80% of the sample had subscribed to commercial or government Web sites by exchanging personal information for free services. Thus, the sample meets the criterion of having experience in subscribing to a Web page and purchasing online. Collecting and interpreting data about Internet use is not straightforward because of inadequacies in the sampling frame. A number of factors compromise random sampling statistical measures in Internet research, such as users holding multiple e-mail accounts and maintaining different identities to log into different commercial and non-commercial Web sites. Therefore, while numerous Internet directories are available online, their reliability is questionable compared to a sampling frame such as the commercially controlled and updated telephone listings by telecommunication companies. Consequently,

the Internet population typifies characteristics of a hidden population (Heckathorn, 1997). The defining characteristics of a hidden population are that no sample frame exists, as the size and boundaries of the population are unknown. Other researchers have identified similar problems in conducting Internet research (Aladwani, 2002; Wyatt, Thomas, & Terranova, 2002) and consider the appropriateness of university/college students a useful representative sample of Internet and computer users (Wierschem & Brodnax, 2003). Nevertheless, a convenience sample reduces the generalizability of the findings to the larger Internet population. However, this sample was considered adequate and useful for the current research to address expert Internet users’ privacy concerns. Furthermore, it is argued that the expert online user will continue as the dominant Internet cohort in light of emergent research that indicates that less educated, younger Internet users are logging off (Katz & Rice, 2002). Arguably, education is a significant demographic indicator in continued Internet usage, combined with other access factors (income, etc.). Finally, when it is borne in mind that response rate to sample surveys are often low and declining, the research differences between random and convenience samples in terms of their representativeness is not always as great as some researchers wish to imply (Bryman & Cramer, 2001).

Survey Measures Expert users were surveyed using a self-administered instrument, including a participant’s information sheet and instructions for participants. The survey instrument was developed from three main sources. First, questions relating to e-privacy issues were derived from the Australian Federal Government privacy fact sheet concerning consumers’ shopping on the Internet (Department of Communications Information Technology and the Arts, 2002). This fact sheet was developed as a result of extensive research by the Australian

Privacy, Risk Perception, and Expert Online Behavior

Federal Government and reflects international best practice procedures for online consumer privacy. As such, it identifies key privacy protection indicators applicable to household online users when interacting and purchasing on the Internet. These privacy protection indicators were considered particularly appropriate for use in this study, as they encompass both attitudes and behaviors toward online privacy protection. Second, a three-item perceived risk scale (Jarvenpaa & Tractinsky, 1999) was modified to reflect risk relating to online purchasing and subscription. Specifically, two items (the first pertaining to safety of using a credit card to purchase online and the second to risk online compared to other ways of purchasing) were slightly modified to reflect online purchasing risk. A new item pertaining to perceptions of risk in revealing personal details online, if requested, was included to tap concerns relating to Web site subscription. While the original scale had a Cronbach’s alpha of 0.65, the modified scale had a Cronbach’s alpha of 0.58, which was a little low (Nunnally, 1989) but was accepted as satisfactory for the purposes of this exploratory research. Third, two questions were developed to ascertain whether respondents had purchased online or had disclosed personal information to subscribe to a Web site. These items were aggregated into a single item termed “online subscription and purchasing” further discussed in subsequent paragraphs. The preliminary instrument was pilot tested and reviewed for clarity by postgraduate students and the article’s authors and accepted without further revision. The survey instructions informed participants that the aim of the research was to assess awareness of online privacy issues regarding requested personal information when subscribing to Web sites or purchasing over the Internet and their perception of any risks involved in sharing information online. All privacy and risk items were measured using five-point Likert scales, and questions relating to online subscription and purchase behavior were dichotomous.

A construct representing online subscription and purchasing transactions that involve disclosure of personal information was developed. Online subscription is a form of consumer transaction that can be described as a secondary exchange, where there is a non-monetary exchange of personal information for perceived value from the online organization in terms of quality service, prize incentives, or relationship building (Culnan & Bies, 2003). Online purchasing, on the other hand, is the first exchange, whereby money or other goods is given in exchange for goods or services (Culnan & Bies, 2003). Nonetheless, in the online environment, personal information also must be disclosed in the first exchange. Two dichotomous variables — online subscriptions and online purchasing — which provided data relating to whether respondents actually subscribed and purchased online, were combined to form a construct with ordinal properties representing no online subscriptions or purchases, online subscriptions only, online purchases only, and online subscriptions and purchases.

Study Results Study 1: Dimensionality of E-Privacy Privacy dimensions were developed by submitting 12 privacy items to a principal components procedure with a varimax rotation. This analysis yielded three orthogonal factors with eigen values greater than 1.0, explaining 52.21% of the variance within these data. Factor loadings of less than 0.3 were omitted from the privacy factors, as illustrated in Table 1. The final analysis, therefore, included 11 items, as one did not load above 0.3 on any of the factors. The grouping of statements provided insights into the interpretation of the three privacy factors. As shown in Table 1, four items loaded on Factor I, which explains 21.3% of total variance. Factor I, labeled privacy aware, is reflective of consumer

Privacy, Risk Perception, and Expert Online Behavior

knowledge and sensitivity regarding the risks of sharing selected personal information online. It consists of four items: selective about information provision, awareness of sensitivity of tax file number, awareness of sensitivity of mother’s maiden name, and perception that companies require excessive personal information. The privacy aware factor is illustrative of users who guard information such as their mother’s maiden name and are selective about the information they provide during online exchanges because they are

aware of the risks involved. Significantly, these users feel that companies in the current marketplace require excessive or unnecessary information to complete an online exchange. Factor II, labeled privacy active, illustrates active behaviors that users undertake relating to privacy. This factor explains 16.4% of the variance within the sample (Table 1). Four items load onto this factor: seeking detailed information about privacy policies, demanding detailed information before purchasing online, requesting that

Table 1. Dimensionality of e-privacy factors Privacy Factors Privacy Statements

Mean*

S.D

Factor Loadings I

II

III

Privacy Aware Selective about providing information requested for transactions

3.54

1.10

.786

Aware of sensitivity of tax file number

3.39

1.61

.743

Aware of sensitivity of mother’s maiden name

2.38

1.62

.713

Feel online companies require excessive personal inform

3.34

1.15

-.538

Ask for detailed privacy policy information before purchasing online

2.41

1.44

.740

Look for privacy policies

2.04

1.26

.646

Request firms do not share personal information & details with other organisations

2.91

1.51

.578

Do not regularly use the same password

3.03

1.18

.425

Aware companies plan to share consumers personal information with other companies

3.64

1.20

-.339

Believe companies privacy policies are difficult to find

3.00

.99

Before transacting with businesses online, they check to ensure email and phone numbers are provided

3.40

1.52

.303

Privacy Active

.419

Privacy Suspicious

Eigenvalues % Variance–Factor % Variance–Cumulative

.693

.673

-.415

.570

21.332 21.332

16.421 37.7543

14.457 52.210

*All item means display pro-dimension agreement. Factor loadings of less than .3 have been omitted, and those judged to constitute a factor – the dominant loadings – are in boldface.

Privacy, Risk Perception, and Expert Online Behavior

firms do not share personal details provided by the consumer, and regularly changing passwords to guard their privacy. Those users who take action to guard their privacy are more likely to perceive reduced risk. If the benefits of disclosing information outweigh the risks, it is likely that they will divulge the personal information required for online subscription or purchasing transactions (Culnan & Bies, 2003). However, as Graeff and Harmon (2002) found, experienced online purchasers demand to be informed and/or have a say in the sharing between organizations of their personal information. Factor III, labeled privacy suspicious, highlights consumer concerns about company behavior and explains 14.45% of the total variance (Table 1). For example, these online household end users are concerned about how companies use personal information and potentially divulge users’ details. Three items load onto this factor: awareness of companies’ plans to share personal information, belief that company privacy policies are hard to find, and checking to ensure that e-mail and online phone numbers are provided before transacting with a company. The privacy suspicious construct highlights the point that users’ privacy concerns also extend to suspicions that commercial organizations may fail to guard consumer data and privacy. Previous privacy research (Sheehan &

Hoy, 2000) found that users’ concerns about privacy increased because of company management behavior, such as disclosing a consumer’s personal information without permission. In contrast, users’ beliefs that firms use fair information practices will ease privacy concerns (Culnan & Bies, 2003) and should reduce perceived risk.

Study 2: Relationship Between E-Privacy Dimensions, Perceived Risk, and Online Subscription and Purchasing Behavior Development of Hypotheses Figure 1 illustrates a number of inferred causal relationships between the expert end-user privacy dimensions empirically derived in Study 1: perceived risk and online subscription and purchasing behaviors. These were tested in the second stage of this exploratory research using a probabilistic approach to causation. De Vaus (2001) defines probabilistic approaches to causation as the argument that a given factor increases (or decreases) the probability of a particular outcome. We now develop the hypotheses that drive Study 2 and discuss the relationship between privacy concerns, risk, and outcome behavior in terms of the e-privacy dimensions of privacy awareness, privacy

Figure 1. A model depicting the relationships among privacy dimension, perceived risk, and online subscription and purchasing

Privacy, Risk Perception, and Expert Online Behavior

suspicion, and privacy active derived in Study 1. We present two lines of argument. In the first, we adopt a hierarchy of effects model of expert household end users’ privacy concerns, relating awareness to suspicious to active. The hierarchy of effects is a well-recognized marketing model (Lavidge & Steiner, 1961) that proposes that consumers move through subsequent stages of awareness (think), affection (feel), and conation (do). Moreover, this model has been used specifically in Internet-related research (Huizingh & Hoekstra, 2003) to describe attitudinal changes of online consumers leading to behavioral changes after visiting a Web site. In the second line of argument, the relationships among the stages in the privacy hierarchy and perceived risk are proposed. We then propose a relationship among the end stage of the privacy hierarchy, privacy active, and online subscription and purchasing intentions. We conclude with a proposed relationship among perceived risk, and online subscription and purchasing behavior. The proposed relationships are shown in Figure 1. Privacy awareness and the extent of knowledge about privacy issues have been raised by a number of researchers (Graeff & Harmon, 2002). However, there has been little research to examine the impact of this awareness on subsequent privacy attitudes and protective behaviors. Dhillon and Moore’s (2001) research suggests that as consumers become more aware of privacy issues, they question how firms use the information that is collected about them. These authors suggest that the provision by consumers of such information should be made discretionary. This questioning and apparent suspicion aroused in consumers leads us to hypothesize that higher levels of privacy awareness are related positively to increased levels of privacy suspicion (Hypothesis 1a). In proposing the next stage of the privacy hierarchy, a relationship between privacy suspicious attitudes and privacy active behaviors, we adopt a hierarchy of effects argument. We argue that privacy suspicion of company online behavior may

10

lead to proactive behavior by expert household end users to protect their privacy; that higher levels of privacy suspicion lead to privacy active behavior (Hypothesis 1b). Moreover, consistent with our hierarchy of effects approach whereby awareness leads to affect before action, we propose that there will be no direct link between privacy awareness and privacy active behavior. This leads to Hypothesis 1c that privacy awareness does not directly affect privacy active behavior. We now consider the relationships between the stages of the privacy hierarchy and perceived risk. Research by Novak and Phelps (1995) suggests that privacy awareness leads to a greater perception of threat to consumer privacy. We thus hypothesize that higher levels of end user privacy awareness lead to heightened perceived risk (Hypothesis 2a). Culnan and Bies (2003) argue that consumers will perceive disclosure of personal information to be low risk, if they believe the company to be open and honest about their information practices. Conversely, it can be argued that if end users are suspicious of a company’s honesty, their perceived risk is likely to be heightened. Therefore, we hypothesize that the extent of end-user suspicion of a company will be related positively to perceived risk (Hypothesis 2b). We have argued that a hierarchy of effects exists for privacy and that privacy awareness leads to privacy suspicion, which subsequently leads to privacy active behavior. It is further argued that privacy active behavior by expert end users is also influenced by perception of risk. Thus, a relationship between higher levels of perceived risk and privacy active behavior is proposed (Hypothesis 3). Returning to the end stage of the privacy hierarchy, we suggest that privacy active behavior influences online subscription and purchasing. We argue that expert household end users who take action to protect their privacy are then more likely to subscribe to Web sites and to make online purchases. This leads us to hypothesize that high levels of privacy active behavior are related

Privacy, Risk Perception, and Expert Online Behavior

positively to online purchase and subscription (Hypothesis 4). Finally, relying on research by Miyazaki and Fernandez (2001) and Pavlou (2003), which suggests that risk perceptions of Internet privacy relate to online purchasing behavior, we propose that higher levels of perceived risk negatively influence online subscription and purchasing (Hypothesis 5). H1a: The extent of end users’ awareness of threats to privacy in the online environment will be related positively to their privacy suspicious attitudes. H1b: The extent of end users’ privacy suspicious attitudes will be related positively to their online privacy active behavior.

H1c: No direct relationship will be found between privacy awareness and online privacy active behavior. H2a: The extent of end users’ awareness of threats to privacy in the online environment will be related positively to perceived risk. H2b: The extent of end users’ suspicious attitudes toward company online behavior will be related positively to perceived risk. H3: The extent of end users’ perceived risk will be related positively to active online privacy protection behavior. H4: The extent of end users’ active online privacy protection behavior will be related positively to online subscription and purchasing behavior.

Table 2. Results of regression analysis of e-privacy dimensions influencing perceived risk and privacy active behavior (a) Effect of privacy awareness on privacy suspiciousness Adj R Square = .162 Variable Privacy Aware (H1a)

Sig .000

F= 14.549 β

t

.417

3.814

.000

(b) Effect of privacy awareness and privacy suspiciousness on privacy active behavior Adj R Square = .217 Variable

Sig .000

F= 10.406** β

t

Privacy Suspicious (H1b)

.353

3.001

.004

Privacy Aware (H1c)

.225

1.912

.060

(c) Effect of privacy awareness and privacy suspiciousness on perceived risk Sig .000

Adj R Square = .286

F= 15.039

Variable

β

t

Privacy Aware (H2a)

.041

.369

.713

Privacy Suspicious (H2b)

.535

4.819

.000

(d) Effect of perceived risk on privacy active behavior Adj R Square = .264 Variable Perceived risk (H3)

Sig .000

F= 27.148 β

t

.523

5.210

.000

11

Privacy, Risk Perception, and Expert Online Behavior

H5: The extent of end users’ perceived risk will be related negatively to online subscription and purchasing behavior.

Regression Analyses To test these hypotheses, simple and multiple regression analyses were employed to examine relationships among the privacy awareness, privacy suspicious, privacy active, and perceived risk constructs. Multinomial logistic regression analyses were used to examine the effect of these constructs on online subscription and purchasing. Results of simple and multiple regression analyses are reported in Table 2. Simple regression was used to examine the influence of privacy awareness on privacy suspicious (H1a) and findings show that 16% of privacy suspicious is explained by privacy awareness (Beta = 0.417, p < 01). Thus, Hypothesis 1a is supported. The influence of privacy awareness and privacy suspicious on privacy active behavior also was tested (H1b) and showed that 24% of privacy active behavior is explained with only privacy suspicious as statistically significant (Beta = 0.353, p < 01).

Thus, Hypothesis 1b is supported, and there is also support for the hierarchy of privacy effects model, as there is no direct relationship between privacy aware and privacy active (H1c). In the next step, multiple regression analysis was undertaken to test the influence of privacy awareness and privacy suspicious on perceived risk (H2a and H2b), and results show that 29% of variance is explained. However, only privacy suspicious (Beta = 0.535, p < 01) is statistically significant; thus, only Hypothesis 2b is supported. To test whether privacy active behavior was influenced by perceived risk (H3), simple regression was used, and results show that a positive relationship exists with 27% of privacy active behavior being explained by perceived risk (Beta = 0.353, p < 01); thus, Hypothesis 3 is supported. Multinomial logistic regression then was used to examine whether privacy active behavior was positively related to online subscription and purchasing (H4), and perceived risk was negatively related to online subscription and purchasing (H5). Results (refer to Table 3) show that only the negative influence of perceived risk is statistically significant. To establish that there

Table 3. Results of multinomial logistic regression of perceived risk and privacy active behavior on user subscription and consumer purchasing behavior online (a) Effect of perceived risk and privacy active behavior on online subscription and purchasing behavior Variables

-2 Log Likelihood

Chi Square

Sig

Intercept

135.780

16.308

.001

Privacy Active (H4)

122.114

2.642

.450

Perceived Risk (H5)

150.515

31.043

.000

(b) Effect of perceived risk, privacy active behavior, privacy suspiciousness and privacy awareness on online subscription and purchasing behavior

12

Variables

-2 Log Likelihood

Chi Square

Sig

Intercept

135.917

14.593*

.002

Perceived Risk

145.856

24.532**

.000

Privacy Active

123.510

2.186

.535

Privacy Suspicious

123.941

2.618

.454

Privacy Aware

122.090

.767

.857

Privacy, Risk Perception, and Expert Online Behavior

were no influences on online subscription and purchasing from privacy awareness and privacy suspicious, multinomial logistic regression also was undertaken that included all four constructs. Only perceived risk was found to be a significant influence. Table 4 provides a summary of the results of the hypotheses.

Discussion The findings of this exploratory study reveal the dimensionality of e-privacy for a sample of expert online household end users: privacy aware, privacy suspicious, and privacy active. In addition, results suggest the presence of a privacy hierarchy of effects where awareness leads to suspicion, which subsequently leads to active behavior. An important finding was that privacy active behavior, which was hypothesized to increase the likelihood of online subscription and purchasing,

was not found to be significant. This is consistent with Donmeyer and Gross (2003), who found that those who took action to protect their privacy were also less likely to subscribe and purchase online. It seems that expert household end users feel that any privacy active behaviors that they undertake may be necessary but not sufficient to lead them to be more likely to engage in online subscription and purchasing. This is a possible explanation for the previous finding of Miyazaki and Fernandez’s (2001) research on consumers. Perceived risk was heightened by privacy suspicion but not simply by privacy awareness. This finding indicates that there may be some threshold level that must be achieved in the privacy hierarchy of effects before risk is perceived. Perceived risk was found to increase levels of privacy active behavior and decrease online subscription and purchasing behavior. Thus, it appears that it is not sufficient to consider privacy concerns alone but rather the interrelationship between privacy concerns and

Table 4. Summary of results of hypotheses H1a:

The extent of end users’ awareness of threats to privacy in the online environment will be positively related to their privacy suspicious attitudes.

Supported

H1b:

The extent of end users’ privacy suspicious attitudes will be positively related to their online privacy active behavior.

Supported

H1c:

No direct relationship will be found between privacy awareness and online privacy active behavior.

Supported

H2a:

The extent of end users’ awareness of threats to privacy in the online environment will be positively related to perceived risk.

Not Supported

H2b:

The extent of end users’ suspicious attitudes towards company online behavior will be positively related to perceived risk.

Supported

H3:

The extent of end users’ perceived risk will be positively related to active online privacy protection behavior.

Supported

H4:

The extent of end users’ active online privacy protection behavior will be positively related to online subscription and purchasing behavior.

Not supported

H5:

The extent of end users’ perceived risk will be negatively related to online subscription and purchasing behavior.

Supported

13

Privacy, Risk Perception, and Expert Online Behavior

perceived risk, if we are to understand the drivers of online subscription and purchasing for the expert online household end user. As the explanatory power of privacy awareness on suspicion is relatively low, other factors, such as personal attributes (Dowling & Staelin, 1994) and specific experiences, also may need to be considered in future research as additional triggers of perceived risk. The results of this study suggest that the task of building online confidence in terms of privacy issues is a complex one. The results, which indicate that action by users to protect privacy do not positively impact online and purchase behavior, suggest that expert online household end users may feel that at this stage the options available to protect their privacy are not sufficient. This suggests that companies may need to provide more effective privacy protective options to all online users. It is also possible that governments may need to legislate more effectively in this area and make available legal recourse to assist and protect end users. This research has advanced our understanding of e-privacy by putting forward a new typology of expert online household end users’ privacy concerns. The existing typologies (Perry 6, 2002; Sheehan, 2002; Sheehan & Hoy, 2000) are more appropriate to the earlier types of technology, such as e-mail, while our typology is relevant to more sophisticated uses, such as e-commerce. Moreover, the existing typologies construct the end user in a passive role, where they only become aware of privacy issues after a breach has occurred (e.g., when they receive e-mail from an unknown company). Our research acknowledges and incorporates a heightened sensitivity to privacy on the part of the end user, resulting from expertise in the online environment. The previous studies also are largely descriptive and, unlike our study, do not conceptualize privacy in a hierarchy of effects, nor do they link privacy concerns specifically to online behaviors.

14

Management Implications and Future Research The telephone, television, and now the Internet are just some of the technologies available to managers who are responsible for new considerations of issues related to privacy. Internet technologies are important for managers, because they transform the way in which goods and services are bought and sold and provide new opportunities for developing and maintaining longer-term relationships with household end users. However, if these relationships are to be sustained, household end users need to be reassured that organizational collection and use of personal data will not involve invasions of privacy. As suggested by the results in the present study, expert household end users may be concerned about how personal information is collected, shared, and used by companies in today’s marketplace. The important question for managers in the future is how to respond to these issues. Currently, management reactions to privacy concerns include a range of activities, such as adding privacy policies to Web sites, use of encryption methods, and security protocols to guard against misuse of sensitive and private information. Findings from this study suggest that managers need to consider whether technical security solutions are the answer to resolving consumer concerns about privacy online. As Katz (2002) states, a number of encryption methods are flawed, and anonymous remailer and other anonymity-guaranteeing services have been compromised by browser software. It appears that expert users may have become aware of such weaknesses in current technical approaches to Internet security. Expert household end users may respond to the provisions of detailed information and clarification about the steps a company will take to guard their personal information. For example, if companies wish to develop a long-term relationship with expert end users, they may need to

Privacy, Risk Perception, and Expert Online Behavior

provide, for example, details during a transaction of how the information is to be used and then discarded after each individual transaction. Research is needed to investigate expert household end users’ information requirements and their desired level of control over their personal information. Ultimately, improved privacy protection strategies and procedures are likely to enhance a company’s competitive position, because it will be able to retain customers who perceive lower levels of risk and are willing to enter longer-term partnerships. While this exploratory research has gone some way toward elucidating the dimensionality of privacy concerns of expert household end users and understanding the relationships among privacy concerns, perceived risk, and online subscription and purchasing, a more comprehensive study needs to be undertaken to confirm these findings. In addition, research is required to test the crossnational validity of the model. As highlighted previously, research also is needed to understand expert household end users’ information requirements and their desired level of control over the information provided during e-commerce and other transactions. Finally, the research agenda in this field also would benefit from a study on the perceived locus of risk and whether it is at the level of the vendor company, the product, or in the transaction medium itself. More specific information like this will allow management to direct its risk minimization strategies to the correct target and to have greater impact for the expert end user.

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Graeff, T., & Harmon, S. (2002). Collecting and using personal data: Consumers’ awareness and concerns. Journal of Consumer Marketing, 19(4), 302-318. Greenspan, R. (2004). Three-quarters of Americans have access from home. Click Z News Formerly Internet Advertising Report. Retrieved October 18, 2004, from http://www.clickz.com/ news/article.php/3328091 Ho, S. S. M., & Ng, V. (1994). A study of consumers risk perception of electronic payment systems. International Journal of Bank Marketing, 12(4), 26-38. Hoy, M. G., & Phelps, J. (2003). Consumer privacy and security protection on church Web sites: Reasons for concern. Journal of Public Policy & Marketing, 22(1), 58-70. Huizingh, E. K. R. E., & Hoekstra, J. C. (2003). Why do consumers like Websites? Journal of Targeting, Measurement and Analysis for Marketing, 11(4), 350. Jarvenpaa, S., & Tractinsky, N. (1999). Consumer trust in an Internet store: A cross-cultural validation. JCMC, 5(2). Kaplan, L., Szybillo, G.J., & Jacoby, J. (1974). Components of perceived risk in product purchase: A cross validation. Journal of Applied Psychology, 59, 287-291. Katz, J. E., & Rice, R. E. (2002). Social consequences of Internet use: Access, involvement, and interaction. Cambridge, MA: MIT Press. Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Models of competence in solving physics problems. Cognitive Science, 208, 317345. Lavidge, R. J., & Steiner, G. A. (1961). A model for predictive measurements of advertising effectiveness. Journal of Marketing, 25, 59-62. McCloskey, H. (1980). Privacy and the right to privacy. Philosophy, 55(211), 17-38.

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Milne, G. R., & Rohm, A. J. (2000). Consumer privacy and name removal across direct marketing channels: Exploring opt-in and opt-out alternatives. Journal of Public Policy & Marketing, 19(2), 238-249. Mitchell, V.-W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33(1/2), 163-195. Miyazaki, A., & Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. Journal of Consumer Affairs, 35(1), 27-44. Nowak, G. J., & Phelps, J. (1995). Direct marketing and the use of individual - Level consumer information: Determining how and why privacy matters. Journal of Direct Marketing, 9(3), 4660. Nua Internet Surveys. (2002). How many online? Retrieved April 1, 2003, from http://www.nua. com/surveys/how_many_online/index.html Nua Internet Surveys. (2003). Nielsen netratings: Global net population increases. Retrieved October 18, 2004, from http://www.Nua.Com/ Surveys/Index Office of the Federal Privacy Commissioner. (2001a). Privacy and the community [Web]. Retrieved February 2, 2003, from http://www. privacy.gov.au/publications/rcommunity.pdf Orwell, G. (1951). 1984. London: Secker and Warburg. Pavlou, P. (2003). Consumer acceptance of electronic commerce: Interating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. Perri 6. (2002). Who wants privacy protection and what do they want? Journal of Consumer Behaviour, 2(1), 80-100. Rainie, L., & Kohut, A. (2000). Tracking online life: How women use the Internet to cultivate relationships with family and friends.

Retsky, M. L. (2001). Just posting cookies agreement not enough. Marketing News, 35(20), 12-13. Rodgers, S., & Sheldon, K. M. (2002). An improved way to characterize Internet users. Journal of Advertising Research, 42(5), 85-94. Roselius, T. (1971, January). Consumer rankings of risk reduction methods. Journal of Marketing, 35, 56-61. Sappington, D., & Silk, A. (2003). Marketing’s information technology revolution: Implications for consumer welfare and economic performance: Overview of the special issue. Journal of Public Policy and Marketing, 22(1). Saythe, M. (1999). Adoption of Internet banking by Australian consumers. International Journal of Bank Marketing, 17(7), 324-334. Schiffman, L. G., Schus, S., & Winer, L. (1976). Risk perception as a determinant of in-home consumption. Journal of the Academy of Marketing Science, 4(4), 753-763. Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of privacy concern among online consumers. Journal of Public Policy and Marketing, 19(1), 62-73. Sheenhan, K. B. (2002). Toward a typology of Internet users and online privacy concerns. The Information Age, 18, 21-32. Stone, R. N., & Winter, F. W. (1987). Risk: Is it still uncertainty times consequences? In Belk, R.W. et al. (Eds.), Proceedings of the American Marketing Association, 261-265. Sweat, J. (2001). Earthlink: An ISP that customers can trust? InformationWeek, (851), 36. Warren, S. D., & Brandeis, L. D. (1890, December). The right for privacy. Harvard Law Review, 4, 193-220.

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Wyatt, S., Thomas, G., & Terranova, T. (2002). They came, they surfed, they went back to the beach: Conceptualizing use and new use of the Internet. In S. Woolgar (Ed.), Virtual society? Technology, cyberbole, reality. Oxford: Oxford University Press.

This work was previously published in Journal of Organizational and End User Computing, edited by M. A. Mahmood, pp. 1-22, copyright 2006 by IGI Publishing, formerly known as Idea Group Publishing (an imprint of IGI Global).

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19

Chapter 1.2

Gender and End-User Computing Laura Beckwith Oregon State University, USA Margaret Burnett Oregon State University, USA Shraddha Sorte Oregon State University, USA

INTRODUCTION Although gender differences in a technological world are receiving significant research attention, much of the research and practice has aimed at how society and education can impact the successes and retention of female computer science professionals. The possibility of gender issues within software, however, has received almost no attention, nor has the population of female end users. However, there is relevant foundational research suggesting that gender-related factors within a software environment that supports end-user computing may have a strong impact on how effective male and female end users can be in that environment. Thus, in this article, we summarize theory-establishing results from other domains that point toward the formation of grounded hypotheses for studying gender differences in end-user computing.

There has been much background research relevant to human issues of end-user computing, which we define here as problem-solving using computer software, also termed end-user programming in some of the literature (e.g., Blackwell, 2002; Green & Petre, 1996; Nardi, 1993). (See the glossary for definitions of these and related terms.) Despite this, few researchers have considered potential gender HCI issues and gender differences that may need to be accounted for in designing end-user computing environments. The most notable exception is Czerwinski’s pioneering research on the support of both genders in navigating through 3-D environments (Czerwinski, Tan, & Robertson, 2002; Tan, Czerwinski, & Robertson, 2003). Although individual differences, such as experience, cognitive style, and spatial ability, are likely to vary more than differences between gender groups, evidence from Czerwinski’s work as well as work in other domains, such as psychology and marketing, has found gender differences

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Gender and End-User Computing

relevant to computer usage. In fact, some research has shown that some software is (unintentionally) designed for males (Huff, 2002). One reason gender HCI issues in end-user computing are important is that ignorance of gender issues has already proven to be dangerous: today’s low percentage of computer science females (Camp, 1997) has been directly attributed to the past unawareness of gender issues in computer science education and in the workforce. There is a risk that if gender HCI issues in end-user computing environments are ignored, a similar phenomenon could occur with female end users.

WHAT COULD GO WRONG? What gender differences might matter in the design of end-user computing environments? Consider the following scenario in one particular end-user computing environment. Imagine a female teacher engaged in preparing a spreadsheet to track her students’ scores and to calculate ways of providing students with the best grades. Part of her process of preparing her spreadsheet is to test the spreadsheet. While she is engaged in testing, the system surprises her by decorating some of the spreadsheet cells, as in Figure 1.

The surprises were intentionally placed into the software by the designers relying on a strategy for end-user computing environments called Surprise-Explain-Reward (Wilson et al., 2003). The surprise, which was intended to capture the teacher’s attention and arouse her curiosity, reveals the presence of an “information gap” (Lowenstein, 1994). In this case the system is using the surprise to interest her in assertions (Burnett et al., 2003), which she can use to guard against future errors by specifying, for example, that the value of a cell calculating a grade average should always fall between 0 and 100. What could go wrong in surprising the user? According to Lowenstein’s information gap theory, a user needs to have a certain level of confidence in order to reach a useful level of curiosity (Lowenstein, 1994). However, given documented gender differences in computer confidence (Busch, 1995; Huff, 2002), the teacher’s level of computer confidence could interfere with the surprise’s ability to capture her interest. Returning to our scenario, suppose for this particular user, the surprise is effective at arousing her curiosity, she looks to the object that surprised her (the assertion) for an explanation. The explanation, viewed through a tooltip, includes the semantics, possible actions she can take (regarding the assertion), and the future reward(s) of taking the action. See Figure 1.

Figure 1. A spreadsheet calculating the average of three homework scores. Assertions about the ranges and values are shown above each cells’ value. For example, on HomeWork1 there is a user-entered assertion (noted by the stick figure) of 0 to 50. The other three cells have assertions “guessed” by the Surprise-Explain-Reward strategy. Since the value in HomeWork1 is outside of the range of the assertion, a red circle notifies the user of the violation. A tooltip (lower right) shows the explanation for one of the guessed assertions.

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Gender and End-User Computing

What could go wrong with the explanation? According to one theory, males and females process information differently (Meyers & Sternthal, 1991; O’Donnell & Johnson, 2001), and thus both the presentation and the content of the explanation may impact its effectiveness for males versus females. If the information needed by the user is not effectively communicated, the user’s ability to problem solve is likely to be reduced. Another role of the explanation is to help users make a reasonably accurate assessment of the risk in taking some action—but since males and females differ in their perceptions of risk (Byrnes, Miller, & Schafer, 1999), the explanation may need to serve these two populations differently in this respect as well. (An example of risk may be the fear that the user will lose their work if they try a certain feature.) If one gender perceives an explanation of a feature as communicating higher levels of risk than another, the users with higher risk perceptions may avoid supposedly “risky” features that may be important to overall effectiveness. Perhaps the most important role of explanations is to make clear the rewards of using particular features of the software. Providing information about rewards in the explanation is consistent with the implications of the Model of Attention Investment (Blackwell, 2002), an analytic model of user problem-solving behavior that models the costs, benefits, and risks users weigh in deciding how to complete a task. An implication of this model is that if the system provides the user an idea of future benefits, users can better assess if the cost of using a feature (here assertions) is worth their time. The reward aspect of the strategy refers to rewards such as the automatic detection of errors, which is depicted by the red circle around HomeWork1’s erroneous value in Figure 1. What could go wrong with rewards? Since males and females are often motivated by different factors, there may be gender differences in what actually is a perceived “reward.” If the rewards

are only tailored to one gender’s perceptions of rewards, the other gender may not be motivated to use the devices that will help them be effective. In this end-user computing scenario, potential problems arose that may be addressable within the end-user computing software itself. Four issues that arose here were (1) software features whose effects depend upon users’ computer confidence (discussed in the section on confidence), (2) the software’s ability to communicate effectively with users (discussed in support), (3) the possibility of a user’s perception of risk interfering with the user choosing to use appropriate features (discussed in confidence), and (4) possible differences between a user’s actual motivations and the software’s attempt to “reward” users for using particular features (discussed in motivation). These issues together form a useful organizational framework for considering gender HCI.

CONFIDENCE This document uses the term “confidence” for the interrelated concepts of self-confidence, self-efficacy, overconfidence, and perceived risk. From the field of computer science, there is substantial evidence of low confidence levels as computer science females compare themselves to the males (Margolis, Fisher, & Miller, 1999). Of particular pertinence to end-user computing, however, is the evidence showing that low confidence relating to technology is not confined to computer science females (Busch, 1995; Huff, 2002; Torkzadeh & Van, 2002). As a measure of confidence, researchers often use self-efficacy, as was done in the Busch study. Self-efficacy is belief in one’s capabilities to perform a certain task (Bandura, 1994). There is specific evidence that low self-efficacy impacts attitudes toward a new software package prior to its use (Hartzel, 2003). Taken together, this research suggests that a first experience with end-user

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Gender and End-User Computing

computing software can impact attitudes which may in turn impact users’ future choices to (or not to) use some features in their software. Overconfidence matters too, because it can prevent people from suspecting errors, leading to misplaced trust in erroneous programs. In particular, overconfidence in spreadsheet correctness is common (Panko, 1998). There is evidence (Lunderberg, Fox, & Punchochar, 1994) suggesting gender differences in overconfidence just as in under confidence. Hence, designing methods to help alleviate overconfidence in end-user computing needs to be carefully targeted specifically toward overconfident users. Perception of risk is tied to confidence, and impacts the decisions people make. According to the attention investment model (a model of how users allocate their attention in problem solving) (Blackwell, 2002), a user may choose not to follow through a particular action, if they decide that the costs and/or risks are too high in relation to the benefits of taking that action. Perception of risk thus plays an important role in a user’s decisionmaking about whether to use some features, and can lead to differences in actual behavior. Differences in perception of risk have been tied to gender. For example, a meta-analysis of 150 studies on gender and risk taking found that females engaged in less risk taking than males (Byrnes, Miller, & Schafer 1999). The meta-analysis did not address risks of computer use directly. However, it did find that intellectual risk taking, defined as activities involving mathematical or spatial reasoning skills, was greater in males than in females (Byrnes, Miller, & Schafer 1999). To obtain evidence about whether confidence might directly impact gender HCI issues for enduser computing environments, we conducted a preliminary think-aloud study in which participants attempted to debug two spreadsheets, given the support of Surprise-Explain-Reward devices (Beckwith & Burnett, 2004). To our surprise, the females’ confidence levels dropped over the

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course of the study much more than did the males’ confidence levels. This result suggests that enduser computing environment itself—which, like other end-user computing environments, was designed without knowledge of gender-related HCI principles—is not currently serving the females’ needs as well as the males’. A subsequent follow-up study (Beckwith et al., 2005) confirmed this: in the spreadsheet paradigm, ties were found between females’ confidence issues and low utilization of features aimed to support problem solving, resulting in effectiveness problems.

SUPPORT We will use the term “support” to mean built-in aspects of the software, such as on-line help systems and Figure 1’s tooltips that help users learn or understand the environment. The system’s approach to help users achieve mastery in remembering the software’s devices may depend on a user’s learning style. One survey of university students found that students with an “abstract random” learning style were significantly more likely to be female and, as a result, could find computer-based instruction ineffective for learning (Ames, 2003). Other researchers have also found gender differences in learning styles (Heffler, 2001; Severiens & ten Dam, 1997). One implication of these findings is that end-user computing may need to support several learning styles, especially if some users are easily dissuaded by support devices not sensitive to their learning style. Problem-solving style also shows gender differences, at least for computer games (Kafai, 1998). Researchers found that, unlike boys, rather than working in a linear fashion through the game, girls prefer to explore and move freely about a game (Gorriz & Medina, 2000). In another difference in problem-solving style, boys’ games typically depend upon competition, whereas girls

Gender and End-User Computing

prefer collaboration and working together (Kafai, 1998). For end-user computing environments, these problem-solving differences suggest differences in the support provided by the system. For example, supporting both linear and non-linear problem-solving styles and providing avenues for both competition and collaboration may be important for software’s success at supporting both genders adequately. Finally, the theory of selectivity suggests gender differences which may impact how users process the information support devices provide. The theory of selectivity, from research in the area of marketing, states that males and females differ in their information processing strategies (Meyers & Sternthal, 1991; O’Donnell & Johnson, 2001). According to this theory of selectivity, females are more likely to employ elaborative information processing strategies, regardless of whether the task is simple or complex in nature. Males, however, are more likely to select heuristic processing strategies that minimize cognitive ef-

fort and reduce information load for simple tasks, switching to an elaborative strategy only on more complex tasks. These gender differences have been shown to impact diverse software-related activities, ranging from users’ perceptions of Web sites used for ecommerce (Simon, 2001) to users’ performance on auditing tasks (O’Donnell & Johnson, 2001). For end-user computing environments, this research may have implications for informing end users of important information via the software’s support devices.

MOTIVATION Research has shown that computer science females are motivated by how technology can help other people, whereas males tend to enjoy technology for its own sake (Margolis, Fisher, & Miller, 1999). These differences are also found with other females who use technology, such as architects,

Table 1. Summary of gender differences in fantasizing about technology (Brunner, Bennett, & Honey, 1998). Reprinted with permission.

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NASA scientists, and filmmakers. One study (Brunner, Bennett, & Honey, 1998) found that females described technological objects as tools to help integrate personal and professional lives and to facilitate creativity and communication, whereas males described them as technological devices to increase command and control over nature and one another. The gender differences found in that study are summarized in Table 1. The technology acceptance model (TAM) (Morris & Dillon, 1997; Venkatesh & Morris, 2000) provides a model of users’ acceptance and usage behavior of technology. According to TAM, user acceptance, and ultimately technology use, is determined by two key beliefs: perceived usefulness and perceived ease of use (Venkatesh & Morris, 2000). “Perceived usefulness” is the degree to which a user believes that using the system will enhance their performance, and “perceived ease of use” is the degree to which the user believes that using the system will be free of effort. According to one study, the relative importance of each differs by gender (Venkatesh & Morris, 2000); women were more influenced by perceived ease of use whereas men were more influenced by perceived usefulness.

Future Trends and CONCLUSION To date, there has been little research on how the design of software itself may interact with gender differences. Still, foundational work from several domains strongly suggests that such differences may have critical impacts on users’ success in end-user computing. Although research is beginning to emerge providing some insights into gender’s importance in end-user computing environments, it is still largely an open question. Also open are questions of what specific types of differences matter in such environments and what amelioration strategies are possible.

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To help provide a foundation upon which researchers interested in these issues can build, this article has drawn from five domains to summarize literature relevant to these questions. All of the literature surveyed identifies one or more issues that potentially impact end users’ success that are also potentially addressable within the software systems. Together, the open questions and survey are intended to provide a foundation for future investigation.

ACKNOWLEDGMENT This work was supported in part by Microsoft Research and by the EUSES Consortium via NSF grants ITR 0325273 and CNS 0420533.

REFERENCES Ames, P. (2003). Gender and learning styles interactions in student’s computer attitudes. Journal of Educational Computing Research, 28(3), 231-244. Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71-81). New York: Academic Press. Beckwith, L., & Burnett, M. (2004, September 26-29). Gender: An important factor in end-user programming environments? IEEE Symp. Visual Languages and Human-Centric Computing (pp. 107-114), Rome, Italy. Beckwith, L., Burnett, M., Wiedenbeck, S., Cook, C., Sorte, S., & Hastings, M. (2005, April 2-7). Effectiveness of end-user debugging software features: Are there gender issues? ACM Conference on Human Factors in Computing Systems (pp. 869-878), Portland, Oregon. Blackwell, A. (2002, September 3-6). First steps in programming: A rationale for Attention Investment models. IEEE Symp. Human-Centric

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Computing Languages and Environments (pp. 2-10), Arlington, VA. Brunner, C., Bennett, D., & Honey, M. (1998). Girl games and technological desire. In J. Cassell & H. Jenkins (Eds.), From Barbie to Mortal Kombat: Gender and computer games (pp. 72-88). Cambridge, MA: MIT Press. Burnett, M., Cook, C., Pendse, O., Rothermel, G., Summet, J., & Wallace, C. (2003, May 3-10). End-user software engineering with assertions in the spreadsheet paradigm. International Conference on Software Engineering (pp. 93-103). Portland, Oregon. Busch, T. (1995). Gender differences in self-efficacy and attitudes toward computers. Journal of Educational Computing Research, 12, 147-158. Byrnes, J. P., Miller, D. C., & Schafer W. D. (1999). Gender differences in risk taking: A meta-analysis, Psychological Bulletin, 125, 367-383. Camp, T. (1997). The incredible shrinking pipeline. Communications of the ACM, 40(10), 103-110. Czerwinski, M., Tan, D., & Robertson, G. (2002, April 20-25). Women take a wider view. ACM Conference on Human Factors in Computing Systems (pp. 195-202), Minneapolis, MN. Gorriz, C., & Medina, C. (2000). Engaging girls with computers through software games. Communications of the ACM, 43(1), 42-49. Green, T. R. G., & Petre, M. (1996). Usability analysis of visual programming environments: A “cognitive dimensions” framework. Journal of Visual Languages and Computing, 7(2), 131174.

Huff, C. (2002). Gender, software design, and occupational equity. SIGCSE Bulletin, 34(2), 112-115. Kafai, Y. (1998). Video game design by girls and boys: Variability and consistency of gender differences. In J. Cassell & H. Jenkins (Eds.), From Barbie to Mortal Kombat: Gender and computer games (pp. 90-114). Cambridge, MA: MIT Press. Lowenstein, G. (1994). The psychology of curiosity. Psychological Bulletin 116(1), 75-98. Lunderberg, M., Fox, P., & Punchochar, J. (1994). Highly confidence but wrong: Gender differences and similarities in confidence judgments. Journal of Educational Psychology, 86(1), 114-121. Margolis, J., Fisher, A., & Miller F. (1999). Caring about connections: Gender and computing. IEEE Technology and Society, 18(4), 13-20. Meyers-Levy, J., & Sternthal, B. (1991). Gender differences in the use of message cues and judgments. Journal of Marketing Research, 28(1), 84-96. Morris, M., & Dillon, A. (1997). The influence of user perceptions on software utilization: Application and evaluation of a theoretical model of technology acceptance. IEEE Software, 14(4), 58-76. Nardi, B. (1993). A small matter of programming: Perspectives on end-user computing. Cambridge, MA: MIT Press. O’Donnell, E., & Johnson, E. (2001). Gender effects on processing effort during analytical procedures. International Journal of Auditing, 5, 91-105.

Hartzel, K. (2003). How self-efficacy and gender issues affect software adoption and use. Communications of the ACM, 43(9), 167-171.

Panko, R. (1998). What we know about spreadsheet errors. Journal of End User Computing, 10(2), 15-21.

Heffler, B. (2001). Individual learning style and the learning style inventory. Educational Studies, 27(3), 307-316.

