Applied Technology and Innovation Management: Insights and Experiences from an Industry-Leading Innovation Centre

Applied Technology and Innovation Management

Heinrich Arnold · Michael Erner Peter Möckel · Christopher Schläffer Editors

Applied Technology and Innovation Management Insights and Experiences from an Industry-Leading Innovation Centre

1 23

Editors Dr. Heinrich Arnold, Dr. Michael Erner, Peter Möckel Deutsche Telekom AG Laboratories Ernst-Reuter-Platz 7 10587 Berlin, Germany Christopher Schläffer Deutsche Telekom AG Landgrabenweg 151 53227 Bonn, Germany

Project team/reviewers Dr. Heinrich Arnold, Dr. Michael Erner, Marcus Berlin

e-ISBN 978-3-540-88827-7 ISBN 978-3-540-88826-0 DOI 10.1007/978-3-540-88827-7 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2009942429 © Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: WMXDesign GmbH, Heidelberg, Germany Illustrations courtesy of Victoria Arnold Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Applied Technology and Innovation Management

v

Applied Technology and Innovation Management

The Importance of Innovation Management at Deutsche Telekom – Technological Uncertainty and Open Innovation ..................................................................1 Deutsche Telekom Laboratories as a Testbed for Modern Technology and Innovation Management .................................................................5 A. Information Acquisition in a World of Knowledge Strategic Foresight ..........................................................................................................12 Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute ...................................25 B. Organizing for Getting the Most out of Openness Implementing Open Innovation to Benefit from External Dynamics of Innovation ......36 Partnering for Research and Development within an Open Innovation Framework......48 Business (Lead) Customer Involvement in the Innovation Process ...............................59 C. Early Stage Market Research and Innovation Marketing Tools for User-Driven Innovation at Deutsche Telekom Laboratories...........................72 Options for Customer Integration in the Open Innovation Paradigm at Deutsche Telekom.......................................................................................................89 Segmentation and Evaluation Tools to Project Customer Potential .............................100 D. The Early Stages of New Product Development Cross-over Application of Enterprise Architecture and Modularization in Telco R&D ................................................................................................................116 Enterprise Architecture in Innovation Implementation ................................................132 Managing Technology Push and Market Pull within Pre-Product Development .........145 Design Research in University-Industry Collaborative Innovation: Experiences and Perspectives .......................................................................................157 E. Transfer of Results and Exploitation Transferring Technology Innovations to Operating Business Units .............................168 The Project Value Tracking Process at Deutsche Telekom Laboratories .....................180 Venturing for Commercialization of R&D Results .....................................................191 List of authors .....................................................................................................................203 Index ...................................................................................................................................217

vi

List of abbreviations

AAA ACA ASG B2B B2C BGU BMBF BMWi CAPEX CBC CEA CEO CET DRL DVD DVDR DTAG EA EBITDA EICT eTOM ICT IMTV IP IPR IPS IPTV IT ITIL KPI MAPS

Authentication, Authorization, Accounting Adaptive Conjoint Analysis Automatic Signature Generation Business-to-Business Business-to-customer Ben-Gurion University Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) Bundesministerium für Wirtschaft (Federal Ministry of Economics and Technology) Capital Expenditure Choice-Based Conjoint Cross-over application for Enterprise Architecture Chief Executive Officer Customer Evaluation Tool Design Research Lab Digital Versatile Disk Digital Versatile Disk Recorder Deutsche Telekom AG Enterprise Application Earnings before Interest, Taxes, Depreciation and Amortization European Center for Information and Communication Technologies enhanced Telecom Operations Map Information and Communication Technology Interactive Mobile TV Internet Protocol Intellectual Property Rights Intrusion Protection System Internet Protocol Television Information Technology Information Technology Infrastructure Library Key Performance Indicator Matching Analysis, Projection and Synthesis

List of abbreviations

NGN NPD NPV NSP OEM OPEX PBX PDA P&I PPP PSN PVT R&D ROI SBU SDP SME SMS SOA TOGAF UI VCR VDSL VHS VoIP WWW

Next Generation Network New Product Development Net Present Value Network Service Provider Original Equipment Manufacturer Operational Expenditures Private Branch Exchange Personal Digital Assistant Product and Innovation Public-Private Partnership Personal Social Network Project Value Tracking Research and Development Return on Investment Strategic Business Unit Service Delivery Platform Small and Medium Enterprise Short Message Service Service-Oriented Architecture The Open Group Architecture Framework University and Industry Video Cassette Recorder Very High Speed Digital Subscriber Line Video Home System Voice over Internet Protocol World Wide Web

vii

Applied Technology and Innovation Management

1

The Importance of Innovation Management at Deutsche Telekom – Technological Uncertainty and Open Innovation

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_1, © Springer-Verlag Berlin Heidelberg 2010

2

Applied Technology and Innovation Management

“Europe’s wealth lies in the knowledge and ability of its people.” This sentence, from the 2007 Berlin Declaration to mark the 50th anniversary of the signing of the Treaties of Rome, not only highlighted the need for a European innovation policy, it also illustrated that globalization, open-access journals, and the Internet have ushered in a new era in the history of science. From the early 18th century, the major cycles in the history of science were shaped by France, then Germany, the United Kingdom, and finally the United States. However, individual centers in the East and West or entire regions such as Europe are increasingly becoming today’s pioneers. Consistent business innovation strategies – supported by a prudent innovation policy – are the key requirements for exploiting Europe’s wealth and strategically leveraging her people’s talents. Innovations in the 21st century are the result of complex conditions and dynamic processes. This requires adequate innovation management that focuses systematically on adding value. This rationalization and systematization of creativity and the spirit of invention is not contradictory. This is what Thomas Edison meant when he said genius is one percent inspiration and 99 percent perspiration. Creative work is influenced by a large number of external factors. These include a growing technological uncertainty that cannot be overcome using traditional standardization requirements. Fierce competition and shorter innovation cycles increase pressure, while expectations from market players and politicians continue to grow. Globalization has led to a rise in the number of potential innovators, and the interdependence or interaction of products, technologies, and markets is continuing to increase. This applies in particular to Information and Communication Technology (ICT), in which horizontal technologies are the real innovation drivers that are now among the most important economic growth and locational factors. ICT is the basis for new business models that lead to new value-added and synergy effects. This is clearly apparent in the new content and services available on today’s ever trendier devices and continuous refinement of their functions. Other keywords here include eHealth, eEnergy, eJustice, eGovernment, and green IT. The real paradigm shift is the opening up of the international research community, enabling successful innovation alliances between universities and industries. Immense forces have been unleashed by open innovation and the related exchange of knowledge and data, as well as by the increased transparency of research activities and results. A fundamental change accompanied by a shift in mentality has occurred in the telecommunications industry in just a few years. While non-functional criteria used to be the benchmark for innovation potential at telecommunications companies, which merely provided telephone and telegraph con-

The Importance of Innovation Management at Deutsche Telekom

3

nections, functional criteria now appear to have limitless applications. This also creates a new and demanding competitive environment that companies must face. A leading market position and economic power may be advantageous in this competitive environment. However, the key factors determining a company’s sustainable growth are its ability to innovate and its significance as a driver of future innovation. Innovation culture is thus becoming a defining element of corporate culture. Deutsche Telekom responded to this challenge in 2004 by establishing Telekom Laboratories. Work at this R&D center encompasses the entire value chain in the innovation process (including outcome control) and reflects the principles and tools of modern innovation management. This is presented in the accompanying publication and explained in detail using examples. The empirical approach follows the old motto “From the market to the market” – or “user-driven innovation” as it is known today. Data, analysis, and forecast expertise are ensured by a broad range of measures as well as by quantitative and qualitative market research. From a methodological perspective, it is interesting that new scientific concepts are also being developed in-house. As a logical consequence of open innovation, Telekom Laboratories pursues an active policy of partnership with universities and research institutions; since it was launched, Telekom Laboratories has cooperated closely with the Berlin Institute of Technology. In addition, Telekom Laboratories enters into interdisciplinary alliances with business customers who are market leaders in their industries, as well as with innovation-based SMEs. Implementing innovations entails difficulties, as the past has often showed. This has been, and continues to be, a particular challenge for the telecommunications industry, in which former monopolies have rapidly been forced to become multiple providers of infrastructures, services, content, applications, and systems solutions. The main problems when transferring innovation projects to individual business units are the organizational and mental management of complex product innovations, and therefore achieving a timely and consumer-friendly launch. These circumstances also gave rise to the spin-along strategy, where Telekom Laboratories supports innovative concepts devised by its staff by providing entrepreneurial start-up capital. Projects that have proven their excellence on the market can be subsequently reintegrated or can continue to be developed autonomously. Telekom Laboratories’ mission is closely related to Deutsche Telekom’s corporate strategy, which is based on long-term market success and customer loyalty. In line with the principle of open innovation, Telekom Labo-

4

Applied Technology and Innovation Management

ratories also believes that it has an obligation to the entire ICT industry, to Europe as a knowledge location, and to the international community of innovators and researchers. In this book, Telekom Laboratories experts and decision makers take a resume of applying insights from research on technology and innovation management and adapting them to the practical needs building a leading place for research and innovation. The Telekom Laboratories authors describe their state-of-the-art methods of technology and innovation management which have proven successful in their innovation work and have thus created this unique document of participatory research. Christopher Schläffer Chief Product & Innovation Officer Deutsche Telekom AG

Applied Technology and Innovation Management

5

Deutsche Telekom Laboratories as a Testbed for Modern Technology and Innovation Management

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_2, © Springer-Verlag Berlin Heidelberg 2010

6

Applied Technology- and Innovation Management

Deutsche Telekom Laboratories has been set up with the ambition to be one of the leading places for corporate research and innovation in the industry. Consequently, not only the topics that are being worked on must be leading edge but also the way in which work is done. Scientific research in innovation research and management science as well as operational specifics and practical experience are the foundation of applied technology and innovation management at Telekom Laboratories. Telekom Laboratories has completed its first five years of operation – sufficient time to validate which approaches proved successful and which not. In this sense, the implementation and innovation management oriented “Innovation Development” unit of Telekom Laboratories1 has proved a very successful and extraordinary testbed for advanced methods in the management of technology and innovation coming from science (Yin 2008) and, as such, has already frequently been the subject of innovation researchers’ investigations. The methods and tools in this book are all described by the Telekom Laboratories experts themselves, who took findings from science, made them practically applicable as tools, methods, and instruments, and validated their applicability through years of use. In this sense, this work is the result of a participatory test of hypotheses on how modern technology and innovation management should work. As the telecommunications industry is undergoing radical technological change, the approaches described here can be seen as howtos for dealing with technology shocks (Arnold 2003). Within the past decades, the introduction of the overarching All-IP system has changed the entire value creation process and structures of the industry. The network architecture has moved from single “stovepipes” to a delayered and modular production, leading to easier and faster deployment of new services involving more market players, whereas the standardization of interfaces is bringing in millions of additional innovators (e.g., web developers). This shift is comparable to the modularization in the PC industry (Baldwin and Clark 1997; Christensen and Raynor 2003). The multiplication of innovators through technical delayering, the ingress of so-called overthe-top Web X.0 services into the telecommunication domain, and the convergence of communication, commerce, and content services as well as media, telco, and computer markets (Zerdick 2001) have created new competitors and competitive constellations in the industry. This puts innovation under particular stress as innovation and creativity are central keys to competitive advantage and sustainable growth for leading companies (Schumpeter and Röpke 2006) in knowledge-based economies (Leydesdorff and Meyer 2006). Globalized competition and the Internet clockspeed come along with shortened product life cycles and strong increases

Testbed for Modern Technology and Innovation Management

7

in cost pressure. In addition to this overall trend, the ICT sector is facing further challenges. As a result, major network operators (MNO) have to compete in an open, standardized environment with a high degree of complexity caused by the large numbers of products and innovators, as well as the continuous uncertainty of future markets and technologies. The answer to how to create sustainable differentiation in innovation at times of technology shock, where many of the relevant innovators are outside of the companies’ boundaries, cannot lie in competing in a closed way with the outside world due to the sheer imbalance of resources. The answer must lie in the build up of a sustainable innovation system where not everything needs to be done but rather selected smartly. The setup interfaces and integrates with the huge community of innovators through applied innovation and management tools in an open way and differentiates smartly. Thus one of the recurring themes in this book has to do with openness – how to build an open innovation system that still allows for distinct differentiation. Telekom Laboratories has not only been at the forefront of a very recent development in corporate R&D by building on five years of experience in smart openness in research and innovation thus providing an environment suited to dealing with complexity and uncertainty. It has also been extremely thorough in implementing the principles of open innovation in a corporate core unit so close to top level corporate decision makers (Picot and Doeblin 2009). One key element used by globally active companies to embrace openness is the creation of R&D laboratories in cooperation with world-class research institutes – e.g., Bell Labs, HP Labs, IBM Labs, etc. (Saez et al. 2002; Lambert 2003; Lam 2007). Deutsche Telekom Laboratories has been established as a public-private partnership together with the Berlin Institute of Technology in order to enhance innovative capabilities in basic and applied research. Consequently, following the trend of open innovation, Telekom Laboratories provides a suitable context for collaborative research with external partners such as universities, non-university research institutions, and other companies (Rohrbeck and Arnold 2006). These joint research projects also aim at reducing the risk of innovation for the participating partners. Telekom Laboratories consist of Strategic Research and Innovation Development. Strategic Research incorporates four chairs of the Berlin Institute of Technology. Innovation Development is organized in five focus fields of innovation. Highly innovative research is not directly controllable, while

8

Applied Technology- and Innovation Management

innovation often results from serendipity (Mittelstraß 1994; Münch 2007). This is why Telekom Laboratories follows a two-directional strategy: On the one hand Innovation Development fosters innovation extending corporate roadmap, whereas Strategic Research follows a “grass root strategy” (Mintzberg 1989) by cultivating several ideas without directly referring to their use within Deutsche Telekom at all times. Both departments enjoy – although to different degrees – academic freedom and the spirit of corporate entrepreneurship with flexible resources for innovative projects. This environment is essential for creative and innovative performance separate from the day-to-day business (Picot and Schneider 1988). These specific working conditions attract highly innovative researchers from all over the world. The proximity to basic research allows Telekom Laboratories a fast and straightforward integration of state-of-the-art research results into new products and services. At the same time this context offers the flexibility to react to recent developments in markets and technologies. The collaboration is an effective means of learning and knowledge transfer for both sides – academic researchers as well as the participating industry. Therefore, the university and industry research collaboration at Telekom Laboratories is mutually attractive: On the one hand, it offers the results of pure academic research the real chance of becoming applied research and, even more, a real market product. It also bears a relation between university-driven questions and the practical relevance of problems of everyday life. This might sometimes prevent the often bemoaned sitting in the ivory tower, even though academic freedom is one of the cornerstones of research at Telekom Laboratories. On the other hand, industry has the opportunity to take an active part in state-of-the-art research and to put this “advantage of knowledge” to work developing innovative products with the best available technology. International partnerships complete the open-minded atmosphere. Innovation happens at the edge of knowledge fields and disciplines. Telekom Laboratories takes into account the importance of interdisciplinary collaborative research within a multi-cultural organization. Interdisciplinarity is one of the structure-forming elements of organizational design. Within the project fields, computer scientists, economists, software and electrical engineers, designers, psychologists, and sociologists work closely together, leading to a strong, interwoven technological and socio-economic competence. This enables social trends to be identified and extrapolated, and scenarios to be developed for future product development. Interdisciplinary work is fostered through shared office spaces, social integration (e.g., events and shared leisure time activities), central meeting

Testbed for Modern Technology and Innovation Management

9

points, as well as cultural norms and values. The resulting communication patterns have already been the subject of empirical research (von Eggelkraut-Gottanka 2008 and 2009). But all these expectations are hard to meet if the basic conditions of the collaboration don’t establish the required social and legal agreements. At Telekom Laboratories, there are various settlements to ensure close teamwork, which leads to real “open innovation”. In addition to flexible administration, there are also more than 10 different types of employment contracts, so that everybody can find the appropriate working conditions and Telekom Laboratories can commit the people it wants. As one of Deutsche Telekom’s core strategic aims is the delivery of superior user experience, innovation management has to integrate customers into the early stage of the innovation process. The customer perspective is incorporated by a user-driven approach, including customer clinics that gain deep insights into preferences, behaviors, and needs (Presse et al. 2008). From an organizational point of view, Telekom Laboratories forms the exclusive and central R&D department of Deutsche Telekom and reports directly to the Chief Product and Innovation Officer. This positioning leads to a strong interaction with the product management and technical departments thus allowing results to be applied to new products and services quickly. This may sound obvious, but quite often high potential innovations are not incorporated by the organizations they originate from but are taken up elsewhere (Spiegel Online 2008). In this sense, the track record of successfully implementing Telekom Laboratories results into the operational business of Deutsche Telekom is an aspect that makes Telekom Laboratories distinct and reconfirms the strength of Europe-based research and innovation. The book’s logic does not aim at completeness, but rather dives into selected core topics of innovation management: A. Detecting weak signals in the environment, acquiring information in a world of knowledge, and identifying the areas of differentiation through R&D. B. Organizing to get the most out of openness with the appropriate structures as well as integrating research and business partners. C. Integrating customer feedback as an essential aspect of early market research in the open innovation processes. D. Methods of early stage new product development based on enterprise architecture, modularization, and the idea of building blocks. E. Implementing and tracking the exploitation of innovation results in the business units and applying new alternative methodical routes of with venturing aspects.

10

Applied Technology- and Innovation Management

This book results from the conviction that seclusion rarely leads to superiority and that it is for the better that the good constantly needs to be exposed and challenged by the state-of-the-art for it to stay at the leading edge. In this sense, we share our findings on applied technology and innovation management approaches as part of the ongoing discussion with our environment and invite your feedback on both research on the management of technology and its application in practical work. 2009, Berlin, Bersheva, Bonn, Darmstadt, Los Altos Peter Möckel Leiter Deutsche Telekom Laboratories

Dr. Heinrich M. Arnold Leiter Innovation Development

References Arnold, H. M. 2003. Technology Shocks: Origins, Managerial Responses, and Firm Performance. Physica-Verlag Springer-Verlag: Heidelberg, New York. Baldwin, C. Y. and Clark, K. B. 1997. Managing in an age of modularity. Harvard Business Review 75(5): 84–93. Christensen, C. M. and Raynor, M. E. 2003. The Innovator’s Solution: Creating and Sustaining Successful Growth. Harvard Business School Press. Lam, A. 2007. Knowledge networks and careers: Academic scientists in industry-university links. Journal of Management Studies 44(6): 993–1016. Lambert, R. 2003. Lambert Review of Business–University Collaboration: Final Report. Leydesdorff, L. and Meyer, M. 2006. Triple Helix indicators of knowledge-based innovation systems. Introduction to the special issue. Research Policy 35(10): 1441–1449. Mintzberg, H. 1989. Mintzberg on management inside our strange world of organizations. Free Press et al.: New York. Mittelstraß, J. 1994. Die unzeitgemäße Universität. Suhrkamp: Frankfurt am Main. Münch, R. 2007. Die akademische Elite. Suhrkamp: Frankfurt am Main. Picot, A. and Doeblin, S. Ed. 2009. Innovationsführerschaft durch Open Innovation, Chancen für die Telekommunikations-, IT- und Medienindustrie. Münchner Kreis, Springer: Berlin, Heidelberg. Picot, A. and Schneider, D. 1988. Unternehmerisches Innovationsverhalten, Verfuegungsrechte und Transaktionskosten. Betriebswirtschaftslehre und Theorie der Verfügungsrechte, ed. D. Budäus, E. Gerum and G. Zimmermann, 91–118. Gabler: Wiesbaden. Presse, V., Steinhoff, F. et al. 2008. User clinics as efficient tool for identifying and addressing segment-specific customer requirements in R&D projects. R&D Management Conference: Emerging methods in R&D management, Ottawa, Canada. Rohrbeck, R. and Arnold, H. M. 2006. Making university-industry collaboration work – a case

Testbed for Modern Technology and Innovation Management

11

study on the Deutsche Telekom Laboratories contrasted with findings in literature. ISPIM Annual Conference: “Networks for Innovation”, Athens, Greece. Saez, C. B., Marco, T. G. et al. (2002). Collaboration in R&D with universities and research centres: an empirical study of Spanish firms. R & D Management 32(4): 321–341. Schumpeter, J. A. and Röpke, J. 2006. Theorie der wirtschaftlichen Entwicklung. Duncker & Humblot: Berlin. Spiegel Online. 2008. Exportweltmeister für Ideen: In Deutschland erfunden, in Japan gebaut. EINESTAGES. September 3, 2008. von Eggelkraut-Gottanka, Thomas. 2009. What does indoor location tell us about interpersonal communication? – An analysis using real-time location tracking data. Presented at KITESCespri, University of Bocconi, Italy. von Eggelkraut-Gottanka, Thomas. 2009. The daily working behavior of R&D personnel: An Analysis of mobility patterns and communication using real-time location tracking data. DRUID-DIME Winter Conference, Aalborg, Denmark. von Eggelkraut-Gottanka, Thomas. 2008. Analyzing communication behavior and communication networks in the context of R&D. Presented at Deutsche Telekom Laboratories, Berlin, Germany. Yin, R. 2008. Case Study Research: Design and Methods (Applied Social Research Methods). Zerdick, A. 2001. Die Internet-Ökonomie: Strategien für die digitale Wirtschaft. Springer.

Endnotes 1

Deutsche Telekom Laboratories consists of two logical and organizational constituents: Strategic Research, for academic research, as an institute at the Berlin Institute of Technology; and Innovation Development, as the implementation-oriented corporate research and innovation unit.

12

A. Information Acquisition in a World of Knowledge

Strategic Foresight

Strategic foresight activities allow companies to identify weak signals of change and react to opportunities and threats in their environment. This chapter describes the strategic foresight practices of Deutsche Telekom and their relation to the innovation and applied R&D activities. Differentiating between continuous foresight (undirected scanning of the environment) and project-based foresight (issue driven), methods, tools, and processes are described. Activities include consumer foresight, competitor foresight, and technology foresight.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_3, © Springer-Verlag Berlin Heidelberg 2010

Strategic Foresight

13

Introduction Most industries repeatedly face disruptions from emerging technologies, political and legislative regulation, alternative business models, and socio-cultural shifts (Christensen 1999; Day and Schoemaker 2004). This also applies to the telecommunication industry (see Figure 1). For the incumbent telecommunication operators, the introduction of mobile telephony in 1986 was one disruption that has been responded to successfully. Mobile telephony came as a so-called technology shock (Arnold 2003), obliterating the incumbent companies’ core competencies and replacing them with new product properties. At the start, it was uncertain if the demand would emerge, if other companies would invest in mobile networks, and how much it might cannibalize the revenues from fixed-line telephony. Investing heavily in mobile telephony has proven to be the right answer for most operators.

Technology

Disruptions

Market & Technology

Political

! 1986 introducing of mobile telephony ! A disruption for the value distribution in the industry

! Recently the introduction of the all-IP-network ! Introduction of disruptive services: VoIP, IPTV, VoD

! Liberalization of the EU telecommunication market ! Recent regulation of roaming tariffs

Figure 1 Disruptions in the telecommunication sector (examples)

Another major disruption has come from the Internet Protocol (IP) and the “horizontalization” of the network it enables. Offering voice services used to require building and maintaining a proprietary network and developing intelligent switching technologies to automate call routing. Today, any small service provider can offer voice and other IP services requiring only software and an Internet connection in their peer-to-peer mode. This has resulted in a scattered and non-transparent competitive landscape with providers of Internet services active in the telco domain; the incumbent operators are still adjusting to this disruption.

14

A. Information Acquisition in a World of Knowledge

A third major disruption for telecommunication providers has been the liberalization of the telecommunication market. The incumbent operators had to open up their networks to third-party operators and are now only permitted to charge a regulated tariff for network usage. Further political disruptions include the limits on roaming charges, where the European Commission has defined price limits for voice minutes when mobile phones are used abroad. In order to identify such disruptions and future developments early on and trigger appropriate reactions, Deutsche Telekom has established a strategic foresight system in its product and innovation unit. Strategic foresight aims to identify discontinuities and trends, as well as to initiate appropriate reactions (Krystek 2007; Liebl 2005). To do so, weak signals in the environment of an organization have to be detected, analyzed, and assessed (Day and Schoemaker 2005). The resulting information helps management make decisions when reacting to disruptions, e.g., defining new markets, products, and services (Slaughter 1998). At Deutsche Telekom several methods and tools of strategic foresight have been implemented regarding different environmental areas. Continuous foresight activities involve undirected scanning of the environment for changes in technology, competition, and customer behavior and needs. Furthermore, directed project-based foresight activities are performed that not only scan but also monitor developments and trends related to a certain temporary issue.

Continuous foresight Continuous foresight activities at Deutsche Telekom screen customers, competitors, and technology (see Figure 2). They are carried out by the product management and a central staff unit at product and innovation, and by Telekom Laboratories as the corporate research and development unit. They all report directly to Deutsche Telekom’s Executive Board, where the results of the foresight activities are used to make strategic decisions. Competitor foresight is performed by product and innovation. A special tool, the Product & Service Radar, is used to identify and monitor market- and competitor-driven innovation. At Telekom Laboratories, experts for “customer behavior, needs, and markets” from the different project fields perform customer foresight using six major tools: laddering technique, diary research, day-in-the-life visits, insight clinics, the regular megatrends study, and the customer evaluation tool. The results are on the one hand an integral part of the R&D work in the project fields, on the other hand, the foundation for dedicated reports. Technology foresight is performed by Te-

Strategic Foresight

Competitor Foresight

Technology Foresight

15

Customer Foresight ! Explorative interviews ! Diary research ! Day in the life visits ! Insight clinics ! Megatrend study ! Customer Evaluation Tool Competitor Foresight ! Product & Service Radar

Customer Foresight

Technology Foresight ! Technology Radar

Figure 2 Methods and tools of strategic foresight

lekom Laboratories’ Technology Exploration team, which issues the Technology Radar 1 and uses it as a tool to identify and assess technological developments and trends. Collection

Interpretation

Utilization

! Identification of weak signals by a global scouting network

! Analyzing technological developments and trends

! Launching new projects based on identified opportunities or threats

! Ordering or purchase of external studies

! Analyzing new products and services in the market

! Analyzing new products and services in the market

! Desk research on publications and online content

! Evaluation and anticipation of consumer needs

! Sourcing of know-how through acquisition or co-operation ! Strategic decisions on activities and budget allocation

Figure 3 Process of strategic foresight

The three continuous foresight activities are guided by an overall process that can be divided into three steps: collection of information, interpretation, and usage of the insights obtained for decision making (see Figure 3). Within each step, different methods are applied. Customer foresight

Customer foresight is related to existing and potential customers and the collection of customer-related information and early identification of customer needs (Trommsdorff and Steinhoff 2007). Meeting the needs of customers is essential for innovation success. However, knowing the current needs is not sufficient; it is much more about identifying future needs. While building on customer feedback results in incremental innovation, identification of their

16

A. Information Acquisition in a World of Knowledge

future needs often allows disruptive change to be projected. To identify future customer needs, customer foresight estimates particular changes in values and lifestyles as these influence customer behavior. Telekom Laboratories’ customer foresight aims to: • Generate ideas for innovation and prioritize their attractiveness • Validate concepts • Select (product and service) features Six major tools proved helpful in gaining deep customer insights (see Table 1). Method or tool

Description

Laddering technique

• Execution of (online) interviews in order to identify latent needs and barriers • Introduction and prioritization of new product/service functions • Categorization of preferences

Diary research

• While using ICT, participants capture incidents in diaries over a predefined period of time • Diaries can be pre-structured or unstructured

Day-in-the-life visits

• Observation of customers in their personal environment • “User in the box”: Vivid documentation of their ICTinfrastructure and usage patterns

Insight clinics

• Confrontation of users with prototypes, mockups, or concepts • Observation of usage patterns to identify barriers, or group discussions to identify latent needs

Megatrends study

• Identification of long-term socio-cultural, political, and technological trends • Deriving implications for Deutsche Telekom’s corporate strategy and R&D strategy

Customer evaluation tool

• Web-based forecasting of revenue potentials based on past data

Table 1.

Deutsche Telekom methods and tools of customer foresight

The laddering technique is used in order to elicit customers’ preferences towards certain products or services. Interviews with customers are performed to identify latent needs and barriers. Within an interview, new product or service functions are introduced. Customers’ preferences are categorized into three classes: attributes of a product or service, consequences, and values.

Strategic Foresight

17

This systematic interviewing method reveals the correlation between product characteristics and their significant value as perceived by the interviewee. Diary research gains insights from topic-specific diaries used by target groups over a pre-defined period of time. Customers keep a diary on all experiences with Information and Communication Technology (ICT). The diaries can be pre-structured, e.g., concerning referring needs, usability requirements, or drivers and barriers. This method allows for the documentation of barriers in using ICT, which would not be remembered by the customer in a retrospective interview. Day-in-the-life visits are 24-hour observations of customers revealing those needs that are hidden from traditional market research. In the case of Deutsche Telekom, cross-functional teams personally visit the customers and observe them in their own environment. The visits result in a vivid documentation of customer ICT-infrastructure and -usage patterns. Insight clinics are a means of personal, direct interaction with the customer. The method is derived from the automotive industry, where test vehicles are presented to human test subjects, followed by detailed interviews. Depending on the topic, the insight clinic can be designed as a confrontation with products or services (in order to identify barriers), or as a group discussion (in order to identify latent needs). The megatrends study is conducted on an annual basis, aiming to assess the long-term impact of social-cultural, political, and technological trends. The study is used to derive implications from the identified trends for Deutsche Telekom’s corporate and R&D strategy. The customer evaluation tool is a web-based solution for predicting the revenue potential of products that are currently in the conceptualization or developing phase. These forecasts are based on reference cases of market adaptation, product lifecycle algorithms, churn rates, and known psychological price barriers. In summary, the customer foresight activities trigger innovation and validate concepts by giving insight into future demand. In contrast, technology foresight and competitor foresight activities aim at identifying technologies and other developments in the ICT market that will enable the building of innovative products and services as well as the development of new business fields. Technology and competitor foresight

Technology foresight is a process which aims to identify future technological developments and disruptions in order to support decision making related to

18

A. Information Acquisition in a World of Knowledge

future R&D activities (Lichtenthaler 2002). It links technological maturity and relevance for the corporation. The process consists of the identification (technology scanning) and observation (technology monitoring) of upcoming and existing technologies, assessment of their potential and relevance, and the storage and dissemination of the gathered information (Reger 2006). Technology foresight can prevent technical surprises and prepare for opportunities based on technological feasibility and/or differentiation. Competitor foresight is linked to technology foresight, as it also concerns technological moves of competitors. But while technology foresight goes beyond information related to competitors, competitor foresight does not only refer to technological aspects. It identifies and assesses the products of competitors, alternative business models, and collaborations among competitors. The collection of information on technological developments and competitor movements is supported by a global network of scouts. These scouts come from internal or external organizations. They are all characterized by a broad knowledge in their search fields and have large social networks which is used to get first-hand information on current activities. Currently, scouts are located in the US, Europe, Israel, China, and Japan. To interpret the gathered information, two radar visualizations are used: the Technology Radar and the Product & Service Radar. Experts select topics with regard to their newness and relevance from the scouted information. The relevance of technological trends is discussed by an expert panel and rated according to two properties: size of opportunity/threat (potential market size, cost savings, disruptive potential) and practicability expressed as technological realization complexity (complexity, implementation risk, development costs). These two scales prove well suited to suggest the level of attention (high, medium, low relevance) a topic deserves from the recipients of the Technology Radar. The relevance of market- and competitor-driven developments is rated concerning external criteria (market volume and growth, intensity of competition, sustainability of idea, and implementation obstacles) and internal criteria (strategic fit, market share, cannibalization/ retention effects, image, synergies, and implementation obstacles). The accumulated insights from both Technology Radar and Product & Service Radar are put into the so-called radar screens. These offer an overview of all observed developments and their relevance. The Technology Radar screen is divided into six generic thus stable technological fields, which are typical telecommunication operator domains (see Figure 4). Furthermore, the screen displays the development phase of the observed technologies, starting with the outside circle for basic research and moving inwards up to market presence.

19

Strategic Foresight

The Product & Service Radar screen is designed from a market perspective and divided into five focus areas following the product framework of Deutsche Telekom product and innovation unit. The life-cycle stage of the observed products and services is displayed, starting in the outside circle with the concept phase and moving inwards up to launched products and services available on the market. Netwo r k Se rvic

twork e Ne Cor

d-U

etw o

rk

En er v

Acc ess N

S ser

Market Presence Market Ready Product Concept

l

DTAG Relevance

Cross-Func tiona

evices Mobile D

ices

d& Fixe

es

Applied Research Basic Research

High Medium Low

Figure 4 Technology Radar screen

The results of technology and competitor foresight are used for the development of new product concepts and the assessment of the competitiveness of existing product concepts. The analyses are used for strategic decisions on innovation portfolios and budget allocation. Furthermore scouts can facilitate the sourcing of know-how. In an environment with high technological complexity and volatile market needs, external technology sourcing is becoming increasingly important in ensuring the competitiveness of a company (Rohrbeck 2007). Based on the insights gained from continuous foresight activities, new topic and business areas are identified. These need to be further assessed before investment decisions can be made.

20

A. Information Acquisition in a World of Knowledge

Project-based foresight with strategic roadmapping Building on continuous foresight activities, project-based foresight allows for the exploration of new topic and business fields. This is particularly important because corporate planning often does not focus enough on developing new business, but rather puts emphasis on the incremental improvement of the existing lines of business. Traditional methods for the assessment of new business do have limitations. For example, when using the issue analysis technique, the overall question of whether or not to invest in new business is subdivided into smaller issues that are assessed separately. This results in a reduction of complexity, which is desired, but also in neglecting the interdependencies between the sub-issues. The strategic roadmapping technique can overcome such limitations. Furthermore, the “roadmapping” element in this approach can bridge the gap between planning and implementation and can facilitate the interdisciplinary coordination between market and technology. By building on scenario planning the inherent uncertainty of any planning situation is taken into account and actively managed. Roadmapping and scenario planning are considered to be very common methods of practice used by companies (Bürgel et al. 2005; Lichtenthaler 2005). Although these two management planning tools have evolved independently, their combination has been used in several future studies (Drew 2006; Lizaso and Reger 2004). Strategic roadmapping combines the macro-level thinking of scenario planning with a detailed, micro-level thinking of “technology roadmapping”. Strategic roadmapping consists of four major steps: environmental analysis, scenario development, roadmap development, and navigation board development. Environmental analysis

Within the environmental analysis, key influencing factors are identified by scanning the political, market, customer, and technological environment. They reach from current business (factors influencing core activities), to the adjacent environment (influencing factors from neighboring industries), and further up to white spaces (areas which today have no connection to the current business, but might have an important impact in the future). The long list of identified influencing factors is reduced to a short list of approximately 15 factors that have the most influence. A cross-impact matrix can be used as a filter, which plots the influence strengths according to

Strategic Foresight

21

their passive (How strong is this factor influenced by another?) and active (How strong is this factor influencing others?) score. The short list consists of the factors with the highest active and passive scores. Scenario development

For the scenario development possible future projections are identified for each key influencing factor. A set of multiple distinct scenarios can be developed by combining non-contradicting projections. It has to be assured that the set of scenarios reflects any possible future. The interpretation of the scenarios leads to the selection of one or more desired scenarios that reflect a future in which the investment in the new business field would be a success. Roadmap development

The scenario analysis is one pillar of the roadmap development allowing backcasting from the desired scenarios. Backcasting is a process which begins from a future scenario where one can derive what developments have to have taken place in order to reach that scenario. In that sense all scenarios are taken into account to map the steps the company needs to take in order to drive the development of a business field towards the desired scenarios and away from the undesirable. The needed developments are structured in different layers – external influences, products (own and from competitors), technologies, and capabilities – and are mapped over time (see Figure 5). In addition to backcasting, the roadmap is also defined working from the present towards the future. All insights from the three continuous foresight activities are taken into account. Through customer foresight future demand can be anticipated, competitor foresight reports on announced and expected product launches and technology foresight identifies key and bottle-neck technologies. In addition, an internal analysis of current activities can identify Deutsche Telekom’s capabilities that support the desired scenario. Roadmap development needs to be a process involving all the different stakeholders, which is based on interactive workshops and iterative, in order to identify the interaction between external influences, products, and technologies.

22

A. Information Acquisition in a World of Knowledge

Layers

Customer Foresight

External influences

Competitor Foresight

Product

Technology Foresight

Technologies

Internal analysis

Key influencing factors

Timeline

Capabilities

Figure 5 Generic roadmap

Navigation board development

Based on the roadmap, a navigation board is composed that allows the progress towards the desired scenario to be monitored. It consists of a set of up to 10 key indicators for which a needed status is defined and the current status is tracked. It also has pre-defined suggestions for actions in case of deviations from the needed status. The navigation board simultaneously tracks developments that can and cannot be influenced by the company. It can be used both for understanding the assumptions prior to investing in the new business field and throughout its development. Further, it can be used as an overview of the progress for operational innovation management as well as a steering document for top management.

Conclusion From the experience at Telekom Laboratories and the product and innovation unit at Deutsche Telekom, a successful strategic foresight activity has to be based on a deep understanding of the need of the decision maker and is thus to some extent specific for an organization and needs to be customized. In general, though, today’s information society usually has to deal with an oversupply of information.Therefore it is essential that foresight insights are delivered in the right format, to the right person, and at the right time.

Strategic Foresight

23

Deutsche Telekom’s tools for communicating gained insights receive a high level of attention because they are designed to match the differing needs of different stakeholders. Consequently the communication of foresighting results does very valuable groundwork for and makes the entire organization familiar with new innovation activities or areas of business. Furthermore, a foresight system should engage many internal and external partners through methods such as scouting, roadmapping and well-designed workshops in order to enrich the data and validate the insights. While integration of internal partners increases the acceptance and absorption of the results later on, collaborating with other companies in foresighting might be the trigger for joint development.

References Arnold, H. M. 2003. Technology Shocks: Origins, Managerial Responses, and Firm Performance. Heidelberg, New York: Physica-Verlag Springer-Verlag. Bürgel, H. D., Reger, G. and Ackel-Zakour, R. 2005. Technologie-Früherkennung in multinationalen Unternehmen: Ergebnisse einer empirischen Untersuchung. In TechnologieRoadmapping – Zukunftsstrategien für Technologieunternehmen, ed. Möhrle, M. G. and Isenmann, R., 27–53. Heidelberg, New York: Springer-Verlag. Christensen, C. M. 1999. The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business School Press. Day, G. S. and Schoemaker, P. J. H. 2004. Driving Through the Fog: Managing at the Edge. Long Range Planning 37: 127–142. Day, G. S., Schoemaker, P. J. H. 2005. Scanning the Periphery. Harvard Business Review 83: 135–148. Drew, S. A. W. 2006. Building Technology Foresight: Using Scenarios to Embrace Innovation. European Journal of Innovation Management 9: 241–257. Krystek, U. 2007. Strategische Frühaufklärung. Zeitschrift für Controlling & Management (Sonderheft 2): 50–58. Lichtenthaler, E. 2002 Organisation der Technology Intelligence – Eine empirische Untersuchung der Technologiefrühaufklärung in technologieintensiven Grossunternehmen. Verlag Industrielle Organisation. Lichtenthaler, E. 2005. The Choice of Technology Intelligence Methods in Multinationals: Towards a Contingency Approach. International Journal of Technology Management 32:388–407. Liebl, F. 2005. Technologie-Frühaufklärung: Bestandsaufnahme und Perspektiven. Handbuch Technologie- und Innovationsmanagement: Strategie Umsetzung – Controlling, ed. Albers, S. and Gassmann, O., 119–136. Wiesbaden: Gabler, 2005. Lizaso, F and Reger, G. 2004. Scenario-based Roadmapping – A Conceptual View. EU-US Scientific Seminar on New Technology Foresight, Forecasting and Assessment Methods. Reger, G. 2006. Technologie-Früherkennung: Organisation und Prozess. Quantensprünge in der Entwicklung erfolgreich managen. Management von Innovation und Risiko, ed. Gassmann, O. and Kobe C., 303–330. Berlin: Springer.

24

A. Information Acquisition in a World of Knowledge

Rohrbeck, R. 2007. Technology Scouting – A Case Study on the Deutsche Telekom Laboratories. ISPIM-Asia. Slaughter, R. A. 1998. Futures Studies as an Intellectual and Applied Discipline. American Behavioral Scientist 42: 372–385. Trommsdorff, V. and Steinhoff, F. 2007. Innovationsmarketing. Munich: Vahlen.

Endnotes 1

Deutsche Telekom Technology Radar is a registered trademark of Deutsche Telekom AG in Germany

A. Information Acquisition in a World of Knowledge

25

Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute

International academic institutions produce a rich pool of knowledge which is relevant for innovation processes. The challenge is to find an effective approach to make this knowledge accessible and usable on a larger scale. The structured approach to setting up cooperation between industry and academia described in this chapter helps transfer knowledge between those two parties, regardless of geographical distance.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_4, © Springer-Verlag Berlin Heidelberg 2010

26

A. Information Acquisition in a World of Knowledge

Introduction Open-source software such as Linux and moderated developer communities such as Sun Microsystems’ Java Community Process show that internal ideas taken to market through external channels, outside the current businesses of the firm, generate value – sometimes more than an internal procedure ever could. Ideas can also originate outside the firm’s own labs and can move inside. Academic institutions around the world are a notable home to a tremendous wealth of insight that can be integrated into the innovation process. With the example of an applied project in the area of network security, this section describes how the leading-edge know-how of an international academic research institute has been put to use. The project followed a stepby-step approach with the following phases: Idea, discovery, research, development, innovation, market launch and exploitation. First, the research challenge of the aforementioned innovation project is described. Next, an investigation of related literature (both academic and industrial) provides an important basis to ensure the novelty of the new project. Theory and design considerations underlying the research challenges are then described in detail. In the subsequent phase, a framework (or conceptual architecture) is specified and implemented in order to realize the envisaged solution.

Linking academic research to the firm’s R&D chain for innovation projects There is no one-size-fits-all approach to dealing with external information (Gassmann and Enkel 2004). Based on an empirical database of 124 companies, Gassmann and Enkel (2004) identified three core open innovation archetypes (Figure 1): (1) The outside-in process: Enriching a company’s own knowledge base through the integration of suppliers, customers, and external knowledge sourcing can increase a company’s innovativeness; (2) The inside-out process: The external exploitation of ideas in different markets, selling IP, and multiplying technology by channeling ideas to the external environment; and (3) The coupled process: Linking outside-in and inside-out by working in alliances with complementary companies where give-and-take is crucial for success. According to Gassmann and Enkel (2004), a rather open approach is more appropriate in the case of highly modular products requiring tacit knowledge (i.e., software development) and companies with complex interfaces with stakeholders in their environment.

Integration of Academic Research into Innovation Projects

27

Inside-Out Process Bringing ideas to market, selling/licensing IP and multiplying technology

Outside-In Process Integrating external knowledge, costumers and suppliers

Scanning of new Technologies

Prototypes

Development

Products

Coupled Process Couple outside-in and inside-out process, working in alliances with complementaries

Figure 1 R&D chain for planning and building innovation projects. (Gassmann and Enkel 2004)

Analyzing the IBM case presented by Gassmann and Enkel (2004), it becomes clear that IBM has decoupled the locus of innovation (in terms of applying the idea and transforming it into an innovation) from the locus of knowledge creation (invention or research) and the locus of commercialization (product development or exploitation of the innovation). Companies like IBM can integrate external knowledge by using the outside-in process in order to increase their innovativeness. Also the locus of innovation need not necessarily be the locus of exploitation. Companies can use the insideout process in order to license knowledge and technology to exploit them outside the firm. R&D and corporate innovation projects alike require several steps. A generic approach to project planning and setup in R&D is shown in Figure 2 – an approach congruent with the innovation process presented by Hauschildt (1997). This process serves as orientation to explain how academic research can be interlinked with implementation-oriented corporate innovation activities.

Idea

Discovery

Research

Development

Invention

Market Launch

Exploitation

Figure 2 The innovation process (Hauschildt 1997)

The process consists of several phases, some of which can take place concurrently.

28

A. Information Acquisition in a World of Knowledge

The idea and discovery phases are important prerequisites for the actual research on the subject matter to take place. The idea phase initiates the innovation process by either expressing an innovation idea or a research hypothesis from the researcher or the corporation. In the adjacent discovery phase, contextual aspects relevant to the specific innovation process are collected and acquired. At the beginning of the research phase special attention should be paid to the definition of the projects’ goals and desired scenarios. The former define the planned outcome of the project, whereas the latter aim at the usage context of the project’s outcome. The following example can be considered: A project deals with the realization of a new mobile device capable of displaying newspaper articles distributed via WiFi. The usage context defines the scenario, i.e., whether users read the newspaper in a train, in a café outdoors, or in other settings. These two example scenarios have a huge impact on the system’s architecture and design considerations. In the train context, generally no WiFi connectivity is available. Consequently, the device must be able to store news and fetch fresh content whenever it is within a reasonable proximity of an access point. The outdoors scenario sets requirements for the device itself: an energy supply is not available and occasional direct sunlight makes the development of a device with unique capabilities necessary. Another integral part of the research phase is the definition of a research roadmap to be agreed upon at a kick-off meeting with all participants, where the goals, milestones, timelines, reporting mechanisms, and roles are set. Furthermore, the responsibilities for the work packages and their deliverables must be assigned to the project participants. The most important outcome of this activity is the formation of a coherent cross-partner project group with each member accepting his or her role and responsibilities. During the core part of the research phase, new ideas are explored and theory needs to be synthesized and analyzed by compiling relevant sources from industry and academia. The research phase should produce a preliminary blueprint (i.e., conceptual framework, architecture) in order to transform the underlying theory into practice. During the subsequent development phase an initial prototype is constructed in order to validate the preliminary version of the blueprint. Often during this stage, scientific experiments are conducted and their results are carefully evaluated in order to further calibrate the blueprint. The invention stage defines the product, service, or component of technical infrastructure. It is based on the aforementioned R&D phases and investigates factors such as robustness and usability in collaboration with the customer until the envisaged properties and quality, including a desirable

Integration of Academic Research into Innovation Projects

29

service level or level of reliability, are reached. Frequently, the research phase’s outcome has to be developed into an operational prototype. In this phase, the design of the project outcome, namely the operational prototype, must be tested and improved repeatedly (Englert and Joost 2007). Finally, during the market launch phase, the project is transferred to the Strategic Business Units (SBUs) and a pilot evaluation is conducted on a commercial-grade version of the prototype. The lifecycle of an innovation project ends with the exploitation phase where the old product is gradually removed from the market. The goal of this phase is to enhance the adoption rate of the new product and address any unexpected obstacles to expansion (i.e., innovations by competitor). In the following section, the innovation lifecycle is illustrated through the case of a network security innovation project.

The case of a network security innovation project The eDare project served as a test case for assessing the feasibility of carrying out large-scale R&D-based innovation projects with international research institutes. The following section describes the research challenge, conceptual architecture, empirical evaluation, and some research contributions of the eDare project. The research challenge

The Internet is a growing network of heterogeneous routers interconnecting millions of private and business users. Industry reports suggest that individual users receive malware mainly from the Internet (NCSA and AOL 2005; Prost 2003). During the first six months of 2006, 18% of all distinct malware detected by the Symantec “honeypot” had not previously been seen (Symantec 2006). Related work and evidence for the use case

The anti-malware market is well-recognized and adoption has spread across the complete end-user spectrum: corporations, small and medium enterprises (SMEs), and individual consumers. Usually, corporations and SMEs tend to be as protected as they can, with anti-malware solutions from different vendors in place at different levels of their networks. Nevertheless, consumer and end-user handling of anti-malware products is far from perfect. Frost

30

A. Information Acquisition in a World of Knowledge

& Sullivan estimate that more than half of privately-owned PCs do not have active, up-to-date anti-malware protection and subscriptions. In the academia, solutions and approaches span various research areas. Prominent works include: MINDS (Ertoz et al. 2004); the statistical network traffic normality prediction and impact analysis (Jiang and Papavassiliou 2004); field programmable gate arrays and Bloom filters (Dharmapurikar and Lockwood 2006; Mitzenmacher 2002); Shields (Wang et al. 2004) and the methods of sequential hypothesis testing/credit-based connection rate limiting (Jung et al. 2004); DIB:S/TRAFEN for port-scanning detection (Kanlayasiri et al. 2000); and the vulnerability analysis engine (Hariri et al. 2003). Nevertheless, these approaches tend to focus on a specific malware type and none of them is capable of providing well-rounded protection against all types of malware. The findings of the NCSA study (NCSA and AOL 2005) suggest that humans cannot be relied on to intercept malware effectively. Most end-users do not care to install or update antivirus software packages with up-to-date signature files and even those users who are considered cautious are not protected against unknown malware that propagate at alarming rates during “windows of opportunity”. Theory of the challenge

This research adopts a distributed, net-centric approach to addressing the aforementioned challenges. Under such an approach Network Service Providers (NSP) constantly monitor traffic flowing through their infrastructure in order to detect and remove malware. The goal of the proposed eDare architecture is to implement and evaluate such an approach by harnessing the processing power of a dedicated Intrusion Prevention System (IPS) scanning hardware, reports from end users, automatic reports from agents installed in personal devices connected to the Internet, and, last but not least, the enormous processing power of the expert’s mind. Thus the proposed approach will provide state-of-the-art, multi-layered early detection, alarm, and response to known and unknown malware. Solution and implementation

eDare is designed to provide maximum automation in the cycle of malware interception: detection, analysis, alert, and response (remedy). The system aims to provide a very low number of false positives by integrating multiple sources of information and processing techniques. The system also in-

31

Integration of Academic Research into Innovation Projects

corporates state-of-the-art hardware devices enabling fast scanning of Web traffic at speeds meeting typical throughputs of an NSP’s edge router. When encountering new malware or suspicious behavior, the response time of the system is expedited by the sharing of observations and warnings between users and the system, which further issues alerts sharing the data across the protected network. Finally, the system can accommodate external plug-ins, expert consultation, and risk assessment in a flexible manner. The conceptual architecture of eDare is described in Figure 3. The solution is comprised of the following three components, which operate concurrently: • Cleaning: Real-time traffic filtering based on signatures produced by the detection component. • Monitoring: Sampling network traffic for analysis. • Detection: Intelligent detection of malware supported by an ensemble of machine learning algorithms.

Router

Known Malware Handling Module (KMHM)

data stream

Agent

Anonymity, Privacy and Secrecy

New malware signatures

clean data stream

Router

Data Stream Manager (DSM) Data streams files meta data

Reports & Feedback Configuration

Collaborative Module

Reports & Feedback Configuration

Data

New Malware Detection Module (NMHM)

malware New malware signatures

Data

Signature Builder

Data

Storage Manager (SM)

Data

Control Center

Figure 3 Conceptual architecture of eDare

Configuration & Feedback

32

A. Information Acquisition in a World of Knowledge

Discussion of the use case and the innovation process The eDare project was carried out in congruence with the aforementioned approach for R&D-based innovation projects. This project demonstrates a case of collaboration with an international academic research center. The idea and discovery phases included a preliminary dialog with Telekom Laboratories network security experts and a review of up-to-date research and practice in the area of network intrusion detection and prevention – all in order to accurately crystallize and focus the eDare project. In addition, representative use cases for different types of customers (i.e., enterprise, ISP, mobile devices) were outlined. This phase also included mapping and synchronization between eDare and other related network security R&D efforts within Telekom Laboratories. At the beginning of the research phase, meetings between Telekom Laboratories staff from two different locations (Berlin and Beer Sheva) produced a project plan based on a preliminary design specification, which yielded a set of interrelated work packages. The packages were mapped to various elements in the specification such as the Known Malware Handler, New Malware Detection, Signature Builder, etc. During the core part of the research phase, alternative approaches were explored using discussions and seminars in order to specify the eDare conceptual architecture in more detail. Examples of relevant research activities conducted during this phase (across various modules of the eDare architecture) are: collecting a repository of malware cases and setting up a representative test environment; selection, optimal deployment and testing of cleaning and monitoring devices; specifying and evaluating various algorithm configurations for Automatic Signature Generation (ASG); estimating the Machine-Learning plug-ins’ efficacy at intercepting new forms of malware (i.e., temporal analysis, artificial neural networks, decision trees, Bayesian classifiers, and others). The outcome of this phase was also published in the form of conference and journal papers. Next, during the development phase, the outcomes of the research phase were integrated into a coherent, operational prototype. The prototype was tested on a massive collection of malware cases in a simulated network environment. Results from these experiments enabled the calibration of various elements in the architecture (i.e., similarity thresholds of automatic signature generation, weighted scoring schemes for new malware detection by various plug-ins, cost/benefit analysis of various deployment strategies for cleaning and monitoring devices). During the invention and market launch phases, a hardened version of the eDare prototype was created in the form of a server rack shipped for further

Integration of Academic Research into Innovation Projects

33

testing by Deutsche Telekom’s network security staff. Experiments conducted during this phase enabled Deutsche Telekom to test the prototype on realistic scenarios faced by large-scale NSPs. The feedback provided by Deutsche Telekom enabled Telekom Laboratories at Ben-Gurion University to validate and calibrate the prototype, so it could better meet the stringent performance and security requirements necessary for rollout. Although opening up the innovation process and involving an international research center in the case of eDare (and the IBM cases) seems directly related to the innovation success, Gassmann and Enkel (2004) mention that there should be a serious discussion about when an integrative innovative approach should be implemented and when not. The future of innovation is not about outsourcing all internal innovation activities, but about following a flexible innovation strategy to allow companies to create more and better innovations by combining various strategies (e.g., outsourcing ventures), reintegrating new businesses, scanning and integrating new technologies, commercializing patents, connecting external sources to the internal innovation process, and launching new collaborations during the required period. Factors such as the company’s “absorptive capability” of external knowledge (in the outside-in process), “multiplicative capability” (in the inside-out process), and the “relational capability” of building and maintaining ties with differentiated partner networks (in the coupled process) should be taken into consideration (Gassmann and Enkel 2004).