Severiens, S., & ten Dam, G. (1997). Gender and gender identity difference in learning styles. Educational Psychology, 17(1/2), 79-93. 25

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Simon, S. (2001). The impact of culture and gender on Web sites: An empirical study. The DATA BASE for Advances in Information Systems, 32(1), 18-37.

to increase correctness in end-user programming. ACM Conference on Human Factors in Computing System (pp. 305-312). Fort Lauderdale, FL.

Tan, D., Czerwinski, M., & Robertson, G. (2003, April 5-10). Women go with the (optical) flow. ACM Conference on Human Factors in Computing Systems (pp. 209-215), Fort Lauderdale, FL.

KEY Terms

Torkzadeh, G., & Van Dyke, T. (2002). Effects of training on Internet self-efficacy and computer user attitudes. Computers in Human Behavior, 18, 479-494.

End-User Computing: Computer-supported problem solving by end users, using systems such as spreadsheets, multimedia authoring tools, and graphical languages for demonstrating the desired behavior.

Venkatesh, V., & Morris, M. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139. Wilson, A., Burnett, M., Beckwith, L., Granatir, O., Caasburn, L., Cook, C., Durham, M., & Rothermel, G. (2003, April 5-10). Harnessing curiosity

End User: Users who are not trained programmers.

End-User Programming: A term synonymous with end-user computing. Gender HCI: Human-computer interaction (HCI) work that takes gender differences into account. Overconfidence: Higher self-efficacy than is warranted by a user’s abilities. Self-Efficacy: Belief in one’s capabilities to perform a certain task. Under Confidence: Lower self-efficacy than is warranted by a user’s abilities.

This work was previously published in Encyclopedia of Gender and Information Technology, edited by E. Trauth, pp. 398-404, copyright 2006 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.3

Gender and the Internet User Cynthia Tysick University at Buffalo, USA Cindy Ehlers University at Buffalo, USA

INTRODUCTION Civilization has seen an explosion of information technologies over the last one hundred years. The telephone, radio, television, and Internet have entered the lives of men and women at work and home, becoming the main forms of communication and entertainment. Unfortunately, early adopters and creators of these technologies were men. Women, working primarily in the home, were not exposed to these technological innovations until husbands or fathers brought them into the home. Oftentimes, wives and daughters viewed these “contraptions” as intrusive to the harmony of the home. Therefore, in order to appeal to the widest possible audience these information technologies were adapted, mostly by corporations, to appeal to women through aesthetically pleasing design, creative programming, and product marketing (Shade, 2002). By the end of the 20t h century, the television emerged as the electronic

hearth. Here the family gathered, shared their day, and engaged in entertainment or debate (Tichi, 1991). Today Americans are spending less time in front of the television and more time in front of the new electronic hearth—the Internet. The average American spends close to three hours on the Internet per day, exceeding the number of hours spent watching television by 1.7 hours (Nie, Simpser, Stepaniknova, & Zheng, 2004). The Internet has followed a diffusion of innovation pattern similar to all its predecessors, beginning as a communication tool for white, male scientists to share ideas, eventually being adopted by young male “inventor-heroes” who manipulated and improved it. These improvements motivated white businessmen to use the Internet to improve profits and productivity, gather information, and entertainment. In the end the computer, and as a result the Internet, left the man’s world of work and entered the woman’s domain of the home. Slowly, over the last ten years it has made a subtle impact on the lives of American women.

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Gender and the Internet User

BACKGROUND Over the first ten years of the Internet’s life researchers have attempted to answer a number of questions about equity of access, gendered use of the Internet (both psychological and cognitive differences), and the impact of these answers on society. This article provides an overview of the community of Internet users and discusses these gender differences. The answers may help realize the goal of a genderless virtual world where all citizens participate equally regardless of age, race, creed, or gender.

Demographic Data Although IBM first introduced personal computers for the home in 1981, they did not become commonplace until after 1994 when Netscape introduced their Internet browser, Mosaic. During the first six years of its commercial life the design and use of the Internet was dominated by men (Bimber, 2000; Ono & Zavodny, 2003). Since then women have slowly surpassed men to account for 51% of the Internet population (Pastore, 2000). In 2004, 64% of all homes in the United States had Internet access (Harwood & Rainie, 2004). However, there are still age and gender disparities among Internet users. In 2004 access by age group showed the largest group of Internet users to be 35 to 54 year olds (45%), followed by 18 to 34 year olds (37%), with 55+ being the smallest group at 18% (Mediamark Research, 2004). By January 2005, the 18 to 29 year olds had surpassed 30 to 49 year olds as the largest age group of Internet users (Pew Internet & American Life Project, 2005). There are virtually no gender differences in terms of the percentage of men and women using the Internet, until the senior population is analyzed. In 2003 the number of female seniors using the Internet reached 49%, up from 40% in 2000, but they still lagged behind senior males (Fox, 2003). One interesting gender

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difference deals with seniors who are not using the Internet. According to a survey conducted in 2002 of non-Internet users 61% of women vs. 49% of men said they would never use the Internet (Lenhart, 2003). As for children, USC’s Annenberg School found that 98% of online youth ages 12-15 used the Internet in 2003, up from 83% in 2000. This same study showed that 97% of online teens ages 16-18 used the Internet up from 91% in 2000 (Cole, Suman, Schramm, Lunn, & Aquino, 2004). A report published by the National Center for Education Statistics reported that 58.5% of children ages 5-17 used the Internet with no gender disparity (DeBell & Chapman, 2003). However, there are socioeconomic and access inequalities in Internet use. Those children living in households with higher family incomes and those with more highly educated parents were more likely to use computers and the Internet than those living in lower income households and with parents who were less well educated.

Internet Connectivity While women’s use of the Internet has reached parity with men there are still connectivity disparities concerning location (work, home, other), age of equipment, type of equipment, and connection speeds. A recent survey conducted by Mediamark Research indicated an equal percentage of men and women connect to the Internet only from work while a slightly higher percentage of women connect only from home, 51% women vs. 49% men (Mediamark Research, 2004). Another recent survey conducted by Pew in 2003 found that a gender gap existed between men and women who connected from other locations. Fifty-four percent of men vs. 46% of women connected from locations other than work or home (Harwood & Rainie, 2004). Men were more likely to connect from a friend’s house while women were more likely to use a library. However, of those over

Gender and the Internet User

65, 90% of them connect from home compared to 10% from work (Fox, 2003). When one compares age, location, and gender we see no remarkable connectivity gap between boys and girls (DeBell & Chapman, 2003; Jones & Madden, 2002). The most recent and comprehensive survey, conducted in 2001 by the National Center for Educational Statistics (NCES), shows that overall 78% of children ages 5-17 connect to the Internet from home compared to 68% who connect from school. Aside from location, with home being the most convenient, the age of the equipment used to connect to the Internet is also important. Not surprisingly, women tend to have older and slower equipment. A 2001 survey by the U.S. Census Department found that on average women’s computers were three years older then men’s computers (U.S. Census Department, 2001). It also found that 44% of men owned a computer less than two years old vs. 37% of women. Wireless, hand-held devices, like cellular telephones, PDAs, and pocket PCs are the next wave in information technology. These technologies are positioned to become an integral component of ubiquitous technology that provides instant access to information anytime from anywhere. Unfortunately, to date, they are disproportionately used by men with 65% men to 35% women (eMarketer, 2003). The connectivity speed can often dictate the types of activities being conducted online. Multimedia presentations and images are large files that require a computer with enough memory capacity and the Internet connection bandwidth to view and download. Therefore, the online experience is better if the connection speed is faster and more robust. The latest connection type, broadband, is slowly overtaking the market accounting for 41% of Internet users. According to a 2003 survey by Nielsen/NetRatings, 52% of broadband subscribers are male compared to 48% female (eMarketer, 2003). This is good news for gender equity since the prevalent theory is that a quicker connection

speed allows the user to explore the Internet for itself rather than for specific information.

Internet Use How much time are men and women spending on the Internet? A 2001 National Geographic Survey (NGS) found that men spend 10.41 days/month finding information on the Internet while women spend 7.33 days/month (Kennedy, Wellman, & Klement, 2003). What are Internet users doing online? According to the 2004 Pew Internet & American Life project the most popular online activities conducted from home were: • • • • • • •

E-mail (45%) Getting news (27%) Checking the weather (20%) Looking for political information (13%) Instant messaging (12%) Watching video clips or listening to audio clips (11%) Online banking (9%)

Pew noted men are more likely to use the Internet to get news and sports scores while women are more likely to seek health and child care information (Fallows, 2004). While e-mail is used by almost all Internet users, the Stanford Center for the Quantitative Study of Society found that women use e-mail and instant messaging for social purposes more than men, while men spend slightly more time browsing, checking out newsgroups and in chat rooms (Nie et al., 2004). Women spent 27.53 minutes per day using e-mail compared to 23.50 minutes each day for men. On the other hand, men spent 22.11 minutes per day browsing the Internet compared to women who spent 15.18 minutes per day (Nie et al., 2004). Accessing news online is an activity more likely to be carried out by males than females. In 2004, 33% of men and 25% of women went online for news. When comparing education,

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age, and gender we see very different results. Approximately 74% of male college graduates under 40 regularly go online for news compared to just 45% of female college graduates (Pew Charitable Trust, 2004). Although men and women are engaged in the same online activities, the way they interact with technology varies by gender. A 2000 National Geographic Survey (NGS) found that 60% of women compared to 54% of men felt that the Internet, specifically e-mail, brought them closer to their immediate and extended families. Meanwhile, the General Social Survey 2000 and 2002 (GSS) showed that women were using e-mail to maintain social networks while men were using it to maintain business relationships (Kennedy, Wellman, & Klement, 2003). Both NGS and GSS show three areas where men and women differ in Internet use: communication, information, and recreation. Women use the Internet to build communication networks while men use it to gather information and recreation.

Gender Differences The diffusion of information technologies, like the computer, start with the early adopters who have traditionally been men. Over time they are adopted by women due in large part to economic necessity. This is because men have the social standing and means necessary to adopt new information technologies early. As these men interact with their friends, co-workers, and employees they provide the communication channel necessary to introduce the innovation to the early majority. Because women are usually not members of these two groups, early adopters or early majority, they fall under the late majority, coming to the innovation out of economic necessity; or they have no interaction with the innovation due to limited financial resources. Despite gender parity in terms of percentage of Internet users and time online, women do not have similar experiences to men. Recent research

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has found that cognitively women and men interact with technology differently. Men process information visually, through the right side of the brain, while women process information textually, through the left side; therefore, men navigate the Internet more effectively with a mouse while women navigate best with hypertext links (Calvert, Mahler, Zehnder, Jenkins, & Lee, 2003; Rodgers & Harris, 2003). Low self-efficacy and anxiety are psychological issues that negatively impact a woman’s experience with the Internet (Barron, 2004; Torkzadeh & Van Dyke, 2002; Zhang, 2004). As a result they are less confident in pursuing careers in programming and systems design (Lynn, Raphael, Olefsky, & Bachen, 2003). Women tend to be introverted, using the Internet to connect and find emotional support, while men are extroverted, using the Internet for stimulation either through gaming or pornography (Hamburger & Ben-Artzi, 2000). How the Internet is viewed also varies by gender. A 2002 study of children ages 11-16 showed that boys saw the Internet as a toy to be “tinkered” with and explored for itself while girls approached the Internet as a tool to complete tasks (Colley, 2003). The best method of minimizing computer and Internet anxiety is to promote technology fluency through hands-on training (Broos, 2005). The more someone is familiar with a technological tool the higher their self-efficacy scores. Women, especially young girls, should begin creatively approaching the Internet through programming and design activities (Barron, 2004). These activities allow them to develop technological fluency and in turn minimize anxiety. Both boys and girls find the most effective teaching methods to be independent practice, friends, parents, and classes. Therefore instructors should create tutorials that address both male (visual) and female (textual) learning styles (Mumtaz, 2001). If young girls do not see the Internet as a toy to be deconstructed they run the risk of always having difficulties with emerging technologies (Torkzadeh & Van Dyke, 2002).

Gender and the Internet User

Internet video games are another tool that help young girls become computer literate, improve self-efficacy, and develop cognitive abilities like spatial orientation (Lynn et al., 2003). Further research by the organization Children Now found that 89% of the top selling computer video games contained either violence or subtle sexual content. Those games specifically designed for girls reinforce traditional gender stereotypes with characters like Barbie and Bratz (Glaubke, Miller, Parker, & Espejo, 2001). Game designers should develop games for girls that promote teamwork, transition with text, and avoid violence (Glaubke et al., 2001). One way to keep adolescent girls interested in deconstructing technology is to show the connection between computers/Internet and traditional interests (Lynn et al., 2003). For example, interactive Web sites that utilize design software to create clothing, works of art, or animation teaches girls how to use complex technology, become motivated to use it, and find it relevant to the achievement of their goals. Once young women experience the power of the Internet they will want to become part of its future. This is also the point where women who are in technology professions need to consider becoming role models and mentors to aspiring female innovators (American Association of University Women, 1999).

FUTURE TRENDS The future Internet will most likely not resemble the current Internet. As ubiquitous technology enters mainstream society, information will become accessible anytime, anywhere, and through a multitude of devices. Our stoves will connect us to recipes; our refrigerators will e-mail the local grocery store to hold milk and eggs for pickup; and televisions, the old electronic hearth, will be replaced by multimedia centers complete with Internet access to the latest movies, music, and online college courses. Streaming video will

become an Internet staple due to high-speed broadband or other, faster connections. Textual blogs will give way to video blogs and everyone will have their fifteen minutes of fame. Eventually the lines between work and home will blur further and leisure time will include the Internet as an activity. Will women play a major role in the development and use of emerging Internet technologies? A larger number of women will need to enter computer science professions in order to become the innovators and early adopters of emerging technologies. Teachers will need to adapt teaching styles and employ problem-solving skills familiar to young girls. The media will need to move away from the stereotype of computer expert as nerdy, lonely boy/man. Finally, parents will need to engage their daughters with science and technology that is of a gender-neutral nature. In combination all of these factors will help young women become comfortable with the hardware and software used to create emerging technologies.

CONCLUSION The Internet has helped women expedite tasks that would have taken them away from family, careers, and leisure time. Now they can order dinner, bank online, grocery shop, download do-it-yourself tutorials, and apply for a mortgage from the comfort of their homes while doing traditional tasks like childrearing and housework, things women will always do. With the numbers of young women online fairly consistent with the numbers of young men, it could be said that the gender gap has been closed. But has it really? Women continue to trail behind men in computer related courses and careers and gender related privileges are still being replicated on the Internet through things like pornography, male oriented terminology and cyber harassment (Gorski, 2003; Spender, 1996). Family, educators, the government, and the media need to encourage

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young women to enter into technological fields of study and designers must begin creating either female friendly or gender-neutral hardware and software. It is through these measures that the next generation of women will feel comfortable and confident enough to become the next great technology innovators.

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DeBell, M., & Chapman, C. (2003). Computer and Internet use by children and adolescents in 2001. Washington, DC: National Center for Educational Statistics. eMarketer. (2003). North America online: Access, demographics & usage. New York: eMarketer. Fallows, D. (2004). The Internet and Daily Life (Report): Pew Charitable Trusts. Fox, S. (2003). Older Americans and the Internet. Washington, DC: Pew Charitable Trust. Glaubke, C., Miller, P., Parker, M., & Espejo, E. (2001). Fair play? Violence, gender, and race in video games. Oakland, CA: Children Now.

Barron, B. (2004). Learning ecologies for technological fluency: Gender and experience differences. Journal of Educational Computing Research, 31(1), 1-36.

Gorski, P. C. (2003). Privilege and repression in the digital era: Rethinking the sociopolitics of the digital divide. Race, Gender & Class, 10(4), 145-176.

Bimber, B. (2000). Measuring the gender gap on the Internet. Social Science Quarterly, 81(3), 868-876.

Hamburger, Y. A., & Ben-Artzi, E. (2000). The relationship between extraversion and neuroticism and the different uses of the Internet. Computers in Human Behavior, 16(441-449).

Broos, A. (2005). Gender and information and communication technologies (ICT) anxiety: male self-assurance and female hesitation. CyberPsychology and Behavior, 8(1), 21-31. Calvert, S. L., Mahler, B. A., Zehnder, S. M., Jenkins, A., & Lee, M. (2003). Gender differences in pre-adolescent children online interactions: Symbolic modes of self-presentation and selfexpression. Journal of Applied Developmental Psychology, 24, 627-644. Cole, J., Suman, M., Schramm, P., Lunn, R., & Aquino, J. S. (2004). Ten years, ten trends. USC Annenberg School Center for the Digital Future. Colley, A. (2003). Gender differences in adolescents’ perceptions of the best and worst aspects of computing at school. Computers in Human Behavior, 19, 673-682.

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Harwood, P., & Rainie, L. (2004). People who use the Internet away from home and work. Washington, DC: Pew Charitable Trust. Jones, S., & Madden, M. (2002). The Internet goes to college: How students are living in the future with today’s technology. Washington, DC: Pew Charitable Trust. Kennedy, T., Wellman, B., & Klement, K. (2003). Gendering the digital divide. IT&Society, 1(5). Lenhart, A. (2003). The ever shifting Internet population: A new look at Internet access and the digital divide. Washington, DC: Pew Charitable Trust. Lynn, K. M., Raphael, C., Olefsky, K., & Bachen, C. M. (2003). Briding the gender gap in computing: An integrative approach to content design

Gender and the Internet User

for girls. Journal of Educational Computing Research, 28(2), 143-162. Mediamark Research. (2004). Internet access and usage, percent of adults 18+. New York: Mediamark Research Inc. Mumtaz, S. (2001). Children’s enjoyment and perception of computer use in the home and the school. Computers in Education, 36, 347-362. Nie, N. H., Simpser, A., Stepaniknova, I., & Zheng, L. (2004). Ten years after the birth of the Internet, how do Americans use the Internet in their daily lives? Stanford, CA: Stanford Center for the Qualitative Study of Society. Ono, H., & Zavodny, M. (2003). Gender and the Internet. Social Science Quarterly, 84(1), 111-121. Pastore, M. (2000, 2005). Women surpass Men as U.S. Web users. Retrieved February 23, 2005, from http://www.clickz.com/stats/sectors/demographics/article.php/5901_434551 Pew Charitable Trust. (2004). News audiences increasingly politicized. Washington, DC: Pew Research Center for the People and the Press. Pew Internet & American Life Project. (2005). Demographics of Internet users. Washington, DC: Pew Charitable Trust. Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(3), 322-329. Shade, L. R. (2002). Gender & community in the social construction of the Internet. New York: Peter Lang. Spender, D. (1996). Nattering on the Net. Toronto: Garamond Press. Tichi, C. (1991). Electronic hearth: Creating an American television culture. New York: Oxford University Press.

Torkzadeh, G., & Van Dyke, T. P. (2002). Effects of training on Internet self-efficacy and computer user attitudes. Computers in Human Behavior, 18, 479-494. U.S. Census Department. (2001). Year newest computer obtained and number of computers in use for households, by selected characteristics: September 2001. Washington, DC: U.S. Census Department. Zhang, Y. (2004). Age, gender, and Internet attitudes among employees in the business world. Computers in Human Behavior, 21, 1-10.

Key Terms Bandwidth: The information carrying capacity of a network or connection to the Internet. Blog: (weB LOG) A personal, interactive journal that is available on the Web. Cyber: Prefix to words used to indicate an association to the Internet or virtual world. Cyber Harassment: Threatening behavior towards someone using the Internet. Diffusion of Information Technologies: Based on Everett Rogers’ diffusion of innovation theory that innovation is communicated through certain channels over time among the members of a social system. Digital Divide: Term used to refer to the gap between those who have access to technology and those who do not. Gender Gap: Unequal treatment or access to a product or service due to a person’s gender. Hand-Held Devices: Hardware used to access the Internet wirelessly such as personal digital assistants (PDAs), cell phones, and MP3 players. Ubiquitous Technology: Information is delivered anytime, anyplace, anyway. Futurists

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Gender and the Internet User

see an Internet that is embedded into appliances, consoles, clothing, watches, cars, etc. that can be used to access information, complete a task, or entertain. Eventually information technology is seamlessly woven into our daily lives to the point that going online is a continuous state of being.

This work was previously published in Encyclopedia of Gender and Information Technology, edited by E. Trauth, pp. 488-493, copyright 2006 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.4

Organization and Management Issues in End User Computing Jason C.H. Chen Gonzaga University, USA Robert W. Holt Gonzaga University, USA D. Bruce Sun California State University at Long Beach, USA

INTRODUCTION End-user computing (EUC) or as it is commonly termed, end-user development (EUD), is a concept and capability granted by advancement in technology that allows participants in a business environment to utilize information technology (IT) by developing applications of their own. Traditional methodology required a software program to be developed by trained programmers in the analysis and design stages of the systems development life cycle, where a user had to accept the program as an individual entity with unalterable characteristics. EUC/EUD now enables this person to customize the program around his/her specific demands. The framework of EUC establishes empowerment and capabilities so that anyone can develop entire information systems

(IS) with little or no help from professional systems analysts or programmers, along with accessing data and creating reports (Laudon & Laudon, 2003). EUC/EUD is a topic in the IT environment that cherishes a progressive history spanning from the mid-1970’s to what it as today.

BACKGROUND During the 1970’s, the concept of management information systems (MIS) began which grounded the importance of utilizing IT as a strategic implementation tool in changing business environments. The event produced a two-fold outcome where generalized perceptions about computers changed to include the relevance and effective usage of data to direct most business decisions,

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

An Overview of Multimodal Interaction Techniques and Applications

and technological ideology shifted from an information specific denomination to a belief towards support management. (Charr, 1988). Once support management became recognized, a paradigm occurred where decision support systems (DSS) became the focal point of MIS integration. The new paradigm conceptualized the computer as a necessary tool for the decision making process in accordance with its data storing properties. EUC has become increasingly more available, due to the induction of less complicated programming languages, termed fourth-generation languages (4GL’s), that provide users who may not have a sufficient skill and knowledge of programming expertise, to develop programs, customized to their individual needs. These specific languages signify simplicity, since most users already have an intuitive understanding of the logic and terminology. They are frequently referred to as user-friendly and nonprocedural, which determines that the languages must be given less structured instructions to achieve the same result as earlier capabilities (Charr, 1988). Their relevance to EUC is significant because earlier languages, such as assembly or procedural language, required a high level of education to interpret the meaning of codes and to produce the desired outcome with the program. From a technology perspective, when the last three decades are crucially examined on how EUC has gained its prominence today, three defining events are attributable for this outcome (Charr, 1988). First, is the success of computer engineering to derive programs which grant users, whom are not skilled computer programmers, to manipulate and maintain the software; second, computer training is now a skill that everyone must have to enter the job market, so everyone must demonstrate proficiency in this area; third, the huge growth in the evolution of the use of computers has drastically decreased costs of hardware and software (Charr, 1988). When these three forces are evolved, it is obvious how EUC has radically gained in recognition and how progressive com-

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puter technology has grown to what we know and use today. The computer has developed into a commodity as common as any household item, like the food processor or even the vacuum cleaner. Despite slightly higher costs, people all over the world are finding the investment increasingly beneficial. The number of things one can accomplish in the comfort of one’s home, even with a little PC, is incredibly large. Hence, it is no surprise that the term EUC has earned its position in the world of computer jargon.

Management Issues of EUC Even with the popularity of EUC, the greatest debate substantiated through this capability is: how much will a business benefit from this process, and can certain disadvantages be posed to limit the effectiveness of EUC implementation? This paper is structured to further provide a background on the evolvement of EUC, based in part on research conducted by Brancheau and Brown (1993), to establish a framework which will finally provide the necessary information and address important management issues and perspectives on EUC.

EUC Management Model EUC can be defined as “the adoption and use of IT personnel outside the IS department to develop software applications in support of organizational tasks” (Brancheau & Brown, 1993). The content of Brancheau and Brown’s study was generated from different journal articles and upon conclusion of their findings they developed a model, incorporating all of the aspects identified with EUC (see Figure 1). The model includes three major components: antecedents (contexts), behavior (EUC management) and consequences (outcomes). EUC management is further divided into two customized sections, organizational EUC management and

An Overview of Multimodal Interaction Techniques and Applications

Figure 1. EUC management model EUC MANAGEMENT ORGANIZATION Technology

Strategy

OUTCOMES

CONTEXT

Management Action

(Antecedents)

• Organization Level

• External

• Work Group Level

• Organization • Work Group

INDIVIDUAL

• Technology

End User

Investment

Task

• Individual Level • Application Level

Tool End User Action

individual EUC management. The organizational part of the model represents the planning, organizing, staffing, directing, controlling, supporting, and coordinating functions associated with EUC management. As the model depicts, three relationships occur involving strategy, technology, and management action. Four factors are incorporated within the individual EUC management: the end user, task, tools and end-user action. The antecedents incorporate those factors pertaining to external, organization, workgroup, and technology investment activities as related to the business. Through the integration of EUC, both individual and organizational management styles, four levels of outcomes occur, organization, workgroup, individual and application (Moore & Powell, 2002). With the background pertaining to this model, we propose that four management issues should be addressed: (1) what are the characteristics associated with End-User Computing? (2) what are the necessary tools needed? (3) what are the advantages gained through EUC? and (4) what are the possible risks and security issues associated with EUC?

What are the Characteristics Associated with EUC? The characteristics of the EUC can be explored in three areas: applications, development skills and prototyping. The details are explained in the following sections.

End-User Computing and Applications Early on, EUC was identified as a spreadsheetoriented operation, repetitively calculating large quantities of data and then later graduated to incorporate operational systems throughout entire organizations (O’Donnell & Sanders, 2003). Approximately ten years later, from the mid-1990’s and beyond when graphical user interfaces and 4GL’s advanced in complexity, EUC was elevated to include large database systems, known as data warehouses, and Internet Web sites (O’Donnell & Sanders, 2003). The impact was delivered throughout the EUC establishment, where individual applications can now include a variety of group applications. EUC initially began as only

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an inter-organizational intermediary, but it can now be able to accommodate a wider spectrum of implementations, consisting of inter-organization and organization-to-customer based systems on the Internet (O’Donnell & Sanders, 2003; Christoff, 1990).

End-User System Development Skills It is vitally important before a DSS is designed, that the end-user is first identified by the end-user developer. The end-user’s applications have to be analyzed initially, based upon what they will do with the software and the technical knowledge that they possess. This establishment will govern how complex the program will be for the end-user’s and customize it to fit their individual needs. Enduser developers consist of three types (from the least to the most in complexity and technical skill level): menu-level developers, command-level developers, and end-user programmers (O’Donnell & Sanders, 2003).

Prototype Development The characteristic that defines one of EUC’s unique advantages is “flexibility,” which basically explains its purpose in the technical software world and is encapsulated by the implementation of prototyping. To define prototyping’s specific parameters, a prototype software program must meet these qualifications: quick to create, easy of change, inexpensive to build, and inexpensive to discard (Carr, 1988; Gelbstein, 2003). Advantages of prototyping are severely weakened if any of these requirements are not successfully met.

What are the Necessary Tools Needed? End-users must have the necessary tools available to them, so that they can develop specific software satisfying their individual needs. The categories of

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tools consist of three major types geared toward specific requirements within the business. The three varieties include: office productivity tools, workgroup computing tools and application development tools (Bruni & Stigliano, 2003). The objective of office productivity tools is to enhance the productivity of individuals, by providing computer support for common office tasks. Typical with the commonalities of the business sector, these tools include all of the relevant software applications, such as word processing, database management, spreadsheets, presentation graphics and other similar programs. With a click of the mouse button, the series of functions will orchestrate, illustrating a custom application on the user’s worksheet. In the office environment, the effectiveness of groups is widely understood with workgroup computing tools to support their specific needs. Specifically, the tools “support the efforts of groups of users working on related tasks,” unlike office productivity tools, which are specific to a single user (Bruni & Stigliano, 2003). Examples of tools under this category include, but are not limited to: email, group calendars, workflow systems and other similar applications, providing for effective communication, among employees. The final division of development tools that are vital for the successful implementation of EUC in a business are application tools. Application tools consist of four types: application generators, fourth-generation languages, application development environments and scripted web pages, functioning to “develop reusable programs with which end users create custom applications” (Bruni & Stigliano, 2003). Essentially, these are the most crucial tools of the three categories, since they support the capability for the formal development and maintenance of computer-based applications, along with software problem resolution and customer support.

An Overview of Multimodal Interaction Techniques and Applications

What are the Advantages Gained Through EUC? In today’s environment, every corporate advantage that can be gained, invites the potential for greater revenue achievement, promoting a greater capture of market share. In respect to obtaining distinct advantages, EUC encourages another important behavioral application in addition to “flexibility,” by increasing a program’s “usability.” There are many advantages for a usable software: reduced training costs, reduced support and service costs, reduced error costs, increased productivity of users, increased customer satisfaction, and increased maintainability (Hohmann, 2003; Holsapple & Whinston, 1996). Based on the premise of EUC that the developer is also the user, a key understanding to what the end-user ultimately wants with the product can be achieved. Once the groundwork is laid out, product development can proceed as usual, with fewer modifications and/or errors.

What are the Possible Risks and Security Issues Associated with EUC? Potential risks are involved in a system development with its integration into the company. Four main areas are: poorly aimed systems, poorly designed and documented systems, inefficient use of organizational resources, and loss of security (O’Donnell & Sanders, 2003; McLeod & Schell, 2001). It is logical to determine how each of these risks can limit success upon inviting EUC into the business atmosphere. The purpose of EUC is to mediate the software development problems that are associated with a poorly aimed, designed, and documented computing system, to provide the ultimate end-user with certain specified capabilities (Lucas, 2000). EUC is a system to provide for that allowance of software customization and enhanced programming communication.

It is evident that in today’s world, there is always technical evolution occurring, between computer security systems, increasing in complexity to counteract the continual updated attempts by software hackers to disrupt the software information. This kind of “predator & prey” relationship is an ongoing process and certainly impacts and threatens the business and the EUC software applications. What is essential to know is that the consequences of a business not having EUC software can greatly outweigh the potential security aspect that looms over all types of software programs. Only the business itself can determine that decision and judge the possible outcomes it may encounter, depending upon which decision is chosen.

Costs/Benefits Analysis of EUC The costs of EUC are evident and intensive studies have been completed to examine and determine what these costs entail. The research conducted by James, Ang, Joo-Eng and Shaw (2003) uncovers deep concerns involved in this type of application. They discovered that users without adequate technical knowledge did not conduct activities that required a greater degree of technicality. Furthermore, due to these inadequacies, these users “expected more from the PC, viewing it as a task enhancing tool” (James et al., 2003). These findings illustrate a very essential point relating to EUC which is regardless of the software program’s abilities, if the employees lack the proper expertise to tap into these aspects of the program, the benefits of EUC will never be recognized. No matter how well designed, constructed, and tested a system or application may be, errors or bugs will inevitably occur. The systems developed by the end users must be maintained, and the end users’ knowledge and skills must be updated periodically as part of the maintenance. Along with training and maintenance, the end-user system will be operational if the organization moves to

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An Overview of Multimodal Interaction Techniques and Applications

the next-level programming language or update its software applications and hardware. The benefits of EUC software and its applications are profound, giving modern businesses the distinct edge on their competitors. This provides for a greater level of independence with each employee, so that they do not have to directly rely on a software specialist to solve these problems. One specific characteristic of EUC is that end-users may work directly with software development specialists to organize and design applications which include attributes needed in the program but allows them the “flexibility” to make any necessary manipulations in the future. This benefit substantiates in a business only having to purchase one program which will adapt to a variety of applications, saving time and money.

PERSPECTIVES on EUC The applications of EUC are judged at two distinct levels based on which area in the software development process is effected, either the end-user or the IS specialist. Both of these perspectives are outlined below, detailing the importance of this capability for both constituents.

The End User Perspective In what perspective do end users view EUC? Response to the growth in EUC has been extremely positive, especially from the perspective of the common end user. From Word Processors to Database Management Systems, and from Decision Support Systems to Executive Information Systems, EUC has broken down all barriers between the layman and the computer. The dependence on computers has grown tremendously. The end user perspective is inclined towards formal as well as informal means of computation.

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The IS Specialist Perspective From this angle, EUC has also had ample support. IS specialists initially would solve the layman’s problems from start to end, generating the required output on the computer. With the growth in EUC, end users began gaining independence and so the IS specialists had to adopt the job of training end users, helping them gain some control over computing functions. Information Centers have cropped up in almost all computer-using organizations, where IS specialists perform a variety of functions, from hardware and software purchasing and maintenance to end user support and training functions. It has been argued many times that EUC might be a threat to IS professionals, but upon reflection, it seems unlikely. The end user would be someone whose career focus would be something different from technology itself. He would, however, be using technology as a career and would constantly be a source of assistance to the common end user. Hence, it is important to note the role played by the IS specialist in EUC and conclude that the two categories complement, rather than oppose each other (Alter, 1999).

FUTURE TRENDS and CONCLUSION With the continuing growth in technology and in the capacities of the computer, the trend for the future seems to be towards more emphasis on userfriendliness and ease of handling (Morre & Powell, 2002). EUC has an extremely positive future, even considering the general technology trends - the technology connecting the information resource to the end user. In the future, organizations would be better off moving towards an investment in IT as an integral part of the organizational constitution rather than considering it a functional cost. With such an attitude developing in the business world, EUC has a bright future with many expanding horizons. This would be due to the fact

An Overview of Multimodal Interaction Techniques and Applications

that IS/IT would become such an integral part of the creation and existence of organizations, and that ultimately its role would not have to be questioned or analyzed. As it has been described in this article, EUC has an essential application associated with software development and customization that greatly increases a businesses competitive advantage upon implementation. Specifically, it reduces a business cost in relation to the fewer number of programs that as a result need to be purchased to maintain the corporation’s vitalities. It seeks to further reduce time and complexities through the provision of manipulating software applications around an employee or department’s individual needs. Although providing employees with this specific type of software poses the possibility for security problems, this risk is always a given threat with all software programs. The success of EUC is weighed heavily upon many variables, and various tools have to be available to expand on all of its capabilities. EUC software development can be made more efficiently, when the software vendor can establish a close link of communication with the final end-user and specifically identify and outline their requests. Perspectives on end-user computing have certainly been well supported among both the end-users and the IS specialists, rooting its existence in present day business strategies. Although its history has evolved in less than three decades, end-user computing has established itself in the IT environment and will only further progress to ever increasing heights in the future.

REFERENCES Alter, S. (1999). Information systems: A management perspective. Addison-Wesley. Brancheau, J. & Wetherbe, J. (1993). The adoption of spreadsheet software: Testing innovation diffu-

sion theory in the context of end-user computing. Information Systems Research, 1(2), 115-143. Brown, C. & Bostrom, R.(1993). Organization designs for the management of end-user computing: Reexamining the contingencies. Journal of Management Information Systems, 10(4), 183-211. Bruni, M. & Stigliano J. (2003). End-user computing tools. In Encyclopedia of Information Systems (Vol. 2, pp. 127-139). Carr, H. (1998). Managing end user computing. Prentice Hall. Christoff, K. A. (1990). Managing the information center. Scott, Foresman and Company. Gelbstein, E. (2003). End-user computing, managing. Encyclopedia of Information Systems (Vol. 2, pp. 115-126). Hohmann, L. (2003). Usability: Happier users mean greater profits. Information Systems Management, 66-76. Holsapple, C. W. & Whinston, A. B. (1996). Decision support systems. West Publishing Company. James, S.K. Ang, L-P., Joo-Eng & Shaw, N. (2003). Understanding the hidden dissatisfaction of users toward end-user computing. Journal of End User Computing, 15(2), 1-22. Laudon, K.C. & Laudon, J.P. (2003). Essentials of management information systems. Prentice Hall. Lucas, H.C. Jr. (2000). Information technology for management (7t h Ed). Irwin McGraw-Hill. McLeod, R. Jr. & Schell, G. (2001). Management Information Systems (8t h Ed). Prentice-Hall. Morre, Jo E. & Powell, A. (2002). The focus of research in end user computing: Where have we come since the 1980’s. Journal of Organizational and End User Computing, 14(1).

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O’Donnell, J.B. & Sanders, L.G. (2003). End-user computing concepts. In Encyclopedia of Information Systems (Vol. 2, pp. 101-113).

Key Terms Database Management System (DBMS): A software program (or group of programs) that manages and provides access to a database. Decision Support Systems (DSS): A computer-based information systems whose purpose is the support of (not replacement) decision-making activities. End-User Computing (EUC): Direct, handson use of information systems by end user whose jobs go beyond entering data into a computer or process transactions.

End-User Development: The development of information systems by end users rather than information system specialists. Hacker: A very knowledgeable computer user who uses his or her knowledge to invade other people’s computer through the computer network. Information Systems (IS): A computer-based system helps people deal with the planning for, development, management, and use of information technology tools to help them perform all tasks related to their information needs. Information Technology (IT): A computerbased tool (hardware and software) that people use to work with information and support the information-processing needs of an organization.

This work was previously published in Encyclopedia of Information Science and Technology, edited by M Khosrow-Pour, pp. 2230-2235, copyright 2005 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.5

Fundamentals of Multimedia Palmer W. Agnew State University of New York at Binghamton, USA Anne S. Kellerman State University of New York at Binghamton, USA

Abstract

Introduction

This chapter introduces multimedia, defined as interacting with information that employs most or all of the media: text, graphics, images, audio, and video. Students and faculty need to learn to create and use high-quality multimedia documents, including references, lecture materials, reports, and term papers. The authors provide a framework for understanding multimedia in its rapidly changing context. They discuss a wide spectrum of multimedia end-user devices that range from smart cell phones and powerful PCs to intelligent cars and homes. They also propose a vision of pervasive multimedia any time and anyplace, and discuss related issues, controversies, and problems. Typical problems are excessive complexity and a plethora of choices that paralyze many potential users. The chapter concludes with a discussion of possible solutions to major problems and probable future trends.

Multimedia is interacting with text, graphics, images, audio, and video. Creators and users of multimedia employ end-user devices that range from PCs and interactive televisions to smart phones and PDAs. People exchange multimedia using delivery methods such as dial-up and cablemodem access to the Internet, mailed DVDs, and Internet2. Multimedia communications can be more effective and interesting than communications that are limited to text. Most of us will create, as well as use, multimedia throughout the remainder of our lives. Almost all future work and everyday life will involve dealing with multimedia wherever we are by using the end-user devices at hand. Examples of use include sending images to Aunt Lizzie by way of a cell phone, and writing and wirelessly posting a report on the Internet concerning worldwide petroleum sources, while standing near an oil well in the Middle East.

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Fundamentals of Multimedia

The objectives of this chapter are to:

• • • •

provide a framework for efficiently acquiring new knowledge and skills in the rapidly changing multimedia arena; discuss a vision for effective multimedia creation and use, by nearly everybody, nearly anywhere, and at any time; delineate the major issues, controversies, and problems that litter the path toward achieving that vision; and discuss some corresponding solutions and recommendations, many of which involve skills that instructional technologists, teachers, and students need.

Background Figure 1 shows a high-level view of people and components involved in creating and using multimedia. Some providers create multimedia content, information, titles, or applications that employ multiple media and are interactive. These authors create this content by employing enduser device hardware and software. They then typically store the resulting content on servers. The content is delivered to other, often different end-user devices employed by users, customers, or readers by means of delivery networks that range from mailing diskettes or DVDs to using

Figure 1. Framework

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local area networks or the Internet. By no means are all authors professional creators; the most interesting aspect of multimedia is that it is now sufficiently inexpensive and almost sufficiently easy that almost anybody can create as well as use multimedia. Other providers include a wide range of individuals, companies, and governments that play a wide variety of roles. For example, some providers provide products and services that are important to the delivery of multimedia to end-users. Figure 1 is a framework in which you can add newly acquired knowledge about multimedia. For example, if you think you might want to provide an on-demand multimedia tutorial for your students to use from their cell phones, you need to have an authoring end-user device, a way to deliver this content to your users, and tools to allow you to create content for the desired end-user platform. You should know that, at least in the US, wireless delivery will be problematic. The good news is that network providers are improving their products and services, with the goal of handling wireless high-quality images and video within the next couple of years. Creators and end-users employ a variety of tools. Some tools operate on the individual media. Other tools, called multimedia authoring systems, assemble multimedia media and add interactivity. Individual media tools include editors for text, images, graphics, audio, and video. Tools that

Fundamentals of Multimedia

Table 1. Typical multimedia software Medium

Tools

Text

Microsoft Word, Corel WordPerfect, Tex, Latex

Graphics, i.e. vectors

Corel Draw, Adobe Illustrator, Macromedia Fireworks, Adobe ImageReady, Macromedia Flash

Image, i.e.

Adobe Photoshop, Jasc Paint Shop Pro, Macromedia Fireworks

bit-map Audio

Sony Sound Edit Pro, Sony Sound Forge for Windows, Sony Acid, Cakewalk products

Synthetic video

AutoDesk AutoCAD, Discreet 3D Studio (MAX), Virtus 3D Website Builder, Macromedia

(animation)

Flash, Electric Image Amorphium Pro, Alias Maya

Captured Video

Adobe Premiere, Avid, Media 100 products, Ulead Media Studio Pro, Microsoft MovieMaker, Apple iMovie

Authoring systems

Macromedia Director, Macromedia Dreamweaver, Click2learn Toolbook, Microsoft Front Page,

for all media

Adobe Page Mill, Microsoft PowerPoint with Producer

Table 2. Typical end-user devices Property

Desktop computer

PDA and cell phones

Storage

80 GB hard drive

128 MB Flash RAM card

Processor clock rate

2.4 GHz

400 MHz

Operating system

Windows XP

PocketPC

Display diagonal, inches

17.0

3.8

Network for delivery

Cable modem, wireline, typical

Wireless, typical speed of

speed of 400 K bits/sec

16 Kbits/sec

generate HTML with associated Web browsers are examples of multimedia authoring systems. Table 1 provides some specific examples. A great many can be found at Maricopa Community College’s Web site, Multimedia Authoring (Maricopa Community College, 2004). One correct conclusion that you can draw from the table’s examples is that creators of multimedia

often need to obtain and master separate tools for each medium, as well as one or more authoring tools that combine several media. To the extent that multimedia creators and users are different people, users are also faced with multiple choices of tools with which to play back the media, such as browser plug-ins for a variety of streaming audio and video formats. Users may also need one or

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Fundamentals of Multimedia

Table 3. Sample content-preparation guidelines Guideline Provide user-selectable options for multiple learning styles. If you present text and audio on the same page, make sure that both use the same words. Nobody can read one set of words while listening to different words. Remember that any movement on a page, such as video or animation, strongly distracts a user from non-moving information such as text. Use colors to facilitate readability, appeal to different audiences, and reflect different cultures. Use background music sparingly, to set a mood. Prepare text for skimming rather than for reading long passages in detail. Computer monitors tire the eyes because they have resolutions that are much less than resolutions of printed pages. Organize multimedia information carefully and consistently. Have important information no more than 3 clicks (or levels) deep. Do not use copyrighted media in any content that you sell, unless you obtain the owner’s permission. Be careful what copyrighted media you use, even in an educational context.

more authoring systems’ so-called “run-time environments,” if not the full authoring systems. Multimedia tends to be large in terms of storage space and network bandwidth. Whereas a text page occupies only about 3,000 bytes, five minutes of high-quality video can occupy up to 1 Gigabyte (1 GB). Solutions involve squeezing multimedia by compressing it using a range of sophisticated technologies. As you can see from Table 2, desktop computers routinely come with 80 GB of hard-drive storage space, whereas mobile devices have far less space. Multimedia requires networks that are fast enough to ensure that multimedia plays correctly. Cable modems, Digital Subscriber Lines, and special techniques of streaming are required and often perform adequately, but note the typical network data rate of PDAs in Table 2. Assume that you have set up an environment such as the one in Figure 1. Now comes the really challenging part: creating the content or coaching

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others to create content. If you are coaching students, you should know that creating multimedia projects can help your students to achieve goals that include developing higher-order thinking skills and interpersonal skills, learning content by engaging multidisciplinary subjects, and developing technical competence and media literacy that will empower the students throughout their lives (TeleEducation NB, 2002). Furthermore, employing multimedia will allow you to improve students’ efforts outside formal classroom settings and allow you to piggyback on these efforts inside the classroom. Victory, however, is not merely receiving any old PowerPoint presentation, even with audio, animation, and video. Having an arsenal of past examples and setting up Olympic scoring of students’ results (Agnew, Kellerman, & Meyer, 1996) helps elevate the standards of content quality. Guidelines such as those in Table 3 can help you as either a coach or creator.

Fundamentals of Multimedia

Issues, Controversies, and Problems Early adopters and pioneers routinely create multimedia. Today, with affordable hardware and software, people who are not media professionals can prepare interactive documents that contain multiple media. They can communicate using these media either in real time, using streaming, or asynchronously, using e-mail. Using multimedia tools, they can structure their information to greatly facilitate understanding. They can send this multimedia information to remote locations, with some certainty that it will be received rapidly and understood successfully. Tomorrow, lacking such skills will seriously impact almost all professionals’ and students’ lives, job satisfaction, and job performance. Content created by early adopters and pioneers has made multimedia literacy part of the expected price of admission to many roles. Complexity and too many choices stymie the rest of us. Unfortunately, arcane skills are still required to set up digital environments and then use them for serious information preparation or even for living-room enjoyment. Our professional colleagues and teaching friends confirm that multimedia is changing fast and furiously, making it difficult for them to keep up in a field that they view as diverging from their respective professional areas. They are elementary school teachers, biology teachers, instructional technologists, literature professors, and so forth. Nevertheless, new multimedia capabilities are superb ones that such professionals need. What best characterizes humans, all over the world, is our ability to communicate orally and in writing. Multimedia can make this communication even better, if done well. Dramatic price declines in software and hardware allow many more of us to set up the environments we need to both create and use multimedia. For example, we have watched PC cams (small video cameras that connect to PCs

for both storage and control) drop in price below $100, then below $30, and now below $10. The difficulty all of us find is that to learn what we specifically need to know, we must either spend hours on the Internet or buy books, often blindly. If only the cost and time spent learning to use the affordable hardware and software were decreasing as well. Vendors have conflicting motivations. It would be easier for users if Microsoft could indeed integrate all required support into its operating system. Governments and competing vendors have widely divergent motivations. As a result, authors are required to either guess at which formats and data rates their audience requires or supply several different views of the same content. Book stores display many shelves of books on different media and on enhancing Web pages. You are led to believe, with some truth, that to learn to create multimedia, you need a yard of books. Very few books tell how to create meaningful content where content is targeted to academic or serious professional uses. Mismatch of equipment between home and school. People are using digital cameras, both standalone and inside cell phones, in record numbers. They are learning how to use them on their own, just blundering through. Electronic gadgets including computers at home are proliferating. Home computers tend to be good ones that far outperform what is available in most K-12 schools and many universities. In school, children are exposed to old equipment without getting sufficient quality instruction in its use and without educators taking advantage of the motivational aspects of using the equipment to enhance education. The Salvation Army prefers to reject any computer that is more than three years old; can schools do the same? Creating high-quality content is difficult today, and few are experienced in it. The available tool often drives the content, rather than conversely. A tragically famous example of this is NASA’s chart justifying its tests of foam striking a Space

47

Fundamentals of Multimedia

Shuttle’s wing. The authors produced bullet after bullet of PowerPoint text at random levels of hierarchy and importance. Unfortunately, they buried the only important fact (that their test set-up had been totally unrelated to the real world) in a sentence fragment at the bottom of the page, rather than making it the title, or at least phrasing it as an unambiguous complete sentence. Dr. Edward Ayers (2004) understands that few scholars have done what he has done over the last decade, namely scholarly research on multimedia narration (Ayers, 2004; valley.vedh. virginia.edu).

Solutions and Recommendations Early adopters and pioneers routinely create multimedia. To help students and teachers achieve multimedia literacy as part of life’s expected price of admission, educational technologists must continue learning, coaching, and mentoring the rest of the academic community. Academics in universities must work together. As an example of the kinds of successes cooperation can have, Dr. Meyer (a computer science professor) and Dr. Driver (a literature professor) worked together to inspire literature students to compose multimedia documents. Each provided relevant expertise (Driver & Meyer, 1999). Young people are intrinsically early adopters and pioneers. While it is charming when students teach teachers, it is teachers who can best apply the required pedagogy to achieve the desired results. Complexity and too many choices stymie the rest of us. A viable approach is to identify a small set of tools and stick with it for several years, ignoring incremental advances in hardware and software. This includes taking advantage of tools that ship with major software. For example, both Microsoft and Apple ship moderately functional

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video-editing software as parts of their current flagship operating systems. Mismatch of equipment between home and school. Parents need to get involved, working with teachers to encourage home equipment use for creation of rich media content in support of pedagogical goals. Creating high-quality content is difficult today, and few are experienced in it. As it becomes easier to assign multimedia, this will become more common. Future teachers need coaching in how to use multimedia in the classroom, as in the text, Multimedia in the Classroom (Agnew et al., 1996). Teaching teachers in colleges and universities does not consist of imparting isolated media skills, but rather of showing how to put it all together in a learning environment. One key is exhibiting exemplary multimedia content achieved by relevant people. It is not useful to present George Lucas’s work for students to emulate; work produced by students in prior years is far more helpful. Like many subjects, the hardest part of teaching multimedia is getting started. Teachers need to develop pedagogical goals for both teaching basic multimedia literacy and using multimedia to teach other subjects. This requires that they understand what is required to create and use multimedia to achieve pedagogical goals and that they then implement paths to reaching these goals that are consistent with their existing environments. They must be able to assess results along multiple dimensions. Merely using multimedia is not enough when the goal is quality content supported by rigorous arguments. Teachers and educational technologists will routinely need to advise, recommend, approve, and obtain parts of the framework required for multimedia. Educators will need to improve their multimedia creation and playback environments, as prices and quality of tools continue to decline, and in step with the mainstream and with overall mission objectives. For example, the military now uses high-speed computers, high-bandwidth de-

Fundamentals of Multimedia

livery connections, and sophisticated multimedia simulations to instruct soldiers on how to carry out missions. Whereas K-12 teachers might have more modest requirements, everyone involved in multimedia must learn to communicate intelligently and efficiently with vendors of multimedia products and services. A major part of this is learning when to search the Web and when to telephone a help line. Experience will assist educators in knowing what multimedia they and students can produce and use with what they have on hand, and what multimedia is suitable for what their intended users will have on hand. Everyone should be able to recognize and produce quality multimedia content. Those who can do this will need to be generous with their time and talents, and help others to do likewise. Available tools must not drive content in undesirable directions, such as PowerPoint’s leading from sentences to sentence fragments to sloppy thinking. Using tools must not be allowed to become the end in itself. Suitable goals include preparing teachers and students who are routinely able to create and deliver a multimedia proposal, resume, presentation, set of instructions, or report using appropriate selections from alternative media. In many cases the media will need to be suitable for nationwide or even worldwide distribution over the Internet. This requires some understanding of the available bandwidths of connections that range from reasonably fast broadband to abysmally slow cellular wireless. It also requires selection between real-time and asynchronous communication and suitability for a wide variety of end-user devices. Creating multimedia requires people to be able to:

• •

acquire and prepare images and graphics for inclusion in documents; repurpose slides and analog photos into digital forms for playback on TVs, PCs, cell phones, and PDAs;

• • •

create and incorporate video using either economical PC cams or high-quality MiniDV camcorders; prepare animation for illustration and instruction; and employ voice recognition software for communicating with devices that are too small to have workable keyboards. Examples of specific skills include:

• •

•

converting media from one form to another for use in various situations and on various devices; using tools that allow processing of several media files in one batch, such as in converting an entire folder of several images from one format to another at once; and organizing raw and completed information for effective later re-use and collaborative use.

All of these skills require particular attention to what is actually feasible to achieve meaningful results. Moreover, everyone must know about the rights that educators and others have with respect to using media created by others. Rights may differ between content used as is and altered content.

Future Trends For better or for worse, future uses of multimedia in educational and other contexts will be driven by improvements in technologies. Moore’s Law famously predicts that processor power and internal memory capacity will continue to double every year or two, without significant increases in cost. Less famously, other important technologies are improving at even faster rates. A decade ago, the available hard drive space on a typical home computer sufficed for storing barely 30 minutes of moderate-quality video; now a typical computer

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Fundamentals of Multimedia

can store about 30 hours of high-quality video. Increasingly, economical hard drives have allowed many users to afford personal video recorders, such as TiVo, as well as sophisticated in-home video editing. A decade ago, transcontinental telephone connections were so scarce and expensive that long-distance telephony was a cash cow; now a glut of worldwide fiber-optic cable bandwidth allows Dell to route help-line calls to Indonesia as cheaply as to Indiana. Companies including Intel, IBM, and AT&T are severely threatened by rapid technological progress that gives customers the opportunity to pay vastly fewer dollars for the same products and services. Many companies see multimedia as their only hope for convincing customers to purchase far more products and services, for at least the same number of dollars. It works. Most owners of digital still cameras now use more hard-drive space for storing images than they used for all purposes just a few years ago. New, tiny video cameras that temporarily store video on expensive flash RAM cards will permanently fill up even more storage space on hard drives. This trend toward rapid improvement in technologies means four things to us all, as creators and users of multimedia. First, we must beware of pressures to use more of the products and services that do not benefit us, merely because they are increasingly economical. Second, we must try to make excellent use of newly affordable methods for achieving our goals. Third, we must encourage providers of multimedia products and services to make major improvements in ease of learning and ease of use, rather than merely dumping in more and more capabilities with attendant increases in confusion. Fourth, we must continually keep abreast of research on assessments of multimedia technologies, especially ones which include best practices. One way to keep abreast is to subscribe to Kotlas’s newsletter, CIT Infobits (2004) and review the list she compiled on Assessments of Multimedia Technology in Education.

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Conclusion We will all communicate using technologies that range from today’s e-mail and Word documents to tomorrow’s video instant chats. Each communication will be significantly enhanced by our ability to easily and effortlessly use appropriate media and to allow the recipient to selectively access content in a variety of forms. We will build on skills acquired at home, in K-12 schools, and in higher education. We can expect to learn continually as technologies improve. As vendors expect to sell more of their hardware and software, we hope their motivations will include making equipment easier and quicker to learn and use, as well as more economical to buy. We must help one another, even if it requires cross-disciplinary activities. The best authors need to take advantage of multimedia to create multimedia and to help the rest of us learn to create it and use it.

References Agnew, P., Kellerman, A., & Meyer, J. (1996). Multimedia in the classroom. Needham Heights, MA: Allyn & Bacon. Agnew, P. & Kellerman A. (2002). Distributed multimedia—technologies, applications, and opportunities in the digital information industry, a guide for users and providers (2nd edition). Cincinnati: Atomic Dog Publishing. Assessments of Multimedia Technology in Education: Bibliography. (Information Resource Guides Series #IRG-11), Institute for Academic Technology (IAT). Compiled by Carolyn Kotlas, MSLS, University of North Carolina. This document includes government, private industry, and K-12. Retrieved February 10, 2004, from www. unc.edu/cit/guides/irg-11.html Ayers, E. (2004). Doing scholarship on the Web: 10 years of triumphs—and a disappointment.

Fundamentals of Multimedia

The Chronicle of Higher Education, (January 30), B24-B25. Driver, M. & Meyer, J. (1999). Engaging students in literature and composition using Web research & student constructed Web projects. Retrieved February 10, 2004, from csis.pace. edu/~meyer/hawaii/ Educase. (2003). Information resources library on articles with multimedia applications and campus learning. Retrieved February 10, 2004, from www.educause.edu/asp/doclib/subject_ docs. asp?Term_ID=364

Koltas, C. (2004). CIT infobits. Retrieved February 10, 2004, from www.unc.edu/cit/infobits/index. html Maricopa Community College. (2004). Multimedia authoring. Retrieved February 10, 2004, from www.mcli.dist.maricopa.edu/authoring/ TeleEducation NB. (2002). The significant difference phenomenon. Retrieved February 10, 2004, from teleeducation.nb.ca/significantdifference/

This work was previously published in Technology Literacy Applications in Learning Environments, edited by D.D. Carbonara, pp. 263-273, copyright 2005 by Information Science Publishing (an imprint of IGI Global).

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Chapter 1.6

Mobile User Data Mining and Its Applications John Goh Monash University, Australia David Taniar Monash University, Australia

INTRODUCTION With the increasing penetration rate of mobile technologies among the marketplace (Goh & Taniar, 2004b; Lim, Wang, Ong, & Hwang, 2003), businesses have adopted various types of mobile products, such as personal digital assistants, mobile phones, and wireless laptop computers, in order to help improve the efficiency of one’s daily life. The increasing adoption of such equipment allows the opportunity for the collection of data about their usage and movement that can then be further analyzed (Goh & Taniar, 2004b, 2004d). The analysis of such data collected from mobile devices and mobile users for determination of patterns is called mobile user data mining (Goh & Taniar, 2004a, 2004b, 2004c, 2004d, 2005). In the mobile environment, there are devices offering service to mobile equipments. These are often known as static devices (Goh & Taniar, 2004b, 2004d), as they stay static and provide services

for the mobile devices. These mobile equipment operate in a network where data can be readily transferred and services can be readily rendered (Goh & Taniar, 2004c). Data mining can be performed in various domains such as the time series domain (Han, Dong, & Yin, 1998, 1999; Han, Pei, & Yin, 2000), Web domain (Christophides, Karvounarakis, & Plexousakis, 2003; Dourish, 2004; Eirinaki & Vazirgaiannis, 2003; Kastaniotis, Zacharis, Panayiotopoulos, & Douligeris, 2004; Kim, Kim, & Kim, 2004), market-basket analysis domain (Agrawal & Srikant, 1994, 1995), geographical information system domain (Koperski & Han, 1995), performance improvement domain (Han et al., 2000; Li, Tang, & Cercone, 2004; Miyahara et al., 2004; Thiruvady & Webb, 2004; Yip, Wu, Ng, & Chan, 2004), security domain (Oliveira, Zaiane, & Saygin, 2004), and mobile domain (Goh & Taniar, 2004a, 2004b, 2004c, 2004d, 2005; Wang, Lim, & Hwang, 2003). The existing methods of data mining include association rules (Agrawal &

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Mobile User Data Mining and Its Applications

Srikant, 1994) and sequential patterns (Agrawal & Srikant, 1995). The existing methods of mobile user data mining include frequency patterns (Goh & Taniar, 2004b), location-dependent mobile user data mining (Goh & Taniar, 2004d), and parallel patterns (Goh & Taniar, 2004c). The aim of this chapter is to provide an insight on the background of mobile user data mining and potential application areas of mobile user data mining in different industries. The potential application is viewed from the aspects of the banking industry, marketing industry, and retail industry. As the mobile user data mining methods are getting more developed, they could be implemented one day in areas where interactions are required with highly mobile customers. This chapter is organized as follows. The next section provides a background on mobile user data mining. We then highlight the benefits of adopting mobile user data mining and provide detail about how banking, marketing, and the retail industry could benefit from mobile user data mining. Next we provide an overview of future challenges such as security and privacy, and finally summarize the chapter.

MOBILE USER DATA MINING Mobile environment (Goh & Taniar, 2004a, 2004d) refers to an environment where free movements of human beings are possible. The mobile environment is an area where a human being carries devices that can be handheld (mobile devices), and seeks information and services from service providers (static devices) that is within the coverage of that area or available through subscription. Mobile environment therefore can also be referred to as the mobile network environment (Goh & Taniar, 2004d). In the mobile environment, many devices are being used. The static devices are nodes in the mobile that never moves around, but are stationed in the mobile environment just to provide services

such as data, network, and processing to their clients, which are mobile devices (Goh & Taniar, 2004b, 2004c, 2004d). These mobile devices are wirelessly connected together to a server. The mobile devices can come in many forms. Some of the existing forms include mobile phones, personal digital assistants, and laptop computers. Mobile devices are the devices that follow or are being carried by mobile users (Goh & Taniar, 2004b). They generally have less processing, network, and data capacity, and need to request service from static devices to become useful. Mobile users are human beings that carry the mobile devices in the mobile environment for the purpose of finding out location-dependent information about a current locality (Goh & Taniar, 2004d), navigating using mobile devices (Lim et al., 2003), and communicating with other mobile users in the mobile environment (Goh & Taniar, 2005; Lim et al., 2003). Mobile user data mining (Goh & Taniar, 2004a, 2004b, 2004c, 2004d, 2005) is the activity that uses data collected from mobile equipment for analysis. Mobile equipment in this context can include mobile phones, personal digital assistants, and laptop computers. Data mining allows large amounts of data to be analysed in order to find out interesting patterns about the data. Two examples were given: 1.

2.

The use of data mining techniques to find out whether consumers tend to visit location A immediately after location B (Goh & Taniar, 2004d, 2005). The use of data mining techniques to find out whether consumers tend to buy product A and product B at the same time in one single shopping trip (Goh & Taniar, 2004d).

The prerequisite for mobile user data mining (Goh & Taniar, 2004a, 2004b, 2004c, 2004d, Mar 2005; Lim et al., 2003) to work is the availability of a large amount of dataset collected from the mobile user (Goh & Taniar, 2004b). This prereq-

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Mobile User Data Mining and Its Applications

Figure 1. Frequency pattern Mobile Node

Confidence=0%

en fid

Mobile Node

uisite should be achieved as much as possible. The increasing rate of adoption of mobile phones by the world population to use as a main method for telecommunication helps achieve this prerequisite. The wide adoption of mobile phones provides the opportunity for the data to be gathered for mobile user data mining (Goh & Taniar, 2004a, 2004d). By obtaining these data, the following are possible to gain more insight about the mobile users: a.

b.

c.

Hypothesis testing, such as testing the data whether mobile users in the population visit location A immediately after location B. Data analysis, such as performing statistical functions in order to report on the statistical report on the population and categorize the population into different segments for other purposes such as target marketing. Knowledge generation, such as applying data mining methods on the vast amount of data gathered to generate a list of rules and patterns about mobile users. Rules and patterns are then refined into knowledge.

Mobile user data mining (Goh & Taniar, 2004a, 2004b, 2004c, 2004d, 2005) involves the identification of a mobile device and mobile user, and then joining the data to represent the activity

54

Confidence=0%

n Co

0% = e c

Mobile Node

Mobile Node

of the mobile user. This data is then used as the source data to be fed into mobile user data mining algorithms in order to find out the underlying patterns on the data. The identification of a mobile device (Goh & Taniar, 2004b, 2004d) might include a mobile phone number, mobile phone model, mobile phone university identification number, owner of a mobile phone, current date and time at the place of a mobile phone, current location of a mobile phone, current context of a mobile phone, current entity the mobile phone is communicating with, current mobile phone power, and memory status. Identification of a mobile user (Goh & Taniar, 2004a, 2004c) involves variables such as user name, address, e-mail, occupation, age, educational background, marital status, family members, personal interest, business interest, current activity, time of peak activity, race, nationality, transportation vehicle detail, criminal history, and religion. Some of the variables are less restricted. Some variables that are more sensitive require more consideration and preparation in the future so that data can be gathered for the benefit of all parties (Goh & Taniar, 2004b). Mobile user data mining (Goh & Taniar, 2004b, 2004d) can thus be summarized as data mining with the use of variables

Mobile User Data Mining and Its Applications

of personal details of mobile users and the current environment details where the mobile user is currently physically located, in addition to the ones normally utilized, such as items bought.

Mobile User Data Mining Techniques Mobile user data mining aims to analyze and predict behavior of mobile users from the data collected by activities generated from mobile users. Some existing mobile user data mining methods are given below.

Frequency Pattern Frequency pattern (Goh & Taniar, 2004b) uses the frequency of communication with pre-specified criteria in order to find out the logical proximity of mobile users. In a mobile environment, it is often the case that mobile users are using the mobile equipment to communicate with another mobile user. This can be done either by voice, voicemail, text, and so forth. Frequency pattern takes account of all these communications and records them into a list. There is a time series which encompasses the time desired immediately from a current time period. Different phases of the time series are given different weight in terms of the relative importance to be significant as frequent communication. Figure 1 illustrates frequency pattern (Goh & Taniar, 2004b). Circles labelled 1, 2, 3, and 4 are the set of mobile nodes that is found to have close relationships, as they are all joined together by using arrows that show high confidence (more than 50%). The relationships between the mobile nodes are determined by finding out their frequency of communication without considering their physical distance among each other, and passing it to a pre-specified criteria which determines which part of the time window is more important. The final output of frequency pattern is a set of mobile nodes connected by relationships (represented

in arrows) which suggests close relationships among them. The generation of time series with the frequency of communication along with the relative weightage is called the pre-specified criteria. Overall, the higher emphasis is usually placed nearer to the current time point. The frequency of communication along with the pre-specified criteria will determine the confidence of frequency of communication in between two mobile users. When two mobile users have been determined as communicating frequently, they will be marked as a frequent relationship in between them. The list of frequent relationships within a mobile environment tells the possibility that the list of mobile users connected by frequent communication may represent a group that is closely related. For instance, they could be a group of close friends or close family members that frequently stay in touch with each other. The limitation of frequency pattern comes from the fact that it is expensive to mine out the frequency patterns when mobile users come in a large volume. Frequency pattern requires the finding of frequency of communication from itself with every single other mobile nodes during each mobile user data mining exercise. This causes the requirement for a high amount of overhead data, which reduces the performance of data mining. Instead of mining by gathering all data, we gather data from the mobile node itself.

Location-Dependent Mobile User Data Mining Location-dependent mobile user data mining (Goh & Taniar, 2004d) is a method that uses user profiles in order to find out knowledge specific to a particular physical location, using previously available data mining algorithms, such as association rules and sequential pattern data mining. It is often useful to find out knowledge specific to a particular location, such as during instances when

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Mobile User Data Mining and Its Applications

Figure 2. Location-dependent mobile user data mining Interactions among Mobile Nodes m

m

m

m

User Profiles for Mobile Users Visited Location A

a decision that relates to a particular physical location has to be made. For example, a manager of a store wishes to find out the behaviour of mobile users at large, but who have visited the store of the manager. User profile (Goh & Taniar, 2004d) is a profile which stores information about a particular mobile user. The knowledge of user profile is organised in the format of mobile_user_identification (theme1 = x%, theme2 = y%, …, themen = z%). The user profile is built up over a period of time by engaging regular communication with static nodes that are constantly serving and recording the resources utilised by the mobile nodes. For example, over time, mobile nodes visited various physical locations. Each physical location is associated with a certain theme, such as shopping, entertainment, academic. These themes are regularly updated to the user profiles; the more occurrences of such events, the higher the confidence value will be associated with a particular theme. Once this process have been occurring over a period of time, the user profiles will be more accurate, as more transactions are fed into the

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User Profiles

User Profiles for Mobile Users Visited Location B

system and the value of the user profiles better reflects the knowledge gathered about the mobile user over a long period of time. Once this has been achieved, then the mobile user data mining process can be started. This is achieved by first identifying the list of mobile users that have visited a particular physical location over a period of time. This serves as a log of who has been visiting the physical location. The process then matches the mobile users identified from the list by querying the information collected about them—that is, the user profiles form a temporary database. This temporary database contains the list of mobile users who visited a physical location, and their visiting behaviours that are drawn from user profiles. Once the matching is done, then it can be passed to any other algorithms in order to find different types of knowledge from the system. Figure 2 illustrates the location-dependent mobile user data mining (Goh & Taniar, 2004d) scenario. The interactions among mobile nodes, which are represented as m1 , m2 , m3 , and m4 in circles are collected and recorded and passed to

Mobile User Data Mining and Its Applications

user profiles. Each time the source data is passed to user profiles, the database is updated by counting the number of visits the mobile nodes have done, and the list eventually becomes more accurate to represent the behaviour of mobile users. Once this is done, the transactions in user profiles are queried by a location-dependent miner, by first identifying all the mobile users that visit the physical location. After this, the list gathered for a particular location can be passed to other data mining algorithms such as association rules or sequential patterns. Location-dependent mobile user data mining (Goh & Taniar, 2004d) has the weakness at the user profile side, in which the accuracy of the user profiles determines the accuracy of the final result. The building of user profiles takes time and coordination among static nodes. It places the requirement at the static node to identify and record incoming and outgoing mobile nodes. Another weakness of location-dependent mobile user data mining draws from the heavy dependence on the user profiles. Issues arise when the mobile

nodes that visited a physical location have not been registered in the user profiles at all, and this will distort the final result of the data mining system. Parallel pattern will solve this method by using the similarities of decisions in order to perform data mining instead of using user profiles. The proposed method further enhances the parallel pattern by examining the relationships among these parallel patterns.

Group Pattern Group pattern (Wang et al., 2003) is a mobile user data mining method which can be used to find useful knowledge from source data collected from mobile users. Group pattern (Wang et al., 2003) analyses the physical distance in between mobile users over a time series and determines the group relationships among mobile users. The physical distance used was Euclidean distance. A list of mobile users are qualified to be a group pattern when there has been at least one occurrence when all of these mobile users have

Figure 3. Group pattern

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Mobile User Data Mining and Its Applications

been physically close to each other (distance < distance_threshold) and over a period of time (duration > duration_threshold). Figure 3 shows a graphical representation of four group patterns (Wang et al., 2003). Group pattern (Wang et al., 2003) is a physical distancedependent mobile user data mining method, in which mobile users who stays close enough together with each other over a certain time threshold will be qualified as one group pattern. The four groups are distinct from each other: since it is physical-distance based, one mobile user can only belong to one group pattern (Wang et al., 2003), and not another, because one mobile node cannot be present at two locations at one time, and thus cannot be a member of two group patterns. The advantage of using physical distance in order to perform mobile user data mining is that in a confined environment, and when there is no communication occurring among mobile users, this method is useful. However, during situations when the geographical area of the mobile users in concern are large and there are many communications which do not need the mobile users to be physically close, frequency pattern will be useful, as frequency pattern (Goh & Taniar, 2004b) does not take the physical distance but frequency of communication into consideration when finding the group relationships of mobile users.

Benefits of Mobile User Data Mining The main advantage of possessing mobile user data mining technology is the ability to deliver personalized services to customers (Goh & Taniar, 2004b, 2004d). The power to personalize comes from the ability to understand individual needs out of a mass amount of customers by the assistance of data warehouses and data mining methods. In order to achieve personalization, the understanding of what the customer does in the whole day, everyday, has to be used as the input for knowledge generation. Mobile phones were used primarily for mobile user data mining to gather

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information about mobile users (Goh & Taniar, 2004b, 2004d). The fact that mobile phones were carried by mobile users all the time fulfills the condition of mandatory need for gathering data regarding day-to-day activity of the customer. The ability to gather and process such complete data that presents the daily life picture of customers enables the generation of precise knowledge about mobile users (Goh & Taniar, 2004c, 2005). The latest mobile phone technology allows the mobile user to be identified. The problem of one mobile phone used by different mobile users is then eliminated. Cost benefit of mobile user data mining can be measured by means of key performance indicators. The cost can be calculated as the cost for generation of knowledge per customer. The cost per customer varies depending on (Goh & Taniar, 2004b): a. b. c.

the data mining algorithm used, the size of customer records, and the pre-existing summarized knowledge about the customer.

In order for a mobile user data mining project to be successfully implemented, the cost of finding knowledge per customer should be lower than the benefits for knowing what the customer wants. The main criterion is that the customer must be a customer that, if the personalized service is provided to them, will return business transactions, which increases business earning. The cost of finding what the customer wants remains high (Goh & Taniar, 2004b). This is due to the high cost of searching for information about the customers. The searching for information about the customers without the use of technology will be prohibitively expensive. The current method of data gathering is limited to the point of sales data gathering. Point-of-sale data gathering involves recording what the customer buys in that shopping center and saves it according to a loyalty program customer

Mobile User Data Mining and Its Applications

Figure 4. Mobile user data mining as an analysis tool for the marketing industry Mobile Phone

Personal Digital Assistant

Other Mobile Devices

Gather Data from Mobile Users

Step

Mobile User Data Mining

Step

Knowledge on Mobile Users

Step

Examples of Knowledge on Mobile Users ) {John, Adrian, Andrew} is a Group of Close Friends ) {John, Adrian, Andrew} Group likes adventures such as water rafting.

identification number. Mobile user data mining, through the use of a mobile phone, enables the ability to gather individual details of customers in a cost-effective way. Therefore, mobile user data mining will continue to exist.

POTENTIAL APPLICATIONS Potential application of mobile user data mining is most promising in the area where frequent interaction with customers that are highly mobile is a norm. Here, three industries—namely marketing, banking, and retail—are selected in order to provide an insight of the potential applications of mobile user data mining techniques in these areas. Mobile user data mining can value add the marketing industry through the gathering of data

from mobile sources about the market population in real time for market research. The banking industry deals with many transactions, including mobile transactions, with a wide range of variety of customers that travel nationally and internationally, and also requires a high degree of data integrity and confidentiality. The retail industry interacts with different customers, who can be identified through their mobile location-dependent and time-dependent variables. Prediction of their behavioural patterns can be found using these as a basis for analysis.

Application in Marketing Marketing is a suitable implementation ground for mobile user data mining. Mobile user data mining value adds marketing through the gath-

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Mobile User Data Mining and Its Applications

ering and analysis of mobile data that provides intensive customer information. The result is better marketing decisions, through better quality of information. Marketing environment involves market research to perform research on data gathered on the market in order to find out trends and behaviors of consumers by means of various methods, including statistics and data mining. Marketing environment also involves supply of market knowledge. The analysis of the market is then documented, and reports were supplied to subscribers from different lines of business from different industries, to provide an understanding of what the market is and what needs to be done. Mobile user data mining provides assistance for the analysis of market data. The market analysis using mobile user data mining is a large-scale analysis in the marketing industry. Mobile user data mining, through the analysis of movements of mobile users, and which includes the time and location of the mobile users, supports the ability to identify and analyze the mobile market population. Figure 4 illustrates where mobile user data mining fits in to the process of building a consumer knowledge base for marketing business. A consumer knowledge base is a base of knowledge in which knowledge about current trends and patterns are stored. A business in the marketing industry gathers data from various sources, including consumer survey, mobile phone, sales data, population, and qualitative analysis. The data mining processes include the traditional data mining techniques and also the mobile user data mining techniques. Data gathered are passed through a process which contains a list of tools. These analysis tools are statistical tools and data mining tools. Mobile user data mining, in particular, focuses on the processing of data gathered from mobile phones. The resulting knowledge generated is then contributed towards the consumer knowledge base. It is a valuable intangible asset for

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the business for later sales to other businesses in different industries. The marketing industry collects data from different segments of populations. This is done by many means, including telephone interview and survey. Such collection method will have the potential tendency to collect inaccurate data. Inaccurate data can be caused either by incorrect recording through asking the wrong question, or the consumer purposely releases inaccurate information. One approach to solve this problem is through the use of database matching, whereby individual records are checked with other sources to ensure that the consumer has released the same information every time. Function Consumer Provides (Marketing Answers) { Checking for Missing Values (Market ing_Answers); Checking for Invalid Values (Marketing_An swers); Check Consumer History (Consumer Mar keting_Answers); If Record on Consumer Found Then { Check Validity (Join(Marketing Answers, Consumer Record)); If Valid then Save (); Else if Invalid then { Mark as Invalid (); Discard (); } } Else { Mark Record as Uncertain (); Save Consumer Record (); } } The second approach for resolving such an issue is through the use of more reliable channels of survey, such as using mobile phone tracking to gather consumer behavior data.

Mobile User Data Mining and Its Applications

The third approach for resolving such an issue is through the use of data mining methods which use predictive analysis to determine the confidence of data. This concept works by means of using data mining to make predictions; for example, if the consumer aged between 20 to 30 and visits a sports and recreational facility very often, the consumer is more likely to buy x y z items in the supermarket. Function Consumer Provides (Marketing Answer){

Prediction = Data Mine (Consumer Back

ground);

Match (Prediction, Marketing Answer);

If Match () = True {

Mark Consumer as Normal Category ();

}

Else if Match () = False{

Mark Consumer as Special Category Requir

ing Human Check ();

with this group. There were costs incurred to gain such knowledge, and the protection of such knowledge through limiting sharing is essential. For instance, a business can buy knowledge about John, but will not gain access to the knowledge related to the whole group. The use of security features is the existing method for resolving these issues. Existing security methods include the use of data protection, such as passwords, and public and private keys, to ensure data confidentiality, integrity, and availability. However, these were not enough in the current world where information sharing is done through real-time sharing over a network which can span across the globe. The protection of such data may hinder the performance of information sharing. In order to resolve this issue, the data mining field has previously proposed concepts of secure sharing of data during data mining.

}

}

Protection of Derived Knowledge During the operation of performing data mining, different entities may require different levels of cooperation by means of sharing of data collected and sharing of knowledge generated from the data collected. As the cost for generation of such knowledge is high, and the cost of losing the data to competitors will cost the entities, it is important to safeguard the information through security means. An example is when derived knowledge such as Group {John, Adrian, Andrew} is a group of close friends who enjoy sailing. This was found because of the data provided from the shop selling sailing equipment and data provided by the telecommunication service. This piece of information is valuable for anyone who wishes to do business

Function Data Sharing () {

Define Attributes to Share ();

Define Individual Levels of Each Attribute

Sharing ();

Transfer Sharable Attributes to Common

Sharing Zone ();

Limit the Duration of Sharing ();

}

Samples of outcome derived from mobile sources through mobile user data mining methods that are applicable to marketing industry are provided. Mobile user data mining contributes to marketing industry through location-dependent, time-dependent, and communication-dependent knowledge previously not feasible to gather. The following knowledge can then be updated to individual profiles in order to enhance the marketing knowledge. Techniques applicable are: frequency pattern, parallel pattern, and location-dependent mobile user data mining.

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Mobile User Data Mining and Its Applications

Application in Banking Industry

Frequency Pattern: {User1, User2, User3} is a group of close friends.

The banking industry involves the activity of gathering financial resources from the population and businesses, and redistributing the financial resources to other entities needing them, benefiting by acting as a financial intermediary. Banking used to be brick-and-mortar based, where consumers have to go to a physical banking outlet to do banking. With the increasing use of information technology, banking systems have developed to be accessible by means of the Internet and mobile devices. The banking industry can capitalize on mobile user data mining by using its strategic position as a financial intermediary. Consumers’ behavior can be represented strongly, simply by viewing what

Parallel Pattern: {User1, User2, User3} tends to move from Sports to Cinema together. Location Dependency. Sports location is frequented by people who like {adventure sports, golf and fishing}. Marketing Profile User1: adds {Sports, Cinema, close To (User2, User30} Marketing Profile User2: adds {Sports, Cinema, close To (User1, User3)} Marketing Profile User3: adds {Sports, Cinema, close To (User1, User2)}

Figure 5. Mobile user data mining as a fraud analysis and prevention tool in the banking industry Market Data Sources

From Business

Banking Data Sources

From Consumer

Bank

Gather Data from Various Sources

Bank

Step

Source Data Mobile User Data Mining

Step

Data Mining Result Knowledge Base On: ) Bank Customers ) Consumers ) Consumers and their Financial Credibility

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Step

Mobile User Data Mining and Its Applications

the consumer buys. Consumers have a limited amount of financial resources, and the decision to buy something will mean that it is something important—that other products or services are passed over in order to take up this product or service. The increasing use of mobile commerce is most visible in the example of being able to purchase meals or buy a soft drink from a vending machine simply by pressing buttons on the mobile phone, deducting the credit from the mobile phone account. With the increasing use of electronic means and mobile means for transactions, these can be gathered easily by the bank, which stores the financial resources of the customers. The bank is in the best position to analyse the behavior of a customer based on the monetary view. The monetary data of the customer is impossible to be gathered by other entities besides the bank itself. Moreover, almost all people and businesses in the population will had to have at least one account with a financial institution. The financial data can be utilized to perform knowledge generation, while preserving the privacy and security of financial data. The key advantages include that knowledge generated can be used to benefit the operation of the bank by providing the most relevant financial services to the customer. The knowledge could be used as an asset for services that provide knowledge about the customer, such as a consumer group’s buying behavior to other business entities while preserving privacy and security. The knowledge can be used to join with other official entities, including the government and other financial institutions, to form an overall picture of the customer to minimize fraud. The technology could facilitate the gathering, recording, and sharing of customer credit knowledge. Figure 5 illustrates mobile user data mining as a fraud analysis tool and a data analysis tool in the context of a bank. The bank has a strategic position

to have the authority to gather and analyze the financial data of customers. The transaction data are gathered from business entities and consumers, all from the market. Data mining, including mobile user data mining, is then performed to produce knowledge on customers. This is stored in the knowledge base. The knowledge base could further be used for: • •

• •

legal enforcement for tracking fraudulent transactions, sale to the service industry on consumer behavior in a specific industry while preserving privacy and security, sale to the marketing industry to better analyze the population behavior, and sharing among financial institutions for the prevention of fraud and credit rating checks.

When confidential financial data of customers are shared among banks, the integrity of those data must be preserved. The bank has the duty of care to ensure that financial data are absolutely accurate, if they wish to participate in data mining. This is an issue when data are shared and processed by data mining software. The existing method for preserving integrity of data is achieved through versioning control and backup storage of data. Customer data (Name, address, balance, check digit) Stored in Location A Customer data (Name, address, balance, check digit) Stored in Location B

There is a need for development of methods built into the data mining methods which could preserve the integrity of customer financial data with minimal amount of data mining performance issues.

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Mobile User Data Mining and Its Applications

Privacy of Customer Identity When data mining is performed, privacy of customer identity must be preserved to ensure that customers cannot be identified if the customer records are shared outside of the organization. This is a research challenge, because it involves the need to shadow the customer identity, but still provide the ability to identify their unique identity and reverse this identity back to the original identity. Existing methods include the use of mapping, which converts the customer identity to another value, which then provides identification on the other end. Customer A

Customer A

(In the Organization)

(Out of Organization)

John

Q23

Mark

F21

Susan

U34

Jane

P23

The map needs to be recorded, and conversions are required to reverse the display of customer identity. The conversion of such customer identity includes Q23, U34, which provides an unknown pattern so that outside organization will not be able to perform the reversal to determine the customer. In the following case, it displays a case where conversion out of the organization without the map is possible. Customer A

Customer A

(In the Organization)

(Out of Organization)

John

M1

Mark

M2

Susan

F1

Jane

F2

The resulting knowledge view out of the organization can be as follows: {Q23, F21, U34}

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is a group of close friends. They enjoy products related to fishing. If a retailer wishes to target this group of customers, a retailer could subscribe to a service or pay per reveal, such as it will cost $100 to find out where the suburbs Q23, F21, and U34 are. It will cost another $100 to reveal their complete mailing addresses. The pattern of the above is clear: M represents Male, and F represents female. Such conversion will lead to easy reversal, which will affect the privacy issues of the originating organization. Samples of outcome derived from mobile sources through mobile user data mining methods that are applicable to the banking industry are provided. Mobile user data mining contributes to the banking industry through the data collected from mobile devices as a result of mobile commerce. In addition, location-dependent information and time-dependent information are also collected so that the movement activities of mobile user and the commercial activities of mobile users are used for mobile user data mining. Techniques applicable are: frequency pattern, parallel pattern, and location-dependent mobile user data mining. Case 1: Banks Location Dependency: Finds out fraud-prone or crime-prone areas. Resources are then directed on these areas for areas for a higher level of security checks. Case 2: Banks Reported that {User1} has fraudulent record or intention. Frequency Pattern Investigates the fraud rings, through the frequency and timing of communications. {User1, User4, User7} often communication with each other, thus suggesting some working relationships among them. Parallel Pattern: {User1: Restaurant - Bookstore}, suggesting that the fraudulent user may be traced at the bookstore, if it cannot be found in the restaurant.

Mobile User Data Mining and Its Applications

Figure 6. Mobile user data mining as a knowledge sharing tool in the retail industry Mobile User

Mobile User

Mobile User

Mobile User

Mobile User

Mobile User

Retail Business

Retail Business

Retail Business

Retail Business

Database On Mobile User Visits

Database on Mobile User Visits

Database On Mobile User Visits

Database On Mobile User Visits

Sharable ?

Sharable ?

Sharable ?

Sharable ?

NO

Private Knowledge

YE S

NO

Private Knowledge

YES

S YE

YES

NO

NO Private Knowledge

Private Knowledge

Common Knowledge Base

Application in Retail Industry The retail industry involves a wide range of businesses selling goods and services to consumers. The businesses in the retail industry are the transaction points where customer transactions take place. The gathering of knowledge in the retail industry works by: •

•

•

the wide range of goods and services sold to consumers which is used to identify the taste and needs of the consumer; the heavy use of electronic transaction means, including mobile commerce, card purchases, and online purchases; and the large network of businesses in the retail industry willing to share their information among each other, while preserving privacy and security.

The retail industry is the largest industry in the discussion of this chapter in terms of the number of businesses and their geographical coverage. The retail industry can capitalize on mobile user data mining by the existing architecture that is widely covered and densely networked. This is a strength whereby knowledge can be derived by sharing the information with privacy and security among other retailers, and is less reliant on the marketing industry to perform the analysis of market. The retail industry style of mobile user data mining uses a distribution strategy. By dividing the task of collecting consumer data to individual businesses in the wide area of the retail industry, these data are then shared on a common knowledge base. Retailers who are on the network could then subscribe to the knowledge base, and gather the market information without the compromise of privacy and security. This knowledge base can also be further shared with other business entities who wish to pay for such service.

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Mobile User Data Mining and Its Applications

Figure 6 illustrates the model in which mobile user data mining is used as a distributed knowledge sharing tool in the retail industry. The retailers have a widely covered and densely networked system where transactions with the consumers are recorded and are shared among other retailers subscribed within the same network. Each will perform its own data analysis and data mining function, including the use of mobile user data mining that utilizes mobile phone data in addition to generic data. The outcome of the analysis is stored in a knowledge base which is shared among subscribers of the network, including entities outside of the retail industry, such as the marketing industry and the banking industry. Samples of outcome derived from mobile sources through mobile user data mining methods that are applicable to retail industry are provided. Mobile user data mining contributes to the retail industry through data collection from mobile devices from mobile users, and the sharing of such individual knowledge among all other retailers through a central service provider so that the knowledge can be well utilized. Techniques applicable are: frequency pattern, parallel pattern, and location-dependent mobile user data mining. Knowledge of Retailers Retailer 1: Knows only about User1 {likes cappuccino}. Retailer 2: Knows only about User2 {likes blue colored basketballs}. Retailer 3: Would like to know about User1, User2, and User3. Mining Performed by Service Provider Frequency Pattern: {User1, User2, User3} are close friends. Physical Parallel Pattern: {User1, User2} often goes from Retailer 2 after Retailer 1. Service Provider Facilitates Knowledge Sharing Location Dependent Mobile User Data Mining:

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Location of Retailer 1 & 2 shown that people who visits there tends to like {newspaper, breakfast}. Retailer 1, 2: Then sells newspaper or complimentary breakfast to attract customers.

FUTURE CHALLENGES Mobile user data mining is indeed a valuable tool worth implementation to assist in finding relevant knowledge about the customers and potential customers. The future remains bright; however, there are some issues that need to be addressed in the future. Selected future challenges include: privacy issue on data handling, protection of data (data security) issue, user identification issue, and handling of massive volume of real-time data issue.

Privacy Issue The privacy issue remains an important element that has been widely implemented in developed countries. The privacy issue exists mainly due to the need for respecting human rights, being able to do things freely with minimal interference from others. This includes the ability to remain anonymous and unknown of their choice and behavior, unless required by a legal or political system. Personal details are being used extensively in mobile user data mining in order to produce a rich dataset. Privacy laws will indeed create barriers to access to such information. This issue could potentially be addressed by means of privacy-preserving methods, such as by a limited amount of view of the personal information through summarized data, or anonymous data which only identifies a person with a primary key of just numbers. While preserving the privacy of customers, the other factor of maintaining data consistency is also important so that the result of the analysis generates accurate knowledge for later use.

Mobile User Data Mining and Its Applications

For example, data gathered contains financial data, location visited data, personal information data, and is shared among different entities from banks, retailers, and market research firms. Not all firms should be allowed to have full view of all data. All of the entities should not be able to reverse the output back to input to gather sensitive information. A technique that divides the data into financial, market research, and personal information that confidentially transfers to the authorized entity when required, in an accurate manner, is the desired result.