Conclusion In this section, a promising methodology for conducting innovation projects with international academic research institutions was defined and presented. The methodology is based on a project approach that facilitates the structured and controlled inclusion of external partners in innovative processes. The practical experience gained during eDare indicates that a roadmap for promoting network security innovation projects in collaboration with academic research centers is feasible and beneficial. Considering the complexity of such projects, the fact that a structured, lifecycle-type methodology was found useful for guiding a large-scale innovation project is encouraging. Moreover, considering the distribution of knowhow in this field, the openness of this innovation project contributed to Deutsche Telekom by strengthening its current business and generating new business models (i.e., security-as-a-service subscriptions). The case of eDare is in line with the trend from closed innovation towards a more open approach visible in numerous sectors (i.e., automobiles, biotechnology, banking, insurance, con-

34

A. Information Acquisition in a World of Knowledge

sumer packaged goods, and more). The locus of innovation in these industries is moving beyond the confines of the central R&D laboratories of the largest companies to start-ups, universities, and other outsiders. Following the archetypes presented in the Gassmann and Enkel’s categorization (2004), the eDare project can be classified as a coupled process that meshes the outside-in process (to gain external knowledge) with the insideout process (to bring ideas to market). Deutsche Telekom and Ben-Gurion University formed Telekom Laboratories at Ben-Gurion University as a bidirectional platform for facilitating this kind of cooperation. In the specific case of eDare, Deutsche Telekom provided both technological and marketing insights, as well as funding for R&D and in-house facilities for testing a large-scale network security solution incorporating innovative ideas developed by Ben-Gurion University researchers. The outside-in approach (Gassmann and Enkel 2004) at Deutsche Telekom can be seen in the case of customer integration in innovation processes at Telekom Laboratories via mechanisms such as lead users, ideas competition, virtual communities, and customer-centered toolkits for innovation (Erner et al. 2009). Lüke and Kapitány (2009) suggest that Deutsche Telekom’s early involvement of business customers in innovation projects yielded various benefits such as minimizing rework, enhancing commitment, and bundling competencies. Nevertheless, since eDare originated as a “technology-push” project (Erner and Presse 2009) in a focused area characterized by highly tacit and specialized technological knowledge, a collaboration between Telekom Laboratories and private/business customers (or suppliers) as part of an outside-in approach was not suitable. On the other hand, the inside-out approach, which commercializes ideas in different industries (cross-industry innovation), has been adopted at the end of the eDare project. Following this approach, a business plan was prepared and a spin-off activity is currently in the pipeline. In addition, due to the modular nature of eDare, parts of the intellectual property developed in the project (i.e., the Automatic Signature Generation (ASG) algorithms for previously unseen malware) can be licensed to vendors in different areas within the network security industry (i.e., anti-virus and intrusion detection/ prevention systems manufacturers).

Integration of Academic Research into Innovation Projects

35

References AOL and NCSA. 2005. AOL/NCSA Online Safety Study. Bauer, E. and Kohavi, R. 1999. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 35: 1–38. Barthélemy, M., Barrat, A., Pastor-Satorras, R. and Vespignani, A. 2004. Velocity and Hierarchical Spread of Epidemic Outbreaks in Scale Free Networks. Phys. Rev. Lett 92.178701 (April). Chen, L. C. and Carley, M. 2004. The Impact of Countermeasure Propagation on the Prevalence of Computer Viruses. IEEE Systems, Man and Cybernetics, Part B 34(1) (April): 823–833. Christodorescu, M. and Jha, S. 2003. Static Analysis of Executables to Detect Malicious Patterns.” Proceedings of the 12th USENIX Security Symposium (Security’03), Washington DC, August 4–8: 169–186. Dharmapurikar, S. and Lockwood, J. W. 2006. Fast and Scalable Pattern Matching for Network Intrusion Detection Systems. IEEE Journal on Selected Areas in Communications 24(10) (October): 1781–1792. Elovici, Y., Shabtai, A., Moskovitch, R., Tahan, G. and Glezer, C. 2007. Applying Machine Learning Techniques to Detect Malicious Code in Network Traffic. 30th Annual German Conference on Artificial Intelligence (KI-2007), Osnabrück, Germany, Sep. 10–13. Englert, R. and Joost, G. 2007. Design and Usability for Personalized User Interfaces of Telecommunication Services. 16th International Conference on Engineering Design (ICED07), Paris, France. Ertoz, L., Eilertson, E., Lazarevic, A., Tan, P., Srivastava, J., Kumar, V. and Dokas, P. 2004. The MINDS – Minnesota Intrusion Detection System, Next Generation Data Mining. MIT Press. Gassmann, O. and Enkel, E. 2004. Towards a Theory of Open Innovation: Three Core Process Archetypes. Proceedings of the R&D Management Conference. Golub, T. et al. 1999. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286: 531–537. Hariri, S., Qu, G., Dharmagadda, T. and Ramkishore, M. 2003. Vulnerability Analysis of Faults and Attacks in Large-scale Networks. IEEE Security and Privacy Magazine (October & November): 49–54. Hauschildt. 1997. Innovationsmanagement. Munich: Vahlen. Jiang, J. and Papavassiliou, S. 2004. Detecting Network Attacks in the Internet via Statistical Network Traffic Normality Prediction. Journal of Networks and Systems Management 12(1): 51–72. Jung, J., Schechter, St. and Berger, A. 2004 Fast Detection of Scanning Worm Infections. LNCS 3224: 59–81. Kanlayasiri, U., Sanguanpong, S. and Jaratmanachot, W. 2000. A Rule-based Approach for Port Scanning Detection. Proceedings of the 23rd Electrical Engineering Conference, Chiang Mai, Thailand. Locasto, M. E., Sidiroglou, S. and Keromytis, A. D. Software Self-healing Using Collaborative Application Communities. The 13th Annual Network and Distributed System Security, Symposium, San Diego, California. Mitzenmacher, M. 2002. Compressed Bloom filters. IEEE/ACM Transactions on Networking 10(5): 604–612. Prost, A. 2003. The Danger of Spyware, Symantec Security Response. Symantec. 2006. Symantec Internet Security Threat Report. Wang, H., Guo, Ch., Simon, D. and Zugenmaier, A. 2004. “Shield: Vulnerability-driven Network Filters for Preventing Known Vulnerability Exploits.” SIGCOMM’04, Portland, Oregon, USA. Aug. 30–Sept. 3.

36

B. Organizing for Getting the Most out of Openness

Implementing Open Innovation to Benefit from External Dynamics of Innovation

Innovation spaces of telco operators and web-based IP services converge; innovation in the telco domain is no longer restricted to operators and their suppliers. It is increasingly brought about by the millions of additional developers of web-based IP services and new IP equipment manufacturers. The choice of alternative technological paths is skyrocketing and renders the traditional closed approach of corporate R&D obsolete. Obviously, competing for the best innovation with the outside world is less an alternative for telco operators than ever before. The challenge for corporations now is to make maximum use of the innovative dynamics around them. Open Innovation is one approach that aims at making the corporation’s borders transparent enough for external contributions, while at the same time limiting the risks of reduced control over the innovation process.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_5, © Springer-Verlag Berlin Heidelberg 2010

Implementing Open Innovation to Benefit from External Dynamics of Innovation

37

Introduction Web-based IP services and all-IP networks have originated company value migrating away from infrastructure based services towards web-based services and equipment for IP networks (Greant 2008; Gutberlet 2008; Wörner 2008). As far as competences are concerned, the mastery of the internet economy has relegated the classical skills of the communication industry and rendered them relatively less important. As such, the advent of webbased IP services has appeared as a technology shock (Arnold 2003) to the classical suppliers of telecommunications equipment and brought millions of IP-based service developers and new equipment manufacturers into the innovation arena of the ICT industry (Arnold and Dunaj 2007). One characteristic of a technology shock is that the structure of competition and valuation of competencies differs after the shock from the situation before. The “IP-shock,” through generally accessible technical interfaces, resulted in millions of innovators being able to better contribute in the innovation space of the telecommunications industry (Arnold and Dunaj 2007). The sheer number of innovators questions the traditional model of closed corporate innovation and makes it impossible to compete with the entire environment. As innovation becomes ever more the decisive driving force behind growth of real value, companies have to find a way to exploit innovation in the “after-shock” regime. Analyses conducted by the National Science Foundation have shown that, for example, almost 50% of the economic growth of the USA is based on the application of technologies and on innovative products and services (Albers and Gassmann 2005). At the same time, IP interfaces and more innovators increase the “clock speed” of innovation. Just a few of the context factors leading to increasing complexity and dynamism in the business environment are the drastic shortening of product life cycles (Simon 1989; Nieschlag et al. 2002), the globalization of competition with the accompanying growth in the number of possible innovators, the interacting influence of products, technologies on international markets, and the increasing difficulty of protecting and monitoring intellectual property and expertise. Moreover, the growth of available venture capital gives young companies the ability to cannibalize established offerings (Chesbrough 2003). Against this background, companies are facing the challenge of reducing development times and costs, as well as timeto-market periods. A current approach of growing importance to integrate the external innovation dynamics for the benefit of the corporation is the concept of “Open Innovation”. In essence, Open Innovation is character-

38

B. Organizing for Getting the Most out of Openness

ized by the collaboration between external and internal partners spanning the boundaries of one focal organization(Chesbrough 2003; Hippel 2005; Prahalad and Krishnan 2008). In doing this, the emphasis is above all on the orientation towards external sources of knowledge (acquisition and cooperation) for knowledge generation (Chesbrough 2003). The Open Innovation Paradigm is thus intended, in particular, to raise potential levels of knowledge and creativity within the company and within networks for innovation by making use of development capacities external to the company and so, among other things, to make a contribution to the faster, more cost-effective and user-friendly development of innovation. Several studies and research have shown that an intensive exchange of information by all the actors involved in the innovation process helps to reduce the timeto-market period, the cost to market, and the risk involved with the development of new products and services (Riggs and von Hippel 1994; Hippel and Katz 2002; Lilien et al. 2002; Chesbrough 2003; Reichwald and Piller 2009). The opening up of company boundaries and the open collaboration with other partners reduces, as mentioned, the risk of technological development through the improved gathering of information, and contributes to the control of increasingly complex and dynamic technological and environmental conditions. On the other hand, this openness raises several questions concerning property rights and how this interaction can be managed and realized within established organizations. So particular organizational demands are placed on the management of innovation by the Open Innovation paradigm itself (Arnold 2008). In particular, the coordination of the vast number of actors involved in the innovation process poses a special challenge, the importance of which was far lower in the previously closed innovation system. Here, two main goals are addressed: first, describing the challenges of Open Innovation processes and second, how Open Innovation can be implemented within innovation and R&D units.

Actors of the open innovation ecosystem A multitude of actors interact with each other within the framework of the “Open Innovation Paradigm”. The central actors in the Open Innovation context include, in particular, the customers and users of innovations, development partners, competitors, as well as both public and partially privatized research institutions (see fig. 1) and last but not least the company itself.

Implementing Open Innovation to Benefit from External Dynamics of Innovation

External Partner

39

External Partner

Internal project Collaborative project External project Company

Project field manager

Figure 1 Actors in the innovation system

Development partners and competitors

The telco industry is a dynamic fast growing environment with a high level of activity towards new standards and technological paradigms. The complexity and dynamics of technological progress and the increasing complexity and interdependence of technologies means that it is barely possible any longer for a single company to develop innovations fully and successfully solely within its own company boundaries. Quite the opposite – it is increasingly obliged to access and utilize external sources and to enter into joint development partnerships. The number of potential and existing innovators has increased dramatically over recent years due to the lowering or total removal of exploitation barriers through the modularization of technology and the standardization of interfaces (Schläffer and Arnold 2007) mainly driven by internet protocol. In the past, the hundreds of developers from major network operators were sufficient for innovating and improving systems and networks, nowadays they are competing with millions of individual or institutionalized developers. Established companies can benefit from the high level of external innovation activity and enter into various types of development partnerships. A precondition for this, however, is that the greater part of the know-how in question is shared on the market.

40

B. Organizing for Getting the Most out of Openness

The framework conditions for innovation outlined above also require attitudes towards competitors to be reconsidered. Competitors can equally be development partners. Cooperation already exists between competitors at the pre-competitive stage of innovation development (Hagedoorn et al. 2000; Barnes et al. 2006). Competitors can, in particular, develop areas together which have a quasi-platform character and which can concentrate on individual areas relevant to their customers in order to achieve the advantages of uniqueness and differentiation. See, for example, IBM with their modularization strategy (Baldwin and Clark 1997), JVC with their VHS standard (Cusumano et al. 1992) or the automotive industry with their platform based approach (Nevins and Whitney 1989; Tully 1993). Research institutions

The demand for the faster introduction of products into the market also requires a more efficient technology transfer from universities. Public and private research institutions have by now become more active in the marketing of technologies (Shane 2002). This is why companies and universities are seeking to integrate the results of research early on in the process of innovation by cooperating more closely. Additionally, research funding agencies in the European Union are facilitating the collaboration of universities, research institutes and the industry, as they are increasingly engaging in technology transfer. The implementation of “Open Innovation” in innovation management is highly demanding for existing organizational structures. Customers and markets

Tailoring the development of innovation to needs necessitates both the continual registration of customer requirements and the early integration of customers into the innovation process, or rather the early orientation of innovation activities towards customer needs (Cooper 1979; Cooper and Kleinschmidt 1987). Socio-economic developments changed customer needs to more individual needs and as a result to more heterogeneous markets. The early integration of customers as important “external actors” within the innovation process decreases the risk of market failure and avoids cost-intensive post-launch product (Gruner and Homburg 2000; Ernst 2002). Customer orientation in the telecommunications industries is mainly directed in two areas. On the one hand, the development of services can be prioritized based on the prognosis and experimental verification of customer needs. So-called

Implementing Open Innovation to Benefit from External Dynamics of Innovation

41

user clinics are becoming an ever more important instrument in the industry. On the other hand, aspects of usability in the sense of customer-friendly user interfaces and intuitive operability can be addressed early on in the innovation process (Herrmann et al. 2007; Dörflinger et al. 2008).

Organizational design and implementation of the open innovation paradigm In order for open innovation to function, organizational structures and processes to anchor openness within the company need to be modified. Furthermore, the establishment of an open culture of sharing and entrepreneurial spirit is necessary to successfully implement open innovation in an organization. The implementation of the “Open Innovation Paradigm” places special demands on structures, processes and the innovation culture being developed by innovation organizations. Structural organization

From the perspective of a major network operator, an integrated approach focusing on decentralized aspects and differences seems appropriate in order to achieve an innovative organization and culture. It is difficult to centrally administer and control the opening up of the innovation process, and with this the organization itself, because a central control is contradictory to the flexibility and adaptability Open Innovation requires within this environment. This is particularly true as the structure and size of the innovation goals change continually. Thus Open Innovation can only be integrated into the whole organization with difficulty through the roll-out of a central master plan. Even harder is the steering of the Open Innovation activities from one central point. Organizational structures and forms must be able to adapt themselves in a self-motivated way to the constant changes within the organizational and inter-company collaboration. Central mechanisms, therefore, prove insufficient for managing the multitude and dynamism of the partners involved, who are moreover often only bound to the company on a temporary basis by project contracts. At the same time, the existence of resilient and flexible organizational structures is indispensable for sustainable management and control. The integration of external development partners is most easily achieved when organizational preconditions are set, under which results-oriented

42

B. Organizing for Getting the Most out of Openness

thinking and action are encouraged. These should not originate from static department orientation, but rather employ resources variably, depending on the task in question. This approach makes collaboration with external development partners easier, but at the same time raises the demands placed on the knowledge organization, as the results from partners must be transferred and integrated sustainably into the company. The tension between creating a flexible and dynamic, yet constant and sustainable organizational structure has been resolved at the Telekom Laboratories through the establishment of topic oriented project fields (see fig. 2). A project field covers an important thematic field of innovation such as “multimedia services” or “security.” With the orientation around a topic area and not around a specialized task, project fields show a high degree of result orientation and commitment to the final success. Being measured by the successful transfer of his R&D results into the business units, the leader of each project field acts as a “corporate entrepreneur” to the extent of an independent entrepreneur within the umbrella of the whole organization if required for the success of a project (Burgelman 1983; Stopford and Badenfuller 1994; Thornberry 2001). Taking care of the entire development chain from topic identification, internal and external presentation to the ultimate success of the project field, including commercial, market and technical aspects, means that the project field leader must employ the best resources available on a flexible need basis. The result of this is that the “project field management” structure is particularly effective in achieving results in the integration of R&D process steps and technological and market aspects.

Customers

Development partners

Company

Competitors

Figure 2 View of the structural organization

Research facilities

Implementing Open Innovation to Benefit from External Dynamics of Innovation

43

True to the entrepreneurial idea, the project field leaders are given access to funds and tasked with investing them to maximize returns, whereby the project field leaders also compete with each other for the allocation of these tight budgets. The decisive factor in this process is not only the economic success or the projected added value, but also the demands of the internal customers. In addition to the company’s internal resources, the project field leader also has access to public and other private third-party funds. The competition between project field leaders for the company’s internal financial resources results in an improvement in resource allocation management. The sourcing and staffing of projects is equally within the free remit of the project field leaders. They have – as does a true entrepreneur – the free choice of innovation and development partners. This makes a temporary partnership with relevant specialists possible. In terms of the company as a whole, the project field leaders should be viewed as gatekeepers placed at the boundaries of the organization, who are independently responsible for maintaining and nurturing their network of partners. Innovative behavior cannot be planned and managed in advance; it requires an atmosphere of freedom and individual choice. Process organization

The integration of the various partners in the innovation process presents the company with an enormous challenge at the process level. The processes and decision-making mechanisms of previously closed innovation organizations must be made transparent so that those involved externally can also initiate innovation efforts and so spark the process. The provision of suitable docking stations and (informal) cooperation opportunities are critical to the success of the overall collaboration. In addition to the opening up of the innovation process, provision must also be made for different forms of collaboration based on the differing types of potential innovation partnership, just as for the differences in the level of intensity of involvement. At the same time, the security of the entire process must be ensured. To further facilitate the innovation process, various supporting processes must be provided to give the project field leaders the freedom to choose resources responsibly and not to facilitate this by short and simple procedures. At Telekom Laboratories, every potential partner can present new innovation ideas. At this point the innovation process has been opened beyond the company boundaries. Innovation proposals do not need to fulfill any for-

44

B. Organizing for Getting the Most out of Openness

mal conditions initially to keep the barrier to entry as low as possible. Therefore, at the idea generation stage, there is increased encouragement for research partners, internal and external customers, public initiatives (such as triggered by the BMBF, the Federal Ministry of Education and Research, or the BMWi, the Federal Ministry of Economics and technology) to submit proposals. In the second phase of the innovation process – the selection phase – research institutions are asked for state-of-the-art evaluations, and with the help of market research, initial forecasts of market trends are created and used for the selection of innovation proposals. Before the next phase – the execution phase – begins, bids are gathered from potential development partners in order to carry out a feasibility study, along with an evaluation of the potential for success. Within the execution phase of the project, “make or buy” decisions are taken, based on the bids tendered and, where necessary, contracts are awarded to the relevant development bodies. As a rule, it is also possible to enter into development partnerships with competitors. The innovations are then marketed in the commercialization phase. This requires implementation by users or the relevant implementation system chosen. Legal and administrative support is offered to the corporate entrepreneur through a local shared services center to accompany the innovation process. Cultural aspects of the organization

As mentioned, a tremendous change is brought about with the opening up of the company boundaries and therefore the involvement and interaction with, and the management of, several actors along the innovation process. Additionally, the implementation of project fields with a high amount of individual responsibility for the innovation managers leads to the requirement that the organization delegates high internal autonomy to the individual project field leader on the one hand, and nurtures a culture of trust within the innovation system on the other. These aspects are essential to ensure freedom to act and corporate entrepreneurship within the organization. In order to foster the collaboration between the individuals and the willingness to share, a strong social identity is necessary and achieved by shared office space, central meeting points, social events together with a focused research mission and a clear innovation strategy. Due to the fact that innovations often result from experimentation or coincidence (Mittelstraß 1994; Münch 2007), Telekom Laboratories are fol-

Implementing Open Innovation to Benefit from External Dynamics of Innovation

45

lowing a two-way strategy. Firstly, projects are initiated and aligned with the corporate product and innovation roadmap. Secondly, various ideas are also cultivated on a smaller scale (“grass root strategy”). The management is aware that only a few ideas will survive and therefore accepts and in that sense also appreciates the failure of these projects. Especially radical innovations arise within an interdisciplinary setting (Hollingsworth 2002) and therefore Telekom Laboratories hires people from international labor markets with diverse academic backgrounds (i.e. computer science, business administration, software and electrical engineering, design, psychology and sociology) to build up a strong, interwoven technological and socio-economic competence. To summarize, Telekom Laboratories’ culture is characterized by academic freedom and corporate entrepreneurship within an open, failure tolerant and diverse setting embedded in a strong social identity and the willingness to share and collaborate across various attitudes and individuals.

Conclusion The Internet Protocol triggered a technology shock valuating new competencies and supporting the emergence of significant innovation activities outside of the classical company demanding a new paradigm of corporate innovation and R&D. The need for the ever faster, more cost-effective and customer-friendly development of innovation goes along with the more demanding environmental factors. The Open Innovation approach is seen as a way out of the innovation dilemma outlined above. What, however, is often not taken into account is the fact that the Open Innovation paradigm itself brings with it certain challenges, particularly those of rendering the management processes more flexible, and thus imposes high demands on the way it is implemented into the business. The integration of innovation actors and the efficient management of partners and resources are critical to the success of innovation management. The framework for managing these demands at Telekom Laboratories comprises increasing the flexibility and the dynamics of the structural and procedural organization in line with a strong culture of openness. This ultimately leads to the organization of entrepreneurial project fields formed around the most important innovation topics, an open innovation process, as well as an interdisciplinary, open, trustful and failure tolerant culture. The combination of these four elements provides the basis for a robust, yet sufficiently flexible organization in the context of the Open Innovation paradigm.

46

B. Organizing for Getting the Most out of Openness

References Albers, S. and O. Gassmann (2005). Technologie- und Innovationsmanagement. Handbuch Technologie- und Innovationsmanagement: Strategie- Umsetzung- Controlling. S. Albers and O. Gassmann. Wiesbaden, Gabler: 3–22. Arnold, H. and M. Dunaj (2007). Enterprise Architecture and Modularization in Telco R&D as a Response to an Environment of Technological Uncertainty. ICIN, Bordeaux. Arnold, H. M. (2003). Technology Shocks: Origins, Managerial Responses, and Firm Performance. Heidelberg and New York, Physica. Arnold, H. M. (2008). Mehr als Methoden, Prozesse und Werkzeuge: Die wichtigsten Treiber für erfolgreiche Open Innovation. Innovationsführerschaft durch Open Innovation: Chancen für die Telekommunikations-, IT- und Medienindustrie. A. Picot and S. Doeblin. Berlin, Springer: 5–24. Baldwin, C. Y. and B. Clark (1997). «Managing in the age of modularity.» Harvard Business Review 75(5): 84–93. Barnes, T., I. Pashby, et al. (2006). «Managing collaborative R&D projects development of a practical management tool.» International Journal of Project Management 24(5): 395– 404. Burgelman, R. A. (1983). «Corporate entrepreneurship and strategic management: Insights from a process study.» Management Science 29(12): 1349–1364. Chesbrough, H. W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Boston, Massachusetts Harvard Business School Press. Cooper, R. G. (1979). «Identifying Industrial New Product Success: Project NewProd.» Industrial Marketing Management 8(2): 124–135. Cooper, R. G. and E. J. Kleinschmidt (1987). «New products: What separates winners from losers?» The Journal of Product Innovation Management 4(3): 169–184. Cusumano, M. A., Y. Mylonadis, et al. (1992). «Strategic Maneuvering and Mass-Market Dynamics: The Triumph of VHS over Beta.» The Business History Review 66(1): 51–94. Dörflinger, T., F. Steinhoff, et al. (2008). «The relevance of usability-enhanced segmentations for new ICT product development.» Proceedings of Anzmac (Australian and New Zealand Marketing Academy) 2007 Conference: “3Rs: Reputation, Responsibility & Relevance” (im Druck). Ernst, H. (2002). «Success Factors of New Product Development: A Review of the Empirical Literature.» International Journal of Management Reviews 4(1): 1–40. Greant, Z. (2008). Open Innovation and Open Source: Lessons Learned in the Mozilla Community. Innovationsführerschaft durch Open Innovation: Chancen für die Telekommunikations-, IT- und Medienindustrie. A. Picot and S. Doeblin. Berlin, Springer: 69–84. Gruner, K. E. and C. Homburg (2000). «Does Customer Interaction Enhance New Product Success?» Journal of Business Research 49(1): 1–14. Gutberlet, M. (2008). Die Auswirkung neuer Geschäfts-, Wirtschafts-, und Gesellschaftsmodelle für die Zukunft der ITK Branche. Innovationsführerschaft durch Open Innovation: Chancen für die Telekommunikations-, IT- und Medienindustrie. A. Picot and S. Doeblin. Berlin, Springer. 1: 5–24. Hagedoorn, J., A. Link, et al. (2000). «Research partnerships.» Research Policy 29(4–5): 567– 586. Herrmann, F., I. Niedermann, et al. (2007). Users Interact Differently: Towards a UsabilityOriented User Taxonomy. Human-Computer Interaction. J. A. Jacko. Berlin, Springer: 812–817. Hippel, E. and R. Katz (2002). «Shifting innovation to users via toolkits.» Management Science 48(7): 821–833.

Implementing Open Innovation to Benefit from External Dynamics of Innovation

47

Hippel, E. v. (2005). Democratizing Innovation. Cambridge, MA, MIT Press. Hollingsworth, J. R. (2002). Research organizations and major discoveries in twentieth-century science. Berlin, Wissenschaftszentrum Berlin für Sozialforschung gGmbH. Lilien, G. L., P. D. Morrison, et al. (2002). «Performance assessment of the lead user idea-generation process for new product development.» Management Science 48(8): 1042–1059. Mittelstraß, J. (1994). Die unzeitgemäße Universität. Frankfurt am Main, Suhrkamp. Münch, R. (2007). Die akademische Elite. Frankfurt am Main, Suhrkamp. Nevins, J. and D. Whitney (1989). Concurrent design of products and processes: a strategy for the next generation in manufacturing, McGraw-Hill Companies. Nieschlag, R., E. Dichtl, et al. (2002). Marketing. Berlin, Duncker & Humblot. Prahalad, C. K. and M. S. Krishnan (2008). The New Age of Innovation: Driving Co-created Value through Global Networks, McGrawHill. Reichwald, R. and F. Piller (2009). Interaktive Wertschöpfung: Open innovation, Individualisierung und neue formen der Arbeitsteilung. Wiesbaden, Gabler. Riggs, W. and E. von Hippel (1994). «Incentives to innovate and the sources of innovation: the case of scientific instruments.» Research Policy 23(4): 459–469. Schläffer, C. and H. Arnold (2007). «Media and network innovation – technological paths, customer needs and business logic.» e & i Elektrotechnik und Informationstechnik 124(10): 317–322. Shane, S. (2002). «Selling university technology: patterns from MIT.» Management Science 48(1): 122–137. Simon, H. (1989). «Die Zeit als strategischer Erfolgsfaktor.» Zeitschrift für Betriebswirtschaft 59(1): 70–93. Stopford, J. M. and C. W. F. Badenfuller (1994). «Creating Corporate Entrepreneurship.» Strategic Management Journal 15(7): 521–536. Thornberry, N. E. (2001). «Corporate Entrepreneurship: Antidote or Oxymoron?» European Management Journal 19(5): 526–533. Tully, S. (1993). «The modular corporation.» Fortune International 127: 52–52. Wörner, H. (2008). Wissensgiganten und Realisierungszwerge in der IT-Industrie – Erfahrungen aus 40 Jahren Täitgkeit in der IT-Industrie. Innovationsführerschaft durch Open Innovation: Chancen für die Telekommunikations-, IT- und Medienindustrie. A. Picot and S. Doeblin. Berlin, Springer: 105–120.

48

B. Organizing for Getting the Most out of Openness

Partnering for Research and Development within an Open Innovation Framework

The increasing stress of competition and technological change reduces the ratio of revenue expectation to internal development costs. As a consequence, more innovation work has to be accomplished for the same funds. Using the external world actively and strategically to enhance one’s own innovation potential provides a solution. An important element is a dedicated partnering concept involving public research institutions as well as industrial peer companies. Building a regional cluster as well as a neutral platform for institutionalizing the collaboration among heterogeneous entities has proven to be the most prominent success factor. Therefore, Telekom Laboratories has cofounded the European Center for Information and Communication Technologies (EICT) which offers catalyzing services for public-private partnerships (PPP). This section highlights the critical success factors for a partnering strategy for modern R&D and innovation following the open innovation scheme in general, and deduces the service offering for PPP and regional clusters.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_6, © Springer-Verlag Berlin Heidelberg 2010

Partnering for Research and Development within an Open Innovation Framework

49

Introduction It is generally agreed that the information and communications technologies (ICT) industry is characterized by quick alterations evoked by strong competitive dynamics and technological change (e.g., Anderson 1997, Meyer 2002, Rothwell 1992). Life cycles of ICT products are very short and leading technology companies generate 50% of their revenue with products less than two years old (Rohrbeck and Arnold 2006). The convergence of IT and telecommunications towards an Internet Protocol (IP)-based ICT will result in even higher competitive pressure and dynamics, as well as a higher “clock speed” of replacing older services through newer ones expanding from the Internet to the entire ICT industry (Shin and Dong 2006). A recent example is Google’s involvement in the 2008 U.S. wireless spectrum auction. Conversely, network operators are looking for new fields in which to offer content, such as Deutsche Telekom which now sells downloadable music from the web. The trend to end user development tools means that computer specialists or even untrained users start to create innovations in the formerly hard-tolearn telecommunications domain. This has led to an enormous increase in the number of innovators worldwide. Furthermore, the use of standardized IP technology in the infrastructures of operators allows for lower equipment prices but also for facilitated provisioning of services through third parties e.g., in the area of Voice over IP, Web 2.0, and beyond. The probability that a disruptive innovation might actually be developed in a seemingly remote region with little infrastructure will increase significantly. The market barrier to entry for competitors decreases as new business models can be realized now with comparably low pre-investment. ICT operators, but also ICT hardware and software vendors, increasingly operate globally and attractive developments can break even supra-regionally in a short time. Furthermore, the ever increasing use of ICT services in other distinct markets such as in the automotive, logistics or health industry, leads to additional dynamics in the industry. The term ‘hybrid value creation’ is used for product bundles where a physical product is bundled together with a service offering (Becker and Krcmar 2008). An example is the bundling of a car with driver assistance services which are basically value-added telecommunication or telematic services. Innovation for such hybrid product bundles has to be created through joint projects and well-orchestrated partnering, e.g., between original equipment manufacturers (OEM) in the car industry and telecommunications operators.

50

B. Organizing for Getting the Most out of Openness

Cooperation models The ratio between turnover from services and infrastructures with innovation aspect and internal R&D resources that can be applied is decreasing, the risk of failure increasing, given the circumstances described above. The classical approach that innovation solely emerges from within the company boundaries and can be derived from internal research results is no longer appropriate. An approach to answer the challenge lies in the opening of the innovation process of the company and thus the strategic use of the outside world in order to enlarge one’s own innovation potential. Through this approach of open innovation (Chesbrough 2003), the company’s own R&D costs can be reduced and, on top, it is easier to trigger additional sources of turnover such as licenses, spin offs or sales (Vanhaverbeke and Peeters 2005). The risk of mismatching the market dynamic to a costly internal development project is reduced. The importance of inter-organizational cooperation for innovation productivity has already been studied intensively (e.g., Faems et al. 2005; Christensen and Overdorf 2000) and can be considered as generally accepted. The main reasons why cooperation contributes to the efficiency and effectiveness of the innovation process are the following: • Access to complementary knowledge and goods covering important segments of a value chain • Mutual transfer of informal know-how that eventually turns out to be imperatively necessary for the creation of innovation • Distribution of research and development costs and risks for big innovation projects This cooperation concept has to be put into effect between companies and public or private universities and research institutions. Industrial collaboration

Industrial collaboration is now being discussed in its two facets: horizontal and vertical. Horizontal collaboration means that companies operating in the same industry segment – in this case telecommunications operators – work together and join forces once a common goal can be identified. Vertical collaboration typically takes place along a value chain and aims at leveraging the different core competencies of each participating company – in our case e.g. telecommunications operators and telecommunications equipment vendors.

Partnering for Research and Development within an Open Innovation Framework

51

This collaboration can be facilitated through institutions that offer and manage a legal, methodological, and managerial framework as well as through tool support for that purpose. For the means of horizontal collaboration, Deutsche Telekom is, for example, shareholder of Eurescom GmbH (http://www.eurescom.eu) where a peer group of European incumbents join forces. This horizontal collaboration among peer telecommunications operators of equal power and status is practiced frequently when the goal is for the benefit of the whole industry segment or to further develop insights and common views through studies and joint technology exploration, helping to create win-win situations. Often standardization issues related to infrastructure are a driver, bringing together otherwise competing operators. Very often this collaboration is of a pre-competitive character and eligible for public funding. The respective role of an enabler of vertical collaboration has developed around Telekom Laboratories and is taken by EICT GmbH (http:// www.eict.de) where – among others – companies from the software, supplier and operator industry segments are working together along the value chain. EICT also acts as a regional cluster which is presented later in this article. For a more detailed footprint of EICT including further roles refer to Bub and Schläffler 2008. EICT partners also include companies from completely different industries like the automotive industry for ICT related hybrid value creation as well as public research institutions. An example of vertical collaboration for telecommunications operators is a collaboration scenario with an equipment vendor where the operational requirements of the eventual customer are being fed into the equipment development at an early phase. The operator has access to, and insight into, competencies and trends that could only be achieved otherwise with considerable internal effort. In return, the vendor receives information about operational know-how and end customer know-how that are necessary for targeted innovation and business models. A prerequisite for a well working ecosystem is an accordingly tuned enterprise architecture and modularization approach (Arnold and Dunaj 2007). Telekom Laboratories, representing the telecommunications operator’s share in the value chain, focuses on the missing functions that cannot be purchased from equipment vendors. Fig. 1 shows how this ecosystem works in Telekom Laboratories’ building block approach. A typical feature of this kind of industrial collaboration is that no money is exchanged, i.e. the companies carry for their own expenses or invest rather in a common consortium infrastructure or partnership fees. The collaboration takes place at ‘eye level’ and within an already established framework with regards to intellectual property rights (IPR) and confidentiality.

52

B. Organizing for Getting the Most out of Openness

Contributions

Final Product Customization

Corporate R&D

Missing function

A

Customization

Vendors B

In-house Functions Vendor Functions

Figure 1 R&D at Telekom Laboratories relies on strong partnering with vendors. Internal innovation at itself, focuses on the so-called missing functions.

Subcontracting

Subcontracting R&D project work has the advantage of limiting research and exploration risks through one-off project structures (as opposed to permanent staff). This allows the company to leverage its innovative capacity better (e.g., [13]). On the other hand, any knowledge built up is also harder to keep and protect. A particular, but very frequent variant practiced by Telekom Laboratories is subcontracting universities and public research organizations. Some of the main factors motivating universities to cooperate with industrial partners are: • Access to practical and applied knowledge; • Access to empirical data; • Access to financial resources. (Rohrbeck and Arnold 2006). The most prominent advantages for companies include: • Access to the most recent results from basic research; • The sourcing of highly qualified project staff, also with respect to future recruitment. For further advantages of this kind of collaboration including a comparison with literature see also Rohrbeck and Arnold 2006 and Soh and Rob-

Partnering for Research and Development within an Open Innovation Framework

53

erts 2005. There is a major conflict of interests to overcome in order to establish a successful cooperation between industry and universities that are predominantly public institutions or private institutions working for the public good. Industrial work in high tech industries is characterized by the continuous attempt to win a technological advantage over the competition, which eventually should result in a short time to market of superior products, and if possible in an exclusive IPR-related to funded research. This contrasts with the universities’ goal of publishing early, disseminating labeled own contributions to science quickly, and thus working for the public good, while gaining recognition and reputation. An increasing number of institutions have an interest in marketing the IPR on their own. As a standard practice, Telekom Laboratories has an interest in receiving the IPR for work that has been initiated and funded by Telekom Laboratories – although the problem might have been scientifically formulated and detailed by a subcontracted institution. Telekom Laboratories proposes a financial compensation model that includes a personal bonus and take over of the financial costs of the patent process. Sometimes, if work on a specific topic has not been initiated by Telekom Laboratories, it is not possible to gain the preexisting IPR including exclusive rights to new incremental IPR. Then the advantage can be early insight and time advance or better licensing conditions. This model has helped either patenting ideas that otherwise might have not been initiated or have been protected by the inventors, or it has helped the institutions market existing IPR better. Telekom Laboratories has installed an IPR process with quick response times that accounts for both sides’ interests and helps to leverage the best of both worlds. While sticking to its commitment of excellence, scientific publications of projects funded by Telekom Laboratories will be submitted to reviewed conferences and journals. Over the duration of the respective secret single-blind or double-blind review process, Telekom Laboratories will start the IPR process and guarantee no delay of the planned publication date of the work. During this time Telekom Laboratories will consider the eligibility of the work for filing a patent or explicitly waive the right of filing a patent. An on-site patent attorney at Telekom Laboratories helps to support the filing of submissions. Telekom Laboratories, or on a broader baseline EICT, recruits staff with background in both scientific work and work on industrial conditions. When recruiting scientists for projects, Telekom Laboratories takes care that the candidates are interested in working on applied topics that are potentially relevant, and, in turn, Telekom Laboratories grants a large amount of academic freedom within these boundaries.

54

B. Organizing for Getting the Most out of Openness

Example of Telekom Laboratories at Ben-Gurion University As the first research site outside of Berlin, Telekom Laboratories has founded a related lab in Beer-Sheva, Israel, on the Ben-Gurion University of the Negev Campus. The agreement is based on project contracts with an organizational framework. Telekom Laboratories has permanently sent a senior employee to the campus who acts as a liaison officer capable of mediating between the specific company needs and the academic world. In addition, the institute has established a director who is a profiled academic with industrial work experience. The project staff is recruited casewise from professors and students who are interested in applied R&D. The selected project work packages should match their academic career. Their work focuses mainly on new algorithms, protocols and prototypes that serve as a model for re-implementations in case of transfer. As a matter of fact, IPR are a crucial key performance indicator for the lab. Labs has established a target-based reward model. Before every project setup, preexisting IPR are communicated and the transfer of new or incremental IPR as well as a license model for the existing IPR is negotiated. The lab focuses on mainly two R&D areas (security and human interfaces), a fact that enables the contributing staff to profit from continuously built up knowledge and industrial insight. Since its inception in 2006, the lab has worked with output highly relevant to industrial business and filed patents. Moreover, it has proven its academic excellence through several papers on prominent international conferences.

Pre-competitive collaboration Especially when there is a given need for standardization, pre-competitive collaboration often makes sense. Common goals can be to develop the market further so that every participant can benefit. Collaboration with competitors can include work on interfaces and standardization for the general benefit of society. In this context, public funding also becomes possible. Innovation clusters

The cooperation principle can be leveraged particularly well in clusters where a critical number of institutions and companies are working, linked closely through a common base of understanding and supported, for example, by framework agreements, common topics of interest, social networks, and maybe even geographical proximity.

Partnering for Research and Development within an Open Innovation Framework

55

Clusters facilitate mutual trust and thus the exchange of informal knowledge that is necessary for open innovation and hybrid value creation. Social networks are often cited as the most important building blocks of Silicon Valley (Lee et al. 2000; Saxanian 1994) and other regions in the USA and Asia (Walshock et al. 2002; Miller et al. 2007). The sources consistently cite the necessity for research institutions with excellent scientists, as well as the need for an organization that serves as a hub and catalyst for communication and for setting up the project. Regional cluster of Berlin

The cooperation principle can be leveraged particularly well in regional clusters, where a large number of institutions are located within a small geographical area. The region of Berlin contains three universities working in the ICT domain, four public Fraunhofer Institutes for applied research as well as further universities of applied science, and so provides a very good culture medium for cluster building, according to the criteria above. The attractiveness of the dynamic city for young people has an enormously positive influence on the development of human resources of international character. The ICT and media sector in Berlin boasts 8,000 companies and 100,000 employees, generating a critical mass according to the criteria above. Telekom Laboratories is only one example of a highly rated institution to have grown out of this successful ecosystem by leveraging a wide partner network of open innovation. That is, a small team of Telekom Laboratories employees serve as contractors on many projects for a huge number of external institutes and companies or for industrial collaboration at eye level. It is important to mention that the majority of the projects are financed by industry, with the goal of inventing new protected products and developing prototypes which can be passed directly to product marketing for further productization. Regional partnerships with the Berlin Institute of Technology of and the Berlin Fraunhofer institutes have proven to be particularly helpful for idea generation and realization. The European Center for Information and Communication Technologies

Fully-meshed innovation clusters go further than the simple cooperation of individual companies and public research institutions. The partners do not cooperate bilaterally, instead many companies and public research institutions link their R&D activities in a public-private partnership. The Eu-

56

B. Organizing for Getting the Most out of Openness

ropean Center for Information and Communication Technologies (EICT) in Berlin runs a partner program where Berlin Institute of Technology and Fraunhofer Society are public members and four industry companies (among them Deutsche Telekom as founding member) are industrial and private members. It has been cited many times as a successful model of a public-private partnership (PPP) (e.g., Frank et al. 2007; Wissenschaftsrat 2007; Kotschatzky et al. 2008). The EICT understands its role as a service company and catalyst for providing the framework and facilitating the setup of joint projects, also beyond the pre-competitive part, i.e., also in the exclusive areas surrounding products. The EICT can be regarded as a neutral “cluster operator.” One successful service offered by PPP is the generation of a social network, facilitating the exchange of informal and implicit knowledge (Rice 2006; Nambisan and Sawhney 2007). This is organized through a regular series of events and by supporting and orchestrating regular contacts between the partners. In addition to this, the EICT has launched an open innovation portal where partners can access and exchange EICT-confidential information. The potential of IT-Tools for the promotion of open innovation is regarded as high (e.g., Dodgson et al. 2006). A continuous expansion of the open innovation portal of the EICT is on-going. The EICT partners mutually consider themselves as “friends and family.” For example, when sourcing subcontractors, Telekom Laboratories double-checks EICT partners before requesting external partners. A stumbling block when starting a project cooperation is often the tedious compilation of contracts and IPR regulations. The EICT offers framework contracts with respective patent regulations so that where there is a specific need, individual contracts can be set up quickly instead of fresh negotiations being required. A common test and validation infrastructure is currently being set up, for example an inter-organizational IP test lab. The EICT also offers project management, tools, and know-how for setting up projects in both the European Union and German public funding arenas. The EICT facilitates access to both industrial and public funding. The character of joint projects can be both product-oriented as well as pre-competitive. The PPP ensures that a big pool of overarching ideas is available and social networking within and outside the partner area is promoted. The EICT does not create innovations per se – this is done by the partner entities – however, it does act as a catalyst and provides a trusted framework for open innovation among its member organizations.

Partnering for Research and Development within an Open Innovation Framework

57

Conclusion Research and development for the dynamic ICT industry requires a powerful framework for cooperation outside a company’s boundaries. This framework has to address both horizontal and vertical collaboration. An optimally tuned balance of interests of all participating entities is necessary for successful implementation of open innovation with partner organizations. Functioning models of cooperation always require clear rules for ownership of IPR. The possibility to acquire IPR from academic institutions increases their attractiveness for companies. Clusters help to make companies’ borders more transparent and increase the exchange of innovation. Founding mediating companies like EICT helps in finding a common baseline and in establishing a framework of trust in order to open up and share.

References Anderson J. 1997: “Technology foresight for competitive advantage,” Long Range Planning, Vol. 30, No. 5, pp. 665–677. Arnold H., Dunaj M. 2007: “Enterprise Architecture and Modularization in Telco R&D as a Response to an Environment of Technological Uncertainty,” Proc. ICIN, Bordeaux. Becker J., Krcmar H. 2008: “Integration von Produktion und Dienstleistung – Hybride Wertschöpfung”, Wirtschaftsinformatik 50 (2008) 3. Bub U., Schläffer C. 2008: “Umsetzung von offener Innovation durch industrielle Cluster und Public Private Partnerships”, Bullinger (ed.) “Beschleunigte Innovation mit regionalen und industrienahen Forschungsclustern”, Fraunhofer IRB. Chesbrough, H. 2003: “Open Innovation: The New Imperative for Creating and Profiting from Technology,” Harvard Business School Press. Christensen C., Overdorf M. 2000: “Meeting the challenge of disruptive change,” Harvard Business Review, Vol. 78, No. 2, pp. 66–76. Dodgson M., Gann D., Salter A. 2006: “The role of technology in the shift towards open innovation: the case of Procter & Gamble,” R&D Management, Vol. 36, No. 3, pp. 333–346. Faems D., Van Looy B., Debackere K. 2005: “Interorganizational collaboration and innovation: Toward a portfolio approach,” Journal of Product Innovation Management, Vol. 22, No. 3, pp. 238–250. Frank A., V. Meyer-Guckel V., Schneider C. 2007: “Innovationsfaktor Kooperation – Bericht des Stifterverbandes zur Zusammenarbeit zwischen Unternehmen und Hochschulen,” Edition Stifterverband Berlin: Stifterverband für die Deutsche Wissenschaft, pp. 148. Koschatzky K., Hemer J., Stahlecker T., Bührer S., Wolf B. 2008: “An-Institute und neue strategische Forschungspartnerschaften im deutschen Innovationssystem,” Fraunhofer IRB. Lee C., Miller W., Hancock M., Rowen H. (eds) 2000: “The Silicon Valley Edge,” Stanford Business Press, Stanford CA.

58

B. Organizing for Getting the Most out of Openness

Meyer J.-A. 2002: “Knowledge and use of innovation methods in young SMEs,” International Journal of Entrepreneurship and Innovation Management, Vol. 2, No. 2/3, pp. 246–267. Miller S., Hancock M, Miller W. (eds) 2007: “Making IT – The Rise of Asia in High Tech,” Stanford Business Press, Stanford CA. Nambisan S., Sawhney M. 2007: “Marktreife Erfindungen,” Harvard Business Manager, Juni 2007. Rice J., Galvin P. 2006: “Alliance patterns during industry life cycle emergence”: the case of Ericsson and Nokia, Technovation, Vol. 26, No. 3, pp. 384–395. Rohrbeck, R. and Arnold, H. M. 2006. Making university-industry collaboration work – a case study on the Deutsche Telekom Laboratories contrasted with findings in literature. ISPIM Annual Conference: “Networks for Innovation”, Athens, Greece. Rothwell, R. 1992: “Successful Industrial-Innovation -Critical Factors for the 1990s,” R&D Management, Vol. 22, No. 3, pp. 221–239. Saxenian, A. L. 1994: The regional advantage – Culture and competition in the Silicon Valley and Route 128. Shin H., Dong H. 2006: “Convergence of telecommunications, media and information technology, and implications for regulation,” Info – The journal of policy, regulation and strategy for telecommunications, Vol. 8, No. 1, pp. 42–56. Soh, P.-H., Roberts E. 2005: “Technology Alliances and Networks: An External Link to Research Capability,” IEEE Transactions on Engineering Management, Vol. 52, No. 4, pp. 419–428. Vanhaverbeke W., Peeters N. 2005: “Embracing Innovation as Strategy: Corporate Venturing, Competence Building and Corporate Strategy Making,” Creativity and Innovation Management, Vol. 14, No. 3, pp. 246–257. Walshok L., Furtek E., Lee Carolyn, Windham P. 2002: “Building Regional Innovation Capacity,” Industry & Higher Education, February 2002. Wissenschaftsrat 2007: “Empfehlungen zur Interaktion von Wissenschaft und Wirtschaft,” Drs. 7865-07, Oldenburg.

B. Organizing for Getting the Most out of Openness

59

Business (Lead) Customer Involvement in the Innovation Process

Increasing competition and the resulting shortening of product lifecycles give an advantage to those enterprises that focus their innovation efforts on early involvement with business customers. This section deals with the involvement of business customers in the innovation process at early stages, describes typical requirements for business customer involvement, and outlines the typical involvement of business customers in the innovation process used at Telekom Laboratories.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_7, © Springer-Verlag Berlin Heidelberg 2010

B. Organizing for Getting the Most out of Openness

60

Introduction According to Hauschildt (1997) the concept of innovation is best described as the result of a process which consists of a set of activities and a sequence of events (Figure 1).

Idea

Discovery

Research

Development

Invention

Market Launch

Exploitation

Innovation Process

Figure 1 The innovation process. Source: following Hauschildt (1997)

The increased heterogeneity of consumer needs for novel and better products and dramatically short, truncated product lifecycles have put substantial pressure on innovative companies (Christensen 2000), while also inducing higher product development costs (Gupta and Wilemon 1990). The early stages of the innovation process are predestined to act as the innovation integration point. The accessibility of single constituents can be considered early on, but can also help in eliminating rework and reducing costs: 75–85% of product life-cycle costs are accounted for in the early product innovation process, while only assuming approximately 5–7% of the total expenses of a project. Moreover, the early stages also determine about 80% of roadmap and deadline coordination and 70% of the quality (Bürgel and Zeller 1997). Finally, early involvement empowers downstream participants, i.e., they have a say before decisions are finalized. The combination of highly constraining factors and intriguing opportunities has led enterprises to look for a strategy of how to deal effectively with this competitive environment and how to include the involvement of external innovation more efficiently into their own internal processes. Enterprises recognize the high potential of optimized planning to create a win-win situation, resulting in mutual benefits and competitive advantage, while significantly reducing the level of uncertainty in the commercialization of new products.

Involving corporate partners as an element of open innovation Building up interactive relationships with external partners along the entire innovation process and a successful transfer into the company’s own pro-

Business (Lead) Customer Involvement in the Innovation Process

61

duction pipeline, represents the ultimate goal when referring to integrating new components into the framework of an existing innovation process. This process, often employing at least the partial externalization of the innovation process, most commonly known as open innovation, has been championed by Chesbrough (2003) in particular (see Figure 2 and Figure 3). However, the concept of incorporating customers – especially experts – into an innoBoundary of the Firm

The Market

Research Projects

Research

Development

Figure 2 Closed innovation model. Source: Chesbrough (2003) Boundary of the Firm

New Market

The Market

Research Projects

Research

Development

Figure 3 Open innovation model. Source: Chesbrough (2003)

62

B. Organizing for Getting the Most out of Openness

vation process is not new and was first brought forward by von Hippel in 1986. According to Laursen and Salter (2004), open innovation is regarded as cooperation towards innovation, encompassing a broad range of vertical and horizontal networks of competitors, startups, suppliers, customers, and universities. Partnering with any of these external entities often significantly facilitates a company’s access to information, as immediate contact with suppliers and business customers constitutes a more reliable source of information in contrast to conventional market research. Moreover remodeling costs for a product can be cut, and any lack of internal expertise and knowledge can be supplied by third parties. Piller (2006) assumes these additional resources can greatly enlarge a company’s capacity to generate solutions and McDermott and O’Connor (2002) concur by recognizing that firms seek involvement with external constituents to fill either technical or market-based competency gaps. As an overall result, the concept of open innovation allows companies to reduce the risk associated with investment in innovation activities.

Open innovation

Customers are independent innovators

Customers are equal partners of the organization

Closed innovation

Initiated dialogue with manufactures

Enterpriseinitiated dialogue with customers

Customers are passive target of observation

Indirect collection of market/customer information

Figure 4 Forms of interaction and cooperation between companies and users. Source: following Reichwald and Piller (2005).

Von Hippel (2005) defines users as “firms or individual consumers that expect benefit from a service”. In practice, end-user involvement and integration has gained more importance within the reference frame of open innovation than the focus on longer-established cooperation with business customers, e.g., suppliers, etc. (Prahalad and Ramaswamy 2004, Enkel et al. 2005). While in the field of end-user-centered marketing the importance of customer involvement has been the subject of several studies and is regarded as one factor in a firm’s success (Kleinaltenkamp and Jacob 2002), there are few studies dealing with business-customer-driven innovation. Parthasarthy and Hammond (2002) propose that external integration can impact on both innovation frequency and speed by facilitating interaction with boundary groups. An analysis conducted by Jacob (2006) instructively demonstrates

Business (Lead) Customer Involvement in the Innovation Process

63

that a significant amount of market success depends on, and can be attributed to, customer involvement in internal processes. However, Jacob (2006) puts emphasis on the fact that certain prerequisites regarding competence have to be met for a successful involvement or integration of external constituents into internal processes. Where these requirements are not met, the inevitable result will, in most cases, be failure and the loss of the company’s valuable resources. The reason for the failure of many well-established enterprises can be seen in the fact that they entered a given market as late followers instead of focusing on the early stages of an innovation process beforehand. Competitors in these given markets already possessed resources specific for the market and a consistent framework of processes and activities. Late followers either had to generate these resources or secure them by acquisitions, which again needed to be integrated consistently into their own company before being utilized effectively. The result was a strategic competitive disadvantage for the diversifying companies (Chandler 2001). AT&T can be regarded as a symptomatic example of this process. In the mid-90s, telecommunications giant AT&T ventured away from its incumbent position in order to take advantage of converging ICT and directly market a product to the end-user. On account of competency gaps, the development of the product, a PDA, was achieved by partnering with software enterprises and consumer electronic firms. Profits were to be generated by the sale of additional communication services and not through the devices themselves. However, not even 4,000 devices were sold. AT&T’s failure can be attributed to the non-user-centric focus on the actual end device, the complexity of the product, and the lack of competence (Stieglitz 2004). On account of increasing pressure, AT&T eventually abandoned its original plans. Similarly, IBM also tried to take advantage of a converging ICT sector. After its own internal efforts to develop private branch exchanges (PBX) as a late follower, IBM also failed in using a collective strategy with PBX manufacturer Mittel (Gambardella and Torrisi 1998). Eventually, IBM acquired the leading PBX company Rolm for $1.5 billion. Like AT&T, IBM was not capable of integrating Rolm’s strategic resources; in fact the acquisition rather destroyed Rolm’s consistent system of activities. IBM lacked insight both into Rolm’s PBX product and the market, and by trying to force Rolm to fit into a mainframe computer business model, Rolm’s actual capabilities were prevented from unfolding. Moreover, the situation also caused key technical employees to leave the company (Stieglitz 2004).