Data Protection Protection of privacy of customer data involves protection of such data so not to be accessed by unauthorized parties. Protection from duplication or sharing is the key aspect of data protection here, as this knowledge has been gathered through investment and is an asset to the business. These assets are valuable for business in general, as the ability to have such information provides better decision making, creating competitive advantage. Due to the existence of knowledge in electronic form, duplication and sharing is likely. A strategy coupled with technology and legal solution is required to preserve the value of the asset. For example, protection of data generated is an essential tool. Consider a business, having invested in the gathering of data through a high amount of effort, finding useful patterns from the data. The firm could provide service for other firms, while retaining its benefit of possessing the data through investment by preventing other firms from sharing the data.

User Identification Issues As mobile user data mining will involve the identification of many different users in the mobile environment, errors and confusions might occur. Identification has many different purposes, including distinguishing one from another to

ensure that for a series of identification numbers 1 to infinity, each represents a different human being. The identification of a user is based on the purpose and requirement of the technique used. If a technique requires the finding of similar activities performed by a single human being, then the identification of the person in question is required, such as through a national identification system. If the technique focuses the items bought and location visited, then the identification requirement is only the identification of the mobile device. This can be done through the use of a network identification number, which is a unique number for every single network hardware that can be used to connect to a network. The selection of the proper identification strategy and technique will be based on the identification requirement of the purpose of mobile user data mining. For example, consider that a group of mobile users needs to be identified for tracking their financial records. The mobile user can be identified individually through the use of username and password, or credit card details, as only one individual can logically be associated with the identification means. On the other hand, when identifying users that have visited a mall and the place that they have visited soon after, it is only required to identify the mobile device through a number, such as the serial number of a network device identification number such as 00-F0-304E-4A.

Handling of Massive Volume of Data When operationally active, mobile user data mining often will handle a massive amount of data in real time. This requires hardware and software that is capable of processing these data with the least amount of downtime. The processing power and data storage size required increases as the number of mobile users are involved and also as the area of the mobile environment enlarges.

67

Mobile User Data Mining and Its Applications

Future challenges in this area are the development of hardware that are capable to process faster, as well as software that can filter the irrelevant data from mobile users and also summarizes the data from mobile users in order to provide a shorter input to the processing system. Other strategies for such issues include the simplification of technique or performance enhancement of techniques in order to reduce the resources required to process such data. For example, consider the sources of gathered data from many different places. These data can easily contain error, or be duplicated and misrecorded. To improve performance and accuracy, if there is a probability of error associated with the data, the system could be designed to drop the data, not taking it into consideration and thus improving data accuracy and performance at the same time. Filtering techniques could also be provided to intelligently identify and filter through dropping data or summarizing data. Consider there were duplicate data about Adam, but they are slightly different. Through cross-checking with another data source, the system is able to properly identify whether the data are reliable or whether there is more than one Adam.

CONCLUSION The future of data mining can be enhanced by capitalizing the increasing use of mobile phones. This gives another mobile dimension of data analysis which produces knowledge that is of higher accuracy. The future use of mobile user data mining technology is described in three different models—the marketing industry model, the banking industry model, and the retail industry model. The marketing industry and banking industry models are highly centralized. The retail industry model demonstrates the use of mobile user data mining in a distributed manner. Overall, the main benefits provided by mobile user data mining are:

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a. b. c.

reduced searching cost of consumer data; ease of tracking, updating, and sharing consumer knowledge; and the ability to generate knowledge out of a wide amount of data sources with the use of technology that enhances the speed of the process.

The main challenges for mobile user data mining in the future are privacy issues and knowledge security issues. The research into mobile user data mining continues to provide insights of solutions in different areas, such as security, privacy, and implementation architecture.

REFERENCES Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Santiago: Morgan Kaufmann. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Taiwan: IEEE Computer Society Press. Christophides, V., Karvounarakis, G., & Plexousakis, D. (2003). Optimizing taxanomic Semantic Web queries using labelling schemes. Journal of Web Semantics, 1(2), 207-228. Dourish, P. (2004). What we talk about when we talk about context. ACM Journal of Pervasive and Ubiquitous Computing, 8(1), 19-30. Eirinaki, M., & Vazirgaiannis, M. (2003). Web mining for Web personalization. ACM Transactions on Internet Technology, 3(1), 1-27. Goh, J., & Taniar, D. (2004a, December 13-15). An efficient mobile data mining model. Proceedings of 2n d International Symposium on Parallel and Distributed Processing and Applications, Hong Kong (LNCS, pp. 54-58). Berlin: SpringerVerlag.

Mobile User Data Mining and Its Applications

Goh, J., & Taniar, D. (2004b, December 20-25). Mining frequency pattern from mobile users. Proceedings of the Knowledge-Based Intelligent Information & Engineering and Systems (LNCS, Part III, pp. 795-801). Berlin: Springer-Verlag. Goh, J., & Taniar, D. (2004c, August 25-27). Mining physical parallel pattern from mobile users. Proceedings of the International Conference on Embedded and Ubiquitous Computing (LNCS, pp. 324-332). Berlin: Springer-Verlag. Goh, J., & Taniar, D. (2004d, August 25-27). Mobile data mining by location dependencies. Proceedings of the 5t h International Conference on Intelligent Data Engineering and Automated Learning, Exeter (LNCS, pp. 225-231). Berlin: Springer-Verlag. Goh, J., & Taniar, D. (2005, January-March). Mining parallel pattern from mobile users. Proceedings of the International Journal of Business Data Communications and Networking (Vol. 1, No. 1, pp. 50-76). Hershey, PA: Idea Group Publishing. Han, J., Dong, G., & Yin, Y. (1999). Efficient mining of partial periodic patterns in time series database. Sydney, Australia: IEEE Computer Society. Han, J., Gong, W., & Yin, Y. (1998). Mining segment-wise periodic patterns in time related databases. Menlo Park: AAAI Press. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. New York: ACM Press. Kastaniotis, G., Zacharis, N., Panayiotopoulos, T., & Douligeris, C. (2004, May 5-8). Intelligent Web prefetching based upon user profiles—the WebNaut case. Proceedings of the Conference on Lecture Notes in Artificial Intelligence, Samos (pp. 54-62).

Kim, B. J., Kim, I. K., & Kim, K. B. (2004, May 26-28). Feature extraction and classification system for nonlinear and online data. Proceedings of the 8t h Pacific-Asia Conference on Knowledge Discovery and Data Mining 2004, Sydney, Australia (pp. 171-180). Koperski, K., & Han, J. (1995). Discovery of spatial association rules in geographical information databases. London: Springer-Verlag. Li, J., Tang, B., & Cercone, N. (2004, May 26-28). Applying association rules for interesting recommendations using rule templates. Proceedings of the 8t h Pacific-Asia Conference on Knowledge Discovery and Data Mining 2004, Sydney, Australia (pp. 166-170). Lim, E.-P., Wang, Y., Ong, K.-L., Hwang, S. Y. (2003). In search of knowledge about mobile users. ERCIM News, 1(54), 10. Miyahara, T., Suzuki, Y., Shoudai, T., Uchida, T., Takahashi, K., & Ueda1, H. (2004, May 26-28). Discovery of maximally frequent tag tree patterns with contractible variables from semi-structured documents. Proceedings of the 8t h Pacific-Asia Conference on Knowledge Discovery and Data Mining 2004, Sydney, Australia (pp. 133-144). Oliveira, S. R. M., Zaiane, O. R., & Saygin, Y. (2004, May 26-28). Secure association rule sharing. Proceedings of the 8t h Pacific-Asia Conference on Knowledge Discovery and Data Mining 2004, Sydney, Australia (pp. 74-85). Thiruvady, D. R., & Webb, G. I. (2004, May 26-28). Mining negative rules using GRD. Proceedings of the 8t h Pacific-Asia Conference on Knowledge Discovery and Data Mining 2004, Sydney, Australia (pp. 161-165). Wang, Y., Lim, E.-P., & Hwang, S.-Y. (2003, May 26-28). On mining group patterns of mobile users. Proceedings of the Conference on Database and Expert System Applications, Sydney, Australia (pp. 287-296).

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Yip, A. M., Wu, E. H., Ng, M. K., & Chan, T.F. (2004). An efficient algorithm for dense regions discovery from large-scale data streams. Proceedings of the 8th PacificAsia Conference on Knowledge Discovery and Data Mining 2004, Sydney, Australia (pp. 116-120). This work was previously published in Handbook of Research in Mobile Business, edited by B. Unhelkar, pp. 216-232, copyright 2006 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.7

ICT, CoLs, CoPs, and Virtual Communities Antonio Cartelli University of Cassino, Italy

INTRODUCTION Every day, information and communication technologies (ICT) are extending their influence on knowing and transmitting knowledge. They act on humankind at different levels: the individual, the society, and the community/organization. The Internet more than other instruments in the past is changing human customs and knowledge strategies mostly due to the online information systems developed during last few years.

BACKGROUND The experiences described below were from the author in cooperation with M. Palma (professor of Latin paleography) while working on special Web sites to be used in paleography for research and teaching. The Web sites were not e-learning platforms but were used as content management systems (CMS), learning management systems (LMS), computer-supported collaborative learning systems (CSCLS), and knowledge management systems (KMS).

The use of the above systems is based on the hypothesis the author shares with M. Palma: that ICT and especially the Internet cancelled the temporal gap existing between research and teaching time (at least in paleography). Due to the Internet, in fact, scientists can immediately publish the research results; it becomes, then, more and more difficult to separate the proposal of new scientific paradigms from their translation into educational and didactical materials.

Web Site “Didactical Materials” for Latin Paleography In the Middle Ages different scripts were used for handwriting documents and their study is based on the analysis of charters and manuscripts. The main problem scholars and students have today is often the simple access to these materials due to security and preservation reasons. Furthermore, proceedings of conferences and meetings are usually printed some years later and it is very difficult, if not impossible, for scholars, to report the meaning of a hypothesis or the relevance of a

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

72

discovery to their students, contextually to their proposal. To respond to the above problems, a Web site was planned to make available the following:

they were written, (c) the date or the period they belong to, (d) the authors and titles of the texts, (e) the bibliography or its source of information.

Didactical materials for a course on Latin paleography, that is, the plates reproducing the pages of manuscripts and the transcription of their texts. Texts freely extracted and translated from printed or electronic documents, or made available from the authors, and collected in various sections: from codicology to cataloguing and preservation, and so forth.

Furthermore it appeared important to show for each woman the manuscript(s) she wrote and vice-versa, and if possible and available, at least an image of the copyist’s hand. The site has two separated sections: one being operated only by editors (to insert, modify, delete the data stored in the database), thus ensuring the scientific validity of the information reported; the other is at everyone’s disposal to obtain the list of all women and manuscripts in the database, or to make queries concerning women and manuscripts with specific qualifications.

1.

2.

It has to be noted that the experience of the didactical materials’ Web site is mostly unique, not only for the systematic nature of the plates and for the presence of their transcriptions, but also for the documents reported among the texts; many of them are, in fact, papers concerning recent research topics, produced for special events (i.e., mainly conferences) and made available from the authors for didactical purposes.

The Web Site “Women and Written Culture in the Middle Ages” The main aim of this dynamic Web site (Cartelli, Miglio, & Palma, 2001) was the systematization of the data emerging from the research while leading to an instrument helping scholars to find new elements for further studies. Data appearing relevant to the scientific community were: •

•

For the Scribes: (a) the name of the woman as it appears in manuscripts, (b) her qualifications (i.e., if it is known whether she was a nun or a layperson), (c) the date or the period she belonged to (up to the 15t h century) For the Manuscripts: (a) their shelf marks (i.e., town, library, and number of the manuscript), (b) the place and the country where

The Open Catalogue of Manuscripts and the Martyrology of Arpino At the basis of the Open Catalogue of Manuscripts are the results of recent studies concerning the use of the Web for the publication of catalogues of manuscripts (Cartelli & Palma, 2003b). In its final structure, it is an information system devoted to the management of documentary information in ancient libraries and it is based on the use of the Internet and especially of the Web for managing and accessing data. It is composed of five sections: (a) the first one contains documents explaining the history of the library and of its manuscripts; (b) the bibliography ordered by shelf mark and, eventually, alphabetically and chronologically, is housed in the second section; (c) in the third section are the descriptions of the manuscripts; (d) the fourth section is devoted to the images of the highest number of manuscripts in the library (potentially all); (e) the fifth and last section consists of a communication subsystem granting the easier acquisition, writing, and editing of texts (Cartelli & Palma, 2002). In Italy the Malatestiana Library in Cesena, an ancient library founded in late 15t h century

ICT, CoLs, CoPs, and Virtual Communities

decided to adopt the Open Catalogue for its 453 manuscripts and their catalogues and activated all the sections the author hypothesized for its structure. On another hand the Martyrology of Arpino, as a single manuscript kept in the church devoted to Our Lady in Arpino (a small town in central Italy), had the suitable features for the creation of an Open Catalogue (Cartelli & Palma, 2003a) made by a single manuscript and a special site for it was carried out in the Faculty of Humanities.

The Bibliography of Beneventan Manuscripts The BMB experience (Bibliografia dei Manoscritti Beneventani—Bibliography of Beneventan Manuscripts) started in 1992 with the main aim of collecting the quotations of Beneventan manuscripts (i.e., medieval books written in the South Italian national script) by means of a program called BIBMAN. In 1997, the first Web site was developed to make it faster and easier for scholars to download new bibliographic data (nearly monthly). Recently many problems with the BIBMAN program resulted in a plan to carry out a new Web site: the BMB online (Cartelli & Palma, 2004). It is an information system where various people with permission can store the quotations of Beneventan manuscripts and it can be freely queried by general users. Persons entrusted with the task of collecting the quotations of Beneventan manuscripts are grouped into three categories: 1.

2.

Contributors, who can write, modify, and delete bibliographic data for the materials assigned to them Scientific administrators, who can manage all data and write, modify, and certify bibliographic materials (this last operation being done only once because certified records cannot be accessed for revision)

3.

System administrator, who is allowed to do all operations including the modification or deletion of certified data

The access to certified bibliographic materials is possible according to different query pages: (a) by author, (b) by manuscript, (c) by contributor, (d) by one or more words or part of them concerning the title, location, or bibliographical abstract of a given publication. It has to be noted that in the system are also implemented: (1) a closed communication subsystem made by an electronic blackboard guaranteeing an easy exchange of messages among contributors, (2) some special functions, available only to system administrator, for the production of the printed version of the yearly collected data.

ICT, CoLs, CoPs, AND VIRTUAL COMMUNITIES IN PALEOGRAPHY The systems and Web sites described above were carried out by the author at different times but were suddenly introduced in everyday teaching and research work. The effects they produced are analyzed in what follows: •

The students attending the paleography course found the plates on the Web site of Didactical Materials (both for the analysis of the scripts and for training themselves in the transcription of the texts) very useful and were instantaneously led to the themes of most recent research studies and to the debates in the paleographic community. Furthermore, after a very short starting phase, during which people had to be repeatedly invited to submit materials for publishing on the Web site, the number of scholars who autonomously propose the publication of their works is now growing. At last it has to be noted that many students in other Italian universities use the same materials and ask

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•

•

•

74

for explanations via e-mail, thus creating a virtual community on paleography studies. Many students not only used the materials reported in the Women Copyists Web site, but were also involved in the description of manuscripts and in the collection of the plates reproducing texts written from women (i.e., they learned to distinguish the different hands of women copyists and their way of writing manuscripts). With time the number of bibliographic notes increased from scholars all over the world. As a consequence the project of an information system which allowed people to autonomously manage the bibliographical data and an enhancement of the virtual community (it is also a community of practice) working on women copyists have been planned, Some students were involved in the description of manuscripts of the Malatestiana Library and accessed the communication subsystem of its Open Catalogue so that they could share with the subscribers of the newsgroup the texts they wrote. It has to be noted that the Malatestiana Library staff reports of an increasing number of scholars and students asking for access to the communication subsystem of the Open Catalogue and of a continuously growing number of reports and discussions concerning the manuscripts in the library. By comparing the above results with what is already known on CoPs and virtual communities, it seems that people involved in the Malatestiana Open Catalogue have all the typical features of those kinds of communities. The Martyrology of Arpino, on another hand, has been used for teaching as follows: (1) some make-up courses centered on it were designed for students with gaps in their basic knowledge of History and Latin (i.e., the manuscript was used as a chronicle from

•

14th-16th century and the historical events reported there had a counterpart in relevant events of that period); (2) students were directly involved in the production of the Web pages of the Martyrology site (with the acquisition and editing of texts and images), in the description of the manuscript and in the transcription of the text. As a result, the students involved in the digitization of the pages of the Martyrology and in the editing of its texts had fewer problems than their friends in learning computer use and there were no cases of rejection of its use as often happened in institutional computing courses in the Faculty. Many students, after having attended the basic courses on cataloguing, were asked to become contributors for BMB online and to produce bibliographic materials. The discussions they had with administrators, professors, and among themselves, the use they made of the electronic blackboard and of the e-mail services for the exchange of messages, and last but not least, the chance of working in little groups on the same problems helped them very much in the acquisition of the knowledge and development of the skills they would need (as paleographers) in their everyday work.

CONCLUSION In the author’s opinion the effects induced on knowledge construction/sharing processes can be analyzed from three different points of view: 1.

The first one concerns the communities of students involved in the experiences described above and what we know on CoLs. It has to be noted that the groups of students involved in the experiences (a few elements each time and in the best case no more than 19 subjects) really had the

ICT, CoLs, CoPs, and Virtual Communities

2.

features of CoLs or FCL as described by Brown and Campione (1996). Furthermore the students were satisfied with the new approach to the discipline and got better results in their exams, that is, they had better scores and developed better skills than their counterparts in traditional courses (as it was recognized by the professors). Some new effects never observed before were also detected in the following skills: working in a group (in traditional courses it is a very rare experience), easier facing of complex tasks (thanks to the help each student could have from their colleagues), and raising of the individuals’ peculiarities within the community. The ICT also had a great role in all experiences because they helped students in experimenting a metacognitive environment and cognitive apprenticeship strategies, involved them in the discussion and evaluation of the procedures they took part in, and let them experiment with meaningful learning (Varisco, 2002). The second point of view is the one of the communities of practice (CoPs). But is it suitable to look at groups of students as CoPs? With respect to corresponding communities in organizations and corporations, which are autonomously created and have no hierarchies, the presence of a hierarchical structure has to be noted (professors and their collaborators organize the work, suggest what to do, support everyday activity, etc.). Further differences between groups of students and CoPs are (a) community skills are mostly induced/transmitted by professors and not freely shared among individuals, (b) community memory is not made of the repositories of expertise but it is made of the data in the Web pages and in the databases (i.e., it is mostly represented by the scientific knowledge available from sites browsing and database querying). Nevertheless, in the author’s opinion, the

3.

answer to the above question must be affirmative because (a) there is a common task shared among all community members, (b) there is a reciprocal commitment regulating interactions and sharing of experiences among the subjects in the community, (c) there is a shared repertoire of knowledge, instruments and methods by means of which common knowledge is preserved and transmitted (Wenger, 1998). The third and last point of view is the one of virtual communities. It has to be noted that many scholars cooperated in the various projects via the Internet (by sending papers or other texts for the Didactic Materials, by intervening in the discussion groups of the Open Catalogue, or by compiling bibliographic cards as contributors of the BMB online) and often they did not know people carrying out those projects or other people working on them. Nonetheless their contributions were very important for students and for the construction of the community memory. In the author’s opinion it can be deduced, at least in the case of paleography, that virtual communities can act as CoPs without needing presence meetings, thus contradicting the third law of Denning (2000). From what has been reported until now, it seems that the overlapping of virtual communities and CoPs features are possible if people joining them have the same highly specialized skills and agree on the target to be hit (also if they do not know one another).

FUTURE TRENDS From what has been said until now some hints for future research seem possible: 1.

If, as emerged above, the contribution to problems analysis and solution coming from different kinds of communities seem

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2.

to produce better results with respect to the ones coming from individuals or single types of communities, further research has to be carried out on the relationships existing among them and on the consequences that the presence of the same individuals in more than a type of community has on knowledge construction processes. If, at least in the case of paleography, the presence of a virtual community contributes in increasing scientific knowledge construction (and not only teaching), other best practices have to be encouraged and the opportunity for academic and research institutions of making stable and durable their intervention in problems analysis and solution has to be analyzed.

REFERENCES Brown, A. L., & Campione, J. (1996). Psychological theory and the design of innovative learning environments: On procedure, principles and systems. In L. Schaube & R. Glaser (Eds.), Innovation in learning (pp. 289-375). Mahwah, NJ: Lawrence Erlbaum. Cartelli, A., Miglio, L., & Palma, M. (2001). New technologies and new paradigms in historical research. Informing Science (the International Journal of an Emerging Discipline), 4(2), 61-66. Cartelli, A., & Palma, M. (2002). Towards the project of an Open Catalogue of Manuscripts. Proceedings of the 2002 Informing Science + IT Education Conference, Cork, Ireland. Cartelli, A., & Palma, M. (2003a). Il Martirologio di Arpino come oggetto di ricerca e strumento didattico. Tecnologie Didattiche, 28(1), 65-72. Cartelli, A., & Palma, M. (2003b). The Open Catalogue of Manuscripts between paleographic research and didactic application. In M. KhosrowPour (Ed.), Proceedings of the IRMA 2003 Confer-

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ence “Information Technology & Organization: Trends, Issues, Challenges and Solutions” (pp. 51-54). Hershey, PA: Idea Group Publishing. Cartelli, A., & Palma, M. (2004). BMB on line: An information system for paleographic and didactic research. In M. Khosrow-Pour (Ed.), Proceedings of the IRMA 2004 Conference “Innovation Through Information Technology” (pp. 45-47). Hershey, PA: Idea Group Publishing. Denning, S. (2000). The springboard: How story telling ignites action in knowledge-era organizations. Boston: Butterworth-Heinemann. Varisco, B. M. (2002). Costruttivismo socio-culturale. Genesi filosofiche, sviluppi psico-pedagogici, applicazioni didattiche. Rome: Carocci. Wenger, E. (1998). Communities of practice. Learning, meaning and identity. Cambridge, UK: Cambridge University Press.

Key Terms Cognitive Apprenticeship: It is originated from traditional apprenticeship, which has the following well-known features: modeling, coaching, scaffolding, and fading. When it is applied to educational and research situations, the new features of articulation, reflection, and exploration must be added to the ones above. Community of Learners (CoL): It is a community of students, teachers, tutors, and experts marked by the presence of the following elements: (1) multiple ZPDs (the zones of proximal development of the subjects in the CoL); (2) legitimated peripheral participation (the respect of the differences and peculiarities existing among the various subjects in the community); (3) distributed expertise; (4) reciprocal teaching and peer tutoring; (5) various scaffoldings; (6) cognitive apprenticeship; and (7) metacognition.

ICT, CoLs, CoPs, and Virtual Communities

Community of Practice (CoP): It is a community of individuals having the following elements in common: a) a joint enterprise, as understood and continually renegotiated by its members; b) a mutual engagement binding members together into a social entity; and c) a shared repertoire of communal resources (routines, sensibilities, artifacts, vocabulary, etc.) that members developed over time. Information System: It is the set of all human and mechanical resources needed for acquisition, storing, retrieving, and management of the vital data of a given system. With human resources are usually intended both the individuals involved in the use of the system and the procedures they have to carry out. With mechanical resources have to be intended both the hardware and software instruments to be used for the management of data.

Online Database: It is a special Web site interfaced with a database (usually relational). Special languages have been developed to make easier for programmers the creation of Web pages for the updating and querying of databases. Paleography: It is the discipline studying the ancient script from 9t h to 15t h centuries (until first printing of documents). Dating and localizing a medieval script, as well as identifying a scribe, are the paleographer’s essential tasks on which all historical speculations are founded. Virtual Community: It extends the sociological definition of community by including the groups of subjects who can never meet themselves or physically know one another but who use the Internet for their interpersonal communication, that is, for sharing information, building new knowledge, and socially interacting.

This work was previously published in Encyclopedia of Virtual Communities and Technologies, edited by S. Dasgupta, pp.248252, copyright 2006 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.8

A Comparison of the Features of Some CoP Software Elayne Coakes University of Westminster, UK

INTRODUCTION There are now significant numbers of software houses supplying services and solutions for community collaboration. In this article we briefly review the requirements for virtual support and the current offerings. This is not intended as a comprehensive survey, but rather an overview of what might be available.

BACKGROUND In 2004 the Directorate of Science and Technology Policy (DSTP) in Canada produced a report reviewing portal technology. In particular, DSTP reviewed a specific subset or portals for community support. They looked at four specific program offerings, operating under portals, across eight areas of functionality. These eight areas were: 1. 2. 3. 4.

Ongoing interactions Work Social structures Conversation

5. 6. 7. 8.

Fleeting interactions Instruction Knowledge exchange Documents.

These program suites—Tomoye, community Zero, iCohere, and Communispace—were all strongly oriented towards Fleeting interactions and Instruction (apart from iCohere), but weakly supportive of social structures, knowledge exchange, and documents. In addition, all software suites contained taxonomy, a local search, an experts database, discussion, and an events notification facility. None provided audio- or video-supported meetings or webinars, and only Communispace provided a (limited) virtual meeting space. All, except for Tomoye, provided community governance and polls.

Other Software Offerings Enable2 was not considered by DSTP. It is provided by Fount Solutions, who claims that it provides the essential capabilities required for CoP sup-

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Comparison of the Features of Some CoP Software

Table 1. Core technology features Relationships Member networking profiles Member directory with ‘relationship-focused’ data fields Subgroups that are defined by administrators or that allow members to self-join Online meetings Online discussions

Learning Recorded PowerPoint presentations E-learning tools Assessments Web conferencing Online meetings Online discussions Web site links

port. These, they say, would include: content management (to generate domain-specific content), discussion forums, document management, member profiles, and a search engine. As we see, the ‘missing’ capabilities of this software suite are also missing from the software reviewed by the DSTP—that is, support for audio and video meetings, webinars, and virtual meeting spaces. Fount also recommends the provision of Weblogs so that users can publish specific content and a tool called really simple syndication or RSS. RSS is used to enable users to subscribe to content sources that match their specific interests. Livelink for Communities of Practice is relatively new software that was launched in 2004 by OpenText ™Corporation (www.contentmanager. net and www.opentext.com/solutions/platform/ collboration/communities-of-practice). Livelink also provides Weblogs, FAQs, webcasts, an experts database, forums with threaded discussions, and role-based permissions for community users so that they can perform specified tasks. Sitescape (BillIves, 2004) also launched new CoP software in 2004. This software provides both synchronous and asynchronous communication facilities, document management, shared scheduling, and instant messaging, as well as a number of task- and process-based tools. Web

Knowledge Structured databases ‘Digital stories’ Idea banks Web conferencing Online meetings Online discussions Expert database and search tools Announcements Web site links

Action Project management Task management Document collaboration File version tracking File check-in and check-out Instant messaging Web conferencing Online meetings Online discussions Individual and group calendaring

meetings, white boards, videoconferencing, and voiceover IP are also supported. iCohere in its CoP Design guide (available from www.icohere.com) states that there are four focal areas for CoPs—relationship building, learning and development, knowledge sharing and building, and project collaboration. The company also provides Table 1, which allocates core technical features to each focal area. Obviously, iCohere considers that its software offering provides these necessary features.

FUTURE TRENDS The software market for KM and IC management is competitive. Features and facilities are changing rapidly and developing in complexity. Increasingly the software for community support is being subsumed into the general KM management software, which is, in its turn, being incorporated into organisational portals. DSTP (2004) expects to see a rapid growth in the portal development market, with organisations integrating their applications to “facilitate creation, sharing and preservation, and intellectual capital management…This trend is eroding the benefits of specific community of practice tools” (p. 3).

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A Comparison of the Features of Some CoP Software

CONCLUSION

REFERENCES

Whilst this article was not intended as a comprehensive review of software for supporting CoPs, it has shown that many offerings lack some (apparent) essentials for a virtual community. Whilst many would be useful additions for a ‘physical’ community by providing shared documents and databases, few provide virtual meetings spaces and the possibilities for synchronous communication and the sharing of tacit knowledge—this latter being, of course, the prime driver behind the development of CoPs.

BillIves. (2004). Retrieved from http://billives. typepad.com/portals_and_km/2004/07/supporting_com.html

NOTE The May/June issue of KM Review (Melcrum Publishing) also contains a useful comparison of CoP and collaboration software.

Content Manager. (2004). Retrieved from www. contentmanager.net/magazine/news _h9697_ opentext-launches_ DSTP. (2004). Retrieved from http://pubs. drdc_rddc.gc.ca/inbasket/dguertin.040317-1030. p521012.pdf Fount Solutions. (n.d.). Retrieved from www. enable2.com

KEY TERMS Portals: “Frameworks for integrating tools, applications, collaboration, and information that is shared across an organisation” (DSTP, 2004, Abstract, p. 3). Collects applications and Web sites together to provide a common look and feel. Webinars: Seminars conducted ‘on the Web’ through the use of an intranet or the Internet.

This work was previously published in Encyclopedia of Communities of Practice in Information and Knowledge Management, edited by E. Coakes & S. Clarke, pp. 89-91, copyright 2006 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global).

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Chapter 1.9

Introducing the Check-Off Password System (COPS): An Advancement in User Authentication Methods and Information Security* Merrill Warkentin Mississippi State University, USA Kimberly Davis Mississippi State University, USA Ernst Bekkering Mississippi State University, USA

BACKGROUND Despite continuing improvements in computer and network technology, computer security continues to be a concern. One of the leading causes of security breaches is the lack of effective user authentication, primarily due to poor password system management (The SANS Institute, 2003), and the ease with which certain types of passwords may be “cracked” by computer programs. Yet even with today’s high-speed computers, an eight-character password can be very secure indeed. If a Pentium 4 processor can test 8 million

combinations per second, it would take more than 13 years on average to break an eight-character password (Lemos, 2002). However, the potential for password security has not been fully realized, and a security breach can significantly compromise the security of information systems, other computer systems, data, and Web sites. Furthermore, the increasing degree to which confidential and proprietary data are stored and transmitted electronically makes security a foremost concern in today’s age of technology. This is true not only in civilian use, but also in government and military use.

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Introducing the Check-Off Password System (COPS)

A primary objective of information system security is the maintenance of confidentiality, which is achieved in part by limiting access to valuable information resources. Historically, user authentication has been the primary method of protecting proprietary and/or confidential data by preventing unauthorized access to computerized systems. User authentication is a foundation procedure in the overall pursuit of secure systems, but in a recent e-mail to approximately one million people, Bill Gates (Chairman of Microsoft Corporation) referred to passwords as “the weak link” in computer security, noting that most passwords are either easy to guess or difficult to remember (Gates pledges better software security, 2003). Gates correctly identified a classic trade-off that system and network administrators must face when considering various password procedures for adoption. Specifically, there is an inverse relationship between the level of security provided by a password procedure and ease of recall for end users. When end users select their own easily remembered passwords, they are easier to crack than longer passwords with a greater variety of characters. The longer the password and the more variability in the characters, the higher the level of security provided by such a password. However, human memory has significant limitations, and such passwords tend to be more difficult for end users to remember. Typically, human short-term memory can only store seven plus or minus two (7 ± 2) “chunks” of information (Miller, 1956), and alphanumeric characters such as punctuation marks and other symbols are not easily combined in a chunk with other characters. For example, the letters “b,” “a,” “n,” and “d” can be easily stored together as a single chunk, but it is difficult for humans to combine symbols such as the vertical bar ( | ) and tilde ( ~ ) with other characters to form a chunk. The problem of striking a balance between security and ability to remember passwords will become more acute as the number of passwords per user increases.

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In a survey with 3,050 distinct respondents (Rainbow Technologies Inc., 2003), the following picture emerged: • • • • • •

Respondents used, on average, almost 5 ½ passwords 23.9% of respondents used eight or more passwords More than 80% were required to change passwords at work at least once a year 54% reported writing down a password at least once 9% reported always writing down their passwords More than half had to reset business passwords at least once a year, because they forgot or misplaced the password

The 352 participants in the present study reported using an average of 3.90 passwords at the time of the study and 4.53 passwords in the six months. Further, 35.5% reported writing down at least one password. Clearly, the use of multiple passwords constitutes a burden to users.

PASSWORD STRATEGIES Because of the trade-offs detailed above, and because methods and technologies employed by crackers are constantly improving, new security strategies with improved password procedures are required. Traditional methods include allowing users to select their own password and assigning passwords to them, both of which may be subject to restrictions on password length and character choices. The efficacy of both systems depends on the ability of end users to recall such passwords without writing them down. The Federal Information Processing Standards (FIPS) publication 112 includes guidelines for different levels of password security (National Institute of Standards and Technology, 1985). At the highest

Introducing the Check-Off Password System (COPS)

level, these guidelines include passwords with six to eight characters composed from the full 95 printable character ASCII set. Furthermore, the guidelines specify using an automated password generator, individual ownership of passwords, use of non-printing keyboards, encrypted password storage, and encrypted communications with message numbering. The theoretical number of passwords using the FIPS procedure is approximately 6.7 x 1015 ( = 958 + 957 + 956 ). However, to utilize the full set of characters, all printable non-alphanumeric characters must be included in the set and have an equal chance of selection as the alphanumeric characters. But passwords with non-alphanumeric characters can be hard to remember. Consider, for example, passwords such as “° ,swFol=; °” or “° >_F’

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Protecting Privacy Using XML, XACML, and SAML

Box 1.

element

string. The XACML listings shown later include detailed comments describing them. Like comments in programming code, XML comments are considered to be extraneous with regard to processing.

XML Cryptography Though not shown in the listings, the authenticity, integrity, and confidentiality of all the XML datasets discussed in this chapter can be protected using digital signatures and encryption. Digital signatures guarantee authenticity (knowing who the signer is) and integrity (knowing whether or not a dataset has been maliciously or accidentally modified). Encryption can ensure confidentiality of data while that data is encrypted. The W3C “XML Signature Syntax and Processing” and “XML Encryption Syntax and Processing” specifications describe how to implement XML-aware digital signatures and encryption.

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Example: Patient Medical Record Listing 1 shows an example of the full XML document being used in the scenario at hand. In the scenario, various hospital staff will access information in the above medical record: •

•

•

Patient Judy’s Physician can access the entire medical record—all the XML data in Listing 1. Administrators can access only Judy’s identity information—the content of the element. University Researchers can access only Judy’s physical data and medical conditions—the contents of the and elements.

In the scenario, it is assumed that the XML instance shown in Listing 1 is located, along with many others, inside a patient medical record da-

Protecting Privacy Using XML, XACML, and SAML

Listing 1. Patient Judy’s medical record Judy 29 Quinine Lane Female 39 Dr. Livingston Diagnosed ... Patient recovering Diagnosed ... No... ...improvement ... Condition is now stable.

tabase. It is further assumed that the database is XML-aware, that is, it can use XML techniques for identifying and retrieving data from the XML instance.

Expressing and Enforcing Privacy Policies in XACML XACML is an OASIS specification now in its second version. XACML, in conjunction with a security framework model, is an extremely robust language for expressing and enforcing

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Protecting Privacy Using XML, XACML, and SAML

distributed, flexible, and abstract access control policies. By “distributed,” it is meant that different XACML policies within different organizations or organizational levels can be drawn together to determine whether a particular access request is to be allowed or not. By “flexible,” it is meant that XACML enables rules and policies (sets of rules) to be specified, merged, and analyzed according to the unique needs of an organization. By “abstract,” it is meant that XACML can express both data-level (e.g., “you can only modify this data node if you are an Administrator”) and much more abstract types of rules (e.g., “persons under 18 years old may only buy this product with their parent’s consent”). XACML centers on the notions of: Action: As defined by the XACML specification, an action is “an operation on a resource.” Resource: Any object that can have an action performed on it. Subject: A user or device performing an action. Note that a subject is NOT defined as the one whom the resource is about; though it is certainly possible for an XACML subject to be one described in the resource if that person (or entity) is trying to perform an action on that resource. Environment: Any attributes pertaining to an authorization decision but are unrelated to the subjects, resources, or actions, for example, a calendar date (that an action may only be performed after).

•

• •

•

There are three top-level XACML elements: •

•

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: A set of policies and/or subsets

of policies that, when evaluated against an \, produce an authorization decision response for that policy set. : A set of related rules that, when evaluated against an authorization decision

•

request, produce an authorization decision response for that policy. : A statement that indicates whether an action is permitted or not given a set of subjects involved in the action and a resource. In addition, a element may also contain conditions that go beyond the basic combination of subjects, resource, and action and that may further influence the outcome. XACML defines as a top-level XACML element to make it easier to generate policies out of independently stored rules; XACML does not define the processing of rules in isolation.

In addition, there are two other important XACML elements: •

•

: A tetrad of (a) one or more subjects, (b) a resource, (c) an action, and (d) environment attributes. When a element is directly under a (or ) element, it defines to what targets the policy (or policy set) applies. When a element is directly under a element, it defines to what targets the rule applies. : Obligations to be performed when an applicable policy results in an action being permitted or denied (each obligation specifies what policy effect requires the obligation’s fulfillment). The construct is particularly important for systems implementing privacy policies such as, “If this citizen’s record is updated, the citizen must be notified.”

Policies Figure 3 illustrates the structure of an XACML policy ( element). As illustrated, an XACML policy starts with a element, which defines what subjects, resources, and actions are covered by the policy.

Protecting Privacy Using XML, XACML, and SAML

Figure 3. Structure of an XACML policy ( element)

As a child of the element, the element has no impact on the outcome of the policy; it only indicates whether the policy is applicable to a particular authorization decision request. Following the policy element is a set of elements. Each element contains a element that indicates whether the rule is applicable to the authorization decision request being processed. If the rule is applicable, and any conditions specified in the (optional) element are met, then the rule will result in either a “Permit” or “Deny” effect. The evaluation of the rules in a policy may result in more than one rule being applicable, and, of those applicable rules, there may be both “Permit” and “Deny” effects. These differing rule effects must be combined to form the outcome of the policy

as a whole. XACML provides several in-built algorithms such as permit-overrides (any “Permit” effect takes precedence) and deny-overrides (any “Deny” effect takes precedence) but also lets policy designers implement their own rule combining algorithms. Finally, an XACML policy may conclude with a set of obligations contained in the element. A policy may specify both obligations that must be fulfilled upon a “Permit” effect and those that must be fulfilled on a “Deny” effect. An example of a privacy-related obligation stemming from a “Deny” effect might read (in human terms), “If an unsuccessful attempt was made to modify this citizen’s record, report the attempt to the auditor.”

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Figure 4. Structure of an XACML policy set ( element)

Policy Sets It is often useful to organize policies into one or more sets (perhaps using the same policy in different policy sets intended for different areas of applicability). To support this, XACML provides the construct. The structure of an XACML policy set is shown in Figure 4. Like an XACML policy, an XACML policy set contains a target (to determine whether the policy set is applicable to an authorization decision request) and may contain a set of obligations. Unlike a policy, which contains rules, a policy set contains only policies and/or other policy sets. Upon evaluating an authorization decision request, some of the applicable policies within a policy set may result in “Permit” effects and others “Deny” effects. Similar to the rule-combining algorithm discussed previously, XACML enables

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a policy-combining algorithm to be specified in order that the overall effect of evaluating a set of policies, and the rules within those policies, can be determined. In this chapter, only a few aspects of XACML can be highlighted, and this brief description has necessitated some oversimplification. For a compete description of XACML, see the “eXtensible Access Control Markup Language (XACML) Version 2.0” (Moses, 2005).

XACML Support for Privacy XACML is, as its name says, an “extensible access control” markup language. In the area of XML Security standards, the term “profile,” when applied to a standard, means a way of using that standard to achieve a certain task. For the “eXtensible Access Control Markup Language

Protecting Privacy Using XML, XACML, and SAML

Listing 2. Construct for using XACML privacy-related attributes …

(XACML)” (Moses, 2005) specification, one of its profiles is the “Privacy Policy Profile of XACML” (Moses, 2004), which defines standard extensions to XACML to facilitate its use in privacy applications. The extensions are two XACML attributes that enable a “purpose” to be ascribed to resources and to actions. When ascribed to a resource, the purpose attribute indicates for what the resource may be used; when ascribed to an action, it indicates why the action is being taken. For the action to be taken for a particular resource that has a purpose attribute attached to it, the action’s purpose attribute must be compatible with the resource’s purpose attribute. Listing 2 illustrates the general construct (adapted from the OASIS “Privacy Policy Profile of XACML” specification) for using these attributes in privacy applications. In Listing 2, the element of the rule requires that the (XACML-defined) purpose attribute of the resource match the (XACMLdefined) purpose attribute of the action in order for the rule to result in a “Permit” effect.

Example: XACML Policy Set for Controlling Access to Patients’ Medical Records Listing 3 shows a simple XACML policy for the scenario introduced earlier. It controls access by Physicians, Administrators, and Researchers to patients’ medical records such as that illustrated in Listing 1. Though this particular policy focuses only on the read action, XACML (being eXtensible) can support other types of actions associated with privacy. For example, a privacy policy may want to restrict the purposes for which data can be published. It should also be noted that in real applications, Administrators of privacy policies would typically work with XACML through a human-friendly interface, not with the raw XACML shown in the listings. Not only do such tools simplify working with XACML, but they can also help to ensure that the actual policies are accurate, complete, and reliable.

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Listing 3. XACML policy set for controlling access to patients’ medical records element may hold both policies and policy subsets. Its PolicyCombiningAlgId attribute specifies the algorithm to be used to determine the overall result when more than one of the contained policies is applicable to an incoming authorization decision request. In this case, if either policy results in a “Permit” decision, then the policy set will echo that “Permit” decision. --> element indicates that if the resource is a medical record and the action is to read it, then this policy set may be applied to determine the authorization decision response. The element defines the applicable resources or resource types and, similarly, the element defines the applicable actions or action types. A element may also contain a element which defines which subjects the policy set or policy is applicable to. Because this element does not have a element, it does not filter any subjects. This policy set is organized so that filtering of subjects (are they hospital staff or not hospital staff) is done at the level of the two contained policies. --> http://example.org/schemas/Hospital/Medical_Record.xsd read element is XACML’s top-level element for holding a policy. Its RuleCombiningAlgId attribute describes how to determine the effect of the rules within the policy. In this case, the value of the RuleCombiningAlgId attribute indicates that if any rule evaluates to a “Permit” decision, the effect of the policy will be to permit the request. --> This XACML policy controls hospital staff’s read access to patients’ medical records. element indicates that the policy only applies to hospital staff. Recall that the top-level element of the policy set specified that the policy set as a whole applies to read access to patients’ medical records, so it is unnecessary to re-specify those parameters in this element. However, if this policy were to be evaluated outside of this context, it would be prudent to include the and elements of the top-level element in this element. --> urn:example:Hospital:subject:Staff:true Physicians may read all aspects of the medical record. urn:example:Hospital:Staff:subject:role:Physician

Hospital Administrators’ may only read the patient’s identity data. urn:example:... ...Hospital:Staff:subject:role:Administrator

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Listing 3. continued /med:Medical_Record/med:Patient/med:Identity_Data This XACML policy controls non-hospital-Staff read access to patients’ medical records. urn:example:Hospital:subject:Staff:false

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Listing 3. continued Researchers (those affiliated with the University) may, for the purpose of medical research and only for that purpose, read all of the medical record except the patient’s name and address. urn:example:Hospital:Non-Staff:... ...subject:affiliation:University urn:example:... ...Hospital:Non-Staff:subject:role:Researcher /med:Medical_Record/med:Patient/med:Physical_Data /med:Medical_Record/med:Medical_Conditions

element of this rule requires that purpose of the action match the purpose for which the resource may be used. This fulfills the primary tenet of privacy practice. -->

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XACML in Action So far, we have created a set of information (the patient medical record) and an XACML policy for specifying the privacy purpose requirements surrounding that information. Now, we are ready to see how an identity and access management (I&AM) system would work during actual e-service requests for information in that record. In the fictitious scenario, our forward-thinking hospital has, in addition to using XACML to protect patients’ privacy, also introduced the most advanced specifications for integrating security infrastructures (SAML) and e-services (Web Services). It should be pointed that XACML can still be used with legacy data, security, and e-services systems. For example, it would be perfectly possible to use XACML with purely relational databases, ASN.1 security, and pre-XML Web protocols. However, in order to show how the suite of modern XML-based e-service, security, and privacy technologies can work together, we focus on those in this chapter. Figure 1, which introduced the patient privacy scenario, includes four components of the XACML model: •

•

•

PEP: The policy enforcement point where a service request is intercepted and an authorization decision request is issued. The service request is only allowed to continue on to the enterprise application if there is a positive authorization decision response. PDP: The policy decision point where the authorization decision is made. The policy decision point will normally access one or more policies from policy administration points (PAPs) and other information such as additional subject, resource, action, and environment attributes (from policy information points). PIP: A policy information point where information necessary to evaluate the policy is obtained. The Hospital I&AM infrastructure

•

acts as a PIP because it provides information to the PDP regarding the purpose of the request (which it actually gets from the University I&AM infrastructure through SAML). PAP: The policy administration point where policies are managed and published.

Figure 5 focuses in on the PDP and the PEP to illustrate how XACML policies are processed in response to a service request. It introduces one more XACML component, the XACML context handler, which coordinates the PEP, the PDP, and PIPs. In Figure 5, these steps take place: 1. 2.

3.

4.

A service request is intercepted by apolicy enforcement point. The policy enforcement point analyzes the request and creates an authorization decision request (may be a proprietary format), which it sends to an XACML context handler. The XACML context handler creates an XACML context request out of the PEP’s authorization decision request. The XACML context request can be thought of as an XACML-formatted version of the authorization decision request enriched by using attribute names and values particular to its operating environment. The Attribute collection and Resource data collection pathways shown highlight that the XACML context handler works closely with the PIPs to provide the information needed by the PDP to evaluate the request. The PDP receives the XACML context request and locates the appropriate policies by matching the subjects, resource, and action specified within the XACML context request with each policy target. As shown in Figure 3, an XACML target consists of one or more subjects (representing those associated with the action being performed on the resource), a resource, an action, and

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Figure 5. XACML policy being evaluated against an XACML context request

5.

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environment attributes. “Matching” needs not, and will often not, be a matter of direct, one-to-one, equivalence. XACML supports a varied, extensible approach for determining which policies and rules apply to a particular request. (Note: The PDP in Figure 5 could be shown containing a policy set containing multiple policies and policy subsets such as that depicted in Figure 4. For clarity, a single policy is shown). Once an applicable policy has been found (one in which the policy target matches the request target), then a matching rule, or rules, must be found to determine the

overall effect of the policy. Again, XACML supports varied, extensible approaches to how rules are matched and interpreted with respect to the overall effect of the policy. For example, an XACML policy might require that all rules result in a “Permit” effect for the policy itself to result in a “Permit” decision. Another XACML policy might result in a “Permit” decision if any particular rule, with a “Permit” effect, matches. Evaluating a matching rule may require information external to the PDP. As stated, the PDP works with the XACML context

Protecting Privacy Using XML, XACML, and SAML

6.

7.

8.

handler to collect any information needed to process policies. Once the applicable policies have been processed, the PDP forms an XACML Context Response indicating the authorization decision. The XACML Context Response is sent to the XACML context handler. The XACML context handler converts the XACML context response into the authorization decision response format required by the PEP. Upon receiving the authorization decision response (in this case a “Permit” decision), the PEP allows the service request to proceed to, and be processed by, the enterprise application. Had a “Deny” decision been received, the PEP might have sent a fault to the service requestor (if the service interface supported and required faults to be sent). With reference to privacy policies, a “Deny” decision may result from the purpose of a service request not being aligned with acceptable uses of a resource (as defined by XACML purpose attributes).

Now, let us apply these steps to the medical record scenario of Figure 1 focusing on the Researcher because she represents the case with the most elaborate privacy issues. For the moment, assume the Researcher (named M. Curie) has been authenticated to the Hospital I&AM infrastructure and is using an application to query the treatments of medical conditions. Recall that the Researchers are not allowed to see patients’ identifying data and must only access medical records for the purpose of research. In conducting her query, the Researcher’s application sends a service request to the hospital’s medical records database. The service request specifies: • •

That the requestor is a Researcher at the University That the physical data and medical conditions section of the Patient Judy’s medical record

is to be returned (it is understood that the Researcher does not know it is Patient Judy’s medical record, rather than simply a medical record selected from a search of female patients being treated for Tendonitis). In order to form the XACML context request, the XACML context handler must obtain any necessary information that is not directly in the service request. Recall that Patient Judy has consented to allow Researchers to access the medical conditions part of her patient record but only for the purpose of research. Because, in our scenario, that information is not in the service request, when Researcher M. Curie’s service request attempts to operate on the Patient Judy’s medical record, the XACML context handler will need to obtain that information about the purpose of the action. One way of obtaining the purpose would be through an SAML attribute request. If Researcher M. Curie has not already stipulated she is running the queries for the purpose of research, the attribute request could result in M. Curie’s application notifying her that she must verify that she is running the query for research. Such verification could then be securely logged for future reference in privacy audits. With the necessary attribute information, the XACML context handler would form an XACML context request like the one in Listing 4. The XACML context request contains the following elements: • •

•

specifies the subject as a Researcher from the University specifies the element of Patient Judy’s Medical Record as the resource specifies read as the action

As the PDP evaluates the XACML context request (Listing 4) against the policy in Listing 3, the following steps occur:

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Listing 4. XACML context request for the medical record scenario urn:example:... ...Hospital:Non-Staff:subject:affiliation:University urn:example:Hospital:Non-Staff:subject:role:Researcher Patient_Record__Judy.xml /med:Medical_Record/med:Medical_Conditions read

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1.

2.

3.

4.

The XACML context request is evaluated against the policy set target, which indicates that the policy set applies to read operations on patient medical records. Because the resource and action specified in the XACML context request match the target of the policy set (which does not filter subjects), the PDP determines that the policy set is applicable to the request. The XACML context request is evaluated against the “Hospital Staff” policy target (which indicates the policy is only for hospital employees). As the subject of the XACML context request is not hospital staff, this policy is ignored by the PDP. The XACML context request is evaluated against the “Non-Hospital Staff” policy target, which indicates the policy is only for subjects outside the hospital. Because the subject of the XACML context request indicates she is one, the “Non-Hospital Staff” policy is deemed applicable, and so its rules are evaluated against the XACML context request. The target of the sole rule of the “Non-Hospital Staff” matches the subject and resource attributes specified in the XACML context request. As the rule contains a element, the conditions therein must also be evaluated to determine the effect of the rule. The conditions enforce the privacy rule that the action on the resource (reading the data about Patient Judy’s medical conditions) must be for the purpose of research. Once the PDP (through the XACML context handler), in conjunction with the Hospital I&AM infrastructure, has determined this to be true, the rule effect of “Permit” takes force. Because the “Non-Hospital Staff” policy’s rule combining algorithm and the policy set’s policy combining algorithm are both set as permit-overrides, the PDP cre-

5.

6.

ates an XACML Context Response, shown in Listing 5, stating a “Permit” decision. The XACML context handler transforms the XACML Context Response into a format pertinent to the policy enforcement point and forwards it. The PEP forwards the Researcher’s service request to Hospital Patient Medical Records database and retrieves Patient Judy’s physical data and medical conditions information.

SAML and Privacy SAML, the Security Assertion Markup Language developed by OASIS, enables enterprise applications to share authentication, attribute, and authorization decision assertions. An authentication assertion testifies that the subject of the assertion has been securely identified. In the context of the current discussion, a user could identity himself on one system (an identity provider) and have an authentication assertion sent by that identity provider to another system (a service provider), allowing the user to procure services from the service provider’s system just as if he had signed on to it directly. Attribute assertions provide attribute information about a subject such as characteristics and preferences. And an authorization decision assertion indicates whether an action by a subject on a resource is authorized. A PEP could use the SAML authorization decision assertion protocol to obtain an authorization decision from another organization’s PDP. Or, as described previously, a PDP could use SAML to obtain attributes about a user from another organization’s I&AM infrastructure. SAML’s enabling of authentication, attribute, and authorization decision sharing across trust domains makes it invaluable, both in its own right and as a companion to XACML, for facilitating the implementation of privacy policies.

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Listing 5. XACML context response for the medical record scenario Permit

Pseudonymous Identifiers The latest version of SAML, “Assertions and Protocols for the OASIS Security Assertion Markup Language (SAML) V2.0” (Cantor, 2005; Madsen, 2005), incorporates techniques from earlier work by the Liberty Alliance Project (Simon, 2003) that allow a user to coordinate e-services of different organizations across trust domains for a particular task yet ensure that only the minimum amount of information about that user necessary to accomplish that e-service is shared among them. This aspect of SAML 2.0 is particularly valuable in applications such as consumers who may want to use a vendor’s Web site to make an auxiliary purchase but who do not want the vendor to be tracking their purchasing habits or collecting other, potentially personal data. As long as domain B trusts the assertions of domain A that entity X is qualified to do action Y or has certain attributes, then domain B need not know anything more about entity X. In the medical records scenario, the Researcher (whose identity is managed by the University) would be able to collect data from the Hospital’s patient medical records for research without divulging her identity to the Hospital. Through SAML, the Hospital is able to verify that the requestor of the patient data has been authenticated through the University and is a Researcher. Figure 6 illustrates how pseudonymous identifiers would be used in a slightly expanded version of the

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medical records scenario where the Researcher wants to use an e-service of Pharmaceutical Inc. to analyze the data collected from the hospital. In Figure 6, the Researcher signs on to the Hospital (a service provider), which, behind the scenes, obtains an authentication assertion from the University (acting as an identity provider). The assertion from the University assures the Hospital that the user signing on is an authentic University Researcher but does not need to divulge which one; it just specifies that the Hospital is to use the identifier “Rad_Chic” for the Researcher. In conducting her research through the Hospital’s e-services, the Researcher decides to use Pharmaceutical’s e-services, so she also signs on to the Pharmaceutical enterprise applications using a second authentication assertion from the University accomplished in the same way as with the Hospital except that the Pharmaceutical identifier assigned for the Researcher will be “RX_1867”. The Hospital and the Pharmaceutical can then perform services on behalf of the Researcher but cannot cross-link her identity because each has a different identifier for her. To use the pseudonymous identifiers within the service request, the sender-vouches technique described in the Web Services Security: SAML Token Profile specification (Hallam-Baker, 2004) could be employed. With this technique, the Web Services SOAP message is signed by an attesting entity (the sender), who also vouches for the subject’s identity through an associated (signed)

Protecting Privacy Using XML, XACML, and SAML

Figure 6. SAML — Using pseudonymous identifiers for privacy

reference to a SOAP security token. If that SOAP security token uses SAML 2.0’s opaque identifier functionality, the receiver would not be able to deduce the identity of the subject. The Patient Medical Record database of Figure 1 would then be accepting requests signed by a notary acting on behalf of the Researcher rather than the Researcher herself.

Communicating Consent Besides pseudonymous identifiers, SAML also protects the privacy of its users through a Consent attribute that can be specified on SAML requests and responses. This mechanism allows, for example, for federating parties to indicate that consent was obtained from a user during single sign-on. A service provider’s privacy policy could then require that information may only be collected about a user if that user’s consent has been so indicated. In Figure 6, the Researcher has, through her University acting as an identity provider, requested

drug information from the Pharmaceutical organization. The Pharmaceutical organization may wish to gather information about the Researcher such as the name of her University department. SAML’s consent mechanism enables the Researcher to grant, or deny, that consent.

Example Listing 6 and Listing 7 illustrate a SAML transaction whereby the Pharmaceutical queries the Researcher for the name of her department, and the Researcher responds. In Listing 6, the attribute query issued by the Pharmaceutical identifies the subject as “RX_1867”, the pseudonym specified for the Researcher. Though the Pharmaceutical does not know the identity of the Researcher, it is interested in knowing her departmental affiliation and so requests the value of that attribute. The response to the attribute query contains an assertion, issued by the University, about the departmental affiliation of the Researcher—she

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Listing 6. SAML attribute query by the pharmaceutical https://idp.example.org/Pharmaceutical

Listing 7: SAML Attribute Assertion Response by the Researcher https://idp.example.org/University http://sp.example.org/Pharmaceutical Department of Medicine

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is from the Department of Medicine. Like the attribute query of Listing 7, the Researcher is identified through her Pharmaceutical-provided pseudonym “RX_1867”. It is important to note that the Consent attribute in element indicates the response is sent with the consent of the subject (who is specifically the Researcher herself, not the University). Also note the element by which the issuer (the University) identifies the intended recipients of the assertion information. The SAML specification details how to use XML Signature to sign SAML messages and assertions in order to assure their integrity and authenticity. For readability, the signatures are not shown in Listing 6 and Listing 7.

Future Trends At the beginning of this chapter, it was stated that new Web technologies such as Web Services and Service-Oriented Architecture (SOA) will make possible levels and types of e-services far beyond the sophistication of what is seen today. Both SAML and XACML are important technologies for realizing the next generation of e-services not only because of their security capabilities for cross-domain authentication and authorization, but also because they can support the protection of individuals’ privacy. The potential for linking various domains’ eservices is powerful, yet it raises many questions regarding privacy. For example, not only might a different domain have a different privacy policy, but also it may be located in a different jurisdiction and thus be governed by different privacy laws (though it should be noted that many governments are working to ensure cross-border compatibility of their privacy legislation). In addition, technologies such as XACML and P3P (Platform for Privacy Preferences) can allow individual users to specify their privacy preferences, which could

then influence the definition and/or the result of privacy policies. Determining the proper application of privacy laws and policy policies across domains is not new in itself. What is new is how this may be done in an automatable manner now that technologies like SAML and XACML make it possible to exchange and process high-level, machine-readable, privacy-related information. As an example, a Web vendor’s e-service may in turn require a type of e-service for credit checking. In order to find a particular instance of that service, it may turn to a registry in order to determine what instantiations of that type of credit-checking e-service are available. Besides the usual criteria such as price and features, an additional criterion based on the content of the e-services’ privacy policy (e.g., how long client information will be retained) and how well it fits with that of the vendor’s privacy policy and the privacy preferences of its client, can come into play. The challenge, then, is to explore how crossenterprise (and perhaps cross-jurisdictional) e-services can be engineered so that the respective privacy requirements of all the privacy stakeholders can be satisfied in a fully automated manner.

Conclusion XACML and SAML are important technologies for expressing and enforcing privacy polices in a world of e-services. XACML policies can specify what data is to be protected, who can access it, what actions can be performed on it, and require that actions be performed for a limited set of purposes. SAML assertions can be used to authenticate users and provide attributes about them without revealing the full details of their identity. It is important to understand that while information security and privacy are connected, they are not the same—security tends to be the art of

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making an intentionally malicious act difficult to achieve, whereas privacy focuses on stating rules of behavior, assuming (with the encouragement of audit trails and the law) that humans and applications will behave accordingly. For example, even though SAML pseudonymous identifiers prevent normal collusion among service providers, this feature would likely not thwart hackers who might use traffic analysis to cross-link network identities. XACML and SAML provide the foundations for privacy, but fully protecting privacy requires techniques beyond the scope of this chapter. The science of applying XACML and SAML security technologies to privacy issues is in its earliest stages. As products supporting XACML and SAML become ensconced in enterprise infrastructures, there will no doubt be further interest in exploring how they can be harnessed to support privacy protection. For example, with regard to XACML in particular, it will be important to ensure that enterprise-wide, and even inter-enterprise privacy policies can be efficiently managed and tested.

Acknowledgment Thanks to Tim Moses of Entrust and Paul Madsen of NTT for their reviews and comments.

References Cantor, S., Kemp, J., Philpott, R., & Maler, E. (2005). Assertions and protocols for the OASIS Security Assertion Markup Language (SAML) V2.0. Retrieved May 16, 2005, from http://docs. oasis-open.org/security/saml/v2.0/saml-core2.0-os.pdf Hallam-Baker, P., Kaler, C., Monzillo, R., & Nadalin, A. (2004). Web Services security: SAML token profile. Retrieved May 16, 2005, from http://docs.oasis-open.org/wss/oasis-wss-samltoken-profile-1.0.pdf Madsen, P. (2005). SAML 2: The building blocks of federated identity. Retrieved May 16, 2005, from http://www.xml.com/pub/a/2005/01/12/ saml2.html Moses, T. (2004). Privacy policy profile of XACML. Retrieved May 16, 2005, from http://docs. oasis-open.org/xacml/access_control-xacml2_0-privacy_profile-spec-cd-01.pdf Moses, T. (2005). eXtensible Access Control Markup Language (XACML) version 2.0. Retrieved May 16, 2005, from http://docs.oasis-open. org/xacml/access_control-xacml-2_0-core-speccd-04.pdf Simon, E. (2003). The Liberty Alliance Project. In M. O’Neill (Ed.), Web Services security (pp. 203-226). New York: Osborne.

This work was previously published in Privacy Protection for E-Services, edited by G. Yee, pp. 203-233, copyright 2006 by IGI Publishing, formerly known as Idea Group Publishing (an imprint of IGI Global).

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Chapter 3.25

When Technology Does Not Support Learning:

Conflicts Between Epistemological Beliefs and Technology Support in Virtual Learning Environments Steven Hornik University of Central Florida, USA Richard D. Johnson University of South Florida, USA Yu Wu University of Central Florida, USA

Abstract Central to the design of successful virtual learning initiatives is the matching of technology to the needs of the training environment. The difficulty is that while the technology may be designed to complement and support the learning process, not all users of these systems find the technology supportive. Instead, some users’ conceptions of learning, or epistemological beliefs may be in conflict with their perceptions of what the technology supports. Using data from

307 individuals, this research study investigated the process and outcome losses that occur when friction exists between individuals’ epistemological beliefs and their perceptions of how the technology supports learning. Specifically, the results indicated that when there was friction between the technology support of learning and an individual’s epistemological beliefs, course communication, course satisfaction, and course performance were reduced. Implications for design of virtual learning environments and future research are discussed.

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

When Technology Does Not Support Learning

Introduction Advances in information technology have enabled organizations and educational institutions to deliver training and learning initiatives free from time and/or place constraints, creating virtual learning environments (VLEs).1 These environments are becoming central to the design and development of both corporate training programs and university curricula. While there are multiple ways to design these environments, common characteristics of virtual learning environments include the mediation of course interactions and materials through information and communication technologies (Alavi & Leidner, 2001) and greater control over the learning environment (Piccoli, Ahmad, & Ives, 2001). The market for this type of training is substantial, with recent estimates suggesting that the industry will generate nearly $25 billion by 2006 (IDC, 2003) and grow annually at approximately 37% (Mayor, 2001). Universities are also undertaking distance initiatives, with estimates suggesting that nearly 90% of public universities offer distance education courses, over three million students participate in these courses, and these numbers are projected to grow (Wirt & Livingston, 2004). The major push behind these initiatives has been both convenience and cost. These initiatives have both potential and pitfalls as can be seen through the findings of two recent studies. Although the potential for cost savings is large, with some large companies finding cost savings of between $30-$400 million dollars per year and reductions in training costs of nearly 50% (Salas, DeRouin & Littrell, 2005), another study has suggested that as many as 80% of employees drop out of these programs before they are complete (Flood, 2000). Thus, it is important to understand the factors that affect the successful implementation of VLE initiatives. Previous research has suggested that instructor characteristics, pedagogical approach or learning models, learner/user characteristics,

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and the technology each play a key role in creating successful outcomes (Alavi & Leidner, 2001; Piccoli et al., 2001; Webster & Hackley, 1997). Recently it has also been argued that a key to the successful implementation of these environments is the convergence between the technology used in the learning environment and the implemented learning model (cf. Benbunan-Fich, 2002; Leidner & Jarvenpaa, 1995; Robson, 2000). However, when the technology used to support learning is designed to support a specific learning model, this can often lead to a compulsory learning process that users must follow to reach the course objectives (Vermunt, 1998). For some users, the learning approach supported by the technology can be in direct conflict with their beliefs about how learning should occur (i.e., their epistemological beliefs) (Bakx, Vermetten, & Van der Sanden, 2003; Schommer-Aikins, 2004). Relatively little is known regarding the implications of the conflict between an individual’s epistemological beliefs (EBs) and the learning environment supported by the technology, but given the centrality of technology to the learning process in VLEs and the central role of EBs in how individuals approach learning and how they learn (Marton, Dall’Alba, & Beaty, 1993; Marton & Säljö, 1976; Perry, 1968; Vermunt, 1996), the relationship between the two is likely to be important. Thus this research represents the beginning of a systematic examination of the role of EBs in VLEs. Drawing from research on EB, evidence suggests that when users do not perceive that the technology supports their optimal learning approach (i.e., there is friction between the individual’s EBs and the learning approach supported by the technology), there will be both process and outcome losses. If negative expectations regarding the ability of the technology to adequately support a learning environment consistent with the user’s EB emerge it can be difficult for the user to accept this novel way of course delivery (Vermunt & Verloop, 1999, 2000). We argue that

When Technology Does Not Support Learning

when users perceive a mismatch between their EBs and the learning model that the technology supports, learning processes and outcomes will be impacted. Thus, the following research question was investigated: Are learning processes and outcomes negatively affected when there is friction between a user’s perceptions of what learning model the technology supports and his or her personal epistemological beliefs? The remainder of this article is organized as follows. First, the paper briefly introduces the virtual learning environment context. Second the paper discusses EBs and how these beliefs influence individual learning processes and outcomes. Next, the paper further builds the argument that friction between individual EBs and beliefs about the technological support of learning models can affect learning processes and outcomes. Fourth, the research context and methods are then discussed. Finally, the results are presented, along with a discussion of the findings, implications, and directions for future research.

Virtual Learning Environments While training has traditionally taken place in a face-to-face setting, technology has enabled new forms of learning, unconstrained by time or place. In these virtual learning environments, learning processes, communications, shared social context and learning community are mediated through information technology, creating a novel learning environment for users. Specifically, VLEs are characterized by high levels of learner control, computer mediation of communication, and the flexibility for learners to restructure learning in nontraditional ways (Piccoli et al., 2001). As with traditional environments, researchers have focused on how effective VLEs are at producing effective outcomes such as learning, performance, and affective reactions to the training setting.

Previous research has found that VLEs can be as effective as face-to-face environments in supporting both learning and affective reactions to the learning environment (cf. Hiltz & Wellman, 1997; Piccoli et al., 2001). In the development of these environments, the design will reflect some pedagogical approach, or learning model (Leidner & Jarvenpaa, 1995). As such, the learning environment will reflect the instructor’s beliefs about what the best way to transfer knowledge is and how the technology will be designed to support this pedagogical approach. However, it is important to note that it is not the methods implemented, but rather student perceptions of these methods that most strongly affect student learning most directly (Entwistle, 1998 a, b; Entwistle, McCune, & Hounsell, 2002). Although, there are many learning models that an instructor can choose from this study focuses on three that are among the more widely accepted and which have been of interest to information technology researchers – the objectivist model, constructivist model and collaborative model (cf. Alavi, 1994; Alavi, Marakas, & Yoo, 2002; Liedner & Jarvenpaa, 1995). In the objectivist model, learning is seen as a process of transferring objective knowledge of an expert to the novice. To facilitate this transfer, VLEs will typically provide capabilities such as online presentation of syllabi, lectures, lecture notes, and so forth, in a non-interactive format. In constructivist models, learning is seen as a process where individuals discover knowledge through active participation in the learning process. Constructivist learning best occurs as individuals actively pursue new knowledge. The collaborative model extends the constructivist model by suggesting that learning occurs as individuals work together to create a shared understanding based upon the contributions of multiple individuals. Typically, these latter learning models employ interactive capabilities designed into the VLE including asynchronous communication capabilities such as e-mail, discussion, or chat. Whatever underlying pedagogical

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approach is desired, we argue that it will be most effective when there is a fit between the technology design and the learning model implemented by the instructor. Research has also begun investigating the processes through which effective VLEs are developed, finding that effective VLEs are not simply created by the technologies used, but instead are enabled through information and communication technologies (ICTs) as students create a shared social context and feel part of a learning community (Rovai, 2002). It has been argued that for this to occur, learners must communicate and perceive the social presence of others, or “the degree of salience of the other person in the interaction and the consequent salience of the interpersonal relationships” (Short, Williams, & Christie, 1976, p. 65). While communication can facilitate the exchange and sharing of information, social presence enables the connections between learners that create course community and improve learning and satisfaction (Tu & McIsaac, 2002; Tu, 2000). With ICTs mediating VLE processes, it is also important to understand the influence of user’s perceptions about whether or not the technology supports the learning environment and if this perception affects their learning processes and outcomes. To do this, we first focus on an individual’s beliefs about learning, or their EBs.

Epistemological Beliefs Beyond the technical and pedagogical considerations that go into the design of effective VLEs, instructors and designers should also consider student perceptions of how learning best occurs. Just as instructors design the environment around a particular learning model, the users of the system will also have specific beliefs about how learning best occurs. Van der Sanden, Terwel and Vosniadou (2000) describe these beliefs as Individual Learning Theories or internalized frameworks of instruction and learning, which

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influence the approach individuals take when encountering new learning situations. These EBs are beliefs individuals have regarding knowledge and knowing, including their beliefs about what knowledge is and how one acquires knowledge (Schommer-Aikins, 2004; Schommer, 1994). Schommer also states that individual’s EBs affect learning outcomes and suggests that these beliefs are a system of independent dimensions with the following anchors: (1) Certainty—knowledge ranges from absolute to tentative; (2) Structure— knowledge is considered to be either organized as distinct bits or as highly interwoven concepts; (3) Source—knowledge is handed down by authority or derived by reason; (4) Control—An individuals cognitive ability is fixed at birth or their ability can be changed; and (5) Speed —knowledge is either acquired quickly or gradually.2 Elen and Lowyck (2000) also identify another aspect of EBs, learning conceptions. Learning conceptions are learner perceptions about what is the most effective way of learning. In this study, we chose to focus on an individual’s learning conceptions for two reasons. First, learning conceptions have been shown to affect the learning process and outcomes (Entwistle, 1991; Marton et al., 1993; Vermunt, 1996) including the extent to which learners utilize the capabilities of the environment (Elen & Lowyck, 1998). Second, learning conceptions should be thought of as the mirror of the pedagogical approaches implemented by the instructors. Just as instructors have a specific learning pedagogy in mind when implementing the course, so also learners have specific EBs about the best approach to learning. These EBs are triggered as individuals engage in the learning process (Hofer, 2004) and are thought to affect how individuals approach learning (cf. Marton et al., 1993; Vermunt, 1996), how individuals learn (Entwistle, 1991), how they devise learning plans and strategies, their self-assessment and monitoring of comprehension (Schommer, Crouse, & Rhodes, 1992), their preferred learning situations (Bakx et al.,

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2003), and the extent to which they leverage the environment to their advantage (Elen & Lowyck, 1998, 1999). EBs are argued to be even more important to learning than the learning model chosen by the instructor because these beliefs filter the learning models implemented by the instructor (Bakx et al., 2003). For example, Bakx et al. (2003) examined the relationship between selfperceived competence, EBs and preferred learning situations, finding that an individual’s EBs about learning were related to their preferred learning situations. When individuals believed that learning best occurred as part of an interactive, constructive process they were more inclined to prefer situations that encouraged interaction and more active shared learning. Thus, differences in learning outcomes can be attributed to individual differences in beliefs about the process of learning (Marton & Säljö, 1976). One manner in which users’ EBs can act as a filter is through the congruence or friction that is created between the users’ EBs and the technological support of learning in the VLE.

Technology and Beliefs: Congruence or Friction Discrepancy theory states that individuals hold a set of expectations about their environment and also perceptions about how well their expectations of the environment are met (Locke & Latham, 1990). In turn these expectations affect how individuals interpret and interact in their environment. When expectations are met, individuals are positively disposed to the environment, and are more satisfied with the environment than when they are not met. The findings of this stream are also consistent with research on expectationconfirmation theory (ECT), which suggests that when individuals expectations about a product or technology are not met (i.e., they are disconfirmed) they are less satisfied and less likely to continue to

use the product or technology (Anderson & Sullivan, 1993; Bhattacherjee, 2001; Oliver, 1980). In VLEs, although the instructor may design the technological support around a particular pedagogical approach (learning model), users will have their own expectations regarding the effectiveness of the chosen learning approach (Vermunt & Verloop, 1999) and how technology can best be leveraged. As suggested by discrepancy theory, when a match exists between an individual’s EBs and the technologically supported learning model, the two are considered to be in congruence. When there is a discrepancy between these beliefs and the technologically supported learning model, friction occurs. In turn, perceptions of congruence or friction affect the learner’s expectations of how positive or negative their learning outcomes will be. These expectations often become self-fulfilling, especially in novel learning settings (O’Mara, Allen, Long, & Judd, 1996). As such, congruence and friction are expected to have an impact on both course processes such as course communication, and perceptions of social presence, as well as course outcomes such as performance and satisfaction (Figure 1). Friction can affect both the user’s approach to learning within the environment as well as causing an underutilization of the tools available to them (Lowyck & Elen, 1994). This can occur because individuals do not see the value of the technology in the setting for supporting their learning. The technology can be seen as a barrier to learning, something placed between the user and the learning outcomes that obfuscate the necessary conditions for learning to occur (Fiore, Salas, Cuevas, & Bowers, 2003). In other words, for these users, cognitive focus moves away from learning processes and towards use of the technology; making communication and participation in the environment more difficult, as well as decreasing the value the technology brings to the course. The effect of focusing on the technology as opposed to the training material

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Figure 1. Epistemological beliefs, technology support and VLE outcomes Epistemological Beliefs

Learning Processes & Outcomes Congruence/ Friction

• • •

•

Communication Social Presence Satisfaction Learning

Technology Support

may lead users to disengage from the course, or to not engage it at all. As individuals disengage, they will interact and communicate less. Conversely, those who perceive congruence will perceive the technology as an important tool in support of learning and be more likely to use the technology capabilities, thus communicating and interacting within the VLE. In the case of friction, as communication is reduced, individuals will find fewer opportunities to ease isolation and develop perceptions of social presence (Burke & Chidambaram, 1999; Gunawardena, 1995; Walther, 1995). Conversely, those whose EBs are congruent with the technology supporting the learning process, will have greater opportunities to create connections using the technology and therefore, be more likely to feel the connections of the other learners in the environment and feel like they are part of a learning community with increased perceptions of social presence. Thus the following hypotheses were investigated: H1a: When there is congruence between a user’s epistemological beliefs and beliefs about the learning model supported by the technology, the user will communicate to a greater extent than when there is friction.

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H1b: When there is congruence between a user’s epistemological beliefs and beliefs about the learning model supported by the technology, the user will perceive greater social presence than when there is friction. We also expect friction to have an impact on learners’ affective reactions to the VLE. As learning in VLEs can still be novel experiences for learners, users are utilizing technology to communicate in ways in which they are potentially unfamiliar and uncomfortable. Evidence has shown that when faced with a conflict between technology functionality and the way the users wish to use the system, users will attempt to match their approach to the approach designed into the system (cf. Todd & Benbasat, 1991). Vermetten, Vermunt, and Lodewijks (2002) also suggest that learners tend to make best use of the elements in the learning environment that fit their preferred way of learning and ignore or underutilize those that do not. In VLEs, this suggests that while users may feel compelled to adopt a learning strategy supported by the technology, they may not effectively utilize the tool. Friction between the user’s EBs and the learning model supported by the technology can exacerbate negative feelings about the environment, because of the user’s need to adapt their learning conceptions even though they may not see it as appropriate. As discussed

When Technology Does Not Support Learning

above, the technology can be seen as a barrier to the learning process, increasing the user’s frustration with learning in the VLE. Together, these outcomes would be expected to lead to lower levels of satisfaction with the VLE. Finally, we believe that friction will also lead to a reduction in learning outcomes (Vermunt & Verloop, 1999). An individual’s prior educational experiences are reflected in their EBs, which in turn play a role in forming the learner’s perceptions of the instructional measures. The match between those perceptions and the types of teaching-learning environment affects the quality of learning achieved (Entwistle et al., 2002). Among other things, for learning to be most successful, connections between individuals are needed (Vygotsky, 1978; Feuerstein, Rand, Hoffman, & Miller, 1980). When friction occurs, learners are more likely to disengage from both the learning process and from their peers, engaging in fewer behaviors that can lead to successful learning outcomes. With other studies also arguing for the importance of ongoing observational learning process (Bandura, 1986; Yi, & Davis, 2003) where individuals learn new behavior and skills through attending and processing the behavior of others, any reduction in connections can lead to reduced attention to the behaviors, ideas and contributions of others. When this occurs, system users are likely to be exposed to less information, process less information, and therefore have reduced learning. In turn, the likelihood of successful learning outcomes will be reduced (Geiger & Cooper, 1996; Harrell, Caldwell, & Doty, 1985). Thus, the following hypotheses were investigated: H2a: When there is congruence between a user’s epistemological beliefs and beliefs about the learning model supported by the technology, the user will be more satisfied than when there is friction. H2b: When there is congruence between a user’s epistemological beliefs and beliefs about the learn-

ing model supported by the technology, the user will perform better than when there is friction.

Method Research Setting The study was conducted in an MIS fundamentals course at a large university in the United States. This course was a required course for all business majors and was taught exclusively online using WebCT. The course was taught over 15 weeks and was divided into 6 modules. Each module focused on different topical areas, such as the strategic use of information and technology, e-commerce, decision support, and so forth, with each module lasting approximately two weeks. Students were assigned to groups of approximately 30 and were asked to both post comments to case questions and respond to the comments of others in their group. Student assessment occurred at the end of each module through the use of individual multiple-choice tests. The course was managed by one instructor and two graduate assistants (GAs), who communicated exclusively online (all were not physically on campus during the semester), and one GA held office hours both online and in person.3 At the end of the fifth module of the course, the survey was made available for one week using WebCT. The course was designed based on principles from the constructivist and collaborative learning models. To encourage active discovery and knowledge construction (i.e., constructivist learning), students were required to gather information from a variety of sources including text, video, and cases and to integrate these into their understanding of the topic. In support of the collaborative model of learning students wrote, read and posted responses to case questions associated with each module.

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Research Participants A total of 332 students participated in the study, of which usable data was obtained from 324. The sample consisted of 156 males and 152 females.4 The average age was 25.9 (SD = 6.5), with a range of 19-54. All of those participating in the course indicated that they were currently employed and had previous computer and Internet experience with over 50% indicating that they had high levels of experience in both.

Measures Satisfaction Satisfaction was measured with an 8-item Likerttype scale developed by Biner (1993). The scale used a 7-point strongly disagree to strongly agree response format. The coefficient alpha reliability estimate for this scale was 0.87.

Learning Two types of learning outcomes, considered to be important by training researchers, were assessed in this study: cognitive/knowledge based outcomes and skill based outcomes (Kraiger, Ford, & Salas, 1993). Each of these outcomes was measured in this study. The form of cognitive knowledge assessed in this study was declarative knowledge. Declarative knowledge was assessed using a 50-point end of module exam score from

the next to last course module (Module 5). This exam was chosen because, by the time the participants got to the end of this module, they had enough exposure to and interaction with WebCT for the effect of technology mediation to be manifest. The correlation between Module 5 exam score and participants’ overall course grade is 0.41 (p < .001). Thus, it is indicative of the participants’ overall performance of declarative knowledge. Skill development was measured using the 6-item perceived skill development scale developed by Alavi (1994). This scale used a 7-point strongly disagree to strongly agree response format. The coefficient alpha reliability estimate for this scale was 0.86.

Social Presence Social presence was measured with a 5-item scale developed by Short et al. (1976). For each question, respondents evaluated the characteristics of the environment using a 5-point, Likert-type scale with anchors such as “unsociable-sociable” and “impersonal-personal.” The coefficient alpha reliability estimate for this scale was 0.80. A complete list of all scale items used in the study is found in Appendix A.

Communication Communication was measured using three types of course communication: the number of discussion postings read, the number of original

Table 1. Learning model identified as desired by learner or supported by WebCT Learning Model Objectivist

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Frequency Desired

Technology Supported

80

17

Constructivist

98

118

Collaborativist

146

189

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discussion postings, and the number of follow-up discussion posts. Each of these was standardized and then an aggregate measure was created to represent communication.

Friction Friction was measured as a gap between what the system users believed was the best way to learn and what they felt the technology supported. Participants were first given descriptions of objectivist, constructivist, and collaborative approaches to learning that an instructor might use. These are shown below: 1.

2.

3.

In the objectivist model, learning takes place as the student absorbs the knowledge of the instructor. Therefore, it is the efficiency by which the instructor can transmit his knowledge that will improve a student’s ability to learn. In the constructivist model, learning only takes place as the students construct knowledge for themselves. The learners do this through active discovery supported by the instructor. In the collaborative model, students create learning by interacting (discussing and sharing information) with other students.

Next, participants selected the learning approach that would be the most effective way for them to learn. Following this, participants selected the learning approach that they felt WebCT supported. Each scale was scored as follows: 1-objectivist, 2-constructivist, 3-collaborative. Using these two scales, if there was no difference between the learning method they learn best in and the one they feel that WebCT supports, it was coded as a 0. If there was a difference in the approach selected, it was coded as a 1. Overall, 142 people felt that that there was congruence between the technology support of learning and their EBs, while 182 perceived friction to exist.

Thus, over 55% of the individuals felt friction between the technology support of learning and their EBs.

Preliminary Analysis As a manipulation check, we assessed whether or not individuals correctly identified the learning model supported by the technology. Recall that the instructor designed the course to include elements of both constructivist and collaborative learning models. Of those participating, 95% identified WebCT as supporting either the constructivist or collaborative learning models (Table 1). Those who were unable to identify the supported environment were dropped from further analysis, leaving a sample of 307. Of these, 140 individuals felt that there was congruence (45.6%) and 167 individuals perceived friction (54.4%). To confirm no significant demographic differences between the groups, the groups were compared on a number of variables, including age, gender, GPA, computer experience, Internet experience, confidence in using computers, and previous VLE course experience. Evidence from this analysis suggested that the groups were not different.

Results Table 2 shows the means and standard deviations of the learning process and outcomes based upon whether the learner perceived congruence or friction and Table 3 shows the correlations among the dependent variables. Learning, communication, social presence, and course satisfaction were all correlated (p < .001). An initial multivariate analysis of variance (MANOVA) was run. The overall test was significant (Wilks’ lambda F (4,301) = 4.01, p < .01), which allowed for an individual ANOVA to be performed for each process and outcome variable. As shown in Table 4, the results of the ANOVA on communication were significant, providing

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Table 2. Means and standard deviations of processes and outcomes Variable Social Presence

Comm.a

a

Group

n

Congruence Friction Total

Satisfaction

Declarative Knowledge

M

SD

M

SD

M

SD

M

140

0.12

.76

3.15

0.86

5.25

1.10

167

-0.08

.77

2.97

0.85

4.76

1.31

307

0.01

.77

3.05

0.86

4.99

1.24

Skill Development

SD

M

SD

41.66

8.22

5.52

0.97

38.83

13.43

5.13

1.15

40.12

11.42

5.31

1.09

The values listed represent standardized scores.

Table 3. Correlation of study dependent variables Construct

1.

1.

Communication

2.

Social Presence

.22***

3.

Satisfaction

.24***

4.

Declarative Knowledge

.24***

5.

Skill Development

2.

3.

4.

5.

—

.09

— .50*** .06 .26***

— .17**

—

.35***

.16**

—

* p < .05, **, p < .01, *** p < .001

support for H1. Users who perceived congruence between technological support and EBs communicated more (M = .12) than when friction was perceived (M = - .08), F (1,305) = 4.98, p < 0.05. H2 predicted that when users perceived congruence, they would experience enhanced social presence (M = 3.15) than when they perceived friction (M = 2.97); support was not found for this hypothesis (F (1,305) = 3.08, p =.08). Supporting H3, users who perceived congruence between their EBs and technology support of learning were more satisfied (M = 5.25) with the learning environment than those who perceived friction (M = 4.76) F (1,305) = 11.97, p < 0.001. Finally, support was found for H4, with users who perceived congruence between EBs and technology learned more than when friction was perceived. This was true

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both for declarative knowledge (i.e., score on the skills test) (M = 41.66 vs. 38.83, F (1,305) = 4.76, p < .05) and for skill development (M = 5.52 vs. 5.13, F (1,305) = 10.06, p < .01).

Discussion Summary of Findings The results of this study provide evidence that congruence between the technology support of learning and an individual’s conceptions of the best way to learn can create more effective VLE processes and outcomes than when friction occurs. Specifically, when users feel congruence, they communicate more, learn more, and are more

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Table 4. Results of analysis of variance on course process and outcomes Source of Variation

SS

df

MS

F

Communication Group

2.91

1

2.91

Residual

178.44

305

0.59

Total

181.35

306

2.26

1

2.26

Residual

223.78

305

.73

Total

226.04

306

4.98*

Social Presence Group

3.08a

Satisfaction 17.88

1

17.88

Residual

Group

455.57

305

1.49

Total

473.45

306

613.36

1

613.36

Residual

39309.19

305

128.88

Total

39922.55

306

11.61

1

Residual

351.74

305

Total

363.35

306

11.97***

Declarative Knowledge Group

4.76*

Skill Development Group

a

11.61

10.06**

p < .10, * p < .05, ** p < .01, *** p < .001

satisfied with the learning experience than when there is friction. While previous research was justified in calling for a fit between the technology used to support learning and the learning model implemented by the instructor, these calls may not go far enough. The shortcoming is that they do not take into account the relationship between an individual’s EBs and the technology support of learning. When friction occurs, it becomes difficult for individuals to leverage and appropriate the technology to support their optimal learning strategy or to adapt their learning to match the model supported by the technology. Instead, the findings suggest that the learners will disengage

from the course by communicating less, perform less effectively, and become less satisfied with the learning environment.

Implications One of the greatest advantages from using VLE’s is the inherent flexibility of VLEs. Thus, the primary implication of this research is the need to flexibly design VLEs such that the technology support of learning is flexible and adaptable to match users’ learning conceptions. Beyond matching the technological support of an instructor’s chosen learning model to the

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instructional design of the technology, the technology should be flexible enough to adjust to multiple user’s beliefs, as some users may not be able to adapt their preferred learning model to the one supported by the technology (Vermunt & Verloop, 2000). Associated with this, while the technology exists to deliver training via multiple methods, doing so does not currently seem to be the predominant model for delivering VLE initiatives. It is our experience that the vast majority of VLE training and educational settings use the same model despite the realization that people have different preferred learning environments. One reason for the simplification of design is that organizations and instructors choose a specific learning model to implement and then design the technology to support that environment. Various constraints, such as time, technical expertise, and effort to leverage technology for ways in which it was not designed, can make it difficult for the designers of VLEs to take into account these multiple approaches to learning pursued by those using the system. Using a development system based on the convergence of instructional design and Web design, Janicki and Steinberg (2003) found that increased learning occurred when flexible learning content was delivered via multiple methods such as narratives, examples and hands-on exercises. The same type of system could be developed for delivery of learning content based on various learning conceptions and matched with a user’s preferred learning approach. The problem is that when static approaches to technological support occur, some learners are put at a learning disadvantage over their peers. Unlike face-to-face settings, where the instructor can better gauge a persons’ learning conceptions, in a VLE they can remain hidden. Thus, these findings suggest that a process for discovering the EBs of those in VLEs needs to be developed. If a learner’s beliefs are found to be in conflict with the model used in the VLE, adaptive VLE’s

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can provide the learning content in a manner best suited for the user. Alternatively in situations where users’ will be engaging in multiple VLE experiences over time, those experiencing friction could be provided with training or social behavior modification to change there EBs, as these beliefs have been found to be malleable as one proceeds through various levels of education (Perry, 1968). Echoing the developmental nature of learning conceptions, Vermunt and Verloop (1999) suggest that friction can have positive consequences by catalyzing users to develop new learning strategies. Future research needs to investigate the effectiveness of the various approaches for aligning a VLE technological support of learning with users’ learning conceptions. Finally, although this research has focused on the negative consequences of friction, more needs to be done to investigate the potential positive effects that friction might create by changing a user’s learning conceptions, specifically what circumstances are needed within the VLE context for this to occur.