64

B. Organizing for Getting the Most out of Openness

General requirements for innovation involvement The involvement of business-customer-driven innovation may lead business customers to function as strategic collaborators. Such a partnership is characterized by mutual trust, competence, open communication and long-term cooperation between the two partners. The collaborators work together to achieve both low costs and product quality with both enterprises profiting from the benefits. Despite the integration of processes both companies remain in charge of their internal processes and decide for themselves when and how to integrate components developed by the partner. Business customer integration seeks to involve partners early in the product development phase to maintain low costs and even allow immediate corrections to a project. The close integration of efforts afforded by a partnership provides a solid basis for responding to market changes and for meeting customer requirements. The crucial difference is that while end-user–driven innovation does not have an agenda for process development and is applied rather to future improvements for many off-the-shelf products, lead business customers and enterprises have to stick to their established internal innovation processes. Business-customer-driven innovation processes are based on distinct sets of complexities and require a different approach to the involvement of innovation than an end-user-driven model. Compared to business-to-customer organizations, companies active in the business-to-business sector have distinct advantages in certain forms of innovation involvement, since they govern a service or end product, while business-to-customer activities require the consideration of many constituents before the actual creation of an end product. The high level of complexity mainly associated with the innovation process arises from the fact that it comes in so many shapes and sizes. Picking the best approach and most appropriate form of innovation – open or closed, disruptive or incremental, driven by which unit, and considering socio-cultural shifts – can not only be very difficult but can even pose a threat to corporate culture (Day and Shoemaker 2004). In order to successfully integrate innovation processes it is imperative for prospective partners to be aware of mutual requirements and determine competences when joining forces in the course of innovation. Requirements generally involve many different constituents that not only differ internally and externally but also from project to project and from business sector to business sector. According to Capron et al. (2001), organizational transactions, i.e., acquisitions or processes involving the integration of external processes into the internal framework, involve three key re-

Business (Lead) Customer Involvement in the Innovation Process

65

quirements. They can be defined as physical assets, employee competence and organizational processes. Physical assets mainly refer to financial resources, partnering possibilities, and R&D infrastructure of an organization. Understanding employee competence, and hence the competence of an organization, has its origins in the behavioural sciences, but its applications have long been transferred to questions concerning management and organizational levels (e.g., Sirdeshmukh et al. 2002). Ritter (1999) differentiates dimensions of competence by applying arguments from psychology, which assume that the competence of an organization not only depends on the extent of task fulfilment but also on the formal qualification of its members. Finally, integrating processes and combining activities and resources are indispensable to conducting such a business operation and to generating financial benefit. Of course, the requirements mentioned above have to be met by both a company and the lead business customer before process involvement can be considered (Figure 5). Organizations (internal)

Lead business customer (external)

Assets Assets

Processes Processes

Employees Employees

Figure 5 Lead customer process involvement.

The model does not include all complexities, but illustrates the major focus of this chapter, i.e., process involvement. The form of business-customer– driven process involvement can be determined along two dimensions. Process involvement varies significantly, depending on its continuity and how actively or passively business customers wish to engage in the process. It can range from a one-time interaction with a business customer for only a

66

B. Organizing for Getting the Most out of Openness

Active engagement Passive engagement

Degree of integration

specific task, to a long-term relationship with continuous interaction during an entire development project or even several projects. Altogether four different scenarios can be observed along the two axes of such a model (Figure 6). Short term partnering may be appealing to companies because of the seemingly low level of commitment or short duration. However, depending on the uncertainty of an innovation, i.e., the difference between required processes and competence and the level at which these constituents are already available at an enterprise to cope with the task, more or less effort has to be made in coordination. Consequently, the more complex the environment, the more it is likely to be counter-productive for short-term involvement as it introduces more equivocality and, even if information is available, constituents may find it demanding to cope with ambiguity. The higher the desired complexity and the number of processes in place, the more businesscustomer-driven process involvement belongs in the long term/continuous dimension.

Process integration

Process integration

Larly stage integration

Larly stage integration

Innovation community

Innovation community

Transfer into product pipeline

Transfer into product pipeline

Reduction of cost & complexity

Reduction of cost & complexity

Process integration

Process integration

Early stage integration

Early stage integration

Innovation community

Innovation community

Transfer into product pipeline

Transfer into product pipeline

Reduction of cost & complexity

Reduction of cost & complexity

short term / one time

Time frame

long term / continuos high low

Figure 6 Dimensions of business-customer-driven process involvement.

Business (Lead) Customer Involvement in the Innovation Process

67

Business-customer process involvement While relatively good models exist for product innovation and customer integration both from a theoretical and a practical viewpoint (e.g., Koufteros et al. 2005), there are few empirical studies on business-customer process involvement. Due to the fact that business-customer involvement in the early stages can improve market success significantly, the following section focuses on business-customer process involvement. Business customers should not only be involved in the late stages of the innovation process but also, in particular, during the early stages. The early stages of the innovation process seem to be predestined to act as the innovation involvement point (see Figure 7) and, furthermore, feature some significant advantages which are described below. Gate 3 Gate 1

Gate 4

Gate 2 R&D Project Implementation

Utilization of results in operation units

Product Portfolio

Gate 5 Idea Generation / Proposal

Project Scheme

Project Plan R&D Project Implementation

Gate 1

Transfer preparation

Operating units Product Generation Processes

Transfer Project

Product Portfolio

Gate 2 Gate 3

Gate 4

Gate 5

Early Stages Business Customer Involvement

Figure 7 Business-customer process involvement Source: following Cooper et al. (2002)

1. Reducing rework costs: Several empirical studies have surveyed the cost allocation of product life-cycle costs and found that 75–85% of these costs are accounted for in the early stages of the product innovation process. It is apparent that through early-stage process involvement, business partners can significantly eliminate these rework costs (Bürgel and Zeller 1997). 2. Long-term commitment (partnering): Business-customer process involvement can be characterized by a longterm commitment, better communication, and a mutual trust between the collaborators. The partners work together in a closer and a more coordinated way in terms of innovation development. Results can significantly facilitate transfers into new business models, products, and services.

68

B. Organizing for Getting the Most out of Openness

3. Bundling of competencies: Different corporate cultures and complex processes can bring innovation involvement or integration to a standstill. The involvement of business customers in the innovation process refers to the ability to bundle the competencies of the business customers in an efficient manner, not only in terms of idea generation in the first stages, but also concerning the important issue of transferring the results to the respective product portfolio. 4. Collaboration at early stages: Innovations need to consider different types of elements for idea creation at early stages (see Figure 8). a) Foresighting: Strategic foresight considers market foresight (the environment), end-customer involvement, and technology aspects from the beginning of the scouting process. Strategic Foresight

R&D Community Collaborations

Market foresight technology intelligence customer foresight

Collaborate with leading university, research centers and industries Ideas Genesis

Opportunity Analysis

Ideas Selection Early Stages

Opportunity Identification

Concept & Technology Development

Involve Business Customer Early

Identification of business customer needs and involvement in early stages

Figure 8 Early stage involvement: business customer Source: following Rohrbeck et al. (2007)

Market foresight: A major task in this process step is the assessment of competitors, the identification and assessment of products, as well as the availability of product or services in lead markets. Technological intelligence: The pace of technological change and the demands for better and novel products and services require compa-

Business (Lead) Customer Involvement in the Innovation Process

69

nies to innovate continually and very quickly. The major task within this process step is the identification, assessment, and use of information on emerging and regressive technologies. Customer foresight: The identification and anticipation of consumer needs, lifestyle, and socio-cultural trends will increasingly become a key factor of success in understanding customers better. This knowledge area helps to ensure that the products and services offered actually provide customers with what they actually demand. b) R&D Community Collaboration: Following the open-innovation idea, a strong partner network with leading national/international universities, research institutes, and industry partners is used to create a wide network of collaboration. This step in the process deals with the idea that open innovation provides an increase of revenue through additional commercialization paths and the reduction of development costs. c) Involvement of business customers: The involvement of business customers at early stages can enhance idea generation and provide an insight into developing innovations for common use (see above). Telekom Laboratories use different ways to collaborate with other important companies within and outside the ICT industry: through Cluster Framework, joint development projects, and consortia projects.

Conclusion This section gives evidence that business-customer involvement at early process stages can provide beneficial improvements for the collaborating partners in the area of innovation development. A set of general requirements for innovation process involvement for business customers has been developed with a detailed and adjusted stage gate process that covers organizational and process competencies, as well as process integration. In order to extend this concept, a qualitative analysis has shown, that businesscustomer involvement at early stages offers basic advantages to the collaborating enterprises. In future, empirical studies will have to be carried out in order to verify a quantitative correlation between business-customer process involvement at early stages and market success.

70

B. Organizing for Getting the Most out of Openness

References Balachandra, R. and Friar, J. H. 1997. Factors for Success in R&D Projects and New Product Introduction. IEEE Transactions on Engineering Management 3: 276–287. Booz, Allen, and Hamilton. 1983. New product management for the 1980s. New York: BAH Inc. Bullinger, H. J. 1990. IAO-Studie: F&E heute – Industrielle Forschung und Entwicklung in der Bundesrepublik Deutschland. Stuttgart: Gesellschaft für Management und Technologie. Bürgel, H. D. and Zeller, A. 1997. Controlling kritischer Erfolgsfaktoren in der Forschung und Entwicklung. Controlling 9(4): 218–225. Capron, L., Mitchell, W. and Swaminathan, A. 2001. Asset Divestiture Following Horizontal Acquisitions in Europe and North America, 1988–1992. Strategic Management Journal 22: 817–844. Chandler, A. D., Jr. 2001. Inventing the Electronic Century: The Epic Story of the Consumer Electronics and Computer Industries. New York: Free Press. Chesbrough, H. 2003. The Era of Open Innovation. Sloan Management Review 44: 35–41. Chesbrough, H. 2006. Open innovation: The new imperative for creating and profiting from technology. Cambridge, MA: Harvard Business School Press. Christensen, C. M. 2000. The Innovator’s Dilemma. Cambridge, MA: Harvard Business School Press. Cooper, R. G. , Edgett, S. J. & Kleinschmidt, E. J. 2002, Optimizing the stage-gate process: What best-practice companies do-I, Research Technology Management, Vol. 45 (5): 21–27. Day, G. S. and Shoemaker, P. J. H. 2004. Driving Through the Fog: Managing at the Edge. Long Range Planning 37: 127–142. Enkel, E., Kausch, C. and Gassmann, O. 2005. Managing the Risk of Customer Integration. European Management Journal 23(2): 203–213. Gambardella A. and Torrisi, S. 1998. Does technological convergence imply convergence in markets? Evidence from the electronics industry. Research Policy 5: 554–463. Gupta, A. K. and Wilemon, D. L. 1990. Accelerating the Development of Technology-Based New Products. California Management Review 32(2): 24–44. Hauschildt, J. 1997. Innovationsmanagement. München: Vahlen. Jacob, F. 2006. Preparing industrial suppliers for customer integration, Industrial Marketing Management 35: 45–56. Laursen. K and Salter, A. 2004. Open for Innovation: The Role of Openness in Explaining Innovation Performance among UK Manufacturing Firms. Working Paper, Tanaka Business School, Imperial College London / Copenhagen Business School. Presented at the AOM 2004 Meeting in New Orleans, LA, 2004. Kleinaltenkamp, M. and Jacob, F. 2002. German approaches to business-to-business-marketing theory. Journal of Business Research 55: 149–155. Koufteros, X., Vonderembse, M. and Jayaram, J. 2005. Internal and External Integration for Product Development: The Contingency Effects of Uncertainty, Equivocality and Platform Strategy. Decision Sciences 36(1): 97–133. McDermott, C. M. and O’Connor, G. C. 2002. Managing radical innovation: An overview of emergent strategy issues. Journal of Product Innovation Management 19(6): 424–438. Moore, W. L. and Pessemier, E. A. 1993. Product planning and management: designing and delivering value. New York: McGraw-Hill. Parthasarthy, R. and Hammond, J. 2002. Product innovation input and outcome: Moderating effects of the innovation process. Journal of Engineering and Technology Management 19(1): 75–91. Piller, F. T. 2006. User Innovation: Der Kunde als Initiator und Beteiligter im Innovationsprozess.

Business (Lead) Customer Involvement in the Innovation Process

71

Open Innovation. Freier Austausch von Wissen als soziales, politisches und wirtschaftliches Erfolgsmodell, ed. Drossou, O. and Krempl, S. Hannover: Heise-dpunkt. Porter, M. E. 1990. The Competitive Advantage of Nations. New York: Free Press. Prahalad, C. K. and Ramaswamy, V. 2004. The Future of Competition – Co-Creating unique value with customers. Boston, MA: Harvard Business School Press. Reichwald, R. and Piller, F. T. 2005. Open Innovation: Kunden als Partner im Innovationsprozess. Munich: TUM. Ritter, T. 1999. The networking company. Industrial Marketing Management 28(5): 467–479. Rohrbeck, R., Arnold, H. M. & Heuer, J. 2007, Strategic Foresight – a case study on the Deutsche Telekom Laboratories, ISPIM-Asia Conference: New Delhi, India. Schumpeter, J. A. 1934. Theorie der wirtschaftlichen Entwicklung. 4th ed. Leipzig: Dunckner & Humblot. Sirdeshmukh, D., Singh, J. and Sabol, B. 2002. Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing 66: 15–37. Spencer, W. J. 1990. Research to product: A major U.S. challenge. California Management Review 32(2): 45–53. Stieglitz, N. 2004. Strategie und Wettbewerb in konvergierenden Märkten. Wiesbaden: Deutscher Universitäts-Verlag. Tollin, K. 2002. Customization as a Business Strategy: A Barrier to Customer Integration in Product Development. Total Quality Management 13(4): 427–439. Von Hippel, E. 1986. Lead Users: A source of novel product concepts. Management Science 32: 791–805. Von Hippel, E. 2005. Democratizing Innovation. Cambridge, MA: MIT Press.

72

C. Early Stage Market Research and Innovation Marketing

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

Users are important actors in innovation projects: The market success of new products and services depends highly on addressing the right customer requirements without overloading them with too many new features and technologies (Lettl and Gemünden 2005; Mason and Harris 2005). Going beyond traditional market research and integrating customers intensively into the innovation process is an important measure of market-oriented innovation management (Ernst 2002; Iansiti and Clark 1994). Deutsche Telekom Laboratories reduces market uncertainties in new product and service projects by applying the concept of user-driven innovation. User-driven innovation is based on innovative customer research tools specifically tailored to four innovation phases: exploration (e.g., day-in-the-life visits), ideation (e.g., lead-user workshops), selection/execution (e.g., user clinics), and commercialization (e.g., field tests). Deutsche Telekom Laboratories applies a variety of these “intelligent”, user-driven innovation tools in order to guarantee a phase-specific, integrated customer orientation. This section gives a methodological overview and examples based on the case study of interactive mobile TV (IMTV).

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_8, © Springer-Verlag Berlin Heidelberg 2010

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

73

Introduction The telecommunication sector depends heavily on successful innovation (Deloitte 2007; Bitkom 2007) – methods to predict what innovation the customer wants and expects from new product and service offerings give companies decisive competitive advantage. Innovation market research and the integration of customers in the innovation process are important ways to reduce uncertainties (Ernst 2002). The position of the customer has successively changed over the last 30 years from a passive recipient to an active co-designer in the creation of value. Successful innovators use competence within an extended network, which includes the competence of customers in particular (Prahalad and Ramaswamy 2000; Gemünden et al. 1996). In this context, the ability to integrate customers is decisive. Iansiti and Clark (1994) understand this to mean the ability to allow information about customers and their needs to flow into the process of innovation on the basis of mutual learning processes. However, capturing and defining customer needs is not an easy task. Many of the methods used in traditional market research (e.g., quantitative surveys and forecast models) are unsuitable for evaluating the market potential of highly innovative offerings (Trott 2002; Wind and Mahajan 1997). Innovation management can nevertheless draw on a number of “intelligent”, user-driven innovation tools that produce reliable market information, even in cases where customers find it difficult to envisage the involved product (Rosenthal and Capper 2006). These include exploration tools (e.g., day-inthe-life visits, diary research), ideation tools (e.g., information pump, leaduser workshops), selection/execution tools (e.g., user clinics), and commercialization tools (e.g., field tests). For example, lead-user workshops generate information about the needs of visionary customers at an early point in time (Lüthje and Herstatt 2004; von Hippel 1986). The use of so-called user clinics enables the screening of the match between new service/product concepts and prototypes and the needs of different customer segments at later stages (Burmann 1994). This section gives a methodological overview of the userdriven innovation tools applied by Deutsche Telekom Laboratories.

Theoretical foundations Innovation and innovation phases

Innovations are new products or services that significantly improve on a previous state, through the combination of purpose, the need addressed, and

74

C. Early Stage Market Research and Innovation Marketing

means, the technology used (Hauschildt and Salomo 2007). Newness can be determined not just by looking at facts but also by looking for a gradual difference compared to the previous state. Innovations with a high degree of newness, so-called discontinuous innovations, are products that use relatively new technology, e.g., cars driven by fuel-cells; that provide a comparatively large, new benefit to the customer in the marketplace, e.g., 3M Post-it notes; or innovations which can be classified as relatively new in both areas, e.g., the first mobile phones (Bergstein and Estelami 2002). Empirical findings point out that discontinuous NPD projects present a particular challenge to management, owing to the different opportunity-to-risk ratio compared to incremental innovations (O’Connor and Veryzer 2001). In literature there are many models for the process of innovation, which vary in the terminology used, by the number of process phases, and by the diversity of the structuring and presumptions about activities being sequential or in parallel. Generally, and thus largely independently of the sector or situation, one can differentiate between the phases exploration, idea generation, selection/execution, and commercialization (Trommsdorff and Steinhoff 2007; Verworn and Herstatt 2002; Gerpott 1999): 1. The exploration phase refers to the initial fuzzy front end of an innovation project. Exploration aims at a deep and integrated understanding of current and future customers, e.g., in terms of their living/working situation, unsolved problems, needs, and wants. The desirable results of this innovation phase are so-called customer insights, vivid descriptions of unsolved problems or unmet needs presented from the customer’s perspective – their perception of, their beliefs about, and their feelings toward the problem. 2. The idea generation relates to the search for ideas for innovations as well as any initial pre-selection. For market pull innovations, demand presents the starting point for innovation; while technology push innovations are initiated by technical ideas or inventions, which then result in a search for an application (Chidamber and Kon 1994). Creativity is required, which can be supported by creativity techniques where – apart from internal sources – especially external sources such as customers come into question. 3. In the third phase, selection/execution, the investigation of the feasibility and the return on investment of the innovation in the marketplace take priority. Selection means that ideas for innovations are reduced to those that could potentially be successful. When assessing the commercial feasibility,particular care must be taken to see if and when the innovation will be accepted by the target customers (Ram and Sheth 1989).

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

75

The investment decision whether to pursue the idea further or to cancel the initiative comes at the end of this phase. This decision is usually based on a business plan or on a well-formulated concept. In the execution phase, the emphasis is on development activities, which are generally dominated by the production and testing of prototypes (Gruner and Homburg 2000). Iterative and parallel alternative paths are frequently pursued to solve technical problems. 4. The commercialization covers the introduction of the innovation to the market. Normally, the product has already been successfully tested in pilot installations, so that in this phase the emphasis is on addressing the wider market. In the interest of designing the operational processes as efficiently as possible, product changes at this stage are only marginal in nature. During the introduction, the marketing mix must be implemented by referring to the strategy followed with the innovation. Communication takes a particularly prominent role here. The innovation can only succeed on the market when the product advantages are perceived by the target customers and are understood as beneficial (Rogers 2003). Customer research and the limits of traditional tools

Market uncertainties arise due to inadequate knowledge about the market and the target customers. Finding the answers to market-related questions such as “What are concrete customer needs?” presents a major challenge to innovating companies (Rice et al. 2002). Innovation projects require a relatively large amount of information about the market (Leifer 1998; Gales and Mansour-Cole 1995). One decisive option to generate information with the objective of reducing market uncertainties consists of carrying out innovation market research and integrating the customer into the innovation process. Customer research potentially covers all phases of the innovation process and can be classified by various descriptive determinants – e.g., primary vs. secondary data, qualitative vs. quantitative methods, etc. (Berekoven et al. 2004). Systematic innovation customer research designed for the broad market should be reinforced with information related to individual customers (Workman 1993). The ability to do this can be seen as a part of the broader network competence which makes it possible for companies to establish and successfully use relationships to external partners (including customers) within their innovation processes (Gemünden et al. 1996). However, reducing market uncertainties for innovative new products and services is not an easy task. Traditional market research tools, such as on-

76

C. Early Stage Market Research and Innovation Marketing

line concept tests/surveys and quantitative forecast models, have proven to be inappropriate for evaluating the market potential of highly innovative offerings (Hoeffler 2003; Trott 2002). This is mainly due to two shortcomings: (1) Traditional methods are too superficial and have a strong tendency to associate with the past. This makes them unsuitable for identifying latent and future customer requirements (Day 2002; Ekström and Karlsson 2001). (2) Traditional approaches focus on evaluating solutions and implicitly assume that target customers already have sufficient knowledge of the products in question (Deszca et al. 1999). However, it can be assumed that target customers are often not as yet sufficiently well-informed to offer a valid assessment of specific functions and preferences in an ad hoc manner. There is a risk (often referred to as the risk of incrementalism) that respondents could prematurely reject innovative concepts (Christensen and Bower 1996). Bennett and Cooper (1979, 78) describe the limits of traditional market research very illustratively: “Picture the would-be market researcher eighty years ago attempting to gauge market reaction to a proposed new product, the automobile. Respondents to any questionnaire would have assured the market-oriented innovator that cars would frighten horses, make too much noise, run too fast, and be generally unreliable. The competition of that time, the horse, would be judged just too strong for a successful market entry.”

Some authors even claim it is better “to ignore the customers” in discontinuous innovation projects (Martin 1995, 83). This position refers to the thesis that customers are mentally bound to (product) functions they already know – so called functional fixedness (Ulwick 2002, 92). However, any decision to completely avoid customer research would have serious consequences; there is a substantial risk of bypassing customer requirements in the development process. There is plenty of empirical evidence of this effect in the high flop rates of otherwise excellent and highly innovative new products (Beverland et al. 2006; GfK 2006): • Flop rates go up to 70% – often because new products do not address customer needs • 60% of innovations fail due to conceptual, 40% due to commercial mistakes • Only about 30% of the introduced innovations have an adequate price-performance ratio, over 50% fail due to over-promising Customer orientation represents one of the strongest innovation success factors (Trommsdorf and Steinhoff 2007; Steinhoff 2006; Henard and Szymanski 2001). It can be concluded that there is a strong need for intelligent, userdriven innovation tools.

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

77

User-driven innovation at Deutsche Telekom Laboratories Use case: interactive mobile TV

Mobile service providers want to offer their customers a mobile TV experience to enhance customer loyalty. Mobile TV is regarded as a key service and as a market differentiator for future revenue generation (Seong 2008; Barrett 2006). Thus, mobile TV is accelerating the trend away from the passive linear TV experience by focusing on a more non-linear TV viewer experience, enabling more customer interactivity, and offering a broad spectrum of user-generated content. The levels of interaction with the system and with other viewers or communities are enlarged considerably by new, innovative technologies. It goes beyond more simple interaction scenarios, such as live user voting in a broadcast program. Full-scale interaction between the TV consumers themselves becomes possible, i.e., interaction within a community during a TV broadcast. So-called interactive mobile TV (IMTV) enables new, interactive, rich media services on mobile phones. In sum, there is huge potential for live mobile TV and program-related interactive services on mobile phones, such as interactive TV shows, interactive games, advertising, blogging, and shopping (Orgad 2006). Capturing and defining customer needs is not an easy task, especially for IMTV, as outlined above. This is because IMTV involves a high level of innovation, not only from the technological point of view, but also from the market’s perspective. IMTV innovations need to be seen not only in the light of those factors which promote its adoption, but also – and especially – in those which tend to inhibit it (Rogers 2003; Ram and Sheth 1989). Although future IMTV applications generally have tremendous potential to offer a new type of customer service – “a new mobile television experience” – they also demand considerable behavioral changes from the target customers. The required learning processes entail significant investment in terms of cognition, time, and money. For target customers, this represents a huge hurdle on the road to adoption. Such barriers to adoption not only have an influence on the purchasing decision, but also represent a considerable degree of market uncertainty for the innovating company. Mobile TV service providers need to ask themselves questions such as: “Which prospective customers actually have a need for interactive ‘lean-forward’ applications? What do different TV customer segments actually want in terms of interactive services? What are the product advantages of IMTV versus traditional (mobile) television that should be highlighted in the communication process? How much are prospective customers willing to pay for IMTV services?”

78

C. Early Stage Market Research and Innovation Marketing

By focusing on notions of interaction which, for the most part, are currently the preserve of TV experts, IMTV projects address unconscious and potential future customer needs, rather than requirements that have already been articulated by customers. Furthermore, it can be assumed that target customers are not, as yet, sufficiently well-informed to be able to offer a valid assessment of specific IMTV functions and preferences in an ad hoc manner. From this, one can conclude that using traditional market research methods, such as standardized surveys (including naive questions on public acceptance such as: “Would you buy this, if .....”), would produce inaccurate forecasts. User-driven innovation for interactive mobile TV

Product innovation research can draw on a number of “intelligent” customer research methods, which produce reliable market information, even in cases where customers find it difficult to envisage the product involved. Methods that break through the restrictions of traditional approaches make it possible to ascertain the level of acceptance of an innovative product well in advance of its introduction (Rosenthal and Capper 2006). Figure 1 contains a general overview of user-driven innovation tools used at Deutsche Telekom Laboratories. Exploration

! Day in the life visit ! Diary research ! Insight clinic ! Explorative interview

Idea Generation

! Information pump ! Ideation workshop, e.g. ! Lead user workshop ! Expert workshop

Selection & Execution

! Evaluation clinic ! Online survey/ new media panel

Figure 1 Overview: user-driven innovation toolbox

Commercialization

! Test market simulation ! Field test

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

79

First Phase: Exploration

As stated above, the main goal during this early phase of the innovation process is to discover the unsolved problems, needs, and wants of current and potential customers, and thus to detect market potential. Naturally, the question arises under which circumstances people are willing to reveal their needs and wants, for example, in the context of mobile service innovations. A spontaneous answer to that question might be: “We should just ask them”. While that simple method will work for a lot of incremental innovations it will most probably not do the job when aiming at the discontinuous innovations that a lot of mobile services are. In order to illustrate the problem, let us consider the following scenario. If, 15 years ago, mobile service user had been asked to name a few of their unsolved problems in the realms of telecommunication, would they have come up with things such as SMS, instant messaging, location based mobile services, and the like? Most likely a lot of ordinary mobile service users would not have. Nevertheless, nowadays these innovations are accepted services in the mobile telecommunications industry. So, what promising options do exist when trying to reveal latent (implicit, unknown) problems, needs, and wants? One possible answer to that question is the user-driven innovation toolbox. It contains a number of methods that are especially suitable for the early phase of exploration: 1. Day-in-the-life visits: The personal visits to (potential) customers are an integral part of this method. The customer is observed during his or her daily routine and interviewed whenever deemed necessary. These visits are usually conducted by small cross-functional teams. Depending on the branch of business, these teams may consist of market researchers, psychologists, product/marketing managers and/or engineers, etc. During and after the direct interaction with the customer, a vivid documentation is drawn up of, for example, the ICT infrastructure and the usage patterns of the person in question; this acts as a picture of the “user in the box” (e.g., Leonard and Rayport 1995; Mrazek et al. 1995). 2. Diary research: This is another very interesting research method especially suitable for the exploration phase. Target groups (e.g., lead users) are asked to keep topic-specific (online) diaries over a predefined period of time. The diaries in question are pre-structured according to the questions the researcher is interested in. They can, for example, focus on latent needs/wants, usability requirements, or drivers and barriers of a product or service innovation (e.g., Samli 1996). 3. Insight clinic: The origin of the user clinic method is the car clinic used in the auto industry. The name of this method results from the test person being invited to a special location (e.g., a workshop or a laboratory

80

C. Early Stage Market Research and Innovation Marketing

in the development department) where they are treated as “outpatients”, as if it were a visit to a medical clinic (Kunkel 2006; Burmann 1994). The car clinic method can be adapted for other sectors (Ozer 1999). In the case of insight clinics, direct personal interaction is realized along different “insight stations”. This can include current products and services and/or visualizations of possible future usage scenarios. The particular insight clinic can be designed for a specific topic: It can involve, for example, product/service confrontation to identify barriers, group discussions to identify latent needs, or the identification of means-end chains via a laddering technique (Braunstein et al. 2000). 4. Explorative interview: The exploratory interviews also allow latent needs and barriers to be identified. An example of a technique deployed during an interview is the introduction and prioritization of future usage functionalities – so-called mini concepts (Durgee et al. 1998).

Example tool: diary research for IMTV In June 2007 Deutsche Telekom Laboratories carried out a diary research in order to gain customer insights on the topic of IMTV. Participants of earlier research projects at Telekom Laboratories served as the basis for lead-user identification. Respondents’ attitude towards innovation and new technology as well as their attitude towards mobile TV determined selection. To begin with, participants were invited to briefing sessions in order to introduce them to the concept of mobile TV. The main goal of this introduction was to mentally focus the participants not only on content questions but also on the research topic, namely, interactivity in Mobile TV. Two mobile devices were presented with preinstalled interactive quizzes to give an impression of possible formats. Participants were then given the diary package consisting of the actual paper-based diary in the form of a little book and a voice recorder. A first chapter in the book gave an overview of the diary structure, detailed explanations on where and how to record thoughts and requirements, and instructions for the voice recorder. Each of the predefined areas of application (entertainment, commerce, communication, and information) was explained and visualized with corresponding examples. The remaining pages were provided for participants’ entries. During the 10-day diary phase participants were regularly contacted to assure constant project awareness. They were given trigger information and regular tips via SMS and email as to what areas could be of interest for interactive services. This way, potential ideas could be triggered in contexts participants had possibly not yet thought of. Participants were also called on a regular basis in order to offer assistance with the diaries if needed. The respondents posted more than 400 statements regarding needs and wants, requirements, and potential barriers to adoption.

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

81

Second Phase: ideation

The user-driven innovation toolbox also provides a number of methods to facilitate the search for, and the pre-selection of, new ideas to be used in the second phase of the innovation process, the idea generation: 1. Information pump: This approach is based on the results of game theory research conducted at the Massachusetts Institute of Technology (Dahan and Hauser 2002). It is essentially a web-based discussion forum similar to the well-established Delphi method. Participants articulate their ideas for future products and services. The ideas are then evaluated in a second round by the other participants (Müller 1997). 2. Ideation workshop: During ideation workshops, creativity techniques, such as trend cards, learning from other brands, and building the future world via Lego bricks, are employed in order to elicit creative input. In other words, the intention here is to delve deeply into the creative potential of the participants and to develop idealized designs (Magidson 2004). Ideation workshop participants can either be experts or lead users (Lüthje and Herstatt 2004) with regards to the topic of the workshop – or a combination of both.

Example tool: lead-user workshop IMTV Following the diary research described above the participants were invited to take part in two one-day lead-user workshops in order to gather detailed ideas on the development of innovative services and products for IMTV. In order to trigger segmentspecific ideas, the original group of diary participants was separated into two independent workshop groups: “commuters” and “young and technologically versed”. After a brief introduction of the participants as well as the workshop’s approach and goal, several creativity techniques were applied. In order to create a less tense and therefore more open-minded and creative atmosphere, a Lego brick game was chosen to start off the actual workshop. Participants were asked to build future mobile TV scenarios. Communication between the participants was encouraged and first results in the form of usage scenarios in a wide variety of contexts (e.g., while riding the subway, during outdoor activities) were achieved. Another tool used in order to enhance creativity was the so-called character puppets method. These puppets represent a specific target group in a stereotypical way. Examples of these character puppets are “young conservatives” or “seniors with need for comfort”. Participants were asked to think of possible IMTV services or products each of the puppets might already use or may otherwise be interested in. The ben-

82

C. Early Stage Market Research and Innovation Marketing

efit of this activity is a change of perspective, which leads to new ideas. Additionally, it opens up the possibility to state ideas that one may be uncomfortable with – simply by attributing it to another person. Furthermore, constant triggering information, such as short movies on future trends, was provided throughout the workshop. Ideas with sufficient analogy were combined into use cases. At the end of the workshop, participants were asked to evaluate the 57 use cases developed during the workshops, in terms of benefit and effort expectancy, and to name their favorites. They were asked to state the reasons for their judgments in order to check consistency.

Third phase: selection/execution

Uncertainties about the market can lead to two types of incorrect decisions. The first type of incorrect decision occurs if management invests or continues to invest in an innovation project, although the potential expected success is classified as low. The consequence is that the innovation will result in a disappointing performance in the marketplace. If the results are a long way from the expectations, then it will be seen as a classic flop. The second type of incorrect decision occurs when there is an idea for a product which could be successful in the marketplace, but management chooses not to invest or not to continue to invest in the NPD project. This means that the high potential for success is not recognized, and the chance for success in the marketplace is unfortunately missed (Eliashberg et al. 1997). An improved information base and more precise estimations of the potential market for the new product increase the probability of correct decisions. Potentially successful new product development projects can then be tracked as effectively as possible and potentially unsuccessful projects can be cancelled as soon as possible (Eliashberg et al. 1997). Again the userdriven innovation toolbox provides us with several methods that can help us gain the respective information: 1. Evaluation clinic: The so-called evaluation clinic method presents an interesting approach in the context of the selection phase. Evaluation clinics can yield information about a variety of areas such as product use, understanding customer preferences in detail, product modifications, and learning behavior. Data collection can be based on observations, questionnaires, in-depth interviews, and/or group discussion. The clinic method consists of three basic steps: determining the basic unit and recruitment of the test subjects, planning and execution of the data collection, and data analysis as well as implementation of the results. The

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

83

optional use of conjoint analysis assures valid and reliable results for the prioritization of product and service functions. Conjoint analysis is based on the fact that relative values of attributes can be measured better jointly than when considered in isolation. The respondent’s benefit perception can then be displayed by using utility points (Dahan and Hauser 2002). The main difference between evaluation clinics and traditional market research methods lies in the separation between an intense learning phase (vivid presentation and explanation of new service ideas) and a subsequent preference measurement (e.g., via conjoint analysis). Depending on the readiness for production, differing presentation concepts for products and services can be integrated into an evaluation clinic: use cases, visualizations, mockups, prototypes, up to final products.

Example tool: IMTV evaluation clinic A series of evaluation clinics with potential mobile TV users was conducted to gather empirical data on preferences for new IMTV services. Using existing T-Mobile life-stage customer segmentation, 36 participants were recruited for each one of five relevant customer segments to ensure an appropriate audience sample for preference measurement (in sum n=180). To begin with, participants underwent an intense learning phase during which IMTV services were vividly explained using visualizations and demonstrators. Prior to every module (mobile entertainment, interactive mobile TV services, and mobile TV), an introduction was given and demo-handhelds were integrated to illustrate the service ideas. To clarify features of services and offers, handouts were given to respondents after the presentation of features on flipcharts. Afterwards, participants were asked to answer questions about the IMTV services they had just been introduced to. The questions were programmed as an online survey and participants had laptops at their disposal. On the basis of these questions, an Adaptive Conjoint Analysis (ACA) and a Choice-Based Conjoint (CBC) were conducted to gain insight into the participants’ benefit perception of separate attributes of the services described. To adjust roadmap prioritizations, three strategic adopter groups were defined –innovators or early adopters, majority, and laggards (Rogers 2003). The results of the IMTV evaluation clinics showed a high interest in IMTV on the customers’ side. Conjoint analysis gave results on preferences in terms of mobile entertainment, IMTV services, and mobile TV. Based on the adopter-specific preferences, short-term, mid-term, and long-term roadmaps were derived, and customer price sensitivity was measured.

84

C. Early Stage Market Research and Innovation Marketing

2. Online survey/New Media Panel: In the case of an online survey based on the Telekom Laboratories’ New Media Panel (panel respondents who have sufficient knowledge about new media innovations like IPTV and mobile TV), product concepts or prototypes are posted online according to their features. A web-based conjoint analysis is carried out in order to obtain valid and reliable information about the prioritization of products and service functions from the (potential) customer’s point of view. Service or product ideas that have been found to be highly attractive can also be subject to deeper analysis (e.g., usability specification analysis/ testing, pricing models, etc.).

Example tool: IMTV field test The next step in the IMTV innovation project will be the introduction of selected services to a mini-test market in Berlin. Experience has shown that insights gained through new product research in other countries (e.g., USA, UK, Japan) cannot be easily extrapolated to the German market. Results concerning benefits, price sensitivities, and adoption probabilities will have a higher validity and reliability for the German market and will reduce the probability of innovation failure. The basis of the IMTV field test is a Telekom Laboratories Innovation Panel consisting of 1000 active panelists located in Berlin.

Fourth Phase: Commercialization

Even in the later stages of the innovation process, such as the commercialization, a lot of mistakes can be made that may result in enormous and costly disappointments. The user-driven innovation toolbox also contains specific methods for the commercialization phase that can help to avoid such mistakes. Depending on the readiness for production, one of the following might be the method of choice (Trommsdorff and Steinhoff 2007): 1. Test market simulation: Test markets can be simulated in laboratories and/or virtual environments. Participants are able to gain experience in the usage of products and services and can then state purchase intentions (Jeppesen 2005). 2. Field test: An innovation is introduced into a real test market. The continuum of possible field tests stretches from market testing with selected users up to local market introduction (e.g., Berlin). The testing criteria may be benefits, usability, bugs, and the like.

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

85

Conclusion The active use of customer competence via the integration of customers is an essential characteristic of customer-oriented innovation processes (Steinhoff 2006; Lüthje 2002). Customer orientation is a critical factor, both for the success of the company (Singh and Ranchhod 2004) as well as for the success of the new product (Kahn 2001). Despite this, a lack of customer orientation continues to be a frequent phenomenon in the process of innovation (Mason and Harris 2005; Ekström and Karlsson 2001). Overcoming the bottleneck factor of customer orientation translates into the need for information. Both the generation of information via innovation market research and the integration of customers in the process of innovation serve to reduce uncertainty about the market (McDermott 1999). This ability can be seen as a part of broader network competence, which makes it possible for companies to establish and successfully use relationships to external partners (including customers) within their innovation processes. By concentrating as early as possible on the product functions most preferred by the target customer, the duration and costs of the product development process can be lowered. By meeting the customer’s needs as optimally as possible, the diffusion process (Rogers 2003) can be influenced positively. Market information enables initial estimates to be made of the potential market, reducing both the first and second types of incorrect decisions. The concept of user-driven innovation as applied at Deutsche Telekom Laboratories offers a systematic toolbox, which can be used for customer orientation in innovation projects. This section introduced exemplary tools based on the case study IMTV. Compared to the “traditional” TV world, the effort to build a convincing next generation IMTV value proposition is a much greater challenge. This not only requires a careful analysis of customer needs and their adoption rate of new services, but also an education-oriented marketing strategy and an efficient organization of the whole value chain for service orchestration and fulfillment.

86

C. Early Stage Market Research and Innovation Marketing

References Barrett, J. 2006. Mobile TV in Europe: Who needs a Standard? A Parks Associates White Paper. Bennett, R. C. and Cooper, R. G. 1979. Beyond the Marketing Concept. Business Horizons 22 (3): 76–83. Bergstein, H. and Estelami, H. 2002. A survey of emerging technologies for pricing new-to-theworld products. Journal of Product & Brand Management 11 (4/5): 303–318. Beverland, M. B., Ewing, M. T. and Matanda, M. J. 2006. Driving-market or market-driven? A case study analysis of the new product development practices of Chinese business-tobusiness firms. Industrial Marketing Management 35 (3): 383–393. Bitkom. 2007. Zukunft digitale Wirtschaft: Volkswirtschaftliche Bedeutung der ITK-Wirtschaft, Strategische Wachstumsfelder und Empfehlungen an Politik und Wirtschaft in Deutschland. 1–173. Braunstein, C., Hoyer, W. and Huber, F. 2000. Der Means End-Ansatz. Kundenorientierte Produktgestaltung, eds. Herrmann, A., Hertel, G. and Virt, W., 85–101. Munich: Vahlen. Burmann, G. 1994. Automobilmarktforschung: Faszination mit Fallgruben? Marktforschung, ed. Tomczak, T., 172–180. St. Gallen: Thexis. Chidamber, S. R. and Kon, H. B. 1994. A research retrospective of innovation inception and success: the technology-push, demand-pull question. International Journal of Technology Management 9 (1): 94–112. Christensen, C. M. and Bower, J. L. 1996. Customer Power, Strategic Investment, and the Failure of Leading Firms. Strategic Management Journal 17 (3): 197–218. Dahan, E. and Hauser, J. R. 2002. The virtual customer. Journal of Product Innovation Management 19 (5): 332–353. Day, G. S. 2002. Managing the market learning process. Journal of Business & Industrial Marketing 17 (4): 240–252. Deloitte. 2007. Telecommunications Predictions. Technology Media & Telecommunications Trends 2007. 1–24. Deszca, G., Munro, H. and Noori, H. 1999. Developing breakthrough products: challenges and options for market assessment, Journal of Operations Management 17 (6): 613–630. Durgee, J. F., O›Connor, G. C. and Veryzer, R. W. 1998. Using mini-concepts to identify opportunities for really new product functions. Journal of Consumer Marketing 15 (6): 525– 543. Ekström, K. M. and Karlsson, M. 2001. Customer oriented product development? An exploratory study of four Swedish SME›s. FE-rapport 2001-380. Göteborg. Eliashberg, J., Lilien, G. L. and Rao, V. R. 1997. Minimizing technological oversights: A marketing research perspective. Technological innovation: oversights and foresights, eds. Garud, R., Nayyar, P. R. and Shapira, Z. B., 214–230. Cambridge et al.: Cambridge University Press. Ernst, H. 2002. Success factors of new product development: a review of the empirical literature. International Journal of Management Reviews 4 (1): 1–40. Gales, L., Mansour-Cole, D. 1995. User involvement in innovation projects: Toward an information processing model. Journal of Engineering & Technology Management 12 (1/2): 77–109. Gemünden, H. G., Ritter, T. and Heydebreck, P. 1996. Network configuration and innovation success: An empirical analysis in German high-tech industries. International Journal of Research in Marketing 13 (5): 449–462. Gerpott, T. J. 1999. Strategisches Technologie- und Innovationsmanagement: Eine konzentrierte Einführung. Stuttgart: Schäffer-Poeschel.

Tools for User-Driven Innovation at Deutsche Telekom Laboratories

87

GfK. 2006. Launches and Relaunches als Motor der Wertschöpfung: Was ist Top, was ist Flop? GfK ConsumerScan Innovation Day, Nuremberg. Gruner, K. E. and Homburg, C. 2000. Does customer interaction enhance new product success? Journal of Business Research 49: 1–14. Hauschildt, J. and Salomo, S. 2007. Innovationsmanagement. Munich: Vahlen. Henard, D. H. and Szymanski, D. M. 2001. Why Some New Products Are More Successful Than Others. Journal of Marketing Research 38 (3): 362–375. Hoeffler, S. 2003. Measuring Preferences for Really New Products. Journal of Marketing Research 40 (4): 406–420. Iansiti, M. and Clark, K. B. 1994. Integration and dynamic capability: Evidence from product development in automobiles and mainframe computers. Industrial and Corporate Change 3 (Special Issue 1): 557–605. Jeppesen, L. B. 2005. User toolkits for innovation: Consumers support each other. Journal of Product Innovation Management 22 (4): 347–362. Kahn, K. B. 2001. Market orientation, interdepartmental integration, and product development performance. Journal of Product Innovation Management 18 (5): 314–323. Kunkel, D. 2006. How to host Car Clinic. Ward’s Dealer Business (January): 44. Leifer, R. 1998. An information processing approach for facilitating the fuzzy front end of breakthrough innovations. Proceedings International Conference on Engineering and Technology Management, ed. Peters, L. S., 130–135. Troy, NY, USA. Leonard, D. and Rayport, J. F. 1995. Spark Innovation Through Empathic Design. Harvard Business Review 75 (6): 102–113. Lettl, C. and Gemünden, H. G. 2005. The entrepreneurial role of innovative users. Journal of Business & Industrial Marketing 20 (7): 339–345. Lüthje, C. 2002. Kundenorientierung im Innovationsprozess: Eine Untersuchung der KundenHersteller-Interaktion in Konsumgütermärkten. Wiesbaden: Deutscher Universitäts-Verlag. Lüthje, C. and Herstatt, C. 2004. The Lead-user method: an outline of empirical findings and issues for future research. R&D Management 34 (5): 553–568. Magidson, J. 2004. Shifting Your Customers into “Wish Mode”: Tools for Generating New Product Ideas and Breakthroughs. The PDMA Toolbook 2 for New Product Development, eds. Belliveau, P., Griffin, A. and Somermeyer, S. M., 235–268. Hoboken, New Jersey: Wiley & Sons. Martin, J. 1995. Ignore your customer. Fortune, May 1, 83–86. Mason, K. and Harris, L. C. 2005. Pitfalls in evaluating market orientation: An exploration of executives’ interpretations. Long Range Planning 38 (4): 373–391. McDermott, C. M. 1999. Managing radical product development in large manufacturing firms: a longitudinal study. Journal of Operations Management 17 (6): 631–644. Mrazek, D., Dray, S. and Dyer, N. 1995. Day-In-The-Life-Visits: How to make them happen globally – or discovering unstated needs in a family environment. European Society For Opinion And Marketing Research (ESOMAR), Making the decision: 48. ESOMAR Marketing Research Congress: 353–359. Müller, S. 1997. Die Delphi-Befragung. Ein qualitatives Prognoseverfahren. Marktforschung und Management 41 (1): 26–32. O‘Connor, G. C. and Veryzer, R. W. 2001. The nature of market visioning for technology-based radical innovation. Journal of Product Innovation Management 18: 231–246. Orgad, S. 2006. This box was made for walking: How will mobile television transform viewers’ experience and change advertising. Nokia report: 1–24. Ozer, M. 1999. A Survey of New Product Evaluation Models. The Journal of Product Innovation Management 16: 77–94. Prahalad, C. K. and Ramaswamy, V. 2000. Wenn Kundenkompetenz das Geschäftsmodell mitbestimmt. Harvard Business Manager 22 (4): 64–75.

88

C. Early Stage Market Research and Innovation Marketing

Ram, S. and Sheth, J. N. 1989. Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing 6 (2): 5–14. Rice, M. P., Leifer, R. and O›Connor, G. C. 2002. Commercializing Discontinuous Innovation: Bridging the Gap from Discontinuous Innovation Project to Operations. IEEE Transactions on Engineering Management 49 (4): 330–340. Rogers, E. M. 2003. Diffusion of Innovations. New York: Free Press. Rosenthal, S. R. and Capper, M. 2006. Ethnographies in the Front End: Designing for Enhanced Customer Experiences. Journal of Product Innovation Management 23 (3): 215–237. Samli, A. C. 1996. Developing Futuristic Product Portfolios: A Major Panacea for the Sluggish American Industry. Industrial Marketing Management 25 (6): 589–600. Seong, S. 2008. Mobile TV in Japan. Ovum report: 1–18. Singh, S. and Ranchhod, A. 2004. Market orientation and customer satisfaction: Evidence from British machine tool industry. Industrial Marketing Management 33 (2): 135–144. Steinhoff, F. 2006. Kundenorientierung bei hochgradigen Innovationen. Konzeptualisierung, empirische Bestandsaufnahme und Erfolgsbetrachtung. Wiesbaden: Gabler. Trommsdorff, V. and Steinhoff, F. 2007. Innovationsmarketing. Munich: Vahlen. Trott, P. 2002. Innovation Management and New Product Development. Harlow et al: Pearson. Ulwick, A. W. 2002. Turn Customer Input into Innovation. Harvard Business Review 80 (1): 91–97. Verworn, B. and Herstatt, C. 2002. The innovation process: an introduction to process models. Working Paper No. 12, TU Hamburg-Harburg: 1–16. Von Hippel, E. 1986. Lead-users: A Source of Novel Product Concepts. Management Science 32 (7): 791–805. Wind, J. and Mahajan, V. 1997. Issues and Opportunities in New Product Development: An Introduction to the Special Issue. Journal of Marketing Research 34 (1): 1–12. Workman, J. P. 1993. Marketing’s Limited Role in New Product Development in One Computer Systems Firm. Journal of Marketing Research 30 (4): 405–421.

C. Early Stage Market Research and Innovation Marketing

89

Options for Customer Integration in the Open Innovation Paradigm at Deutsche Telekom

The consistent application of the open innovation logic leads to the inclusion of the customer. Open innovation helps to open up company boundaries, promoting cooperation with and integration of external know-how brokers to meet the more exacting innovation ecosystem requirements. In addition to subsidiaries, suppliers, competitors, consultants, as well as private and public research institutions; first and foremost, the customer plays a decisive role (Eurostat 2007). Customers are equal partners in the development processes at Deutsche Telekom as part of a consistent open innovation approach. The four customer integration methods – lead user method, ideas competition, virtual communities, and “toolkits for innovation” – are based on theoretical principles and are exemplary for user integration in the open innovation approach.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_9, © Springer-Verlag Berlin Heidelberg 2010

90

C. Early Stage Market Research and Innovation Marketing

Introduction The first task is to answer the question: “To what extent do customers have any sort of innovative potential, which manufacturers can leverage?” Various studies show that users independently develop new products or fine-tune existing products to meet their own needs. Take extreme sports equipment for example, where more than a third of users developed new or improved products (Franke and Shah 2003) This trend is indicative of a change in roles on the part of the customer, shifting from the traditional consumer to the active, shaping innovator. The customer is no longer solely a user that influences the manufacturer’s product development as a “statistical average.” In fact, customers are now aware of their needs, develop ideas independently, and, in certain cases, even develop full-fledged prototypes to solve their problems. As part of research into success factors behind innovation management, the product’s uniqueness in relation to the superior customer benefit plays a particularly decisive role, alongside the classic business administration performance criteria (efficiency, cost, and time). It is not unusual to find that even companies renowned for their highly innovative products, such as Sony, Apple or Philips, have in the past made costly errors in the area of marketing and customer orientation (Rosen et al. 1998). These were essentially the result of misunderstood customer expectations and the associated failure to adapt performance to customer needs. If, however, the customer is involved early on in the innovation process as an equal partner, the customer’s innovation potential can be leveraged. This also reduces the uncertainty of “development not geared to market needs” since the jointly developed products correspond more closely to actual customer requirements. At the same time, the early involvement of the customer reduces time-to-market by eliminating time-consuming iteration cycles and test phases. Studies have also shown that product concepts developed in cooperation enjoy, in certain cases, significantly higher revenue and market share forecasts, a higher level of innovation, and greater potential to set up a separate product family (Lilien et al. 2002). Need and prerequisites for open innovation in the telecommunications industry

The telecommunications sector is also increasingly leveraging the potential of external know-how brokers to develop products in partnership. The technological shift in the telecommunications industry facilitates new forms of

Options for Customer Integration in the Open Innovation Paradigm

91

cooperation and product development. In the early days of telecommunications, services and technology platforms were inextricably linked, such as the telephone in respect to data services and data networks. In particular, the introduction of the packet-based IP network has prompted the decoupling of telecommunications services and networks, bringing the decentralized, parallel creation of multiple services on a single network in its wake. This separation of network and service has also enabled external developers to offer their own services, leading to a sharp increase in external innovators and, in turn, fiercer competition. This has not only led to the need to succeed in an increasingly competitive marketplace. The aforementioned development means that telecommunications providers can also increasingly involve external partners in the innovation process. While open innovation methods may well have been applied generally, or customer integration applied specifically in the past, practical implementation has, however, been simplified enormously thanks to new technological opportunities. While in the past Telekom Laboratories we have been looking almost exclusively at partner companies and institutions, increasing modularization and, in turn, simplification of service development will in future involve customers more and more in the development of innovations, as the following will demonstrate.