Managerial Implications This research also has implications for managers seeking to maximize returns on their investments of VLEs. This study reinforces the importance of matching user beliefs about learning to the technological support of learning. When this congruence occurs, learning processes and outcomes are enhanced. This benefits managers in two ways. First, congruence leads to better learning, which should translate to improved employee performance. Second, less satisfied individuals are less likely to choose to engage in behaviors that they view negatively, such as enrolling or participating in future VLE initiatives (Ajzen, 1988; Bhattacherjee, 2001). The importance of this cannot be underestimated in environments such as the current one, where over 55% of those participating felt that the technology did not support the way they learned best and because

When Technology Does Not Support Learning

one of the biggest threats to VLEs is the large discontinuance rate (Flood, 2000). Finally, managers should consider developing their VLE architectures so that they can be flexible enough to tailor course offerings to the preferences of the users. By adopting flexible approaches, managers can provide learning environments that provide the greatest potential for learning. As an example of flexibility in training approaches, Hewlett Packard has developed their corporate training initiatives to allow regional managers to tailor a mix of traditional, blended, and Webbased initiatives to meet the preferences of the region (O’Leonard, 2004). Given these options, users have been able to self-choose the learning approach that they are most comfortable with. This flexibility has led to improved employee performance and improved customer service (O’Leonard, 2004). Future research should investigate how this flexibility translates to improved learning and transfer.

Limitations As with any study, there are several potential limitations that pertain to the generalizability of these findings. First, the results from this study represent a specific technology implementation in a single course. While there was no evidence to suggest that the results found in this study would be different in different settings, we cannot generalize to other settings. Future research should replicate and extend the findings of this study with different technologies and different contexts. Additionally, participants were required to choose a single learning model between objectivist, constructivist, and collaborative learning models, which did not allow us to investigate the blending of multiple learning models as part of this study. Finally, EBs are more than an individual’s learning conceptions but rather a system of interconnected beliefs about knowledge and how knowledge is accumulated (Hofer, 2004; Schommer-Aikins, 2004; Schommer, 1994); as such a more systematic

investigation of the aspects of a user’s EBs would allow for a deeper understanding of the beliefs on VLE outcomes.

Conclusion This study was motivated by the desire to better understand the implications of friction between the technology support of learning and the EBs of those engaged in the learning process. Results indicated that friction between a user’s belief about their learning approach and that provided by a VLE can lead to reduced participation, peer connections, performance and satisfaction. With technology central to the learning processes and outcomes in a VLE, it is important for those designing VLE initiatives to understand that successful VLEs depend not only on the matching of instructor learning models with technology support, but also allowing the technology to be flexible enough for those with differing EBs to have the opportunity to leverage the technology to support their desired learning approach. Without considering the fit between EBs and technology support of learning, the potential exists for organizations to waste large amounts of resources in their investments in their distributed initiatives and for employees participating in these initiatives to learn less, participate less, and ultimately have reduced skills and knowledge than if fit were considered.

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learning. Dordrecht, The Netherlands: Kluwer Academic Publishers. Vermetten, Y.J., Vermunt, J.D., & Lodewijks, H.G. (2002). Powerful learning environments? How university students differ in their response to instructional measures. Learning & Instruction, 12(3), 263-284. Vermunt, J.D. (1996). Metacognitive, cognitive and affective aspects of learning styles and strategies: Aphenomenographic analysis. Higher Education, 31, 25-50. Vermunt, J.D. (1998). The regulation of constructive learning processes. British Journal of Educational Psychology, 68, 149-171. Vermunt, J.D. & Verloop, N. (1999). Congruence and friction between learning and teaching. Learning & Instruction, 9, 257-280. Vermunt, J.D. & Verloop, N. (2000). Dissonance in students’regulation of learning processes. European Journal of Psychology of Education, 15(1), 75-89. Vygotsky, L.S. (1978). Mind in society. Cambridge, MA: Harvard University Press.

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Yi, M.Y. & Davis, F.D. (2003). Developing and validating an observational learning model of computer software training and skill acquisition. Information Systems Research, 14(2), 146-149.

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Endnotes 1

2

3

4

While multiple terms have been used to describe these environments, such as virtual learning environments (VLE), distributed training, distance learning, e-learning, and technology-mediated learning (TML), we use the term Virtual Learning Environment in this study. Individual differences in learning styles have also been suggested as an important criterion in understanding individual learning. Unlike EB, however, learning styles —which vary in their measurement —pertain more to personality traits often having to deal with how an individual views the world and how that view impacts their learning processes. In contrast EB, as defined and measured in this study, are an individual’s conceptions about the best way in which learning material are best delivered as either an objectivist, constructivist, or collaborative approach. Discussions with the last GA found that other than first week assistance in setting up WebCT by a few students ( Oravec, J.A. (2003). Some influences of on-line distance learning on U.S. higher education. Journal of Further and Higher Education, 27(1), 89-103. Pain, D., & Le Heron, J. (2003). WebCT and online assessment: The best thing since SOAP? Educational Technology & Society, 6(2), 62-71. Postle, G., Taylor, J.C., Taylor, J., & Clarke, J. (1999, July 7-10). Flexible delivery and inclusivity: Pedagogical and logistical perspectives. Paper presented at the 8th European Access Network Convention, Malta. Singh, P. (2004). Globalization and education. Educational Theory, 54(1), 103-116. Sturgess, P., & Nouwens, F. (2004). Evaluation of online learning management systems. Turkish Online Journal of Distance Education, 5(3). Swannell, P., & Taylor, J.C. (1999). It was the best of times, it was the worst of times. Niece, France. Taylor, J.C. (2000). Coach education in the 21st century: Challenges and opportunities. The Sport Educator, 12(1), 11-16.

Turban, E., Aronson, J.E., & Liang, T.P. (2005). Decision support systems and intelligent systems (7th ed.). Upper Saddle River, NJ: Prentice Hall. Vedantham, A., & Breeden, L. (1995). Networking for K-12 education: The federal perspective. Internet Research: Electronic Networking Applications and Policy, 5(1), 29-39. Verma, A., Kochan, T., & Lansbury, R. (1995). Employment relations in an era of global markets. In A. Verma, T. Kochan, & R. Lansbury (Eds.), Employment relations in the growing Asian economies (pp. 1-26). London: Routledge. Vickers, L.A., & McClellan, G. (2004). Bringing the World to North Dakota. Peer Review, 6(2), 22-25. Volery, T., & Lord, D. (2000). Critical success factors in online education. International Journal of Educational Management, 14(5), 216-223. Web Crossing. (2005). Retrieved July 24, 2005, from www.webcrossing.com/Home/ Zemsky, R., & Massy, W.F. (2004). Thwarted innovation: What happened to e-learning and why. University of Pennsylvania. Zhuang, L. (1995). Bridging the gap between technology and business strategy: A pilot study on the innovation process. Management Decision, 33(8), 13-21. Zikmund, W.G. (2003). Business research methods (7th ed.). Mason, Ohio: Thomson Learning.

This work was previously published in Technology Support Learning and Teaching: A Staff Perspective, edited by J. O'Donoghue, pp. 261-276, copyright 2006 by Information Science Publishing (an imprint of IGI Global).

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Chapter 4.2

Supporting the JAD Facilitator with the Nominal Group Technique Evan W. Duggan University of Alabama, USA Cherian S. Thachenkary Georgia State University, USA

ABSTRACT Joint application development (JAD) was introduced in the late 1970s to solve many of the problems system users experienced with the conventional methods used in systems requirements determination (SRD) and has produced noteworthy improvements over these methods. However, a JAD session is conducted with freely interacting groups, which makes it susceptible to the problems that have curtailed the effectiveness of groups. JAD outcomes are also critically dependent on excellent facilitation for minimizing dysfunctional group behaviors. Many JAD efforts are not contemplated (and some fail) because such a person is often unavailable. The nominal group technique (NGT) was designed to reduce the impact of negative group dynamics. An in-

tegration of JAD and NGT is proposed here as a crutch to reduce the burden of the JAD facilitator in controlling group sessions during SRD. This approach, which was tested empirically in a laboratory experiment, appeared to outperform JAD alone in the areas tested and seemed to contribute to excellent group outcomes even without excellent facilitation.

INTRODUCTION There is widespread support for the belief that systems requirements determination (SRD)— discovering and documenting the features that an information system should deliver—is an extremely important but very difficult aspect of software development (Borovits et al., 1990; Byrd et al., 1992; Cheng, 1996; Holtzblatt &

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Supporting the JAD Facilitator with the Nominal Group Technique

Beyer, 1995; Raghaven et al., 1994). This difficulty often leads to systems failures due to both development shortcomings—failure to establish the required features in the required time, and usage factors—and abandonment by its intended beneficiaries (Lyytinen, 1988). Several factors account for this difficulty, but the nature of the interaction among system developers, users, and stakeholders is the prime contributor (Antunes, 1999; Holtzblatt & Beyer, 1995). User-developer communication and stakeholder negotiations assume greatest importance at the requirements determination phase of the systems development life cycle (SDLC). Here, the specific details of the problem to be solved and the needs to be satisfied are clarified. It is here, however, that poor communication is most pervasive (Dieckmann, 1996; Holtzblatt & Beyer, 1995). Joint application development (JAD) is a team-oriented approach that has been widely used to (1) confront the communication barriers to effective information elicitation and (2) increase users’ contribution to this key systems development activity (Byrd et al., 1992). JAD assembles a diverse group of users, analysts, and managers from various sectors of an organization to jointly specify requirements in a face-to-face workshop. Despite its success in comparison to conventional SRD methods, JAD has failed somewhat to deliver on its initial promise to forge the team rapport necessary to alleviate known communication impediments to effective SRD, and has introduced other group-related problems (Dean et al., 1997; Kettelhut, 1993). A major reason for this failure is that JAD workshops are conducted under the freely interacting meeting structure where spontaneous communication occurs among group members with minimal control imposed by the communication structure (Van de Ven & Delbecq, 1974). Groups that deliberate in this manner typically experience many of the problems in which social and emotional dynamics obstruct the accomplishment of the objectives of the meeting

(Kettelhut, 1993). The success of a JAD session is often dependent on the extent to which these problems are alleviated. This places a very high premium on excellent facilitation (Carmel et al., 1995; Davidson, 1999; Wood & Silver, 1995). Facilitators have been offered several prescriptions for minimizing these problems (Andrews, 1991; Carmel et al., 1995; Davidson, 1999; Kettelhut, 1993; Wood & Silver, 1995). Many of these are contained within the nominal group technique (NGT)—a facilitated technique that focuses on alleviating negative group dynamics in meetings where participants interact in a highly structured manner. This technique could be applied in the decision-making stages of a JAD workshop to provide a comprehensive set of procedures for increasing the group’s effectiveness. NGT reputedly increases the effectiveness of creative problem-solving groups (Delbecq et al., 1986). Its easy-to-apply protocol supports facilitators in producing results that fairly accurately reflect the combined judgement of groups engaged in problem-solving meetings (Zuech, 1992). Our thesis is that the application of NGT in the JAD workshop will help to reduce the criticality of excellent facilitation for high-quality JAD results and that this integrated communication structure will induce more acceptable results from less than excellent facilitation. This presumption is very important because excellent JAD facilitation is a scarce commodity (Carmel et al., 1995) despite several years of fairly extensive JAD practice (Davidson, 1999) and increasingly common usage (Dennis et al., 1999; Kettelhut, 1997). In this study, we examine the effects of the integration of NGT and JAD structures on the communication problems that typically beset user-developer interactions in SRD when JAD alone is used. The major objective is to determine whether NGT, in combination with JAD, reduces the facilitator’s burden in curbing dysfunctional group behaviors and thereby contributes to improved performance.

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REVIEW OF RELEVANT LITERATURE The prevailing viewpoint is that SRD, which is a complex process incorporating a variety of features and often conflicting stakeholder interests (Vessey & Conger, 1994), is a critical determinant of system development success or failure (Byrd et al., 1992; Cheng, 1996; Raghaven et al., 1994). Unfortunately, the dominant experience is that inadequate interaction and poor communication among system developers and users characterize this process (Holtzblatt & Beyer, 1995). A variety of SRD approaches have been used to elicit information from knowledgeable managers and users. These include, but are not limited to, interviewing, survey by questionnaire, JAD, focus group meetings, brainstorming, prototyping, goal- and scenario-based techniques, critical success factor and task and protocol analyses, and ethnographic techniques. Interviewing has been the most prevalent and best-known technique (Raghaven et al., 1994; Watson & Frolick, 1993). But this approach has proven inadequate for resolving competing requirements and securing stakeholder agreement (Dennis et al., 1999; Dieckmann, 1996). It is also difficult both to determine whether all problems are unearthed and all requirements captured, and gauge the adequacy of the participation of those interviewed. These deficiencies seriously challenge the accuracy, completeness, consistency, and clarity of the resulting requirements (Dean et al., 1997). JAD was therefore designed to correct these problems (Dennis et al., 1999; Purvis & Sambamurthy, 1997). While JAD is not as widely practiced as interviewing (Purvis & Sambamurthy, 1997), it has gained in popularity (Jackson & Engles, 1996) and is increasing in common usage (Dennis et al., 1999; Kettelhut, 1997). Researchers (Dean et al., 1997-1998) and practitioners (Spina & Rolonda, 2002; Jones, 1996) consider JAD best practice for structuring group interaction in par-

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ticipatory environments and for operationalizing user involvement (Uden, 1995). It has been used increasingly with rapid application development (RAD) projects (Rist, 2001) and dynamic systems development method (DSDM)—a RAD-based technique used extensively in the UK (Barrow & Mayhew, 2000; Beynon-Davies et al., 2000). JAD is known by several other names including facilitated technique, facilitated workshop, joint application review, accelerated design, and user-centered design (Carmel, et al., 1995; Dean et al., 1997-98). Several derivatives exist (Asario 2000), and some organizations have made their own modifications to the formal JAD structure (Davidson, 1999). In addition to its application in systems development, JAD (under any of these names) has also been used in several other organizational decision-making contexts (Kettelhut, 1997; Davidson, 1999). JAD places significant emphasis on the communication aspects of requirements elicitation (Liou & Chen, 1993-1994; Purvis & Sambamurthy, 1997). System developers, users, and managers assemble in a synchronous, three- to five-day workshop to specify information requirements and make system decisions under the guidance of a trained facilitator (Andrews, 1991; Wood & Silver, 1995). One of JAD’s important intentions is to develop the necessary team rapport in order to bridge the communication gap and exploit potential synergistic opportunities to produce higher quality system requirements (Dean et al., 1997; Purvis & Sambamurthy, 1997). The steps in the JAD process, as described by Wood & Silver (1995), are highlighted in Table 1. Agile development methods are being used increasingly in systems development paradigms such as extreme programming (XP), featuresdriven development, adaptive software development, and DSDM (Highsmith & Cockburn, 2001). These methods typically collapse several life cycle stages for speed of delivery and produce deployment-ready modules iteratively and/or incrementally. Traditional methods, however, still

Supporting the JAD Facilitator with the Nominal Group Technique

Table 1. The five phases of JAD JAD Phases

Key Activities

1. Project Definition

Agree scope and objectives; secure management commitment and willingness to release experts (Liou & Chen, 1993/4)

2. Background Research 3. Preparation for Workshop

4. The Workshop (or Session)

5. Preparation of the Final Document

Acquire knowledge about existing business processes and problem domain Finalize meeting logistics and facilities Offsite meeting to minimize potential interruption; facilitator demonstrates excellent interpersonal relationship skills and understanding of group dynamics (Liou & Chen, 1993/4); the facilitator should be neutral and objective (Anson et al., 1995; Schuman, 1996), and should strive for broad user participation and focus on agenda (Carmel et al., 1995) Review document is in the presence of participants and sponsor(s), confirm, and get approval

account for a large percentage of development efforts1, especially for large systems with several stakeholders and in environments with fairly stable business processes. JAD may also be used with newer methods even when requirements are not completely pre-specified. For example, it is often used with RAD and to help generate use cases in object-oriented development. JAD could conceivably be used in short bursts at the commitment stage of XP’s planning game to prioritize “story cards” and identify implementation risks. The performance of JAD facilitators during the critical interactions of the JAD workshop is pivotal to the success of the meeting (Carmel et al., 1995; Davidson, 1999; van Murik, 1994). They, without a great deal of assistance from the JAD communication structure, bear the responsibility of guiding the session toward the attainment of the desired objectives (Dean et al., 1997) and securing decision outcomes that reflect the combined judgment of the group (Wood & Silver, 1995). Anson et al. (1995) and Dowling and St. Louis (2000) found that the quality of facilitation significantly influenced relationships and moderated process outcomes. Some of the potential group problems that challenge JAD facilitators are listed in Table 2.

Meeting structures may influence the conditions responsible for process loss or gain, but outcomes are more often determined by the extent to which the intended structure is appropriately invoked (Bostrom et al., 1993; Gopal et al., 1992-1993; Poole & DeSanctis, 1990). Groups that faithfully apply the intended communication structure outperform those that do not (Anson et al., 1995). The crux of our argument is that it is the facilitator’s responsibility to inspire faithful appropriation of the adopted meeting structure (Bostrom et al., 1993; Schuman, 1996, van Murik, 1994). While some meeting techniques provide a challenge for facilitators, NGT is considered to be conducive to faithful appropriation (Ho et al., 1999), and groups react very positively to the technique (Wood & Silver, 1995). Many of the recommendations for offsetting the dysfunctional effects of the freely interacting group technique used in JAD workshops (brainstorming, anonymity; prescriptions for reducing destructive dominance and increasing participation, strategies for precipitating consensus [Wood & Silver, 1995], and proposals for overcoming groupthink

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Supporting the JAD Facilitator with the Nominal Group Technique

Table 2. Potential JAD problems Problem Conforming Behavior Search Behavior Destructive Dominance Anchoring

Description Participants acquiesce to the emergent group norm (Delbecq et al., 1986) Inadequate diagnosis and premature specification of solutions (Delbecq et al, 1986) Less desirable contributions of powerful participants overwhelm useful ideas from others (Wood & Silver, 1995) Excessive focus on tangential issues raised by influential participants causes digression from the main objective (Ven de Ven & Delbecq, 1974)

Groupthink

Over commitment to group harmony such that group cohesion becomes the de facto decision criterion (Kettelhut, 1993)

Risky-shift Behavior

Empirically observed phenomenon where the group shifts from the risk profiles of its individual members (Kettelhut, 1993)

Elective Participation and Free Loading

Group members contribute of their own volition and some may not contribute at all (Carmel et al., 1995)

Commitment Errors

The group arbitrarily enlists the resources of its organization to unattainable objectives (Kettelhut, 1993)

Goal-setting Errors

Scheduling that reflect unrealistic group aspirations (Kettelhut, 1993)

The Abilene Paradox

Conflict avoidance that permits group decisions that are contrary to the desires of the individual members (Kettelhut, 1993).

[Kettelhut, 1993]) seem to be standard features of NGT. NGT is used in problem-solving situations to elicit individual knowledge, views, and opinions (Zuech, 1992). It is particularly useful in situations where group members must pool their judgments to determine a particular course of action from a large number of alternatives (Hornsby et al., 1994; Zuech, 1992). NGT combines the effects of two factors—conveyance (uninhibited idea generation during which “free” interactions are restricted) and convergence (precipitation toward consensus). These are accommodated in the five steps (listed in Table 3) which help to downplay the social and emotional dynamics that affect the

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performance of freely interacting groups (Delbecq et al., 1986; Ho et al., 1999). NGT’s superiority over the interacting group technique has been demonstrated in creative, problem-solving situations (Delbecq et al., 1975; Ven de Ven & Delbecq, 1974) and with heterogeneous groups working with complex problems (Stephenson et al., 1982). Several other noteworthy attributes that have been empirically verified include participants’ satisfaction with the process (Korhonen, 1990), usefulness in identifying different problem dimensions, and error reduction (Frankel, 1987), and its adaptability to a variety of problem domains (Chapman, 1998). NGT has been successfully combined with techniques such as multidimensional scaling (Frankel, 1987), multi-

Supporting the JAD Facilitator with the Nominal Group Technique

Table 3. NGT process Step

Activity

1.

Idea Generation

2.

Idea Recording

3.

Discussion and Clarification

4.

Ranking

5.

Decisionmaking

Description Participants independently and silently generate ideas regarding goals and problem solutions in writing. This separation of creative thinking from idea evaluation reduces emotional attachment to an idea and contributes to greater objectivity (Delbecq et al., 1986; Van de Ven & Delbecq, 1974). The facilitator records one idea at a time from group members in a round-robin format until all participants have completed their list of ideas. This accommodates increased participation (Stephenson et al., 1982; Ven de Ven & Delbecq, 1974). Each idea is discussed for clarification and subsequent evaluation, without either critical evaluation or lobbying, which reduces conformance pressure on lower ranking group members. Participants independently rate and rank all the ideas.

The final decision-making on the priority ordering of the alternatives (if necessary) is based on voting and mathematical pooling of the individual rankings.

attribute utility in decision analysis (Thomas et al., 1989), quality function deployment (Ho et al., 1999), and the analytical hierarchy process (Teltumbde, 2000). It has also been used to identify potential problems in information systems deployment (Henrich & Greene, 1991). Researchers have also evaluated the effect of using group support systems (GSS) to improve negative JAD outcomes (Carmel et al., 1995; Dennis et al., 1999;Liou & Chen, 1993-1994). The need to design structures to improve the conveyance of information and convergence toward consensus for effective, group decision-making are common objectives of NGT and GSS (Beruvides, 1995). NGT is primarily a manual technique, whose deliberations are usually not automatically documented (Liou & Chen, 1993-1994), while GSS uses networked computers and specialized software to allow group members to interact anonymously but fully aware of the responses of others (Beruvides, 1995; Dennis et al., 1999). Thus a GSS is a more sophisticated and efficient application of the basic NGT technique. Carmel et al. (1995) used case studies to compare JAD in its traditional application with JAD using GSS (EJAD), and found that EJAD ses-

sions were better with respect to both the degree of participation they induced and decision time, but not as effective for conflict resolution. They recommended a more active facilitator role for EJAD. In comparing group performances under NGT-like support and GSS support, Watson et al. (1988) and Liou and Chen (1993-1994) found no significant difference in task effectiveness under the two structures. Since then Dennis et al. (1999) have concluded that GSS-enabled JAD has helped to reduce model-building time by approximately 75 percent. NGT in combination with JAD, however, offers an alternative medium for conducting group research, particularly where the deployment of GSS is not yet pervasive.

RESEARCH MODEL AND HYPOTHESES The research model (Figure 1) was adapted from several GSS process models (Nunamaker et al., 1993; Ocker et al., 1995-1996; Pinsonneault & Kraemer, 1989) to abstract the relevant relationships among the variables of interest in our proposed process. It depicts the transformation that

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Supporting the JAD Facilitator with the Nominal Group Technique

Figure 1. General research model Group Factors Dynamics Size Effort

Task

- Effectiveness

Nature Complexity

Facilitation

- Participants’ Satisfaction

Outcome - Quality - Participants’ Satisfaction

Skill Level Control of Structure

Mtg.Structure JAD JAD & NGT

occurs in a facilitated group session as a result of the interaction of the contextual characteristics of the group, task, facilitation, and the meeting structure to contrive some instrumental outcome. The nature of the interactions is determined by this dynamic interplay, which affects process effectiveness (the degree of process gain or loss experienced). The meeting structure (the protocol that governs the pattern of interaction) and the complexity of the task (the activities required to accomplish the group’s objectives) also influence group dynamics. The quality of facilitation both impacts and is impacted by group factors, and a similar, reciprocal relationship exists between the meeting structure and the effect of facilitation. The degree of adherence that the structure itself permits and the skill of the facilitator demarcate this effect. The effectiveness of the process influences both participants’ satisfaction with the meeting and its results, and the quality of the decisions. We believe that facilitators using integrated JAD and NGT in SRD will achieve superior results

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to those using JAD alone. The nature of the stakeholder issues that typify SRD precludes effective results based on conventional JAD facilitation, or makes success attainable by only the very best facilitators. Because of its amenity to “faithful appropriation,” the NGT structure will help to reduce this crucial reliance on facilitator excellence for effective interactions (greater participation, less destructive dominance, convergence toward consensus) and successful outcomes. The theme that underpins our propositions is that the integrated communication structure provides assistance in reducing the performance gap between expert and novice facilitators; the structure contributes more to the expected difference in outcomes than heroic facilitator efforts. The parts of the hypotheses that refer to the effects of facilitation are necessarily exploratory. While it is intuitively appealing to predict that expert facilitators will contribute to more effective results than their less skilled counterparts, there is no theoretical basis to predict that either will inspire more faithful application of the integrated structure.

Supporting the JAD Facilitator with the Nominal Group Technique

The first four hypotheses are concerned with impacts on process effectiveness, an intermediate indicator of successful outcomes. Process effectiveness is concerned with the manner in which the input variables (including the communication structure and the facilitator) interplay to generate process conditions that are satisfying for the participants and are conducive to “right” outcomes. It is partitioned into several impacts: the extent of participation; the degree of destructive dominance, where influential members commandeer the deliberation to the exclusion of useful contributions from other members; the capacity to confront conflict and converge toward consensus, so that unresolved issues are minimized. Under JAD, it is the facilitator who must devise innovative means to draw introspective members “out of their shells” and “chill” dominators (Wood & Silver, 1995). But participation is involuntary within NGT; the structure compels the involvement of all participants, which helps to reduce the facilitator’s burden. Hypothesis 1: The integrated communication structure will contribute to a significantly higher level of process effectiveness but the expert facilitator will not. Hypothesis 2: The integrated communication structure will induce a significantly higher level of group participation but the expert facilitator will not. Hypothesis 3: The integrated communication structure will contribute to less domination, but the expert facilitator will not. Hypothesis 4: The integrated communication structure will help groups attain a higher degree of consensus, but the expert facilitator will not. Satisfaction with the process may be a postrequisite measure of effectiveness. There seems to be an inverse relationship between the existence

of dysfunctional group behaviors and group members’ satisfaction with the process. If indeed destructive dominance is controlled, participation increased, and consensus achieved, more group members should be satisfied with the process. But usually the disparity in background, authority, and knowledge in SRD makes it difficult for facilitators (without the benefit of a supportive meeting structure) to stem the tide of group behaviors that are not conducive to overall group satisfaction. We therefore propose: Hypothesis 5: The integrated communication structure will contribute to a significantly higher level of participant’s satisfaction with the process, but the expert facilitator will not. Satisfaction with the decisions and high-quality requirements, which are desired outcomes of the intervention of the integrated structure, are postulated as important measures of the overall success of the process. We contend that participants who use the integrated structure to generate requirements should identify more with the results and will feel a greater sense of ownership of the decision than JAD users. This increased affiliation derives from the greater involvement in the decision making and satisfaction with the process that the integrated structure supports. More effective participation in the deliberations is also expected to result in process gain, which should help to improve the quality of the output—the requirements. Hypothesis 6: The integrated communication structure will contribute to a significantly higher level of par-ticipant’s satisfaction with the outcome, but the expert facilitator will not. Hypothesis 7: The integrated communication structure will contribute to significantly higher quality requirements, but the expert facilitator will not.

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Because of NGT’s greater amenity to “faithful appropriation,” the integrated protocol is expected to reduce the facilitator’s burden in minimizing group behaviors inimical to good outcomes and help to reduce the reliance on excellent facilitation for high-quality results. The reduction in dysfunctional group behavior (destructive dominance is used as a surrogate measure) exhibited in sessions conducted under the integrated structure and under JAD should therefore be greater for the unskilled facilitator than for the skilled facilitator. Hypothesis 8: The difference in destructive dominance in JAD and the integrated structure sessions will be greater when the facilitator is low-skilled than when he (or she) is highly skilled.

RESEARCH METHOD A completely randomized design was used to conduct the laboratory experiment. In this design, two levels of group communication structure (standard JAD and the integrated protocol) were crossed with two levels of facilitation (expert and novice). The facilitated group session was the unit of analysis.

Procedures Twelve professional facilitators from four facilitator associations, 18 scribes, and 144 role players (75 females and 69 males) participated in this experiment. Each participating facilitator led two sessions—one JAD and one the integrated structure. The 24 experimental groups consisted of a mix of role players from a wide cross-section of IS users, systems developers, business professionals (that accounted for 40% of the participants), senior undergraduate (35%), and graduate students (25%). This diverse combination of participants enriched the study by providing the within-group

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heterogeneity to maximize the influence of the manipulated variables. The professional organizations categorized the facilitators (who were all volunteers) into the two skill levels by years of facilitation experience and the number of sessions conducted. The twelve facilitators (six from each category) were (randomly) pre-selected to conduct the experiments. A facilitator’s packet containing the experimental task with instructions on how to conduct the sessions and a script for the NGT protocol was provided before the day of the session. Before each experiment the facilitators participated in a further one-hour debriefing session. Student volunteers participated for extra credit in their systems analysis and design classes. The four organizations (seven were originally asked) that participated, expressed great interest in the outcome of the experiment and were promised a copy of the findings. These facilitator associations also canvassed several business participants from among their membership, while other professionals participated during training exercises with these firms. Practicing or trainee facilitators were not allowed to participate as role players. The role players were randomly assigned to groups of six. Each group was randomly assigned to one of the four experimental conditions: JAD conducted by an expert facilitator, JAD conducted by a novice facilitator, the integrated communication structure (that combined JAD and NGT) conducted by an expert facilitator, and the integrated communication structure conducted by a novice facilitator. In sessions that lasted approximately two hours, each group was asked to generate high-level requirements for an integrated order processing, inventory management, accounts payable and receivable, and distribution management system to solve information systems problems of a fictitious chain of owned and franchised delistyle sandwich shops. The case was developed by Marble (1992). The documented requirements were then typed and verified by two independent volunteers and

Supporting the JAD Facilitator with the Nominal Group Technique

sent to the three expert judges, who rated them along eight quality dimensions. Participants also completed a pre-session (objective background information), as well as a post-session survey to record their perception of the sessions and facilitator-produced reports containing both perceptual and objective observations.

1. 2. 3.

Level of experience with SRD (single item from the pre-session instrument). Level of business experience (from the presession instrument). The level of effort expended by the group (assessed by the facilitator).

Variables and Measures

DATA ANALYSIS AND RESULTS

The instruments used in this study—demographic information on participants collected before the experimental sessions, a post-session survey (PSS), the facilitator’s report (FR), and the expert judge’s quality rating sheet (QRS)—were adapted from previous research (Anson et al., 1995; Bailey & Pearson, 1983; Green & Taber, 1980; Gouran, 1978) and revalidated. They measured the dependent variables (Table 4) used in tests of hypotheses. The following data were also obtained:

The main statistical procedures used to test the statistical hypotheses was factorial multiple analysis of variance (MANOVA), for multiple dependent variables and analysis of variance (ANOVA) for tests involving a single dependent variable. The analysis of data was conducted in three main areas: 1.

An examination of the demographic profiles of the participants was undertaken to establish whether potentially confounding input variables (not manipulated experimentally)

Table 4. Variables and measures Variable

Measure

Type of Measure

Process Effectiveness - Overall

Seven-item scale (from PSS)

Perceptive

-

Participation

1. 2.

Facilitator count (from FR) Two-item scale (PSS)

Objective Perceptive

-

Destructive Dominance

1. 2.

Facilitator Observation (FR) Two item scale (PSS)

Objective Perceptive

-

Consensus

1. 2.

Count of unresolved items at session end (FR) Two item scale (PSS)

Objective Subjective

Participants’ Satisfaction - With the Process

Four-item scale (PSS)

Subjective

-

Five-item scale (PSS)

Subjective

Aggregation of Judges’ scores (maximum of five points each) for Accuracy, Precision, Completeness, Conciseness, Relevance, Creativity, Consistency, and Feasibility (from QRS)

Expert Rating

With the Outcome

Requirements Quality Rating

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Supporting the JAD Facilitator with the Nominal Group Technique

2.

3.

should be controlled statistically as covariates. This analysis indicated (by the failure to reject the equality hypotheses) that there was homogeneity across groups with respect to the level of effort they expended for each communication structure used (p-value < .093) and for facilitation (p-value < .254). Similar results were obtained for equivalent experience in a professional business environment and with SRD (p-value < .758 and < .290 for communication structure and facilitation respectively). The revalidated instruments indicated satisfactory evidence of internal consistency (Cronbach’s alpha) with reliability ratings of .9082 (post-session), .7931 (pre-session), .8287 (facilitators’ report), and .9754 (judge’s rating sheet). The evaluations of the hypotheses follow.

Summary of Statistical Analyses Table 5, which summarizes results from the test of the hypotheses, indicates those hypotheses that were supported. No significant interaction

effect was found for any of the tests, nor was there was any indication that facilitation made a difference where the integrated structure did not. In all cases it was observed that the integrated structure outperformed JAD regardless of the caliber of the facilitator. The results indicate that skillful facilitators will induce greater participation, inspire a higher level of satisfaction with the process, and contribute to higher quality requirements than unskilled ones under any of the meeting structures. However, there was no significant difference attributable to facilitator competence for process effectiveness, destructive dominance, conflict resolution, and satisfaction with the outcome. The results, where both the integrated structure and facilitation competence were found to contribute to improved group performance, may indicate that the integrated technique makes good facilitation better in these areas. Taken together, the other cases that indicated significant effect due to structure and the insignificant effect due to facilitator competence are also useful results. They provide empirical support for the conclusion that the integrated structure may be able to achieve

Table 5. Summary of results from the tests of hypotheses

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Hypothesis

Type of Test

Difference Due To Structure

Difference Due To Facilitation

1. Process Effectiveness

ANOVA

Y (Sig. = .001)

N (Sig. = .070)

2. Level of Participation

MANOVA

Y (Sig. = .001)

Y (Sig. = .027)

3. Conflict Resolution

MANOVA

Y (Sig. = .001)

N (Sig. = .539)

4. Destructive Dominance

MANOVA

Y (Sig. < .000)

N (Sig. = .306)

5. Satisfaction with Process

ANOVA

Y (Sig. = .034)

Y (Sig. = .042)

6. Satisfaction with Outcome

ANOVA

Y (Sig. = .017)

N (Sig. = .202)

7. Quality of Requirements

ANOVA

Y (Sig. = .011)

Y (Sig. = .027)

8. Difference in Level of Dysfunctional Behavior

MANOVA (equivalent of Hotelling’s T2)

Y (sig. = .032)

Not Applicable

Supporting the JAD Facilitator with the Nominal Group Technique

objectives (reducing destructive dominance, precipitating consensus, and contributing to satisfaction with the requirements) that have eluded even highly skilled facilitators under JAD. Hypothesis 8 (which examined the relative performances of novice and expert facilitators under the integrated structure and JAD respectively) was also supported in the test in which two dependent variables were used in a two-factor MANOVA (Table 5). This is an important result, especially as we accept that excellent facilitation is a scarce commodity. It implies that the integrated structure solution can overcome potential

deficiencies imposed by less than perfect JAD facilitation. Although only destructive dominance was used in the test, the graphs in Figures 2(a) to 2(d) demonstrate that this phenomenon may be true for other important determinants of process effectiveness and other desirable outcomes. These figures demonstrate the disproportionate improvements in performance by unskilled facilitators compared to their more competent counterparts as both groups switch between JAD and the integrated structure. The mean scores (where bigger scores are more desirable) for satisfaction with the outcome, destructive domi-

Figure 2(a). Satisfaction with outcome

Figure 2(b). Destructive dominance

18 0

80

Mean Satisfaction with Outcome

17 0

16 0

15 0

Mean Destructive Dominance

70

60

14 0

Fa cilita tor L eve l

13 0

S killed

12 0

U nsk ille d

JA D

N JA D

Fac ilitator L evel Sk illed Uns killed

50 J AD

NJ AD

G ro up S tructure

G r oup S tructure

Figure 2(c). Satisfaction with process

Figure 2(d). Process effectiveness

150 260 250

140

230

130

Mean Process Effectiveness

Mean Satisfaction With Process

240

220

120

210 200

110

F a c ilit a t o r L e v e l S k ille d

Facilitator Level

190

Sk illed

180

Unsk illed

JA D

U n s k ille d

100 JA D

NJA D

N JA D

G roup S truc ture

G ro u p S tru c tu re

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Supporting the JAD Facilitator with the Nominal Group Technique

nance, satisfaction with the process, and overall process effectiveness respectively, increased as skillful facilitators switched between JAD and the integrated structure respectively. However, unskilled facilitators experienced a much larger increase for a similar shift. These all signal the potentially beneficial effects of the integrated structure in making facilitation less complex.

DISCUSSION AND IMPLICATIONS This research effort was justified on the general acknowledgement that the freely interacting meeting structure often contributes to dysfunctional group behaviors that curtail the effectiveness of JAD. These behaviors impede communication, contribute to process loss, which prevents groups from realizing their true potential, and obstruct the realization of synergy. JAD is therefore critically dependent on excellent facilitation (a scarce resource) to overcome these potential problems. The study was designed to evaluate the legitimacy of the claim that the integration of NGT and JAD could enhance facilitation effectiveness and obviate this prerequisite of facilitation excellence for SRD success. The findings support this expectation in the dimensions tested. However, caution is advised in the general interpretation of these results. They were obtained under experimental conditions where the task was simulated and the time for its accomplishment compressed. Additionally, the high-level requirements generated in the experiments lacked the details that typify normal systems specifications. Further, role playing under experimental conditions cannot realistically capture the effects of power asymmetry, the intensity, emotiveness, and political turf issues that characterize the natural process. On the contrary, participants in the experiments seemed far more willing to make concessions than is typical in natural settings. This research effort

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should therefore be viewed as a laboratory model that requires replication in the field. A further limitation of this study was the difficulty in establishing incontrovertible criteria for classifying facilitators into the two skill levels of expert or novice. Facilitators perform at least three distinctly different but complementary functions (Wood & Silver, 1995). They carry out environmental analysis—high-level data collection to circumscribe the problem domain and the solution goals. They plan and organize the meeting agenda, tools, techniques, and resources for the session, and they facilitate communication during the meeting. Only the latter was required in our study, but success in all three areas characterizes the “goodness” of facilitation. Effective performance at the earlier levels can simplify the effort at the communication level. Even more, good facilitators overall may not be competent at all levels. Although years of experience as a facilitator and the number of sessions conducted were suggested criteria, participating organizations were free to consider other means of classification. Discriminant analysis, however, indicated that the resulting categorization seemed reasonable. It is recognized that facilitation competence levels exist along a continuum, but for this experiment only the levels at the extremes of the continuum were used, which allowed this manipulated variable the maximum potential for exhibiting relative influence. Two other limitations could also have impacted the results. The facilitators were given the freedom to decide the order in which they conducted their two sessions. In hindsight, the claim to any systematic effect of facilitator learning might have been nullified if we had included an experimental condition that required randomization of the order in which each facilitator conducted his (or her) two sessions. Additionally, the facilitators were not paid; they volunteered because of their interest in the outcome. Non-payment could conceivably

Supporting the JAD Facilitator with the Nominal Group Technique

have induced differences in performance levels for some facilitators and not others, although it would not necessarily affect the manner in which each conducted his or her two sessions. The insignificant effects of facilitation competence for process effectiveness, conflict resolution, destructive dominance, and satisfaction with the outcome seem to bear out the suggestion that, for these, the integrated technique had the capability to equalize facilitator performance across the skill level continuum. This inference seems consistent with the accepted role of facilitation as the guardian of the faithful appropriation of the meeting structure. It is precisely the integrated structure’s amenity to faithful appropriation that gave rise to the proposition that even otherwise incompetent facilitators may experience successful results (induced by the structure) that facilitation competence could not by itself enable. Despite JAD’s success in some areas, several authors emphasized the imperative for facilitators to “chill the dominators” and promote consensus. Yet the experience has been that even excellent facilitators have not always alleviated the deleterious impact of dominance by both powerful and extroverted group members in group sessions generally, and in JAD workshops in particular. Similarly, the desire by less influential group members to avoid conflict has tacitly contributed to JAD outcomes that reflect the decision preferences of dominators rather than group consensus. The indication that highly competent facilitators are no more likely to reduce these problems than their less skillful counterparts demonstrates the facilitators’ inability to curb dysfunctional behaviors and enhances the value of the integrated structure. For the other three measures (level of participation, satisfaction with the process, and quality of the requirements), the significant result due to both communication structure and facilitator competence suggests that these effects may be additive; the integrated approach also helps good facilitators produce better results. It may appear

somewhat inconsistent, though, that facilitator competence was shown to have a significant effect on the level of participation—one of the indicators of process effectiveness, but not on process effectiveness itself. Similarly, there is an apparently conflicting indication of significant facilitator effect on participants’ satisfaction with the process and not with the outcome. One credible explanation for the former may be that excellent facilitators have a larger repertoire of artifices to cajole introspective group members toward more active participation (as one of the authors observed during the experiments). But these innovations were not enough to rectify more pernicious problems like destructive dominance, anchoring, or subjective defense of opinions (especially those expressed by influential group members) that are not supported by the group. This result is a good one in the larger scheme of the integration of JAD and NGT; facilitation is still an important component in the idea clarification stage. And ultimately, the objective is not necessarily to equalize participation, but to ensure breadth of participation and influence proportional to the level of knowledge of the participants. It is the dysfunctional consequences of inequality of influence and participation due to power or personality, which does not contribute to consensus that is a problem. It seems the communication structure and the facilitator can play mutually supportive roles to establish this objective. The significant facilitator effect for process satisfaction (but not satisfaction with the outcome) may also be due to the ability of more skillful facilitators to draw on a larger set of tools to keep the sessions fluid, interesting, and more efficient, which helps participants feel better about the process itself. However, it is possible that the memory of “people-related” problems (prior to and even despite facilitator intervention) lingered and affected participants’ evaluation of the actual outcome. The results also supported the proposition that the difference in facilitator effectiveness under the

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Supporting the JAD Facilitator with the Nominal Group Technique

integrated structure and JAD was greater, on the average, for low-skilled facilitators than it was for the highly skilled. Another way to view this is that the integrated structure induces greater improvements in the results produced by groups directed by unskilled facilitators than in those directed by highly skilled facilitators in comparison to similar results under JAD. It reduces the onus for excellent process outcomes on the competence of the facilitator. This is an important finding, which could have potentially far-reaching implications for practitioners. It suggests that superb facilitation is still desirable but it is not a critical success factor under the integrated structure.

CONCLUSION The integrated approach appears to be able to preserve the benefits commonly attributable to JAD (such as speed of SRD and user involvement) and treat the group behavioral problems that mitigated its success. If this occurs, several benefits could accrue to practitioners. First, some JAD efforts are not contemplated because excellent facilitation is not always available; researchers and practitioners agree that highly skilled JAD session leaders are in short supply. The indications from this research could provide the confidence to reduce this quandary (to apply JAD poorly or not at all) and make the technique generally available to address the pervasive problems with systems requirements. In addition, higher quality requirements permit early detection of potential specification errors, which helps to reduce scope creep, prevent postdesign alterations, and ultimately contribute to better information systems. These benefits would contribute to reduced, systems development and maintenance costs. Greater user satisfaction with both the SRD process and its decisions could also promote user ownership—a sense of responsibility for the realization of the system benefits—and help deflect usage-related systems failures.

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These findings suggest some follow-up objectives for future research. An interesting one is to attempt to replicate these results in the field to incorporate the realism of true SRD environments and reduce many of the other limitations of this study. It would also be useful to evaluate the performance of the integrated structure by varying some of the factors that were controlled in this experiment (e.g., group size, and task complexity). Another possible research undertaking would be to study the potential areas of applicability of the combined JAD/NGT approach with newer systems development paradigms. The latter objectives could provide valuable insights that could lead to the establishment of contingency strategies for deploying this technique in several contexts.

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Dekleva, S. M. (1992). The influence of the information systems development approach on maintenance. MIS Quarterly, 16(3), 355-372. Delbecq, A. L., Van de Ven, A. H. & Gustafson D. H. (1986). Group techniques for Program Planning: A guide to nominal group and Delphi processes. Middleton, WI: Greenbriar. Dennis, A. R., Hayes, G. S., & Daniels, R. M. Jr. (1999). Business process modeling with group support systems. Journal of Management Information Systems, 15(4), 115-142. Dieckmann, M. (1996). Making new technology investments pay off. Managing Office Technology, 41(7), 14-16. Dowling, K. L. & St. Louis, R. D. (2000). Asynchronous implementation of the nominal group technique: Is it effective? Decision Support Systems, 29(3), 229-248. Frankel, S. (1987). NGT + MDS: An adaptation of the nominal group technique for ill-structured problems. Journal of Applied Behavioral Science, 23(4), 543-551. Gopal, A., Bostrom, R. P. & Chin, W. (1992-1993). Applying adaptive structuration theory to investigate the process of group support systems use. Journal of Management Information Systems, 9(3), 45-69. Green, S. G.. & Taber, T. D. (1980). The effects of three social decision schemes on decision group process. Organizational Behavior and Human Performance, 25, 97-106. Gouran, D. S., Brown, C., & Henry, D. R. (1978). Behavioral correlates of perceptions of quality in decision making discussions. Communication Monographs, 45, 51-63. Highsmith, J. & Cockburn, A. (2001). Agile software development: The business of innovation. Computer, 34(9), 120-127.

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Henrich, T. R., & Greene, T. J. (1991). Using the nominal group technique to elicit roadblocks to MRP II implementation. Computers & Industrial Engineering, 21(1-4), 335-338.

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Holtzblatt, K., & Beyer, H. R. (1995). Requirements gathering: the human factor. Communications of the ACM, 38(5), 30-32. Hornsby, J. S., Smith, B. N., & Gupta, J. N. (1994). The impact of decision-making methodology on job evaluation outcomes. Group & Organization Management, 19(1), 112-128. Huber, G. P. (1980). Managerial decision making. Glenview, IL: Scott, Foresman. Jackson, R. B. & Embley, D. W. (1996). Using joint application design to develop readable formal specifications, Information and Software Technology, 38(10), 615-631. Jones, C. (1996). Patterns of software system failure and success. Boston: International Thomson Computer Press. Kettelhut, M. C. (1993). JAD methodology and group dynamics. Information Systems Management, 10(1), 29-36. Kettelhut, M. C. (1997). Using JAD for strategic initiatives. Information Systems Management, 14(3), 46-53. Korhonen, L. J. (1990). Nominal group technique. M. W. Galbraith (Ed.), Adult Learning Methods, (pp. 247-259). Florida: Krieger Publishing Company. Liou, Y. I. & Chen, M. (1993/4). Using group support systems in joint application development for requirements specifications. Journal of Management Information Systems, 8(10), 805-815.

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Endnote

1

The International Software Benchmarking Group survey on worldwide business systems projects (June 2001) indicated that 66% of these projects used traditional systems techniques. They also found that RAD/JAD was used in 28% of the overall projects.

This work was previously published in the Journal of Organizational and End User Computing Vol, 16, No. 2, edited by M. A. Mahmood, pp. 1-19, copyright 2004 by IGI Publishing, formerly known as Idea Group Publishing (an imprint of IGI Global).

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Chapter 4.3

Educating Our Students in Computer Application Concepts: A Case for Problem-Based Learning Peter P. Mykytyn Southern Illinois University, USA

Abstract Colleges of business have dealt with teaching computer literacy and advanced computer application concepts for many years, often with much difficulty. Traditional approaches to provide this type of instruction, that is, teaching tool-related features in a lecture in a computer lab, may not be the best medium for this type of material. Indeed, textbook publishers struggle as they attempt to compile and organize appropriate material. Faculty responsible for these courses often find it difficult to satisfy students. This paper discusses problem-based learning (PBL) as an alternative approach to teaching computer application concepts, operationally defined herein as Microsoft Excel and Access, both very popular tools in use today. First PBL is identified in general, then we look at how it is developed and how it compares with more traditional instructional approaches.

A scenario to be integrated into a semester-long course involving computer application concepts based on PBL is also presented. The paper concludes with suggestions for research and concluding remarks.

Introduction It probably would not surprise most Management Information Systems faculty and academics that both accredited and nonaccredited colleges of business continue to struggle with instruction in computer application concepts aimed at undergraduate students. A review of business school Web sites would indicate that a wide array of courses, course names, and course schedules exist: Introduction to Computers, Management Information Systems, and Microcomputer Applications might be some course names that

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Educating Our Students in Computer Application Concepts

could be found. Some of these courses might be sophomore level, whereas others might be found at the junior level. In other instances, more than one course might be found. One class might deal exclusively with computer application concepts, such as Microsoft Office, whereas another might relate more to MIS itself. Furthermore, the topic related to teaching MIS and application concepts is often raised in postings to ISWORLD (www. isworld.org). Thus, it is of little surprise that in the end, questions and uncertainties about these classes exist. The focus of this perception-based paper is toward teaching computer application concepts such as Microsoft Office. That is not to say that Microsoft Office is the sole way to teach these types of concepts. Indeed, some schools might teach other tools such as HTML, JAVA, Visual Basic, and so forth. In effect, however, my thoughts related to teaching these concepts are independent of the particular tool used in the classroom. At the same time, Microsoft Office, with particular emphasis on Excel and Access but with some instruction focusing perhaps on PowerPoint and FrontPage, seems to be the most prominent tool used today to provide students with basic computer application concepts. With numerous surveys, questions posed on ISWORLD, questions raised by faculty at conferences, and continuing efforts by textbook publishers to develop the right set of books for these tools and applications, MIS faculty continue to struggle with this type of class. At the same time, students themselves raise objections. On the one hand, some students are already well skilled in these concepts as a result of taking similar classes in high school or at the community college level. Still others have worked with these tools professionally and do not see the need for taking another class that is perceived to have little to no value. In other instances where two required classes are taught, the overlap between the first and second class is so similar as to again provide seemingly little value to the student.

Instead of rehashing the same material over and over again, however, my thought is to suggest an alternative approach to teaching computer application concepts. The approach is called problem-based learning (PBL). This approach is by no means new or unique. However, it does seem to be somewhat unique to teaching computer application concepts; indeed, it appears to be quite unique in colleges of business. Ideally, readers of the paper may question the approach presented leading them to investigate it more completely in terms of its applicability and use in this type of class. Additionally, new approaches such as this would lead to empirical research as well. In the next section, a brief overview of PBL, its concepts, and how it might be applied to teaching computer application concepts is presented. A suggested research agenda follows. Conclusions are presented last.

Problem-Based Learning In mid-January 2006 I searched one of the online database indices that our University subscribes to, EBSCO (www.ebsco.com/home/). I searched for the term “problem-based learning” and was rewarded with 1,125 hits. Choosing to refine the search somewhat, I again searched on “problembased learning,” this time as part of the title of articles. A total of 383 articles in that database contained “problem-based learning” as part of the title at the time of the search. This very unscientific sampling process indicated that the vast majority of articles are from the medical field: Medical Education, Journal of Clinical Anesthesia, Medical Teacher, and Physiotherapy are representative of the journals containing those articles. In fact, I continued with this process and noted that just one article found was related to colleges of business. That article appeared in the International Journal of Technology Management, and it dealt with entrepreneurship. I was unable to identify any articles dealing with computer tools, such as

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Table 1. Generalized differences between a traditional and a problem-oriented classroom (Adapted from Shepherd and Cosgriff [1998]) Traditional education Curriculum as prescription Instructor-focused Teaching as transmitting Learning as receiving Rigid environment Replicate and apply received knowledge Emphasis on a best solution Evaluation of result

Microsoft Office. To try to uncover anything of a PBL nature and information systems, I again searched EBSCO with “problem-based learning” appearing anywhere in the text of an article along with the following journals: Journal of Management Information Systems, Information & Management, MIS Quarterly, and Decision Sciences. No results were returned. This very unscientific assessment does not come as a surprise, because the concepts of PBL do not appear to be mainstream as far as teaching computer and software concepts is concerned. One article dealing with PBL appeared in the spring 2003 issue of Decision Sciences Journal of Innovative Education (Kanet & Barut, 2003). It dealt with using PBL to teach production and operations management. To begin with, it is perhaps easier to first indicate what PBL is not, or rather how it contrasts with a traditional approach to teaching. The opposite of PBL is subject-based learning. In this pedagogical environment, students are presented first with material that is specific to a discipline, such as medicine, nursing, geography; following this, students encounter or are presented with a scenario to which they apply what they have already been taught (Maskell and Grabau, 1998). In this situation, the instructor may present a lecture and take a very active part in the process. Following that, students, most likely acting alone, work out

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Problem-based learning Curriculum as experience Student-focused Teaching as facilitating Learning as constructing Flexible environment Construct and synthesize knowledge Emphasis on alternative acceptable solutions Evaluation of result and learning process

problems that may be very well structured and directly tied to the lecture presented earlier. Problem-based learning is a process that is rooted in using a problem situation to direct and focus the learning activity. Boud and Feletti (1991) provide guidelines for PBL instruction: Students are presented with an authentic problem, one that is based on a real-world situation. In its purest form, students work in groups to gather their thoughts and prior knowledge they may have and then seek to define the problem broadly. Students develop questions that relate to problem aspects they don’t understand; essentially, they understand what they know and do not know. Students may rank order the issues or questions and may divide the process of obtaining answers among themselves. When students reconvene, they present and summarize their findings as a group and decide how best to proceed. Drennon (2005) indicates that the process outlined above provides students with a problem which then leads them to engage in a “self-directed, reiterative, reflective learning process” (Shepherd and Cosgriff, 1998, p. 50).

Educating Our Students in Computer Application Concepts

One of the major differences between a traditional approach to instruction, such as a standard lecture by the instructor, and PBL relates to the role of the class instructor. Indeed, much of the literature discussing PBL refers to the instructor as a “tutor.” The prime educative task of the tutor is to ensure that students make adequate progress towards formulating the problem, identifying what they need to learn in order to understand it better and deal with it…The tutor does this essentially by questioning, probing, encouraging critical reflection, suggesting and challenging in helpful ways—but only where necessary. (Margetson, 1994, p. 14) Dunlap (2005) iterates that a tutor would not provide direct instruction about the task facing the students or the direction in which the students should proceed. Rather, the instructor/tutor’s role would be subtler, whereby he/she would guide students by asking metacognitive types of questions. Other roles of a tutor would include generating and prioritizing ideas, helping decide what to do next, and determining whether and when a group reached consensus. In summary, there are a number of pedagogical differences between a traditional approach to instruction and that based on problem-based learning. Shepherd and Cosgriff (1998) provide a synopsis of the major differences in these approaches; they are illustrated in Table 1. What may be one of the major differences between the two approaches, a difference that might surely need to be dealt with completely, is the change in the nature of authority in the classroom between the instructor and the students. Drennon (2005) summarizes it very clearly, indicating that PBL removes authority from the instructor and places it in the hands of the students, undoing students’ “… submissiveness to the rules of the established order” (p. 389).

Problem-Based Learning Applied to Computer Application Concepts As taught in many colleges and universities, computer applications concepts often pertain to teaching Microsoft Office tools, especially Excel and Access. That is not to say that some institutions will not include Word, PowerPoint, FrontPage, or even Web tools such as HTML or JAVA. However, for the purposes of this paper, it is assumed that Word and PowerPoint are elementary tools that are common knowledge today or are tools that students have learned on their own. This paper also assumes that FrontPage, JAVA, and HTML are important tools today for the development of Web sites; however, the focus of these programs is quite specialized and might be better left to perhaps a Web development course. That said, it is certainly possible to apply PBL to instruction in these tools. The discussion that follows below is one possible approach to incorporating PBL to teach computer application concepts. It is certainly not meant to imply that it is the only possible approach. By its very nature, PBL can be somewhat unstructured. In addition, the description below presumes that students are computer literate. That is, students would have already completed a literacy course that most The Association to Advance Collegiate Schools of Business (AACSB) accredited colleges of business include in their curricula. The course described here would be a 2nd level class, one that applies more advanced topics in Excel and Access. The class is a three-credit-hour class taught at the junior level. It consists of two meetings per week: a 75-minute lecture on MIS concepts using one of many possible textbooks published for this purpose, such as texts by Turban, Leidner, McLean, and Wetherbe (2006) and Laudon and Laudon (2005). The other 75 minutes are devoted to advanced application concepts involving Excel and Access. The latter portion of the class would

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normally be taught in a computer lab with each student working independently at a computer workstation. Because the class focuses on more advanced concepts in Excel and Access, there are numerous scenarios that an instructor might incorporate in order to achieve the learning objectives. The scenario suggested here relates to 401(k) investments. The scenario indicates that the student has just moved from one job to another and wishes to “roll over” $100,000 accumulated in a 401(k) program begun with the first employer. There are numerous mutual funds that the new employer will allow employees to choose from; these options would also be open to the rollover of the $100,000. For practical purposes, the number of funds suggested for this exercise is in excess of 150, and the student must select at least three funds at the beginning of the project. In addition, each month the student will invest $1,000 of his/her money into the new employer’s 401(k) program, and the new employer will contribute an additional $500, thereby providing the employee with a total new monthly investment of $1,500. The student is free to invest the $1,500 each month in one or more of the three funds selected at the beginning of the project. Or the student may select new funds for which to invest. Finally, the student is not restricted to keeping the same three funds that he/she started with at the beginning of the project. That is, the student may rollover money from one or more funds into one or more new funds. The goal of the project, in addition to having the student learn new concepts in Excel and Access, is to see which student in the class accumulates the most money at the end of the semester. An instructor may choose to reward the winning student (or perhaps the first three students) with some sort of award, such as actual prize money or extra points or credit for the class. It should be stated at the outset that PBL exercises are reportedly based on collaboration with peers, that is, students who work together in groups. The mutual fund exercise described

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above could be designed for persons to work alone. That said, it might be argued that some of the benefits from PBL as suggested earlier by Drennon (2005) would be lost. They include allowing students to better organize ideas, defining the problem better, and deciding as a group on how best to proceed (Drennon, 2005). However, it is possible that the main focus of PBL might still be accomplished by having students work alone. In fact, one PBL study implied that, except for the nature of the problem students were working on, students could accomplish a PBL-based task by working alone (Lam, 2004). Harland (1998) pointed out too that students working in teams or groups can encounter difficulties. These include differential workloads and failing to delegate properly. Thus, team-oriented activities are not necessarily perfect. One of the strengths of the project described here is its applicability to students enrolled in colleges of business administration and the real world nature of the issues that these students would encounter, perhaps sooner than expected but definitely not long after they complete their undergraduate studies. This scenario can serve a much greater good than many projects described in textbooks because of the personal association that students can have with the topic, and such an orientation is a primary objective of PBLbased instruction. Its applicability to students is enhanced too because it is designed as a semester-long project. There are numerous parts to this problem that contribute to its somewhat unstructured nature, another characteristic of PBL-centered problems. These include the necessity of researching mutual funds in terms of fund objectives, types of investments a fund focuses on—stocks, bonds, international—and so forth. Mutual funds also assess various fees, such as management fees, and not all funds charge the same for them. Of course, the nature of returns on investments would be a primary concern of anyone. Additionally, capital gains and dividend histories would be relevant

Educating Our Students in Computer Application Concepts

to long-term growth. The issues just listed are not necessarily exclusive of others that might be interjected into the project, but they are all key to mutual fund investing. More importantly, these criteria lend themselves very well to the use of Excel and Access. There are a number of advanced concepts in Excel and Access that students could investigate and integrate into the project. For Excel, these include numerous analysis tools, such as sensitivity analysis, goal seeking, descriptive statistics, presentation tools such as histograms and pivot tables, ranks and percentiles, and exponential smoothing. In Access, students would be exposed to report writing and query processing as well as perhaps more advanced topics in modeling and database design. The role of the instructor in this scenario would be consistent with the PBL approach to instruction. Recall that PBL approaches usually refer to the instructor as a tutor. The instructor would provide the background information to the students, and that information is consistent with the information provided above, that is, rolling over $100,000, identifying new mutual funds, and so forth. The instructor would not provide direct instruction in Excel and Access as needed to deal with the relevant questions about fund types, expenses and fees, returns, capital gains and dividends, gains and losses, and so on. Instead, consistent with Dunlap’s (2005) suggestions, the instructor would cleverly guide students and their thinking by asking appropriate questions. These could include: • •

• •

What might be your long-term objectives with these investments? What information might you need to more appropriately and clearly make your investment decisions? Have you attempted to summarize what you know or do not know up to now? What type of analysis is necessary to man-

age your investment decisions? How would Excel and Access contribute to facilitating the analysis? The role of the instructor/tutor may also be thought of as a facilitator. In this regard, the instructor would assist students in generating and prioritizing ideas, and expanding their knowledge beyond what they might find in a course text on Excel and Access, perhaps using numerous Web-based resources. Since personal investments centered on 401(k) programs might be new to these students, the instructor would encourage students to do additional research on the topic. It is also important for the instructor to monitor the student’s progress to make sure they are “keeping on track.” This can be accomplished by appropriate feedback in the formal class period as well as through the use of e-mail, chat rooms, and the like. Harland (1998) discussed another potential problem that could occur in the scenario described here: students’ lack of knowledge in basic Excel and Access skills. All students completing this class would have completed a computer literacy class as a prerequisite. However, it is a frequently discussed fact that literacy courses can and do differ considerably. In addition, students may have completed a literacy class years earlier, may have used different tools besides Excel and Access, and may have received varying levels of knowledge and instruction in the literacy class. As such, not all students are similarly prepared to enter the advanced class. Varied backgrounds such as these can necessitate that the instructor ensures that all students are guided properly, that appropriate Excel and Access tools that might be integral to the project are identified to the students who would then do additional research on them.

Potential Benefits of PBL A number of benefits can be achieved by using

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a PBL-based approach to teaching computer application concepts. First, PBL is student-centered. In the current scenario, students are provided with an overview of the topic and problem area and are guided to develop an understanding of the problem. Students are not force-fed instruction about this or that command. The models and types of analyses they develop to have a better understanding of the mutual fund problem are their own ideas, as opposed to traditional approaches that may tell the student to “do this, then do this next, and finally conclude by doing this.” Students may be able to draw upon prior learning much better. A PBL approach may help the students to understand the relevance of earlier material better and allow them to grasp the fact that there is indeed meaning behind coursework. In addition, the nature of the problem presented here, that is, rolling over mutual funds, is very relevant to students enrolled in colleges of business. Many will not seek employment as financial analysts, but most will indeed take a personal interest in their own investment portfolios. Steinemann (2003) refers to this latter point as applicability because students are solving real problems. In general, Steinemann (2003) indicated that PBL provides instruction for students that, in addition to applicability, is more active because students are directing themselves; students are more motivated because they are more interested in a subject when it is more personally oriented; and students can develop professional skills that they can take with them long after completing a semester-long course. The nature of the problem presented here can provide these benefits.

Research Agenda The use of PBL as a method of instruction involving many courses in colleges of business is an approach that needs formal research. As stated previously, there is little evidence found

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in the literature that PBL is common in business classes. The assessment of learning is obvious. Do students learn better in PBL environments than in traditional, lecture-based classes? Longitudinal studies over the length of a quarter or semester can help to answer this question. Dunlap (2005) found that self-efficacy improved in students enrolled in a computer science software engineering capstone course as a result of incorporating PBL as the method of instruction. Could the same results be found in teaching computer application concepts? Another important research question relates to just how students go about the process of learning when following PBL. Closely related to learning as an outcome variable, understanding the process is also important. What did students do? What sources did they seek out to assist them in the problem? What processes did they follow when developing any models they use to track mutual fund performance? Of paramount importance too is the comparison of group-oriented PBL instruction versus instruction that is individually based. As stated previously, PBL is essentially always oriented toward individuals working together in groups. As proposed above, it is suggested that students can work alone and achieve success. This should be investigated. Is there a need for research on just how a PBL method of instruction in a given class affects learning in later related classes? Are students better prepared to take later, more advanced courses if earlier classes are taught following PBL? Finally, it is necessary to determine the willingness of faculty to reorient their approach to teaching this type of material. Sometimes faculty may be resistant to changes, so it is necessary to ascertain what motivators might serve to have faculty institute significant changes in instructional methods.

Conclusion

Educating Our Students in Computer Application Concepts

Colleges of business continue to struggle with teaching computer application concepts. As recent as January 2006, an online survey posted to ISWORLD dealt with topics, tools, and concepts associated with this type of class. Schools seeking accreditation, or those working to maintain accreditation, are also faced with numerous issues surrounding curriculum and curriculum management and revision. It therefore follows that this type of class needs attention. PBL has been used successfully in many disciplines, such as medicine, engineering, computer science, and geography. It follows that it might also be an approach that could meet with much success in business schools as well. In particular, Computer Application Concepts is a business course that is taught in almost all colleges of business. It is a course that is often difficult to teach and manage. PBL is an approach that should be investigated more completely as a possible solution to better instruction and better student learning.

References Boud, D., & Feletti, G. (1991). The challenge of problem based learning. New York: St. Martin’s Press. Drennon, C. (2005). Teaching geographic information systems in a problem-based learning environment. Journal of Geography in Higher Education, 29(3), 385-402.

Kanet, J., & Barut, M. (2003). Problem-based learning for production and operations management. Decision Sciences Journal of Innovative Education, 1(1), 99-118. Lam, D. (2004). Problem-based learning: An integration of theory and field. Journal of Social Work Education, 40(3), 371-389. Laudon, K., & Laudon, J. (2005). Essentials of management information systems: Managing the digital firm and student multimedia edition package (6th ed.). Upper Saddle River, NJ: Prentice Hall. Margetson, D. (1994). Current educational reform and the significance of problem-based learning. Studies in Higher Education, 19(1), 5-19. Maskell, D., & Grabau, P. (1998). A multidisciplinary cooperative problem-based learning approach to embedded systems design. IEEE Transactions on Education, 41(2), 101-103. Shepherd, A., & Cosgriff, B. (1998). Problembased learning: A bridge between planning education and planning practice. Journal of Planning Education and Research, 17, 348-357. Steinemann, A. (2003). Implementing sustainable development through problem-based learning: Pedagogy and practice. Journal of Professional Issues in Engineering and Education and Practice, 129(4), 216-224. Turban, E., Leidner, D., McLean, E., & Wetherbe, J. (2006). Information technology for management. Hoboken, NJ: John Wiley & Sons.

Dunlap, J. (2005). Problem-based learning and self-efficacy: How a capstone course prepares students for a profession. Educational Technology Research & Development, 53(1), 65-85. Harland, T. (1998). Moving towards problembased learning. Teaching in Higher Education, 3(2), 219-230. This work was previously published in Journal of Organizational and End User Computing, Vol. 19, Issue 1, edited by M. A. Mahmood, pp. 51-61, copyright 2007 by IGI Publishing, formerly known as Idea Group Publishing (an imprint of IGI Global). 1329

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Chapter 4.4

Users as Developers:

A Field Study of Call Centre Knowledge Work Beryl Burns University of Salford, UK Ben Light University of Salford, UK

Abstract We report the findings of a field study of the enactment of ICT supported knowledge work in a Human Resources contact centre, illustrating the negotiable boundary between what constitutes the developer and user. Drawing upon ideas from the social shaping of technology, we examine how discussions regarding producer-user relations require a degree of greater sophistication as we show how users develop technologies and work practices in-situ. In this case different forms of knowledge are practised to create and maintain a knowledge sharing system. We show how as staff simultaneously distance themselves from, and ally with, ICT supported encoded knowledge scripts, the system becomes materially important

to the project of constructing the knowledge characteristic of professional identity. Our work implies that although much has been made of contextualising the user, as a user, further work is required to contextualise users as developers and moreover, developers as users.

Introduction In this article we offer insights into how a group of users interact with what can be seen as a knowledge sharing system—an ICT-supported repository used by call centre staff who offer expert advice on employment issues as HR management workers. The study provides a case of ICT-enabled knowledge sharing, insights into complex knowledge

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Users as Developers

work in what is often regarded as a highly standard, rules-based environment and, in particular, we emphasize the role of knowledge in systems development and use. To do this we focus upon the roles of users in the development, tailoring, and maintenance of “the knowledge” component of the system in everyday practice. Our research question was: How is knowledge made by professional users, and given the presence of ICTs in our field site, what is their role in this practice, if any? Drawing upon the social shaping tradition, which we shall expand upon later, we argue for the recognition of the, oft politically constructed and negotiable boundary between developers and users. In the remainder of this section, we briefly set our view of knowledge as a practice. Although there are those who privilege ICTs as “the” mechanism for capturing, storing, and disseminating knowledge, this has been challenged as lacking insight into different kinds of knowledge and its provisional situated nature (Blackler, 1995; Fleck, 1997; Marshall & Brady, 2001; Sutton, 2001). For example, knowledge may be embodied (knowledge about how to do something, gained through doing), embedded (where routine arrangements are deployed), embrained (akin to the holding of conceptual skills and cognitive abilities), encultured (rooted in shared understandings), and encoded (conveyed by signs and symbols) (Blackler, 1995; Collins, 1993). However, it has been further argued that greater insights can be gained by studying the processes of knowledge construction, rather than trying to describe and define its different forms. Knowledge is mediated by various things, situated in a given time and place, provisional in that it is socially constructed, pragmatic in that it is purposive and object oriented, and contested as it has links with power and politics (Blackler, 1995). Blackler (1995) therefore recommends that we focus upon the systems through which people achieve their knowing, on the changes that are occurring within such systems, and on the process through which new knowledge may be generated.

Indeed, it has been further argued that rather than simply define or describe knowledge networks, the challenge is to show how particular practices and discourses sustain networks of power-knowledge relations (Knights, Murray, & Willmott, 1993). For example, historically, task-continuous status organisations were prevalent where functional and hierarchical differentiation coincided. In this environment, positions were defined, by, among other things, knowledge ownership (Offe, 1976). But modern organisations are said to exhibit increased task-discontinuation structuring of status and the function of work performed (Hardy & Clegg, 1996). We therefore emphasize the need to go beyond simplistic notions of knowledge as a commodity to be extracted and transferred (Walsham, 2001). Knowledge may be used in innovation appropriation processes to provide access to other relevant knowledge and systems and as a political tool in support of particular interests (Hislop, Newell, Scarbrough, & Swan, 2000). Knowledge informs and justifies how we act, when it is taken as “truth,” especially when it is understood as neutral and authoritative, then it is powerful (Alvesson & Willmott, 1996). As mentioned earlier, knowledge is situated and therefore it is necessary to understand that knowledge construction is somewhat predetermined by the fact of “growing up” in a society (Mannheim, 2004), in our case, an organisation. Thus we have to be careful to avoid an excessively volunteeristic account of knowledge work in which actors are depicted as autonomous agents who possess sufficient resources to make their network a reality (Knights et al., 1993). Indeed, as Orlikowski (2002, 2006) argues the role of material forms, systems, spaces, and infrastructures in everyday knowledgeable practice are important. In this study we explore the ICT-related organisational practices in a call centre environment, “CarePoint,” where complex forms of knowledge and processes for the construction of knowledge are used in practice. We examine how a group of staff maintain an important knowledge sharing

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system and how this facilitates the appropriation of it into the everyday practices. Through this we illustrate the links among: the development of an ICT system in use, different forms of knowledge, and the roles of users as developers. We point to the use of politicised knowledge in the creation of the negotiable boundary between development and use, and thus the naming of developers and users. In the next section we expand upon the call centre research tradition and its contested relationship to knowledge work. In particular we begin to raise questions regarding call centre working and knowledge where the employees are professionals. In the following section, we introduce a social shaping lens to highlight the negotiable boundary between development and use, and developers and users. Following this, we outline our approach to the field study and then we provide an interpretation of our findings. The findings are then discussed; we present our conclusions and some implications for research and practice.

Call Centres and Knowledge Work The use of call centres has grown over the past 20 years, predominantly in response to the needs for globalisation, the potential they offer for improving organisational efficiency, and the desire to become more customer facing in the light of the business hype surrounding customer relationship management (Light, 2003). Call centre workers often perform roles that, in more traditional organisational settings, would be performed by a number of people. Indeed, where outsourcing companies operate call centres, the workloads of employees can be distributed over a multi-organisational customer base in order that they are most efficiently utilised. In order to further maximise the efficiencies to be had from the call centre model, ICT-enabled surveillance is used extensively to monitor performance. For

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example, the automatic call distribution software used to allocate calls to agents is also used to measure performance in terms of, for instance, length of time taken to answer calls, calls lost, and revenue generated (Taylor, Mulvey, Hymann, & Bain, 2002). Conventional call centre agents have little or no officially sanctioned autonomy; scripts are built into ICTs by developers to guide them through their interactions in an efficient and standardized fashion. Yet, in recent years, there has been a significant drive towards the “professionalisation of call centre operations” with UK organisations such as the Call Centre Managers Association promoting “the profession” (www. ccma.org.uk) and the UK Customer Contact Association (www.cca.org.uk) offering call centre accreditation and portable qualification initiatives for call centre staff. Employers thus, often depict call centres as knowledge-intensive environments where skilled, semi-professional workers are employed for their interpersonal skills (Frenkel, Tam, Korczynski, & Shire, 1998). However, the majority of academic accounts of such environments stress that routinisation, repetition, and employee disempowerment are the prominent features, even though there are studies of employees “fighting back” (Menzies, 1995; Taylor & Bain, 1999; Taylor et al., 2002; Van Den Broek, Callaghan, & Thompson, 2002). Given this context, how then might ICT-enabled knowledge work be performed in a call centre environment which is often organized and managed via scripts, within a unitary organisational frame of reference? More specifically, given our field site, call centre employees have to create, modify, and share knowledge as part of their professional contract—how does this happen in an environment where one would expect high levels of ICT-enabled scripting to take precedence? Moreover, given it has recently been argued that most ICT-based systems are still currently developed as static entities whose purpose is to model a dynamic world (Kanellis & Paul, 2005), and that knowledge is provisional. Then the use

Users as Developers

and development of ICTs in knowledge intensive environments needs further investigation in terms of the role of users and developers might have in making such systems work in practice. In the next section we introduce a social shaping of technology (SST) lens to assist with this.

USER-DEVELOPER RELATIONS: A VIEW FROM SST The social shaping perspective we adopt to study knowledge construction seems particularly appropriate given its genealogy in the sociology of scientific knowledge (SSK). SSK proponents claim that the natural world has a small or non-existent role in the construction of scientific knowledge and emphasises the social influences upon science (Collins, 1993). However, as Orlikowski (2002, 2006) reminds us, it is important to take account of the materiality of knowledge. SST has therefore evolved over time to take account of materiality. Contemporary SST researchers reject technologically, and socially, deterministic accounts of the construction and appropriation of technologies recognising the mutually constitutive nature, and negotiable boundary between, society and technology (for overviews see Bijker & Law, 1994; Mackenzie & Wajcman, 1999; Pinch & Bijker, 1987; SØrensen, 2002). From this perspective technology applications do not have predictable outcomes. Instead, technologies are conceptualised as being shaped as they are designed and used. Therefore, while they may change situations, the technologies themselves may be subject to change resulting in intended and unintended consequences for sociotechnical arrangements. However, as mentioned earlier, ICT-based systems are often reported as being delivered as complete solutions, which are sufficiently specified a priori (consider software vendor websites). The consequence of this is that many systems are still deemed failures at some point in time despite user involvement in system development and implementation (Cavaye, 1995).

This has been termed the design fallacy—the presumption that the primary solution to meeting user needs is to build ever more extensive knowledge about the specific context and purposes of various users into technology design (Stewart & Williams, 2005). Stewart and Williams argue that the problem with this thinking is that it privileges prior design, it is unrealistic and unduly simplistic, it may not be effective in enhancing design/use, and it overlooks opportunities for intervention. Indeed, it has been argued within the field of IS, the reality of the situation is that organisational features are products of constant social negotiation and consensus building and this means we need to rethink how ICTs are developed (Truex III, Baskerville, & Klein, 1999). A further issue is that users are often seen not considered in their context and instead are often thought of in systems development and use, as using a given ICT in isolation from other affiliations, identities, interactions, and environments (Lamb & Kling, 2003). Developers too, have also often been seen as objective experts whose sole aim in life is to build the best system possible for an undifferentiated group of users. However, it is now increasingly recognised that such views are simplistic and that development and use is loaded with power and politics on “both sides” (Franz & Robey, 1984; Markus, 1983; Markus & BjØrn-Andersen, 1987; Yourdon, 1986). Yet, the “two sides” of users and developers in ICT efforts are still a key feature of IS research. We understand that power is exercised by developers over users (Markus & BjØrn-Andersen, 1987), and that certain users may exercise power over developers (Howcroft & Light, 2006), but users as developers exercising power has received minimal attention. Even the long tradition of end-user computing still predominantly refers to users, not as developers, but as users who happen to develop ICT-based systems. Users are rarely discussed in terms of any role they may have as a developer and developers are similarly usually not seen as users (Friedman & Cornford, 1989).

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This is despite case studies of users as developers and their valuable role in product development (cf. Holmström, 2001). Consequently, questions remain about whom users and developers are. We believe this is inextricably linked with the artificial, and arguably political, distinction made between the social and the technical often made in the field of IS, when such distinctions are clearly socially constructed and negotiable (Bloomfield & Vurdubakis, 1994). Drawing upon SST we wish to expand upon this in terms of the roles associated with work deemed social and technical. In sum, we think the boundary between the usage and production (or development) of that deemed social and technical—the socio-technical—is also negotiable (Rohracher, 2005). Therefore, although discussions of producer-user relations have yielded many interesting and valuable insights, we think it might also be useful to recognise the ongoing work that “users” put into socio-technical systems in situ (Fleck, 1994; Rohracher, 2005; Stewart & Williams, 2005). Not only do they use such systems, they produce them in use too. This idea of ongoing development in use is well known within the body of work known as the SST (Fleck, 1994; Rohracher, 2005; Stewart & Williams, 2005). However, within IS the distinction between users and developers remains. In the next section we provide details of our approach to undertaking the field work.

Research Approach This study is part of a wider programme of work looking at the deployment of ICTs in professionally populated environments. The research approach is interpretive and qualitative. The findings are based on primary and secondary data drawn from one of the cases. The case study research method is largely acknowledged and is frequently used to conduct qualitative data informed IS research (Klein & Myers, 1999; Orlikowski,

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1996; Walsham, 1993; Walsham, 1995). A range of techniques associated with the case study method were used given our concern for the effects of the socio-technical arrangements within the company rather than the “technical” aspects alone (Myers, 1997). CarePoint, the human resources contact centre we have studied, employs 78 staff, only five of whom are male; the average age of the staff is 34 years. During our time at CarePoint, we were given company identification badges and were allowed free access to the call centre when visiting. We were not accompanied by a member of staff, unless we requested this for introductory purposes. A cross section of 14 members of staff were interviewed and observed over a number of sessions during a six-month period—this included the call centre manager, policy makers, case workers, and a range of HR advisors. For this study, a number of relevant social groups have been identified by following the actors and historical snowballing in line with Bijker (1994). Moreover, as Bijker suggests “this is of course an ideal sketch as the researcher will have intuitive ideas about what set of relevant social groups is adequate for the analysis of a specific artefact” (p. 77). At the beginning of each interview we explained the study’s purpose and made the participants aware that we intended to publish our findings. However, we assured each participant that they would be anonymised and we gave them the opportunity to decline participating in the interview and the research more generally. We also informed them that they could decline to answer any of our questions and that they could review their transcript (all participants reviewed their transcript and corrected any misinterpretations on our part). It was also made clear that we would only discuss any points of interest with their colleagues, if they agreed to this. No one declined to be interviewed, and everyone was happy to have their thoughts shared with their colleagues. The time scale of individual interviews varied between 0.5 and 1.5 hours. The interviews were semi-structured;

Users as Developers

they were recorded and transcribed providing 81 pages of transcription. Additionally, documentary evidence such as CarePoint policies was referred to as necessary, this was collected from the company intranet and it was given to us by various employees during the process of our investigation. We also drew upon numerous sessions of nonparticipant overt observation and photographic evidence that we collected. Analysis and data collection were simultaneously carried out. This began with the aim of discovering the nature of the contact centre agent’s professional identity, their background and their working environment. Data was then collected, guided by Johnson’s (2001) conceptual map of the characteristics of professions combined with a literature review of knowledge and ICT use in organisations. Johnson supplies us with a list of characteristics often related to professions: mastery of an esoteric body of knowledge; autonomy, formal organisation, and code of ethics; and a social function with occupational autonomy and knowledge being two defining features of this case study. The collected data was coded in relation to the literature and the features of the professions framework, and finally a subset of this related to knowledge was further unpacked eventually leading to the identification of the analysis reported here.

Constructing Knowledge At CarePoint CarePoint is an internal HR contact centre within an international health care product firm. The staff prefer CarePoint to be referred to as a contact centre as opposed to a call centre so as to get away from the traditional portrayal of a call centre. This decision was made because the staff do more than enact standardised scripts; the calls require the practice of knowledge as related to various HR issues. The contact centre staff provide HR support and advice to the 55,000 employees employed by the company. This work is undertaken by HR

professionals and involves advising on complex issues such as maternity leave, retirement policy, and staff recruitment. Employees will telephone the staff with a query, and it is their role to resolve it, escalating the caller to different levels of contact centre workers as necessary. The centre was set up in July 2003 with most of the staff recruited from within existing HR roles within the company. However, two staff with experience of call routing and working in a call centre environment were also recruited from elsewhere in the company. In both cases the existing knowledge base of the staff was seen as crucial to the creation of this new organisational function. The majority of the staff within CarePoint are chartered Institute of Personnel and Development (IPD) qualified or are undergoing one of their programmes of education. They regard themselves as professionals and consider HR to be a profession. The ICT-based system supporting the function was created by integrating pieces of software from existing systems throughout the wider organisation. This comprises a generic help desk application, a call logging system based on a customer relationship management package module, and a bespoke staff scheduling system written in Microsoft Excel. In addition, call scripts are created in Microsoft Word using HTML and these are integrated with the customer relationship management module. The customer relationship management module offers the facility for linking to external documents via HTML. Thus the scripts which the staff use are readily accessible by clicking on a link on the screen layout and this takes them to the HR “e-manual.” This manual contains two kinds of scripts. First, it is a script in terms of the wording staff might use when dealing with an employee or manager’s query. It also acts as a potential script in terms of how they might progress that query. For example, an employee may call in and want to know how much holiday entitlement they have for a given year. The script always begins with a prompt for a series of security checks, they are asked for

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Figure 1. CarePoint conceptual operating model

3 2 1b 1a

0

their staff number and name. Their details are then retrieved by the call centre staff member and they are asked to confirm their date of birth so as to ensure the advisor is speaking to the right person. Once the details have been provided and checked for authenticity, the call centre staff would then, in theory, open the e-manual and find the section on holiday entitlement. This would tell them to ask the member of staff how long they have been working at the company and what grade they are (as entitlements differ accordingly). The call centre staff member will then follow the appropriate link, based on the staff’s grade, and the system will present them with the holiday entitlement information organised by number of years of service. They then report back to the employee what their entitlement is. They would also advise that this is exclusive of any UK bank holiday entitlement.

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Level 3: Policy Owners set the policy. Responsive to the needs of the business. Accountable for the communication of the policy. Level 2: Case Worker for more complex cases. Likely to be in progress longer. May require faceto-face contact. Level 1a: Frontline Advisor Level 1b: Senior Advisor: Advisory service available by phone or email aiming to answer the majority of queries on first contact (no call backs). Level 0: Self Help material available directly to Line Managers. HR Operating Manual, StoreNet and various toolkits .

Who are the Users and What do they Use? CarePoint staff are the first obvious group of users within our case. They operate the ICT-based systems to provide advice to the managers and employees who contact them for guidance. Such knowledge-based interactions are mediated a number of things. A key aspect is the call flow model as shown in Figure 1 and it can be seen that Level 0 introduces another set of primary users, the employees and managers of the organisation CarePoint services. Level 0 is “self help,” where managers and employees can solve minor problems and queries themselves by referring to the standard HR operating manual, Storenet and other “toolkits” such as Breakfast News (a daily bulletin) via the Intranet. CarePoint advisors are organised across the remaining “levels.” Level 1, the advisory service, is split into two sections: Level 1a comprises a

Users as Developers

team of frontline advisors who should be able to answer a broad range of questions and they usually take the incoming calls (when the contact centre is busy, all staff, irrespective of level, take incoming calls directly). Level 1b comprises a team of senior advisors who have the ability to give more expert advice; a case that is referred to this level of staff should take no longer than 20 minutes to complete the call. If a call is more complex and exceeds 20 minutes then the customer is routed to the next level of agent, Level 2, comprising a team of case workers. Case workers deal with cases that exceed 20 minutes that need to be discussed in more detail, such as issues regarding discrimination or bullying. They make the choice on how to best support the line manager or employee, and if necessary, they visit them on site. Case workers also go out on site and deal with any new or extra businesses as they arise, the formation of new sites of operations for example. They also have particular responsibility for the development of the service; they look at how things can be done better. Level 3 hosts a team of policy owners; they deal with legal issues, set out various policies, are responsive to the needs of the business, and are accountable for the communication of changes in policy. However, despite the internal structure of the contact centre appearing to be task-continuous, in this case the staff’s relationship to other members of departments is quite discontinuous. Level 1 staff may deal with high ranking managers throughout the organisation because of their specialist encultured and embedded knowledge and by drawing upon the encoded knowledge in the scripting system. This meant call times could not be standardised. Staff may be dealing with a high ranking manager who does not want to be hurried. Moreover, some callers might have particularly difficult personal circumstances to deal with, for example bereavement or feelings of victimisation. Therefore, the staff we spoke with felt they would be neglecting their professional duties if they hurried people through the calls for

the sake of improving call turnover statistics. This meant that when the call centre was set up, working arrangements had to be configured bearing this in mind, as one employee explained: … and we came from HR, which could be very complicated and we needed to give them the benefit of our experience, so 15 minutes for the average call, and Janet [the call centre manager] wanted it cutting to 3 minutes and that’s all you’ll have and we had this ongoing debate, god you know there’s no way we can share our marvellous knowledge in 3 minutes and if you look at the call stats now we actually do it in 7 minutes. (Adriana, Case Worker) The uses of the “knowledge” component of the system, the scripts, are also deployed more flexibly than might be the case in a traditional call centre setting. Cathy, a senior advisor informed us that: it would generally be the [level 1] advisors that would use the scripts, the stuff in scripts is fairly basic where a case is black or white, if it’s a bit more grey and a bit more in depth then a bit more knowledge is needed Even Level 1 advisors informed us that the scripts were of limited value: I used the script regularly for the first two weeks while training, and sometimes I do still have to use them, but I have only been here three months, but because I come from an HR background I have prior knowledge of some issues, so that helps. (Peter, Junior Advisor) We were told, and observed; that if the caller is a staff member then advisors stick rigidly to company policy on what is discussed referring the caller to the encoded knowledge in the scripts as necessary. However, if line managers call, advisors tend to draw upon encultured and embedded

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knowledge not recordable in the ICT-based scripts so they can give a more rounded discussion on the policy. The system is therefore used by more established members of staff in a supporting role if they are dealing with an unfamiliar case, with trainee staff using scripts in a supporting role, particularly in the first two weeks of training, and if they are new to the HR function. Case workers use the system to coach and develop advisors on legal issues and telephone manner and style; to learn from escalated and tribunal cases; to probe the case issues; and give guidance and advice. In addition all advisors are encouraged to use the system in their spare time to further enhance their knowledge and skills on various issues that they have yet to deal with. We noticed this happened on several occasions and when we asked the staff why they were doing it, they said it was part of their continuing professional development because the IPD was “strong on this.” A further distinguishing feature of this knowledge work environment is the approach to performance management. Data is logged and statistics are produced by senior call centre staff and used for reporting to the company on the number of calls taken, employer relations issues and trends, and to ensure each team is aware of their objectives. However the staff told us that this data was used to improve the service and their working conditions (for example by employing more staff as the number of calls increased) rather than to make them work any faster. We were also told that overt monitoring occasionally takes place for training purposes to ensure advisors are giving consistent advice on issues and in that respect all staff agreed that the system and monitoring taking place is beneficial to the company and the individual. As Peter, a junior HR advisor informed us: We get messages to tell us that calls are monitored…people will always interpret things differently, possibly if you asked everyone here the same question, everyone will give you a slightly

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different answer, so it is important we all sing from the same hymn sheet when giving advice, but hopefully that’s where our knowledge helps, it’s a good thing. The seating arrangements within CarePoint are very different to traditional call centre environments. Levels of staff are mixed together within the “pods” of the contact centre, whereas in more conventional call centre environments junior and senior staff tend to be segregated, as was the previous situation here. This action was taken to develop the group overall, thus reinforcing the group. So now if an advisor has a “tricky” call to deal with, they can put the caller on hold, ask for assistance from senior advisors and share that knowledge, therefore reducing their skills gap and instilling confidence: If I get a strange call or something I can’t deal with we have Senior Advisors seated alongside us, seating has been recently rearranged so that has helped a lot because we’re taking more complex calls now and we can deal with them better because we’re getting coaching from the team so we’re up-skilling. (Heather, Junior HR Advisor) Indeed, this is again an extension of recognising the knowledge base of the staff in configuring working practices. When the contact centre was first set up, staff had to be encouraged to move away from their comfort zone and develop their knowledge base further. As one manager commented: The first challenge for me, as a line manager with my team of seven was because they all had their own jobs, they all stuck to their own jobs and they all had this long service, they weren’t sharing their knowledge, well one of the first things I introduced them to was multi-skilling, so I made sure I had at least three people that could do each task. (Helen, Team Manager)

Users as Developers

Development of the System in Use So far, we have focussed upon the users in our study, now we move on to consider the developers of a particular part of it. Moreover, the users think this is the most important part—the ICTbased scripts that underpin the contact centres operations. As mentioned earlier, a central feature of traditional call centre working is the use of scripts for the purposes of maximising efficiency and reducing the need for skilled workers by imposing a standardised, prescriptive approach. Traditionally call centre scripts are produced by third party companies or in-house developers throughout the requirements gathering process of the development cycle, or in response to user needs. At CarePoint however, Jane, a senior advisor, told us that: Senior Advisors construct the scripting, we have to think about how the questions will be asked in various ways, and look at how these can be answered. The HR manual is also reconstructed as and when necessary. When the script is written it goes to the Case Workers to check their interpretation of the Q and A to make sure it is all done properly, then it is saved. In this case, users are also developers. As they are dealing with provisional knowledge, there is a need to constantly revise the scripts to build the base of encoded knowledge and it is a team effort to determine whether this happens or it just becomes encultured or embedded. As Helen a team manager commented: It’s a team effort though, we have meetings and if issues are raised regarding the scripts and they need to be altered, maybe a new query has been brought to light that we have no answer to, or maybe the answer to the question needed to be put into a different context, then it would be amended or written.