Open innovation through customer integration at Deutsche Telekom The literature focuses on various customer integration tools. This section looks at four methods where the customer is involved as an innovator and not just as a bearer of needs. The methods are initially outlined and then their use at Deutsche Telekom is discussed. At present, all the tools are in use or have already been successfully used at Deutsche Telekom. Lead user method

The lead user method is a four-stage approach to generating ideas and developing concepts. The aim is to identify lead users and incorporate these into the innovation process in order to work together with them to develop new product concepts or improve existing concepts. Users with very high innovation potential are designated lead users, who can then be characterized by means of the following two attributes:

92

C. Early Stage Market Research and Innovation Marketing

• They are currently conscious of needs that will only be relevant to a larger market segment in future. • They expect above-average benefits from solving their problem. Due to their dissatisfaction with the existing market offering, lead users are highly motivated to become innovative. In particular, the first attribute makes them interesting innovation partners for companies since their needs reflect future market segments. The four-stage approach of the lead user method is shown in Figure 1. Four steps of lead user method

1. Start of lead user project: Set up a team and objectives of the project

2. Trend analysis: Identify significant technological and market trends

3. Lead User Identification: Identify lead users, who lead the identified trends

4. Development of solutions: Design concepts in collaboration with identified lead users in a workshop

Figure 1 Lead user method

This manifests itself in the very high innovation potential of the concepts developed using this method. Nonetheless, users require a great deal of specialist know-how in their particular field. The method is now used in disparate industries and renowned companies, such as 3M, Philips, Kellogg, Nestlé, or Hilti, all of whom have already successfully used it (Olson and Bakke 2004). The lead user method has been used at Deutsche Telekom for “online gaming.” The project was broken down into various phases, the kind of approach typically found in the literature: Phase 1 – Define the search field: The project team was made up of staff from the innovation department, students, and course leaders. “Online gaming” was defined as the search field. This is regarded as the central entertainment medium of the future; major future potential is forecasted for multimedia solutions in particular.

Options for Customer Integration in the Open Innovation Paradigm

93

Phase 2 – Ascertain trends: Secondary research and expert interviews were used to identify 16 trends as particularly promising for the “online gaming” search field. From these, the three trends “casual gaming,” “linking real and virtual worlds,” and “all-in-one platform” were selected for further analysis, on the basis of the criteria: relevance and transferability. Phase 3 – Identify lead users: The search was conducted using the pyramiding method based on social networks and posting. Lead users were sought both in the target market and in similar markets such as mobile communications. 46 potential lead users were identified in this way, of which nine were ultimately invited to a workshop. Phase 4 – Lead user workshop: Three groups, each consisting of three lead users, two students, and one company representative, developed three concepts in the workshop. To share their particular concept ideas with the other group members, a plenary session was set up so concepts could be regularly presented and discussed. The experience gained in the project confirms that the lead user method is generally suitable for use within Deutsche Telekom. During the project the requisite methodology expertise was developed, so that this method can be tailored to other innovation projects. However, the three developed product concepts did not live up to the radical level of innovation expected by the company, which is also seen in the aforementioned literature as one of the main drivers for using the lead user method. This issue will be addressed in future lead user projects by modifying the remit. Instead of the clear focus on developing one product idea per group, the aim in future should be to generate numerous ideas in an open-creative session and only prioritize and flesh these out in the final stage. Ideas competition

An ideas competition is an invitation to the general public or a specific target group to submit topic-related contributions within a certain time frame. Submitted ideas are generally assessed by a group of experts on the basis of various assessment criteria and prizes are awarded based on results achieved. A simpler version involves a prize-draw with various topic-related categories. This method is particularly suited to consumer goods markets since it is a simple way to reach numerous anonymous users who do not need any indepth technical product knowledge. By firmly putting the development of function-related future scenarios at center stage, ideas competitions enable a wide range of innovation levels to be reached, right through to radical inno-

94

C. Early Stage Market Research and Innovation Marketing

vations. Nevertheless, the suggestions need to be technically and economically viable. Ideas competitions vary in terms of how specific the task is and how elaborate the submitted ideas need to be. Task specificity can range from a very detailed (e.g., optimization of the product individualization program “mi adidas” at Adidas), through to a very open remit (e.g. ideas competition on the “Car of the Future” at Audi). The scope, quality and nature of the concepts to be submitted can vary from simple user statements through to prototypes (Walcher 2007). Deutsche Telekom has been running the ideas competition “Television of the Future.” This is geared both to developers – who may be individuals, companies, start-ups, and other institutions – and consumers with no technical know-how. The entire range of individuals and institutions submitting ideas is leveraged in line with the remarks made above. Developers are awarded with up to one million euros, distributed to the particular winners in various phases. Basically the remit is extremely broad, with no restrictions on topic selection, to avoid stifling participants’ creativity. In this respect, task specificity and the required level of detail differ for the developers and the consumers; higher levels of development are naturally demanded from the developers. Various possible topic areas such as participation in TV, community applications, or new business models were suggested to developers as possible starting points, while essentially leaving developers free to choose any topic. Interested developers submit written project proposals in the first stage, from which Deutsche Telekom experts in the field of “IPTV” select the 10 best contributions. As the competition progresses, an increasing amount of detail is required. Deutsche Telekom provides the winners with a “Developer Kit,” which they can use to convert their ideas into prototypes. The projects are assessed by a jury, consisting of internal and external experts from the media industry, trade press, and the university sector to ensure the developed prototypes are assessed objectively and professionally. Three of the 10 participants go on to qualify for the next stage. These three finalists then develop their application on Deutsche Telekom’s IPTV platform. The jury ultimately selects the final winner. The application ideas are assessed on the basis of various criteria: usability (ease of use and integrability with the existing interface), customer benefits, and quality of the technical solution. Among consumers, the remit is deliberately kept very general. End customers are asked to submit their own ideas on the “TV of the Future” via video or text messages. No limits are placed on participants’ imagination in order to generate the most innovative ideas. Essentially, the idea is to lev-

Options for Customer Integration in the Open Innovation Paradigm

95

erage the huge ideas potential of the anonymous consumer market; as such, the level of detail required is minimal. In conclusion, Deutsche Telekom utilizes the entire range of options for the ideas competition by involving developers as well as anonymous consumers. Accordingly, as stated above, the task specificity and level of detail also vary. While definitive conclusions cannot be drawn yet, initial results would, however, indicate that ideas competitions should also be used in future as a worthwhile means of integrating customers. Virtual communities

Enterprises utilize various means to tap into the innovation potential of virtual communities. This covers the whole span from the observation of and interaction with members of existing communities to the initiation of own innovation communities whose main purpose is to generate innovations. 1. Community observation involves “scanning” of existing product and brand communities for innovative contributions. These communities allow the anonymous observation of a specific user group in order to identify innovation potential. Experience shows that one contribution out of a hundred includes items relevant to innovation (Henkel and Sander 2007). Analyzing individual contributions for innovative ideas is, however, extremely time-consuming, so that this form of customer integration does not provide satisfactory results. 2. Enterprises can also interact actively with community members in addition to observing them. Particularly active or innovative users can be targeted directly or the entire community asked to submit proposals for new products and changes. In the meantime, various members can comment on proposals made by others and, in turn, further develop these proposals by means of interaction. 3. The third and most active form of customer integration based on virtual communities was used by a Japanese clothing, household goods, and food retail chain. Muji involves its customers in the development process through its muji.net virtual community. This platform asks customers to design new products, which are also assessed by the community members. If one of these products reaches a minimum order quantity stipulated beforehand by Muji, it goes into production and is distributed (Ogawa and Piller 2005). Like the ideas competition, virtual communities are particularly suited to consumer goods markets. Here, too, numerous anonymous users can be tar-

96

C. Early Stage Market Research and Innovation Marketing

geted from whom no specialist knowledge is demanded. The presentation options supported by the Internet, such as 3-D animations or videos, can also be exploited productively. Ultimately, communities can be combined effectively with other methods, such as an ideas competition or the toolkits set out below. Together with other research institutions, Deutsche Telekom is developing a joint development platform for the future Internet. This platform combines multiple developers and organizations, providing access to specific research findings. The initiative is focused on trialing, emulating, testing, and jointly developing new services for the future Internet. The community created here corresponds to the typology stipulated in the points 2 and 3 above. Ideas can be shared in the community and commented upon within the community. Through the access to a large pool of developers specializing in telecommunications and web-based services, the findings of in-house research and development can easily be validated, and their acceptance can be gauged. However, joint developments can also be implemented. The platform is still being set up, so that no final conclusions can be drawn at this point. Opening up to professional customers or research institutions does, however, represent a step in the right direction. In future, an increased use of consumer communities for innovation development can be expected. This makes particular sense since many telecommunications products are tailored to special communities and frequently include specific community elements. Toolkits for innovation

Toolkits for innovation are toolkits the manufacturer provides to customers, so they themselves can implement the needs-related portion of development tasks. The needs-related innovation activity is thus moved into the customer domain. Effective toolkits are generally characterized by five elements, which are shown in Figure 2. The toolkit for innovation allows manufacturers to respond to very heterogeneous product-related demands. In addition, the use of toolkits does away with time-consuming, costly iteration cycles. However, users often need specific skills (e.g., programming skills). Moreover, users of toolkits for innovation always have to keep to the predefined parameters and definition areas associated with design variables and rules. As such, radical innovations are unlikely to happen since the user cannot extend the toolkit’s technological limits.

Options for Customer Integration in the Open Innovation Paradigm

97

Five elements of an effective toolkit for innovation

1. The toolkit offers the user a „trial-and-error“-learning through a simulated feedback of his current solution.

2. It posses about an appropriate solution space that offers the user a sufficient degree of freedom.

3. The toolkit is user-friendly. It allows the user to introduce his abilities and to work with his specific design language.

4. It prepares modular templates. The user has access to standardized components and can focus his creative work on selected aspects.

5.The toolkit is able to transfer the users language into the system language to ensure the manufacturing of the designed product.

Figure 2 Toolkits for innovation

Deutsche Telekom provides a toolkit for innovation to web application developers. This is therefore not geared to consumers, but primarily to enterprise customers and developers. The Deutsche Telekom “Developer Garden” provides network-centric services, such as voice call and short message service, based on open interfaces. A modular approach is adopted – as required above – so that developers can easily integrate core telecommunications network functionalities into their applications and test these without spending a great deal of time and money. For instance, telecommunications services can be easily combined with existing web applications; thus, user friendliness is also ensured. Consequently, new communications solutions can be developed without Deutsche Telekom having to make changes within its core network. The need to implement the solution in the live system is thus also taken into account. The opening up and provisioning of these functions enables developers to generate and, at the same time, fine-tune innovative services. As part of the “Developer Portal,” developers are gradually provided with additional services in order to increase the number of offered functions and,

98

C. Early Stage Market Research and Innovation Marketing

in turn, the variety of innovation. The potential solution space is therefore continually extended. In its existing form, developers and enterprise customers in particular can be targeted with the toolkit. Increasing modularization of service components means consumers are also increasingly able to participate themselves in innovation development. Developments especially in the area of “Web X.0” require and promote customer involvement in the innovation process; toolkits are an important tool in this respect.

Conclusion In the age of open innovation, companies within the telecommunications sector must directly integrate customers and developers in the innovation process in a bid to leverage their creative potential. This approach also reflects the change in how enterprises perceive the customer – over the past few years the customer has developed from a passive to an active component of the product development and marketing process. This section has identified four methods to integrate customers in enterprise development processes, as well as the associated implementation at Deutsche Telekom. The illustrated examples tend to involve developers, in other words users of pre-products, who have been integrated as customers into the development process. The initial results appear to confirm that these methods can be used successfully without wide-ranging, industry-specific modifications. Overall, customer integration is promoted by technological developments, especially the decoupling of technology and services as well as the associated modularization of service components. As such, it is most likely to see end customers also become increasingly integrated into the innovation process in the future.

References Eurostat (2007) „News Release 27 (February 22).“ Franke, N. and S. Shah (2003). „How communities support innovative activities: an exploration of assistance and sharing among end-users.“ Research Policy 32(1): 157–178. Henkel, J. and J. Sander (2007). Identifikation innovativer Nutzer in virtuellen Communities. Management der frühen Innovationsphasen. C. Herstatt and B. Verworn. Wiesbaden, Gabler: 77–110.

Options for Customer Integration in the Open Innovation Paradigm

99

Lilien, G. L., P. D. Morrison, et al. (2002). „Performance assessment of the lead user idea-generation process for new product development.“ Management Science 48(8): 1042–1059. Ogawa, S. and F. Piller (2005). „Collective customer commitment. Turning market research expenditures into sales.“ MIT Sloan School of Management Working Paper. Olson, E. and G. Bakke (2004). „Creating breakthrough innovations by implementing the Lead User methodology.“ Telektronikk(2). Rosen, D., J. Schroeder, et al. (1998). „Marketing high tech products: lessons in customer focus from the marketplace.“ Academy of Marketing Science Review 1998(6). Walcher, D. (2007). Der Ideenwettbewerb als Methode der aktiven Kundenintegration: Theorie, empirische Analyse und Implikationen für den Innovationsprozess Wiesbaden, DUV.

100

C. Early Stage Market Research and Innovation Marketing

Segmentation and Evaluation Tools to Project Customer Potential

Selecting the most promising innovation project requires – among other criteria – a sufficient yet practicable indication about its market potential. This projection takes place in the Information and Communication Technologies (ICT) market, where costumer needs are becoming increasingly heterogeneous. Companies make use of various approaches based on customer and market segmentations in order to reduce the complexity of markets by grouping consumers with similar product interests and buying behavior. This section addresses socio-demographic as well as socio-cultural segmentations and discusses how they can be used in the process of innovation development and the projection of future customer potential.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_10, © Springer-Verlag Berlin Heidelberg 2010

Segmentation and Evaluation Tools to Project Customer Potential

101

Introduction Heterogeneous consumer needs in a rapidly changing market

ICT products, such as mobile phones, broadband internet connections, and mobile connectivity, play an increasing role in the everyday life of consumers. Surveys1 show that, for most customers, especially in younger groups of the population, mobile phones or access to the World Wide Web (WWW) are integral parts of life. Alignment of new products to the requirements of customers increases the probability of market success (Lüthje 2003). Therefore, market segmentations are wide-spread, accepted, and in use in companies all over the world, with different segmentations found in different industries2. Segmentations are used for various purposes, but they are especially valuable for product communication strategies and product development. According to Grover and Srinivasan (1987), they continue to be an important marketing concept in both academic literature and marketing practice. The initial premise for segmenting a market is the assumption that the market in focus is not entirely homogeneous (Beane and Ennis 1987) but rather characterized by a differentiated buying behavior of consumers. Smith (1956) mentions that market segmentations are disaggregative and recognize the existence of several demand schedules from the consumer’s perspective. Furthermore, they involve viewing a heterogeneous market, in this sense characterized by divergent demand, as “a number of smaller homogeneous markets in response to differing product preferences among important market segments”. Practitioners working with market segmentations should remind themselves: There is no silver bullet for segmenting markets either in theory or in practice. The approaches used should vary depending on industry and sales purpose, or specific research and assessment questions. However, according to Kotler (1980) and Kotler et al. (2007), operable and therefore useful segments must possess the following characteristics: “measurability, accessibility, and sustainability”. Moreover, segmentation variables can be divided into four major areas: geographic, demographic, psychographic, and behavioristic. Practical experience and studies such as “Semiometrie”, published in 2001 by SevenOne Media GmbH, show that demographic segmentations mostly do not suffice. More in-depth knowledge on how consumers interact with products and precisely why they do so is needed for a successful market launch and the vital accompanying communication strategies. Here, the relatively distal but still powerful determinants of buying behavior come into

102

C. Early Stage Market Research and Innovation Marketing

play, the so-called personal values (Brangule-Vlagsma et al. 2002). Personal values, which fit into Kotler’s areas of behavioristic, and partially psychographic variables have been used to explain the readiness for outgroup social contact (Sagiv and Schwartz 1995), voting behavior (Rokeach 1973), and the willingness to contribute to charity organizations (Manner and Miller 1978). In addition they influence the use of mass media (Rokeach and Ball-Rokeach 1989), socially conscious behavior (Anderson and Cunningham 1972), and ecological behavior (Ellen 1994). They can also be used to explain the existence of market segments (Brangule-Vlagsma et al. 2002). The following section will describe how an integration of personal values into demographic segmentations can be achieved and then successfully used for marketing and product development purposes. Indications for future product success in the early phases of innovation

Customer goodwill and acceptance play a crucial role in the market success of innovations. In fact, one of the greatest risks for practitioners in the field is the failure to gain this acceptance (Heiskanen et al. 2007). Christensen and Raynor (2004) assert that, in order to determine the success and the potential of a product, practitioners need to find answers to the following first two questions. In addition, another crucial question has to be added to this list for the purpose of defining the necessary segments more closely. 1. Is there a group of customers that cannot use a product or service due to the fact that they either cannot afford it or lack the necessary technical requirements or trained skills to actually use it? 2. Is there a large number of consumers in one of the lower market segments that would like to use a cheaper product with fewer, but still sufficient, functionalities? 3. How many and what type of customers are not willing to use the product? The third question is especially important because the exclusion of nonusers from the target group helps to narrow down the focus of the segments and, therefore, increases the reliability of the remaining segments. To adequately answer these questions, consumer needs have to be considered not only with regards to demographic variables, but also the values described in Section 1.1. This quickly reduces the number of existing segmentations because only a few approaches have incorporated these aspects in their models. The Sinus-Milieus®, the Sigma-Milieu® approach, and the Schober Life-

Segmentation and Evaluation Tools to Project Customer Potential

103

style Segmentation® are just a few examples of qualitative segmentations3. Companies might also choose to develop their own qualitative segmentations; however, they are then mostly used for internal purposes only. Using these segmentations gives practitioners deeper insights into empirically observed and verified user behavior. The benefits of using segmentations with a qualitative base are obvious; aspects such as the way that new technologies are communicated and promoted within specific social groups can be explained, and it is easier to understand where certain barriers with regards to ICT usage come from. A more detailed analysis of the drivers that positively influence and actually foster user behavior is also possible. The question, however, arises of how these segmentations can be used not only to understand one’s potential customers, but also to project the success of future products and services and, therefore, potentially quantify the relative importance of the project compared to other conceivable innovation projects. The following section first aims at describing the concept of social milieu theory and its origins, then an explanation is given of how this approach can be put to use by practitioners when they don’t need to fully understand what is behind these models.

Social milieus as a basis for customer segmentation The origin of social milieus and their implications for market segmentation

Social milieu theory can be traced back to the roots of modern sociology which was established and developed by French sociologists, such as Auguste Comte and Emile Durkheim, in the 19th century. The milieu concept has been constantly elaborated ever since and is in use in many different contexts, ranging from marketing and market research to the analysis of entire societies (Bourdieu 1998). In Germany, work on social milieus has increasingly gained importance since the 1980s due to research conducted into the German social structure. Milieu research can be described as a socio-cultural approach that aims at classifying individual norms, values, or attitudes to certain cultural patterns. These patterns do not only refer to the culture of different countries, but, more importantly, to different sub-cultures within a specific society (Geißler 2000). The idea behind milieu research is to first explore what kind of behavior, personal values, habits, and forms of interaction exist and then, in a second step, to cluster them into similar units or segments. Only after this, an analysis of objective socio-structural variables, such as income, age, sex, place

104

C. Early Stage Market Research and Innovation Marketing

of residence, or political orientation, is conducted within these segments. The approach could therefore be described as “grouping together likeminded people”. If people have similar interests, vote for the same parties, grew up in similar social environments, such as working class or upper class, they are likely to show similar behavioral patterns relevant to their buying behavior. As an alternative, the use of quantitative segmentations merely enables practitioners to determine how much buying power an individual has at his or her disposal, rather than understand the reasons why a person decides to spend a specific amount of money on a specific product. However, qualitative segmentations can, for example, give the insight that people aged between 50 to 60 years with higher levels of education and income only choose a specific brand of a product because they value its rather conservative, and therefore more trustworthy, image. The problem with qualitative insights into customer behavior is that they are usually based on smaller and mostly unrepresentative numbers of interviewees. This makes it complicated for the company to create general assumptions for its specific target groups. Therefore, before being able to apply the insights obtained for product strategy and marketing purposes, a reliable quantification of the milieu-theoretical approach should be pursued. Quantifying the qualitative: inherent challenges when using milieu theory and market studies in the process of new product development

One of the biggest challenges when working in the field of innovation development is to successfully introduce product innovations to the market. Market studies, if not explicitly designed for innovation development, have one major shortcoming: they usually do not survey the consumer’s interest in new products, and hence the potential market size remains unknown. Consequently, in a first step, companies often focus on so-called lead users; i.e., users with high levels of domain knowledge as well as higher levels of education and buying power. These lead users can give first valuable ideas on the acceptance of a product, especially in qualitative user studies, such as personal interviews or user observations. They can be a useful starting point for the development of new ideas and the evaluation of first prototypes or product concepts. This admittedly is not always applicable for every product that is introduced, especially in the field of telecommunications which usually deals with standardized and mass products that need to address the broader spectrum of consumers.

Segmentation and Evaluation Tools to Project Customer Potential

105

In addition, attitudes to price sensitivity can only be usefully applied to existing products because they are associated with specific ideas, values, and prior positive or negative experiences of the respective consumers. Innovative products signify a new and unknown terrain about which consumers might easily make assumptions with regards to usage frequency, practical necessity, and pricing. However, these assumptions are not transferable to broader target groups due to the fact that lead users do not statistically represent the entire market potential. Because most qualitative user studies in innovation development are based on relatively small samples, a quantification of the results obtained is hardly possible. All of these factors lead to a rather unclear picture of the future potential number of customers, and projections have to be made based on experience in the field and on reliable user data. For this purpose, Deutsche Telekom Laboratories developed a tool for projecting customer potential right from the start of the innovation process. The idea behind this so-called Customer Evaluation Tool (CET) is to combine milieu segmentation theory with annual quantitative market media studies, with the goal of estimating the development of future market potential. The web-based CET uses the Sinus-Milieus® segmentation developed by Sinus Sociovison for this purpose. The following chart gives an overview of the Sinus-Milieu® landscape in Germany:

Higher

1

Sinus B1

Sinus C12

Etablierte, 10%

Moderne Performer, 9%

Sinus A12 Konservative, 5%

Sinus B12 Postmaterielle, 10%

Sinus AB2 DDR-Nostalgische, 6%

Middle

2

Sinus B2 Bürgerliche Mitte, 16%

Sinus C2 Experimentalisten, 8%

Sinus A23 Traditionsverwurzelte, 14%

Lower

Social Status Basic Values

Sinus B3

Sinus B3

3

Hedonisten, 11%

Konsum-Materialisten, 11%

A

B

C

Tradition Sense of Duty and Order

Modernization Individualization, Self-actualization, Pleasure

Re-orientation Multiple Options, Experimentation, Paradoxes

Figure 1 The Sinus-Milieus® 2009 in Germany: social status and basic values.

106

C. Early Stage Market Research and Innovation Marketing

The segmentation developed by Sinus Sociovision can be very useful for new product development. Concrete benefits are that important criteria are addressed and surveyed, including monthly spending on ICT, education, and standard of living. In addition, the segmentation describes the values of each segment in detail, ranging from very traditional and conservative attitudes to values such as modernization and individualization, the acceptance of technology, opinion on health issues, and political orientation. By keeping in mind that additional inherent correlations exist between higher and lower levels of education and the respective social status, represented mostly through income and job positions, practitioners can draw valuable conclusions about the behavior of the potential customers in each segment. A second correlation is given between the core values of each segment and the distribution of age; meaning that more conservative values lie in the mindset of older people, whereas values such as personal freedom or individualism can be found in younger segments. All of these factors have an influence on the acceptance of new technologies and products that is often underestimated. A significant shortcoming of this segmentation approach, however, is the fact that industry-specific issues are only addressed marginally. The milieus were not initially developed to meet the requirements of the telecommunications industry and therefore do not provide answers to very specific ICT questions. Since it is possible to integrate specific topics into Germany’s market surveys, the gathering of more in-depth knowledge in the ICT sector is nonetheless possible. The most important issue, however, is the fact that the Sinus-Milieu® approach is used in almost all of Germany’s market studies with sample numbers ranging up to 30,000 respondents4. This allows for an excellent quantification of qualitative consumer data because it is possible to tell how many representatives of each segment exist in Germany and how, judging from the prior consumption patterns that are also collected in the surveys, they can be best addressed with new products. A concrete use case of how to empirically match milieu theory with quantitative data is described in the next chapter in order to show how decision makers can use complex social theory in a useful and practical manner in the process of successful innovation management.

Segmentation and Evaluation Tools to Project Customer Potential

107

Projecting customer potential by using the Customer Evaluation Tool (CET) Translating insights from market segments into the language of product managers and decision makers

The CET was programmed with a practical web interface to provide practitioners and marketing executives with a practical aid for the decision making process. The CET operates by harmonizing market adaptation and product lifecycle algorithms from marketing theory and practical industry-related experience, so that business cases based on specific product data or product analogies can be calculated. The calculation of churn rates and the integration of psychological price barriers are also provided for. As market analyses and customer potential estimations have to be conducted for every innovation project, difficulties often occur because there is no unified basis for the calculation of these cases. This is due to the fact that different departments might choose different approaches and different quantitative input. One of the biggest shortcomings of these business cases, therefore, is the lack of comparability, which is especially relevant for convergent services (i.e., services that incorporate different technologies and consist of several product components, such as IPTV or mobile internet). The target groups behind the business cases are often unspecific, and the diversity of the society and potential target groups is thus not entirely taken into account. The CET was, therefore, made available on the company’s intranet in order to allow for higher levels of transparency and for comparability. Furthermore, it provides easy access to market data and serves as an initial basis for discussion within interdisciplinary teams. Even though the Sinus-Milieus® are not a telecommunication specific segmentation, they were chosen as the basis for the tool because they are incorporated in all relevant market media studies. This makes it possible to differentiate buying behavior not only between the ten different milieus, but also across industries. By using the milieus, the CET draws on a huge amount of market data, with respondent samples of up to 30,000 respondents – that goes far beyond the scope of any company-internal segmentation. They furthermore enable statistical series because the study-base is updated annually, enabling trends to be calculated based on previous empirical values. These factors are the premises for estimating concrete future buying behavior which then serves as a valuable insight for managers in their decision making process. As the underlying studies are representative in nature and depict German society from the age of 14 years upwards, they can be used to make

108

C. Early Stage Market Research and Innovation Marketing

generalized assumptions about German consumption patterns. By integrating this information into the CET, it is not only possible to evaluate how many consumers use which product and to what extent at a specific given time, but to also make a prognosis on how this behavior might change in the future. The following use case aims at explaining the logic and the features behind the tool without going into too much technical or statistical detail.

Online recording devices – a customer evaluation tool use case from the field of IPTV Before going into the detailed discussion of the use case, a further important issue has to be addressed. As descried above, the CET supports the calculation of business cases; therefore, the following four premises have to be taken into consideration before a reliable evaluation can be realized. [A] [B] [C] [D]

What is my potential target group? How much is my product going to cost the end consumer? How will prices develop in the future? What is the estimated churn over a period of five to ten years?