However, the professional identity of the group is also seen as something to be maintained. All codifiable forms of knowledge do not end up being encoded into the scripts. Case workers have monthly meetings where they share their learning and decide who this will be further shared with and how. The system may also not be developed further because of the temporally situated nature of the knowledge they deal with. For example, Heather, a junior HR advisor informed us that: A useful tool we use is the Breakfast News that is communication between the teams about anything that needs to be shared…for instance we’ve got the Pope’s funeral on Friday and we’re starting to get calls about that, so everybody needs to give the same information…It doesn’t go on the scripting because it’s a one off and it’s not in the HR manual. People are querying the one-minute silence. It’s a lot easier to update people this way than to update the scripting. Staff knowledge and expertise is a crucial element of working at CarePoint. Their professional identity is not only recognised by qualifications but in the main by an individual’s knowledge and expertise. Jenny the talent management administrator told us that: …there’s a lot of people who have worked here for a long time and they’ve got a lot of knowledge about a lot of things and that’s professional expertise, there’s people here like that, their knowledge is so invaluable, so professional. Such knowledge constructed through dealing with various cases is shared by pulling out “key learning” from the cases, selectively sharing the findings in regular meetings, deciding if there’s anything they need to share with other staff (formally and informally), and recording it, if necessary, in the scripts. One advisor described the system as a “time saving tool” another as a “learning tool.” This process of selectively shar-

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ing and encoding knowledge is also a powerful political tool. Advisors unanimously agreed that the system enhances their professionalism, finding out information via the intranet is easier, it makes it possible for individuals to have a broader span of what information is available concerning the various issues they are dealing with as Melanie, a senior advisor stated: The system can pull up some wonderful information, it can tell you every minute of what’s happening with a case…if you think about the responsibility we have got here, we support the whole of the company with HR issues, we are very powerful, the technology aids our power and knowledge...it enhances our professionalism, you know you could actually say that the technology in a way is your ‘buddy’ because the system holds all the answers for you to enable you to do your job properly. Critically though, the staff of the contact centre control and enact the development of the scripts on their own terms, within the parameters of their role of course. In doing this they are able to present certain knowledge in an encoded format and label it as fairly basic “the black and white issues” that lay people might be able to understand if they write it correctly and if support is there from them to interpret it as necessary. However, they also get to choose which knowledge remains embedded and encultured and in turn this mystifies their professional position. Ironically, the ICTs that they value so much are also distanced in some circumstances as not being capable of doing what they do, even though they have developed them.

Discussion Our research question was: How is knowledge made by professional users, and given the presence of ICTs in our field site, what is their role in this practice, if any? In sum, we concur with

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prior studies which recognise the political nature of knowledge practice and the role of the material in this (Blackler, 1995; Hislop et al., 2000; Orlikowski, 2002, 2006). In this case, we see the prime materials as professional knowledge and ICTs. Thus, particular forms of knowledge and knowledge practices are implicated in the construction of further knowledge and knowledge practices and ICTs are enacted in a variety a ways in support of this project. Through these practices, the political boundary between users and developers, often constructed itself on the basis of claims to knowledge, is blurred as each becomes the other. We shall now explain this position further. There are three main forms of knowledge that we focus upon in this study, encoded, embedded, and encultured. Encoded knowledge is that which is written into the scripts. Embedded and encultured knowledge is that retained by HR staff either because it is not possible to encode it or it is deemed not desirable to do so. Perhaps most starkly, we see that embedded and encultured knowledge is seen as securing the professional project. HR staff are very careful in determining which knowledge is encoded into the scripts—it is a political practice. Such knowledge is used as a source of power to retain and reinforce professional identity. The role of the ICT-based scripts in this practice is complex. Although, ICTs have been noted as heightening professional identities (Lamb & Kling, 2003) and we see this in our case, it is also clear that considerable work goes into development in situ to fend off any encroachment such systems might enable. Thus, the HR staff simultaneously see the technology of the scripts as, central to the professional project, what one advisor called, “a buddy” and also something that they do not need because what they know could not possibly be encoded. It is also used to reinforce their professional position through their control of its development to delegate certain tasks, which they see as lacking in professional status, to lay employees and managers. They are therefore ex-

Users as Developers

ercising various forms of professional power in IS development (Markus & BjØrn-Andersen, 1987), as other “user/developer” studies have shown (Howcroft & Light, 2006). Their role becomes one of providing access to such “basic” knowledge, only stepping in when expert interpretation is required. This is interesting as Lamb and Kling (2003) state that, at the individual level—social actors exercise limited discretion in ICT choice and use, since, in organisational contexts; they articulate the preferences of a collection of actors. However, while we accept that this may be the case, knowledge is socio-technically structured (Mannheim, 2004), we also show that individuals can have a high level of discretion as to whether they use particular features of an ICT—scripts are optional and often downplayed in terms of their value. Therefore, the scripts are used as materials in knowledge practice in a variety of ways. This usage of ICTs does, we argue, blur the line between development and use. We do recognize that many researchers may not see their creation, tailoring, and general maintenance of scripts as “proper development” but we take a different view. The line between development and use has to be seen as a politically constructed and negotiable knowledge boundary—developers are the experts who know how to develop, users do not. Yet, in our case, we have clear evidence that the HR staff have very definite, knowledgeable ideas about which practices can and should be encoded. Moreover, they are confident in implementing these developments using HTML code. Of course, knowledge is provisional, several years ago HTML would have been seen as the realm of the developer, now of course many would say that anyone can do it, or that “users” only have a rudimentary understanding of the language. The question is, does this matter? In this case, a group of users are shown to be developers of a knowledge management system that works for them in practice. Moreover, in this case, they are the developers of what they deem to be the most important part of the system. If the work

scheduling system or call logging system failed, they could still answer the telephones and give advice using the scripts (accessed via the intranet). If the scripts were not in existence, they may (particularly new staff) encounter difficulties in service provision. The users are the developers of a mission critical system. Finally, it is also worth discussing our findings in relation to the end-user computing literatures more generally. It is suggested that the process of developing an application not only predisposes an end user developer to be more satisfied with the application than they would be if it were developed by another end user, but it also leads them to perform better with the application than they would if it were developed by another end user (McGill, 2004). The implication of McGill’s work is that performance relies on direct, hands-on use of the system. Our study also suggests that end users might perform better with an application they have had a hand in developing because they know when to use it and when not to. In terms of our case, we can think of performance in two ways. First, as related to answering an employees HR query—they can do it quickly without relying on the scripts. Second, in relation to performing the professional project—they can choose when and when not to encode particular knowledge practices. It has also been argued that use of userdeveloped applications requires substantial end user knowledge because of the lack of separation of data and processing that is commonly found (Hall, 1996; Ronen, Palley, & Lucas, 1989). However, in our study, this was not the case, the end-userdeveloped scripts were usable, and used by lay users (managers, employees, and new call centre recruits), because this application was developed for them as well as those who developed it. Again, this reinforces the user-developer’s dexterity in systems analysis and design. However, of course the scripts are not always successfully navigated by users, and thus, like good developers, they engage in refinement of the system and user training on a continual basis. In this case, the socio-technical

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arrangements, as Truex III et al. (1999) suggest, are optimized for high maintenance.

Conclusion Our study provides a case of users interactions with what can be viewed as a knowledge sharing system. Moreover, the case is of interest as a site of work relying on complex knowledge practices in what is often, despite managerial rhetorical efforts, a highly routine knowledge deprecating environment. We gain insights into a different call centre working model which rejects ICT process and socio-geographic configurations based on scientific management principles. Call centres are not, generally speaking, associated with knowledgeable autonomy, knowledge of the rules is required. Given the rise in the call for the professionalisation of call centre working, our site offers interesting insights for those looking to change working arrangements to give employees more credit for the ability to enact more complex knowledge practices. Moreover, we would suggest that further studies of professional user groups in other settings would yield interesting insights for studies of knowledge sharing and ICTs. Our particular focus has been to shed light on a group of users, who traverse the politically constructed knowledge boundary into development, and how they get a knowledge sharing system to work for them in situ. In our case users develop a knowledge sharing system for use by themselves and others, in a variety of ways; as with other development processes, this is political. The implication of this for research and practice is the need to recognise the fact of ongoing ICT development, and the actors who perform this in situ. We would add this might be undertaken by users as developers. Moreover, our work also suggests the need for more attention to be paid to the role of developers as users. Given the rise of social software, open source communities, and the packaged software industry, a broader consideration of ICT devel-

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opers as users is required. In each of these scenarios developers not only use the software as an application, but also as a system to fulfil a particular need—to sell to make a living for example. Much has been made of contextualising the user, further work is required to contextualise the developer as a user and understand the social actors in ICTs environments who straddle both politically constructed knowledge domains.

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Menzies, H. (1995). Whose brave new world? The information highway and the new economy, Toronto, Canada: Between the Lines Press. Myers, M. D. (1997). Qualitative research in information systems. Retrieved November 21, 1997, from http://www.auckland.ac.nz/msis/isworld/ Offe, C. (1976). Industry and inequality. London: Edward Arnold Publishers Limited. (Original work published in 1970) Orlikowski, W. (2002). Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 13(3), 249-273. Orlikowski, W. (2006). Material knowing: The scaffolding of human knowledgeability. European Journal of Information Systems, 15(5), 460-466. Orlikowski, W. J. (1996). Improvising organizational transformation over time: A situated change perspective. Information Systems Research, 7(1), 63-92. Pinch, T., & Bijker, W. E. (1987). The social construction of facts and artifacts: Or how the sociology of science and the sociology of technology might benefit each other. In W. E. Bijker, T. P. Hughes, & T. Pinch (Eds), The social construction of technological systems (pp. 17-50). London: MIT Press. Rohracher, H. (2005). From passive consumers to active participants: The diverse roles of users in innovation processes. In H. Rohracher (Ed.), User involvement in innovation processes: Strategies and limitations form a socio-technical perspective (pp. 9-35). Wien, Austria: Profil.

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Stewart, J., & Williams, R. (2005). The wrong trousers? Beyond the design fallacy: Social learning and the user. In H. Rohracher (Ed.), User involvement in innovation processes: Strategies and limitations from a socio-technical perspective (pp. 39-71). Wien, Austria: Profil. Sutton, D. C. (2001). What is knowledge and can it be managed? European Journal of Information Systems, 10(2), 80-88. Taylor, P., & Bain, P. (1999). An assembly line in the head: Work and employee relations in the call centre. Industrial Relations Journal, 30(2), 101-117. Taylor, P., Mulvey, G., Hymann, J., & Bain, P. (2002). Work organization, control and the experience of work in call centres. Work, Employment and Society, 16(1), 133-150. Truex III, D. P., Baskerville, R., & Klein, H. (1999). Growing systems in emergent organizations. Communications of the ACM, 42(8), 117-123. Van Den Broek, D., Callaghan, G., & Thompson, P. (2002). Call centres: Exploring the team paradox. In Proceedings of the 6th International Workshop on Teamworking, Malmo, Sweden. Walsham, G. (1993). Interpreting information systems in organizations. Chichester, England: John Wiley & Sons. Walsham, G. (1995). Interpretive case studies in IS research: Nature and method. European Journal of Information Systems, 4(2), 74-81.

Users as Developers

Walsham, G. (2001). Making a world of difference: IT in a global context. Chichester, England: John Wiley and Sons Ltd. Yourdon, E. (1986). Managing the structured techniques: Strategies for software development in the 1990s (3rd ed.). Englewood Cliffs, NJ: Yourdon Press. This work was previously published in Journal of Organizational and End User Computing, Vol. 19, Issue 4, edited by M. A. Mahmood, pp. 42-56, copyright 2007 by IGI Publishing, formerly known as Idea Group Publishing (an imprint of IGI Global).

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Chapter 4.5

Online Calculator Training in Mathematics and Technology William Brescia University of Arkansas, USA Tammy Cline University of Arkansas, USA

Executive Summary

Organization Background

This is the case of three institutions attempting to identify and address a need for professional development training for high school algebra teachers. Teachers across the state were facing two problems: (1) a need for training on how to successfully integrate the graphing calculator into the math curriculum and (2) training was not available that would fit into a teacher’s schedule. Teachers rarely had time to attend the instruction necessary to integrate new technology into the curricula being offered at remote sites. With Webbased training, teachers should be able to complete the training at their own pace and convenience. Online Calculator Training in Mathematics and Technology would demonstrate the effective use of online training to teach the basics of graphing calculators and the integration of graphing calculators into the math curriculum.

Organization 1: Center for Science, Mathematics, and Technology Education (CSMTE) at Humongous State University (HSU) The mission of the Center for Science, Mathematics, and Technology Education (CSMTE) has always been to advance science, mathematics, and technology education in the state through the support of the best practices and delivery of programs that enhance the scientific, mathematical, and technology knowledge and skills of the state’s teachers and students. CSMTE offers support, training materials, and resources at no charge to K-12 faculty and students as well as training and professional development for anyone in the region who is interested in science, mathematics, and technology excellence in public schools. CSMTE is staffed by a director;

Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Online Calculator Training

two secondary and three elementary specialists each in science, mathematics, and technology education; two administrative assistants; and a receptionist. The staff provides assistance in science, mathematics, and technology training whenever and wherever it is needed throughout the state. Additional information is available at the CSMTE Web site. The CMSTE annual budget for new program development plus a budget for consultant costs is found in Appendix A. CSMTE has an advisory board made up of representatives from the faculty at each of the school districts in the service area; an equal number of executives from area businesses interested in science, mathematics, and technology; two members appointed by the governor; parents and students; and one representative from the National Association of Curriculum Designers. The Center provides names of qualified faculty who are willing to consult with other schools on how to improve their science, mathematics, and technology programs. CSMTE also has a classroom laboratory, where teachers and students can observe new instructional techniques and receive individual training. With assistance from the requesting school, the Center sends specialists to remote locations to provide training. In these cases, it is usually necessary to bring together science, mathematics, and technology teachers from nearby schools. CSMTE also provides digital images to anyone in the service area who requests them via the Center’s Web site. The Center makes a special effort to provide services to underserved communities and takes seriously the inclusion of everyone who supports the mission of the Center in any of the offered services. The Center actively seeks support from funding agencies both locally and nationally. Currently, the Center is receiving funding from 13 local businesses, eight foundations, and four federal grants. These agencies support special after-school, preschool, bilingual, and special-

education programs in science, mathematics, and technology. While the Center makes available a number of ready-to-use materials and established workshops, it is always open to working with teachers to develop new materials or workshops that address emerging issues in science, mathematics, and technology (Preparing People for Prosperity, 2003).

Organization 2: Computer Education Department (CED) at HSU The Computer Education Department’s mission is to meet the diverse needs of communities throughout the state. CED offers distance, blended, and traditional courses at various sites throughout the state and with a variety of delivery methods. Courses are designed to promote interactivity and to support student learning to allow the state’s residents to pursue excellence in education at all levels. Web-based courses are available to students whenever and wherever they have access to the Internet. CED is headed by the dean, Francis McCloud. She has served as dean since the creation of CED 17 years ago. As dean, she has shepherded the department from a three-person operation to a multi-service agency. There are currently three divisions — Distance Education, Commerce and Industry, and Conferences — each of which is headed by an associate dean. The largest division is Distance Education, employing four full-time instructional designers, two media specialists, a faculty liaison specialist, an administrative assistant, and a secretary. Colleges, departments, and programs wishing to offer distance-learning courses are assisted by the staff to make sure the courses they offer are designed using effective learning principles. CED also offers a number of non-credit courses, all Web-based. These courses include continuing legal education, training in food

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handling and processing, continuing engineering courses, courses specific to partner businesses, and training for bank employees. Also, they have a long history of offering non-credit courses for in-service teachers. Teachers are required by state law to take 60 hours of professional training each year. Given the rural nature of the state, Web-based training is well received by teachers and administrators who are reluctant to travel any distance to receive in-service training due to expense and time away from their classrooms and families. CED also offers support to P-12 teachers who need assistance designing and developing multimedia instructional materials and responds to questions from teachers working in Web-based environments. The department also offers a 24-hour helpline, and on-site consultations are arranged to help school districts plan for integration of technology into the curriculum. CED used existing hardware and software systems. The budget for the Teacher Support function is found in Appendix B. CED has an award-winning technical support system in the Web-based courses it offers. This system responds to questions from students and teachers who may be unfamiliar with the software or have technical difficulties. CED also is committed to access for all learners — it provides special hardware to respond to a wide range of physical and cognitive disabilities. In late fall of 2002, CED instituted a planned change from compressed interactive video to Web-based course offerings. One of the key stakeholders in implementing this change was the College of Education (CoE) at HSU. The CoE used the results of a needs assessment to identify possibilities for offering degree programs that were needed by teachers and administrators statewide. Design and development of Web-based degrees began in mid-December 2002, with implementation occurring during the first summer session of 2003. The list of degrees that are offered include a bachelor’s degree in Human Resource Man-

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agement; master’s degrees in Special Education, Educational Technology, Educational Policy Studies, Adult Learning, Physical Therapy, and Higher Education; and doctoral degrees in Curriculum and Instruction, Public Administration, and Instructional Design. In addition to degree programs, CED also reached agreements with public school districts throughout the state to develop certificate training for school principals and superintendents. Another agreement was made with a professional radiologists’ organization to provide Web-based continuing education for radiologists statewide (Division of Continuing Education, 2004).

Organization 3: Educational Support Cooperative #5 Thirteen Educational Support Cooperatives (ESC) located throughout the state provide services to the school districts in their respective geographic areas. Part of their mission is to provide professional development courses for teachers. The ESCs often form consortiums with local schools and institutions of higher education to provide training and skills development. The ESCs also recommend computer hardware and software purchases to schools to ensure that students moving within their district will have similar resources. The ESC is governed by a board of directors that consists of a representative from each of the counties in a cooperative’s geographic district, two members appointed by the governor (one from each of the large school districts) and one at-large representative appointed by the State Department of Education. The executive director administers a staff of 27. There are support specialists in Reading, Social Studies, Art, History, Math, Science, Technology, Bilingual Education, Physical Education, Counseling, and Special Education. There is also staff who focus on instructional design and media development. Funding is also available to bring in specialists in other areas, as needed. These specialists, who provide assistance directly

Online Calculator Training

to schools, are supported by two administrative assistants, a secretary, and a bookkeeper. These services can be delivered either at the school or on-site at ESC. ESC support personnel work with teachers to find the media and the instructional support they need to improve their teaching. The ESC consultants can help a school district transform to a more productive and responsive learning community. The goal of ESC is to help each and every school in a cooperative to create learning institutions in which everyone is achieving results at a high level (Hamilton County Educational Service Center, 2004). ESC is located in the south central part of the state. The total area covered by the cooperative is 10,204 square miles, making it the largest service district by far. There are two major cities and several small towns in the district; the rest of the area is rural. ESC #5 is the second-largest cooperative in size and the third largest in population. Total enrollment in all grades is 39,473, with 28.2% in high school, 18.1% in middle school, and 53.7% in elementary school. Fifty percent of the students are African American, 45% are White, 4% are Hispanic, 1% are Asian, and .5% are Native Americans. ECS #5 provides services to 2,804 teachers and professional support staff (Appendix C).

Setting the Stage Player One: Amelie Ducotel, Math Instruction Specialist Amelie Ducotel, a Mathematics Instruction Specialist at the Humongous State University’s Center for Science, Mathematics, and Technology Education, holds a B.S.E. from the HSU with a Business Education Certification. As a math specialist, Ducotel’s primary job was to help schools to improve their benchmark and end-of-course exam scores in math. To accomplish this, she provided professional development to teachers. Also, she

went into classrooms and taught model lessons. These lessons were designed to give the teachers new ideas for presenting standards-based lessons; they also provided the students with test-taking tips for the open-response items on the tests.

Player Two: Gaye Dawn, Instructional Designer Gaye Dawn, an instructional designer with the Computer Education Department (CED) at the HSU, attended Northwestern University in Evanston, Illinois, where she received a BS in Learning Sciences and an MS in Human Development and Social Policy. Dawn’s work experience includes being a classroom teacher and curriculum developer in Chicago, Seattle, and Washington, DC, working for school systems, Native-American organizations, the Bureau of Indian Affairs, and Indian Education Programs. She holds a PhD from the Peabody College at Vanderbilt University in Language, Literacy, and Culture, with a minor in Mathematics and Science Education. She met Ducotel while working in a postdoctoral position in a mathematics curriculum development project at the Curriculum and Instruction Department at HSU. The position she holds at the CED focuses on providing support to mathematics and technology teachers working in high schools that predominantly serve underrepresented minorities.

Player Three: Gwen Chelm, Technology Representative: Subject Matter Expert Gwen Chelm, the ESC’s technology representative, serves as a Subject Matter Expert (SME) for graphing calculator training. Chelm has knowledge of resources and activities that can help to meet the objectives of the training. She holds a PhD in Instructional Technology from HSU and

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knows both Dawn and Ducotel from curriculum studies courses at HSU. She is an experienced teacher, having worked at Huntsville, Alabama, public schools where she took advantage of access to classroom technology. She is an accomplished Web designer, who is a strong advocate of using psychological and philosophical research results to support distance learning.

The Situation on the Ground It seems that almost daily the media reports on a new study telling us that the skill level of students graduating from high schools in the United States of America lags behind those of other nations in the areas of math, science, and technology. If students are ever going to become successful in the areas of mathematics, science, and technology, they need to practice these skills in situations where they can solve new problems and figure out when to apply appropriate methods and strategies (Hiebert, 1999). Real-world examples answer students’ eternal question — “When am I ever going to use this?” — with concrete applications that are interesting and important to students (Spicer, 2000). Calculators with appropriate software provide students with the tools necessary to perform complex computations, freeing them to concentrate on the meaning and the why of performing the computations (Day & Kalman, 1999). Graphing calculators (GC) can be used as effective tools to support problem solving in algebra due to the fact that the calculator takes over the dreary calculation. The technology makes it possible for students in first-year algebra to address problems that in the past would have required higher levels of math knowledge and skill (Currents, 2003). While some parents and school boards have expressed concern that using GC might adversely influence students’ ability to perform calculations, research shows that using this technology actually produces students that

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perform higher in these skill areas (Thompson & Senk, 2001, Oldknow & Waits, 1997). Teachers who view algebra strictly as a computational exercise to be mastered rather than a way to solve problems and reason are less likely to use GC effectively (Carlson, 2002). The GC can be used in the classroom for several purposes: a computational tool, a tool for data collection and analysis, a tool for visualizing symbolic functions and interpreting data, and a tool to confirm speculations (Doerr & Zangor 2000). Because the GC makes it possible to represent functions, it allows students the flexibility to attempt to solve problems in various ways and improves their learning of operational concepts (van der Kooij, 2001). Heck (2001) developed five recommendations for teachers using GC in their classrooms: (1) support student learning of where, when, why, and how to use GC; (2) develop activities that give students opportunities to practice basic skills; (3) give students opportunities to work on several systems so they can be prepared for new and uncontemplated results; (4) use explicit symbolism of computers to introduce students to the use of variables; and (5) be a role model by using GC to answer students’ questions. However, not all research supports the notion that GCs are without problems in the classroom. The need to know several different calculators requires instructors to spend additional time in preparation for courses using GC (Mittag & Taylor, 1999). Drijvers (2000) identified five obstacles in using GCs in the classroom that have pedagogical implications: 1.

2.

The difference between the algebraic representations provided by the Computer Algebra Systems (CAS) and those that students expect and conceive as simple. The difference between numerical and algebraic calculations and the implicit way the CAS deals with this difference.

Online Calculator Training

3.

4. 5.

The limitations of the CAS and difficulty in providing algebraic strategies to help the CAS overcome these limitations. The inability to decide when and how computer algebra can be useful. The flexible conception of variables and parameters that using a CAS requires.

These concerns alone should be cause for complete and strict use of instructional design principles.

Case Description Statewide, school boards have placed a great deal of emphasis on student performance in benchmark (Appendix D) and end-of-course exams in all content areas. In the spring of 2003, Ducotel received several calls requesting training in GC. These calls came mainly from high school algebra teachers with long and successful teaching careers. The teachers wanted to incorporate the use of GC into their classrooms in order to motivate their students to focus on the big picture rather than getting bogged down doing the calculations (Spicer, 2000). Ducotel began to believe that high school science and mathematics teachers statewide might need training in this area. She knew from her own experience that, not too long before, many of these same teachers were opposed to the idea of using calculators in their classrooms (Currents, 2003). As the semester progressed, more teachers requested training on how to integrate use of GC in their classrooms. Ducotel was reluctant to develop a workshop because, in the past, when workshops on this topic were offered, few teachers actually had attended. Ducotel believed that GC could help to improve student performance against the benchmarks and in the end-of-course algebra exams. Due to this potential performance increase, it was disappointing that the attendance at these workshops was so low (Appendix E). The course had been

offered once by a professional trainer who had been assigned to teach the session by the calculator company from whom most school systems purchased their equipment. Ducotel decided to work with Dawn and Chelm to determine why the attendance was low and what could be done to increase teacher attendance at the graphing calculator training. With the approval of her supervisor, Charles Brown, Ducotel developed a workshop for high school algebra teachers that was designed as an intensive, two-day, 16-hour training session. Again, attendance was disappointing; however, those teachers who did attend completed evaluation forms and reported that they left knowing they were familiar with all the basic keys on the calculator, many of the functions of the calculators, and a few advanced features of the calculators. The teachers also reported that they were able to implement comfortably the new technology into their classrooms. The ECS was also beginning to perceive a need for improvement in the use of handheld devices in public schools. The Excellence in Science and Mathematics project reached out to the Computer Education Department in an effort to establish a working relationship to address their perceived need: teachers wanted training to help them integrate GC into their curricula. During a meeting of the three key players at the CED, Dawn suggested that, while they all thought teachers in the ESC district wanted training in how to use GC, no one knew for sure. She suggested that one indicator that the need was not so great was that the past face-to-face workshops were not well attended. She suggested that a needs analysis be conducted, targeting all the high school algebra teachers. Ducotel thought that would be a waste of time. She said, “Look, we already have calls in to both of us, requesting training. What more do we need?” But Dawn thought that a needs assessment could answer several questions: (1) What is the nature of the problem? (2) Who has the problem? (3) Do they

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want training? (4) Is this a training problem, and, if it is, how do we solve it? (5) What content is needed? and (6) What are the learning goals? Dawn also knew that the Sternwood Foundation had expressed an interest in funding this project for the first three years as a demonstration project. (Sternwood, a local foundation, has a funding category for seed money to support projects that train math, science, and technology teachers.) CSMTE hoped the other partners would contribute talent, time, and funds to the plan, as well. To begin collecting the necessary information to answer these questions, a needs assessment questionnaire (Appendix F) was developed to be sent to all eighth- through tenth-grade teachers in the educational cooperative area. This survey, it was hoped, would help to understand teachers’ attitudes toward distance-learning experiences, the feasibility of participating in an online course, and the preference for an online course or a traditional course. The Sternwood Foundation has a long history of funding technology projects. W. S. Sternwood was a pioneer in the creation of the cathode ray tube, and after retirement, he and his wife Vivian devoted the rest of their lives to supporting the integration of technology into K-12 and higher-education classrooms. Applications to the Sternwood Foundation are required to use the Grantmakers’ Common Application Form (http://www.nng.org/assets/pdf/cga.pdf). The foundation has carried out the dream of the Sternwoods by focusing on funding programs that help to improve teaching and learning. The foundation’s giving is based on the idea that education can be a powerful tool to change the world for the better by producing individuals who are ready to be contributing members of society. Sternwood has determined to invest in education that improves skills and knowledge and helps to make our workforce competitive in a world marketplace. High-quality teaching was identified as the key to student learning. Sternwood’s Technology and Learning Program

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supports professional development of teachers at all levels. They are interested in using technology to improve instruction in all content areas. The foundation’s materials show that they are interested in Web-based learning. In the past, many Web-based learning projects have failed for a number of reasons. The foundation would like to see online learning become more interactive in three areas: teacher-to-student, student-to-student, and student-to-content. They are interested in funding innovative ideas that will impact the lives of students. While they fund technology in education, they do not fund applications for equipment alone. Equipment must be part of a project that relies on technology in order to be successful. Sternwood grants are limited to $10,000 to $50,000 over a three-year period. Most of the awarded grants are in the $25,000 to $40,000 range. The previous budget, developed from the perspective of a face-to-face course, is shown in Table 1. The three key players decided to explore the possibility of submitting a proposal to the Sternwood Foundation to fund the needs assessment and the training. They started work on a threeyear proposal, with the first year devoted to the needs assessment and research; the second year to design, development, and evaluation of the materials; and the third year to delivery of the professional training. The first draft of the budget is shown in Table 2. A draft Distance Learning Screening Form was developed (Appendix G) in order to provide data for the instructional content. Converting instruction from one medium to another may be impacted by a number of variables, including time and funding restrictions, input from stakeholders, learning goals and objectives, media design and development, and estimated return on investment (Belanger & Jordan, 2000). The results of the survey were used to develop the GC training. The survey results indicated that the training should use the ADDIE model to design the Web-based

Online Calculator Training

Table 1. Provided by each of the participating centers

1. Personnel 2. Participant Costs (100 participants anticipated) A Tuition

None Requested

B. Books

Non Requested

C. Materials GC Guides (1 set for each participant

2,500

Participant Travel @.$.35 per mile

450

Meals @ $15/day X 100 X 2 days

3,000

3. Instructor Travel

300

4. Supplies

500

5. General Office

400 Total Direct Cost

$7,150

Table 2. Category

Unit Cost

Quantity

Total Cost

2 instructors

$300/month

12

$3,600

Lab assistant

$50/month

12

600

Equipment

$50/month

12

600

Maintenance

$100/month

12

1,200

Communications

$50/month

12

600

Administration

$100/month

12

1,200

Overhead

$100/month

12

1,200

Total Direct Costs

9,000

Indirect Total

project (Kruse, 2004). The method was known to all participants. The elements of the model are Analysis, Design, Development, Implementation, and Evaluation (Phelan, Mendoza-Diaz & Mathews, 2002). The group used the survey results to determine that the target audience of the online workshop would be eighth- through tenth-grade mathematics teachers with minimal or no experience with the GC. All the teachers who contacted them

$1,000 $10,000

expressed a desire to use the calculator to enhance instruction in their pre-algebra and algebra classrooms. They wanted to feel comfortable using the calculators and also to learn activities that they could use in their classrooms. The traditional training workshop required 16 hours of intense training. The Web-based training was designed to be available for teachers to work at their own pace. However, certain deadlines had to be met during the training in order for

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the teachers to receive professional development credit for the training. A proposed storyboard of the instruction was created to determine the flow of the online course. The flowchart listed all activities to be completed as well as deadlines the teachers must meet in order to receive credit. All activities were offered using an online course shell. A training manual would be designed to include explanations of the basic operations of the graphing calculator as well as activities to be completed to reinforce the learning of the operations. This manual should help teachers learn the materials and allow them to integrate the instruction into their classrooms. The activities to be included for download were Circles and Eggs and the Fishing Boat Problem (Appendix H). The need for development of a hard-copy training manual to accompany the Web-based training has not been decided. Chelm thought the manual should be placed on the server and made available for the participants to download. The course shell could be used to make all class activities available to teachers. All text materials from the traditional training could be converted to PDF format and made available in the course shell software. The team planned to use graphics to enhance the material and to show different instructional styles, and decided that animation, sound, music, and video might be used in this course. Chelm pointed out that similar courses had been offered in other ESCs (Appendix I) and universities and that she had access to that content. She suggested a few starting places: http://www.mathsnet.net/links/links_graphs. html http://oxavi.org/polynomial_math.htm http://www.eddept.wa.edu.au/graphcalc/res/res1. shtml

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She went on to point out that there were many other resources available to them aside from their own experiences. For instance, HSU has an Instructional Technology Department that has students who are always looking for class projects; Chelm knew that Instructional Web Design is offered each spring and that Instruction on the Web was planned to be offered every fall. With those two classes alone, she said, it should be possible to find any assistance they might need. Ducotel pointed out that the Center had several graduate assistants who could assist with a needs assessment and instructional design, if necessary. When ready, the course would require teachers to register and to begin working through the online training activities. During participation in the training, questioning of the learners through e-mail and discussion forums would help the instructor to determine the understanding of the learners, the pace of the training, and the usability of the training manual and the activities. At the completion of the workshop, the participants would finish an evaluation instrument. This instrument was designed to evaluate the effectiveness of the instruction and the training materials in meeting the learning objectives. The instrument would also give the participants the opportunity to discuss their comfort level in using the technology/manipulative in the classroom activities (Appendix J). The second part of the assessment would determine the need for any future training. This would be a separate evaluation (Appendix K) that would be sent to the participants two months after the workshop. It was projected that two months would be ample time for the participants to integrate the technology into their curricula and reflected on the success of using the technology.

Online Calculator Training

Current Challenges/ Problems Facing the Organization School systems all across the country currently are experiencing the twin crises of budget shortfall and the need to provide students with an up-to-date technology-based curriculum. The Center for Science, Mathematics, and Technology Education at HSU receives continued funding but at an annually reduced rate. This reduction puts the continuation of work on this project in jeopardy. There is some hope that the Computer Education Department at HSU can pick up some of the shortfall. The CED will work to identify graduate students to do some of the development, but it is not currently able to provide the instruction. Educational Support Cooperative #5 still supports the effort and is searching for additional funders; it is coordinating a proposal writing effort as the applicant, with HSU serving as a subcontractor. Also, there seems to be a split developing between teachers who are early adopters, those who want the training now, and those who are late adopters (teachers who remain resistant to receiving the training). The early adopters have learned how to use the calculators on their own, feel comfortable using the technology, and consistently request advanced training in the functions of the calculator. Late adopters make the case that the kids are comfortable with technology and most likely will be able to use the tools more readily than the teachers in a short time. These teachers remain reluctant to give up any training time to learn software over what they see as more important issues. The team is asking that the superintendents of the school districts affected will develop a policy on teacher participation in the training. Unexpectedly, school building technology directors who are in charge of purchasing any hardware related to instruction have expressed a concern regarding the purchase and monitoring of the calculators. If purchase of the calculators

is going to come out of the schools’ technology budgets, the coordinators believe that they should have input into which calculators will be purchased, who will provide the training, and who will be responsible for the maintenance and safe keeping of the calculators. Finally, the state legislature is considering a law that would require all teacher training in science, mathematics, and technology to conform to national standards developed by the National Council for Teachers of Mathematics (http://www. nctm.org/standards/). These standards, for the most part, are in agreement with existing stateidentified algebra benchmarks but do deviate from some of the activities planned for this training. The legislative liaison for the ESC believes that the bill will not come up for a vote for at least another year. The director of the ESC is concerned about developing training materials that might need to be revised in just one more year. He has asked Ducotel, Dawn, and Chelm to write a memo advising him on this issue. The stakeholders consider funding to be the major challenge they face; however, they want to make sure that they address the other concerns and provide the planned GC training.

References Arizona K-12 Center. (2004). Retrieved December 21, 2004, from http://azk12.nau.edu Belanger, F., & Jordan, D. H. (2000). Evaluation and implementation of distance learning: Technologies, tools and techniques. Hershey, PA: Idea Group Publishing. Carlson, S. (2002, September). Back to basics: A bridge too far. π in the Sky. Retrieved December 7, 2004, from http://www.pims.math.ca/pi/issue5/ Cerebellum Corp. (2002a). Algebra: Linier equations. Retrieved December 19, 2004, from http:// www.cfv.org/guides/9746.pdf

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Cerebellum Corp. (2002b). Algebra: The basics. Retrieved December 19, 2004, from http://www. cfv.org/guides/9750.pdf Currents. (2003). Calculating success in mathematics. Research for Better Schools, 7(1). Retrieved December 22, 2004, from http://www.rbs.org/currents Day, J. M., & Kalman, D. (1999). Teaching linear algebra: What are the questions? Retrieved December 4, 2004, from http://www.umassd. edu/SpecialPrograms/Atlast/welcome.html Division of Continuing Education. (2004). The University of Alabama in Huntsville. Retrieved December 19, 2004, from http://www.coned.uah. edu/contact.cfm Doerr, H., & Zangor, R. (2000). Creating meaning for and with the graphing calculator. Educational Studies in Mathematics, 41, 143-163. Drijvers, P. (2000). Students encountering obstacles using a CAS. Journal of Computers for Mathematical Learning, 5, 189-209. Hamilton County Educational Service Center. (2004). About HCESC. Retrieved December 20, 2004, from http://www.hcesc.org/abouthcesc/default.asp Heck, A. (2001). Variables in computer algebra, mathematics, and science. The International Journal of Computer Algebra in Mathematics Education, 8(3), 195-221. Hiebert, J. (1999). Relationships between research and the NCTM standards. Journal for Research in Mathematics Education, 30(1), 3-20. Indiana’s Academic Standards 2004. (2004). Algebra I standards. Retrieved December 20, 2004, from http://www.doe.state.in.us/standards/Docs2004/English/Word/HS-Math/AlgebraI.doc Kruse, K. (2004). Introduction to instructional design and the ADDIE model. Retrieved Decem-

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ber 21, 2004, from http://www.e-learningguru. com/articles/art2_1.htm Mayer, S., & Caruso. (2000). Emotional intelligence as zeitgeist, as personality, and as a mental ability. In Bar-On, Parker (Eds.), Handbook of Emotional Intelligence. Mid-Continent Research for Education and Learning. (2004). Algebra content benchmarks. Retrieved December 19, 2004, from http://www.mcrel.org/ Mittag, K. C., & Taylor, S. E. (1999). The evolution of a graphing calculator course for preservice mathematics teachers. Issues in the Undergraduate Mathematics Preparation of School Teachers: The Journal. Retrieved December 11, 2004, from http://www.k-12prep.math.ttu.edu/journal/technology/mittag01/article.pdf Oldknow, A. J., &. Waits, B. K. (1997). What is the appropriate role, if any, of hand-held computer symbolic algebra in the teaching and learning of mathematics? In Proceedings of the Third International Conference on Technology in Mathematics Teaching, Koblenz, Germany. Retrieved December 15, 2004, from http://euler.uni-koblenz.de/ictmt3/ cd-rom/pdf/oldknow1.pdf Phelan, K., Mendoza-Diaz, N., Mathews, S. (2002). An example of converting a traditional course into distance learning: An open discussion. Paper presented at the 9th Annual Distance Education Conference, Texan A&M University, College Station, Texas, USA. Retrieved December 21, 2004, From http://www.cdlr.tamu.edu/dec_proceedings/dec 202002/Contents.pdf Preparing People for Prosperity. (2003). Retrieved December 20, 2004, from http://uaedi.cast.uark. edu/uaedi/orgfull.asp?ID=14 Research Support & Sponsored Programs. (2004). Research compliance. Retrieved December 21, 2004, from http://www.uark.edu/admin/rsspinfo/ compliance/index.html

Online Calculator Training

Spicer, J. (2000). Why use calculators (and other technologies) in the high school math classroom? Eisenhower National Clearinghouse for Mathematics and Science Education (ENC) Features. Retrieved December 22, 2004, from http://www. enc.org/features/focus/archives/edtech Texarkana Independent School District. (2004). About TISD. Retrieved December 19, 2004, from http://www.txkisd.net/

Thompson, D., & Senk, S. (2001). The effects of curriculum on achievement in second-year algebra: The example of the University of Chicago school mathematics project. Journal for Research in Mathematics Education, 32(1), 58-84. van der Kooij, H. (2001). Functional algebra with the use of the graphing calculator. Presentation at the ICMI Study Group on the Future of Algebra, Melbourne, Australia. Retrieved December 17, 2004, from http://www.fi.uu.nl/twin/en/ presentations/2001icmi.pdf

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Online Calculator Training

Appendix A. Center for Science, Mathematics, and Technology Education (CSMTE) Budget New Service Development Face-to-Face or Web-Based Category

Amount

Personnel

Provided by CSMTE

Travel Three-day Design and Development Two Day Design and Development Three Weeks Design and Development Two Weeks Design and Development

1,085 1,014 3,885 2,960

Total Travel

8,945

Materials Copying Costs Books, Disks and Other Materials

150 1,900

Total Materials

2,050

Total Costs

$10,995

Center for Science, Mathematics, and Technology Education (CSMTE) Budget Consultant Services Budget Fees Three-day Subject Mater Expert Fees Two Days Follow-up Two Days Web Designer Two Days Web Developer Total Fees Travel Three-day Follow-up Two Follow-up Days Total Travel Materials Duplicating Costs Graphics and Web-based Images Software and hardware Total Materials Total Costs

Source: Arizona K-12 Center Web site (2004)

1358

Amount 3,885 2,960 4,500 6,240 17,585 1,485 1,214 2,699 $450 $760 3,489 4,699 $24,983

Online Calculator Training

Appendix B. Teacher Support Services July 1, 2003-June 30, 2004 Instruction Part-time Program Director: $27/hr for 25 hrs/wk for 44 wks

30,000

Clerical support: $9/hr for 440 hrs

3,960 Sub Total

33,960

Benefits Program Director @ 15%

4,500

Clerical support @ 10%

396 Sub Total

4,896

Professional Services Contracted instructional staff 10 staff at $22 hr X 18 hours/wk for 44 wks

174,000

Contracted instructional designer- $50/hr x 300 hrs

15,000 Sub Total

189,000

Sub Total

1,750

Other Purchased Services Staff travel (conference presentations) Lodging, mileage, per diem - 2 @ 875

1,750

Supplies General office supplies and project supplies

796 Sub Total

796

Other Local Travel - 2,000 miles @ .34/mile

680 Sub Total

680

Grant Total

231,082

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Online Calculator Training

Appendix C. Annual Budget 2003 School Year Summary Educational Support Cooperative # 5 Projection August 1, 2003-August 1, 2004 Based on the official 2002-2003 Budget Expenditures Revenues State

Federal

Foundation & Corporate gifts

Individual gifts

Payroll Costs $16,370,000 63% 9,509,000 36%

Purchases of contracted services. Supplies and materials Capital Outlay

370,000 1% 22,000

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