The executive board of company “X” is planning to start the development of a new and innovative digital recording device that enables consumers to record movies or TV shows and save them on a personal web space on the internet in order to watch them at a later period of time without having to store various videotapes or DVDs at home. Before starting, it is crucially important to gain insights into the buying behavior of future customers and the potential market size [A]: Since company X is dealing with a new product that is not yet available on the market, new and costly studies could be done to measure the levels of acceptance and the readiness to pay a specific amount of money to actually use the service. On the other hand the CET could be employed to calculate the market potential by making use of product analogies that can be found in the already extant market surveys. In this case, managers of company X could – as an initial step – take into account all users of Video Cassette Recorders (VCR) or DVD Recorders (DVDR) because they have similar needs with regards to their TV consumption behavior. They all have one crucial thing in common: the latent need to be able to record whatever they might miss and watch it at a later time. In order to do this, the users of VCRs and DVDRs have been willing to spend money on technology that satisfies this need and most of them will continue to do so in the future. These users

Segmentation and Evaluation Tools to Project Customer Potential

109

can, therefore, be taken as a primary basis for focusing on the relevant market segment for the company’s new product. However, further aspects have to be taken into consideration. Even though users might have the common need to record films or TV shows, they do not necessarily have access to the WWW or, in the case of company X, have the high-speed internet access that allows them to easily download and store movies. In a second step, the relevant market segment should, therefore, be limited to those people who possess a VCR or DVDR and the internet connection needed for the product. This is statistically possible by intersecting the two data sets with each other, so limiting the market size to only those consumers that meet both technical prerequisites. The following figure shows how this part of the calculation is realized in the graphical user interface:

Figure 2 Using the CET to get a market overview.

In a third step, the marketers in company X will have to make decisions on product pricing [B] because significant price barriers exist5, even within the quite homogeneous groups of VCR and DVDR consumers. The price of the

110

C. Early Stage Market Research and Innovation Marketing

product to be introduced will therefore greatly influence the sales volume. By using the Sinus-Milieus® and the product analogies described above (users of VCR/DVDR and users of high-speed internet, such as VDSL) as a basis for the calculation, a more detailed understanding of how much the new product could cost can be achieved. After considering the pricing for the product, three price categories have to be entered into the CET system to complete the basis for calculation. The Sinus-Milieu® segments show great differences with regards to psychological price barriers and the willingness to spend money on ICT devices and services fees. By understanding how representatives of each segment perceive prices charged by a company, it is possible to either focus only on a specific circle of users or to diversify the product with regards to the endconsumer price. In addition it is possible to integrate a logarithmic or linear price decline [C] and an estimation of the expected churn rate [D]. The price decline algorithms are especially useful if company X is not the sole player on the market, but has to deal with competitors that might influence the product pricing in the long run. For any reliable forecast the price decline should be calculated for a specific period of time as figure 3 shows: While the CET is a tool that can aid marketers and product managers in the process of ideation and product evaluation, it cannot be used to calculate reliable business cases on its own. Users have to stick to the golden rule of social research – namely know your data – thoroughly develop their idea, choose a matching analogy, and have clear ideas about the pricing as well as the churn rate. The correct choice of analogies from the product world is of crucial relevance especially in the field of ICT innovation because companies have to deal with the introduction of products as first movers, with no comparable product available on the market yet. The CET, therefore, serves various purposes. On the one hand it is useful for the estimation of customer potential and revenues over a period of five to ten years with regards to existing products, on the other hand it has a high didactic value with regards to planning, conceptualizing, and realizing new product launches. The tool should be handled with care, and a thorough understanding of how markets work is crucial. Another practical reason for using the CET is for presenting the results in the easy-to-handle Microsoft Excel format as as figure 4 shows: The tool can assist users in calculating potential customer and revenue numbers; it does not create business cases on its own, but also requires critical reflection and interpretation by its users. To date, no (known) application is capable of taking into consideration all influential factors in calculating complete business cases. A human component is absolutely necessary,

Segmentation and Evaluation Tools to Project Customer Potential

111

Figure 3 Price decline calculation using the CET.

but the CET enables a faster assessment of potential user numbers and revenue figures. Furthermore, it has to be taken into account that the CET was developed to project revenues realized in mass markets. It would be interesting to additionally develop a similar approach for a business-to-business segmentation. However, this would be a much more challenging task since no generalized segmentation of business customers exists and firms tend to develop their own segmentations. The results projected by the CET should also be assessed critically because they present projections of how scenarios might develop, rather than final figures. It is therefore useful to calculate a broader number of cases with similar assumptions in order to create a scenario funnel that gives an overview of best and worst case scenarios. The CET nonetheless provides practitioners with a significant aid due to the high validity of the case calculation, considering that sample sizes of

112

C. Early Stage Market Research and Innovation Marketing

Figure 4 Results obtained from the CET in Microsoft Excel format.

fewer than 500 respondents cannot be used for calculations. With sample sizes of up to 30,000 respondents, this so far has never been the case. The base of the CET, the Sinus-Milieu® segmentation, is diverse in the topics it addresses, so the algorithms used by the tool have so far been tailored to the ICT market to meet the specific requirements of Deutsche Telekom AG. The CET also includes topics that do not address ICT, and the algorithms can be modified and adapted to other industries, such as the automotive or food industries. At present the CET only incorporates data series for the German market, but internationalization is possible due to the fact that similar segmentation approaches exist for other countries.

Conclusion There is no silver bullet with regards to using market and customer segmentations in the decision making process of new product development. A broad variety of segmentations exist that serve various purposes, and practitioners have to carefully decide which segmentation they wish to implement. De-

Segmentation and Evaluation Tools to Project Customer Potential

113

mographic segmentations serve specific products, especially if the differentiating criteria are the factors age, income, or buying power. Qualitative segmentations, on the other hand, serve for more in-depth consumer analysis which can be of crucial relevance in the early stages of the innovation process. Ideally, a qualitative analysis should always precede a quantitative one; the quantification of research results should be pursued only after the qualitative research has lead to detailed insights and preferences that then facilitate the development of market or consumer segments. Developing company-specific segmentations, therefore, does make sense in most cases because the resulting segments then best match the company’s research and development requirements. However, in the field of ICT, the use of multiple segmentations can also provide various practical benefits. Keeping in mind that company internal segmentations are more topic-specific (i.e., more focused on ICT topics in the telecommunications industry or solely focusing on smokers in the tobacco industry), they may lack valuable information on people that are not yet a target group but might become one in the near future. This is especially important in the field of ICT, where a lot of research is conducted in the field of so-called “offline” topics. For example, these offline topics address users who do not yet possess mobile phones or do not have access to the internet or mobile internet, but remain an interesting potential target group. In this case, demographic segmentations do not reveal the factors that explain the resistance to using these products. Here, qualitative data and information on values and attitudes are indispensable. Ideally, the milieu approach to segmentation, which gives detailed insights on these highly influential factors, should be used together with company internal segmentations to better explore the relevant target groups. If these insights can then also be quantified and product potentials projected with statistical tools such as the CET, companies will have established a detailed and reliable base for their research, development, and marketing activities.

114

C. Early Stage Market Research and Innovation Marketing

References Anderson, W. T. H. and Cunningham, W. H. 1972. Socially Conscious Consumer. J of Marketing 36(3): 22–31. Beane, T. P. and Ennis, D. M. 1987. Market Segmentation: A Review. European Journal of Marketing 21(5): 20–42. Brangule-Vlagsma, K., Pieters, R. G. M. and Wedel, M. 2002. The Dynamics of Value Segments: Modeling Framework and Empirical Illustration. International Journal of Research in Marketing 19: 267–285. Bourdieu, Pierre. 1998. Distinction – A Social Critique of the Judgement of Taste. Cambridge, Massachusetts: Harvard University Press. Christensen, C. M. and Raynor, M. E. 2004. Marktorientierte Innovation – Geniale Produktideen für mehr Wachstum. Frankfurt: Campus Verlag. DeSarbo, W. S., Degeratu, A. M., Ahearne, M. J. and Saxton, M. K. 2002. Disaggregate Market Share Response Models. International Journal of Research in Marketing 19: 253–266. Ellen, P. S. 1994. Do We Know What We Need to Know? Objective and Subjective Knowledge Effects on Pro-ecological Behaviors. Journal of Business Research 30(1): 43–52. European Commission. 2007. Eurobarometer Survey: Safer Internet for children – Qualitative Study in 29 European Countries. Geißler, R. 2002. Die Sozialstruktur Deutschlands. Wiesbaden, Bonn: Westdeutscher Verlag. González, A. M. and Bello, L. 2002. The Construct “Lifestyle” in Market Segmentation. The Behaviour of Tourist Consumers. European Journal of Marketing 36(1/2): 51–85. Grover, R. and Srinivasan, V. 1987. A Simultaneous Approach to Market Segmentation and Market Structuring. Journal of Marketing Research XXIV: 139–153. Heiskanen, E., Hyvönen, K., Niva, M., Pantzar, M., Timonen, P. and Varjonen, J. 2007. User Involvement in Radical Innovation: are Consumers Conservative? European Journal of Innovation management 10: 489–509. Kotler, P., Wong, V., Sauders, J. and Armstrong, G. 2007. Principles of Marketing: An Essential Guide to Marketing Planning. Longman. Kotler, P. 1980. Principles of Marketing, 291–309. New Jersey: Prentice Hall. Lüthje, C. 2003. Methoden zur Sicherstellung von Kundenorientierung in den frühen Phasen des Innovationsprozesses. Management der frühen Innovationsphasen. Grundlagen – Methoden – Neue Ansätze, eds. Herstatt, C. and Verworn, B., 35–57. Wiesbaden: Gabler. Manner, L. and Miller, S. J. 1978. An Examination of the Value-Attitude Structure in the Study of Donor Behaviour. Proc. of the American Institute of Decision Sciences, St. Louis, American Institute for Decision Sciences, vol. 12, 532–538. Rokeach, M. 1973. The Nature of Human Values. New York: The Free Press. Rokeach, M. and Ball-Rokeach, S. J. 1989. Stability and Change in American Value Priorities, 1968–1981. American Psychologist 44(5): 775–784. SevenOne Media. 2001. Semiometrie. Der Zielgruppe auf der Spur. Unterföhring: SevenOne Media GmbH. Smith, W. R. 1956. Product Differentiation and Market Segmentations as Alternative Market Strategies. Journal of Marketing 21(1): 3–8. Sagiv, L. and Schwartz, S. H. 1995. Value Priorities and Readiness for Out-Group Social Contact. Journal of Social Psychology and Social Psychology 69(3): 437–488.

Segmentation and Evaluation Tools to Project Customer Potential

115

Endnotes 1 2

3

4

5

The Eurobarometer Survey entitled “Safer Internet for Children” conducted in 2007 is especially relevant in this context. Smith (1956) mentions the cigarette and automobile industries as classic and well-known illustrations for market segmentations. Other examples are the telecommunications industry and the finance sector. Furthermore, a differentiation between business-to-customer (B2C) and business-to-business (B2B) segmentations can be made. More detailed information on these segmentations and the research approach used to develop them can be found on the official websites of the respective institutes: www. sociovision.de, www.sigma-online.com and www.schober.de. To mention a few, the German survey “Typologie der Wünsche Intermedia” by Burda has a sample size of 19,153 in the 2008 edition and the “Verbraucher Analyse” by Axel Springer AG draws on a sample size of 29,621 respondents in its 2007/08 edition. The Stern “Markenprofile” use samples of 10,059 participants. This assumption is based on an analysis of VCRs and DVDRs that are available on the market ranging from quite basic products (> € 200) to high end products with prices of up to € 2,500.

116

D. The Early Stages of New Product Development

Cross-over Application of Enterprise Architecture and Modularization in Telco R&D

This section describes how a cross-over application of enterprise architecture in telco R&D helps to cope with issues in the early innovation stages – complexity management in view of the choice of alternative technological paths and implementation uncertainty in view of distance of strategic and operational levels. Linking enterprise architecture concepts and early innovation stages, it builds on the modularization of R&D results. As a result it provides the ability to effectively face a number of innovation processes, interfaces, platforms and roadmaps and is associated with knowledge of markets and users. The enterprise architecture (EA) framework has been introduced and widely used in the domains of IT and business processes to bring a holistic picture to both of them. It can – together with a modularization concept – offer an important instrument for coping with the challenges to telco R&D. The applicability of the EA framework is extended so as to focus innovation efforts on specific modules or architectures. At Deutsche Telekom Laboratories, cross-over application of enterprise architecture is used as a fast reaction approach to manage reusable and re-combinable modules and to allocate them properly.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_11, © Springer-Verlag Berlin Heidelberg 2010

Cross-over Application of Enterprise Architecture and Modularization

117

Introduction The ingredients for successful development and product innovation are suggested along with formal systematic processes with quality control points – the stage gates (Cooper 1998) or the user integration within development processes (Hippel 1986, 1994; Gruner and Homburg 1999). Also, open innovation fundamentals are widely recommended (Chesbrough 2003) to deal with complexity in the integration of innovators and commercialization of results alongside the value chain. Despite the tools and innovation management approaches used, organizations still have difficulties when it comes to facing the alternative technological paths in new product development. For example an extreme situation is taking place in the information and communication technologies (ICT) or telecommunication industry domain, where addressing the innovation is not solely driven by operators and their supply chains, but increasingly the strategies are also co-defined by millions of developers of web-based services. Hence, multiple development tracks have to be considered. But even if the new product development (NPD) activities happen to be in overlapping and parallel constellation (Brockhoff 1999), the decision makers are often tempted to apply predominantly focusing and prioritization measures (e.g. very often reduced to create a long list and cut a short list through a scoring model). The effect of such NPD practices will not be sufficient to provide the right answer when it comes to the selection of the right technology choice. Systematic and strict processes rigor has to be applied where it is necessary. However the problem is different. When it comes to early innovation stages, the R&D units or high-tech companies face the issue of: (a) complexity management in view of the choice of alternative technological paths; (b) distance between strategic and operational level; (c) synchronization of innovation procedures. Cutting the technology options early might result in lost opportunities. And as markets are dynamic, the right decision in the past might not match new developments in market needs. The variety of technological paths runs parallel to alternative or rather complementary paths in the evolution of customer needs. A number of innovation processes, interfaces, internal platforms, and product roadmaps have to be considered. The question is: How to efficiently manage complementary (technology/development) paths with many options while limiting risk with limited resources?

118

D. The Early Stages of New Product Development

In industries such as automotive or IT, modularization and platform (architecture) innovation became an answer (Baldwin and Clark 1997). Christensen describes the value migration caused by shifts in the value chain following system modularization in the PC industry (Christensen 2003). The idea of “modules” is becoming a solution to growing complexity (Kodama 2004). As a method known from manufacturing industries such as the automotive industry, the module and platform approach has helped to reduce cost despite increased numbers of units, and effectively increased flexibility through the re-combination of modules. Within the boundaries of R&D, the modular approach is an important tool of technology transfer, knowledge management (Probst et al. 1999), as well as archiving and retrieving project results. The thorough implementation of modular-based innovation in the telecommunication and IT domain is currently supported by many industrial standards, e.g., Information Technology Infrastructure Library (ITIL), enhanced Telecom Operations Map (eTOM) – published by the TM Forum – IBM Enterprise Architecture, or vendor-driven service delivery platform concepts (SDP) and service-oriented architecture approach (SOA). As a result both modular and architectural innovations are identified as being necessary in the meantime. As a consequence, the cross-over application of EA is proposed as an approach to combining both EA and modularization principles in order to manage and properly allocate reusable and re-combinable modules to products and solutions from strategic to operational levels. Cross-over application of EA provides a dynamic implementation framework, extends the mainstream thinking on new product development, and links strategic and operational levels together. It can be used to handle more and concurrently – development processes, more options/building blocks, delivery to more architectures, and it is applicable at large enterprise as well as the small contractors – provided that the target architectures and procedures are well known.

Enterprise architecture in telco R&D Enterprise architecture as an approach

The popular TOGAF architectural framework defines EA as a set made up of a complete collection and logical organization of business strategies, metrics, business capabilities, business processes, information resources, business systems, and networking infrastructure within the enterprise sufficient to explain the enterprise level architectural description.

Cross-over Application of Enterprise Architecture and Modularization

119

The following is already discussed by Arnold and Dunaj (2008). EA is a concept used for linking the business development domain with innovations in IT technology (Ross 2003). The aim of EA is to “holistically” address all aspects of the “extended enterprise” and is therefore directly associated with business technology alignment – business structure, business activities, business processes, information flows, information systems, infrastructure, standards, and policies. In its ultimate form, EA connects the decision level of the CEO with the IT support processes at their disposal. EA aims to maximize the return on information while, at the same time, reducing the cost and complexity of IT technology (Schekkerman 2003). Complexity and cost reductions are to be achieved, among others, by platform and module types of systems partitioning1. Building on Zachman’s concept of EA (www.zifa.com and www. zachmaninternational.com), Jaap Schekkerman’s work introduces “technology capabilities” as important components and a linking component “enabling technologies” to technology strategy (Schekkerman 2003). They are shown in italics in Table 1. This is the part where the modularization approach in early innovation stages becomes most relevant. Enterprise strategic alignment Business strategy

Technology strategy

Business concepts

Enabling technologies

Operational capabilities

Technology capabilities

Enterprise business improvement

Table 1.

Key elements of an extended enterprise architecture (Schekkerman 2003).

The consolidated view with an overarching four dimension structure is presented (Pulkkinen 2006) in the so-called EA grid (Table 2). In the holistic view of IT and business, a process is established and the framework of the EA enables, by linking domains of IT and business processes, the identification and orchestration of the right choices for different business roles and their specific requirements. The EA grid distinguishes between business, information, and systems and technology architecture dimensions.

120

D. The Early Stages of New Product Development

Systems (applications) architecture

Technology architecture

Strat. info management considerations, information value chain

Strategic systems Portfolio (application portfolio)

Strat. tech. portfolio; vendor relationships, enterprise technology guidelines and policies

Business and management decisions, portfolio of businesses, mission, business strategies, visions

Services/ Information products in the mgmt. of the domain, business domain processes for their production

Domain systems Technologies Mapping infrastructure: Interoperability platforms, networks, data communication

Business requirements for systems and data management

Systems architecture; application patterns; developer guidelines

System level

Enterprise level

Information architecture

Domain level

The Business EA Grid architecture

Table 2.

Data architect.; data harmonization principles; data storages

System-level technology architecture; technical implementation

EA grid according to Pulkkinen

Present enterprise architecture thinking in a nutshell

Most authors approach EA concepts either from the business/process perspective (e.g., Lankhorst 2005; Winter 2003; Bernus 2003) or from the IT technology perspective (e.g., Johnson et al. 2006; Matthes & Wittenburg 2004; Vasconcelo 2001). Table 3 shows exemplary contributions to EA and their orientation. Author (Year). Publication.

Short description

Zachmann, J. A. (1997). Enterprise architecture: The issue of the century Enterprise Architecture: The Issue Of The Century. Database Programming and Design, 1–13.

Contribution to EA / purpose of EA Defines a framework for EA, rather from the technical side; the goal is to get precise information about the running system state

Cross-over Application of Enterprise Architecture and Modularization

121

Author (Year). Publication.

Short description

Contribution to EA / purpose of EA

Shekkerman, J. (2003). How to Survive in the Jungle of Enterprise Architecture Frameworks: Creating or Choosing an Enterprise Architecture Framework. Trafford Publishing.

Extended Enterprise Architecture (E2A) Framework

E2A is a holistic view covering the key aspects of an enterprise and its environment from the strategic level to the implementation and transformation level represented by 6 core questions: Why? With Whom? What? How? With what? When? – making SOA a subcategory of the How and With What logical and solution representation.

Matthes, F. and Wittenburg, A. (2004). Softwarekartographie: Visualisierung von Anwendungslandschaften und ihrer Schnittstellen. Informatik 2004 Jahrestagung der GI. Ulm, 2004.

Software cartography: Visualizing application landscapes and interfaces

Purpose is a comprehensive and intelligible depiction of complex IT systems. Process landscapes are not in scope.

Lankhorst, M. (2005). Enterprise Architecture at Work: Modelling, Communication and Analysis. Springer.

Enterprise architecture at work: modeling, communication and analysis

Enterprise architecture tries to describe and control an organization’s structure, processes, applications, systems and techniques in an integrated way. The unambiguous specification and description of components and their relationships in such architecture requires a coherent architecture modeling language.

Ross, J. W., Weill, P. and Enterprise architecture Robertson, D. (2006). as a strategy Enterprise Architecture as strategy: Creating a foundation for business execution. Boston, Mass.: Harvard Business School Press.

Understanding business needs for technology management

122

D. The Early Stages of New Product Development

Author (Year). Publication.

Short description

Contribution to EA / purpose of EA

Johnson, P., Lagerstrom, Extended influence R., Narman, P. and diagrams for enterprise architecture analysis Simonsson, M. (2006). Extended Influence Diagrams for Enterprise Architecture Analysis. edoc ‘06, 3–12, from http:// doi.ieeecomputersociety. org/10.1109/ EDOC.2006.27.

Getting a structured analysis method of the EA, this method generates quantifiable results, other papers with specific goals (security, IT management…)

Organizing IT support for Cullen, A., Orlov, L., Radjou, N., Hoppermann, innovation J., Peyret, H. and Sessions, L. (2006). Enterprise Architecture’s Role In IT-Enabled Business Innovation. Forrester Research “EA’s Role In Innovation” series.

IT is supposed to provide structure and process for business-focused technology R&D. EA groups can enable the firm’s innovation pipeline and develop the firm’s innovation network.

Table 3.

Characteristic applications of enterprise architecture (examples)

An analysis of characteristic contributions to enterprise architecture confirms this observation: EA can be very useful for early telco innovation and R&D, but this possibility has not yet been recognized. The reasons for enterprise architecture in telco R&D

As telco R&D is addressing a complex environment of “all layers” and is opened to many innovation departments and needs, identification, development, and transfer of its results obviously requires a systematic approach. NPD brings about further motivations for the possible application of EA. There are issues of (a) complexity management with alternative technology choices, (b) implementation uncertainty due to distance between strategic and operational level, and (c) innovation processes synchronization. (a) Complexity management with alternative technology choices

The current situation in the ICT industry can be characterized as a period of experimentation (Arnold 2003). That, of course, makes it rather difficult to identify the “right” technological path for established telcos.

Cross-over Application of Enterprise Architecture and Modularization

123

The new services offering moves at a “clock-speed” higher than the introduction cycles of new telco services. In the case of Web 2.0, myriads of emerging web services and players are enabled, which brings along innovative concepts – often competing – and usually makes it impossible to distinguish successful approaches from failures from the beginning. In addition, infrastructure is changing from stove pipe architectures to delayered IP-based architecture; third party IP-based services reduce lifecycle times and bring new players to telco’s innovation landscape. In this way the telco options to innovate are directed: • first, by web-based services which benefit from the separation of application features from the network – with full next generation networks architecture (NGN) this will become even more prevalent; • second, by technological choices also moving beyond the application layer with respect to user interface, type of terminal device, and control and access technologies. So, telco innovation needs to have an approach ready to accommodate rapid but uncertain developments in the application domain, as well as technological uncertainty from control and access layers throughout the user interface. (b) Implementation uncertainty due to distance between strategic and operational level

Primarily the external inflow of technology determines the focus of telco R&D on the missing parts or gaps left by suppliers in order to concentrate development resources on missing features and properties that differentiate from competition. Next, the transfer of R&D results has to take into account the different “lifecycles” in product development and in customer and market trends, as well as the long-term character of research results delivery. The longterm trends have to be taken into account before the specific product is designed. The end product specifications and strategic assumptions are subject to change throughout time, so the final expectations can differ from what was expected. As a result the R&D units have to make their own transfer of results compatible with the external inflow of technology and they have to adapt to expectations dynamics. (c) Innovation processes synchronization

The process synchronization issue appears when various technology and marketing departments are interacting. In innovation development there

124

D. The Early Stages of New Product Development

could be two or more innovation procedures (e.g., funnels, stage-gates) running in parallel. This is also true for technology roadmapping, the coordination of different roadmaps, and for managing the expectations of different technology and product-related departments. NPD processes at later stages require the elimination of multiple options and concentration of resources on a few selected ones, which are set to be commercialized. Nevertheless in early innovation stages more technology options are maintained. A co-existence of top down and bottom up approaches is the result. Consequently the general rule to focus and prioritize cannot be applied to such processes in one instance; the flexible treatment of technology options has to take place. A method or platform is needed which enables co-existing top-down and bottom-up approaches.

Modularization as an approach Modularization means the decomposition of the system into small parts (Schönherr 2004). Modularization is an approach established to handle the variability of product development. In the computer industry, modules are a solution to the complexity of IT systems; in the automotive industry, modularization is used as an integrative approach to complicated suppliers chain management (Salerno 2001). The modularization provides the benefits of (a) reducing complexity for specific areas (but might increase it due to a higher complexity in managing the interfaces); (b) flexibility required to deal with uncertainty; (c) cost advantage due to modules reusability; and (d) services customization in a large scale. The term mass-customization2 is used when it comes to searching for product development methods for products that meet customer needs by maximizing individual customization and minimizing the costs base using modular components (Kotha 1995). Present modularization thinking overview

Modularization benefits for organizations are identified as achieving mass customization, shorter product development cycles, faster technological change, and lower costs (O’Grady 1999). Modularization has been described in the computer industry (Baldwin and Clark 2000), in home appliances (Worren et al. 2002), and automakers

Cross-over Application of Enterprise Architecture and Modularization

125

(Cusumano and Nobeoka 1998; Salerno 2001). A large amount of literature on the subject already exists (Belzowski et al. 2003; Graziadio and Zilbovicius 2003; Marx 1997). The other aspects of modularization are considered to be strategic management and supply chain management3. At Telekom Laboratories, Erner & Presse focus on the implication of the modularization on the R&D and innovation management in general. Modularization is a method advocating flexibility in process and complexity management. Modularization should be applied if there is advanced architectural knowledge about the components and their interactions and interdependencies. Modularization from the telecommunication industry perspective

In the telco industry modularization is also known. The mainstream thinking is embodied in SOA. The networks’ architectures follow the design of All-IP, next generation networks and service delivery platforms. The network functionalities are defined as functional modules and put into building block elements. The concepts of enablers have become a standard topic for discussion not only in the traditional telco environment. The trend is to turn network capabilities into enablers, whereas various enablers are identified. The enablers can be defined as modules. As discussed before, those can be functional as well as conceptual blocks or non-incremental solutions originating from basic research. A modular approach in innovation and R&D has to focus on the modules, which are best provided by an operator, and make them interoperable. Interoperability needs to be ensured with both the legacy systems and the NGN components of the operator and service provider itself, and moreover with existing or emerging interesting web-based applications in the market so as to be able to take advantage of their rapid development. Modularization in telco R&D

Modularisation in telco R&D is discussed as following (Arnold and Dunaj 2007) – as a method known from manufacturing industries such as the automotive industry, the module and platform approach has helped to reduce cost despite increased number of units, and effectively increased flexibility through re-combinations of modules. Within the boundaries of R&D, the modular approach is an important tool of technology transfer, knowledge management (Probst et al. 1999), as

126

D. The Early Stages of New Product Development

well as archiving and retrieving project results. For R&D, a module can be defined as a detailed, self-contained project result (Erner and Presse 2007). For the development-oriented part of R&D, modules can exist in the form of functions (e.g., part of software with a designated functionality) or as a concept or architecture; whereas the more research-oriented part of R&D is expected to come up with non-incremental solution approaches to a problem, which then can be further developed into functions. A key requirement for the functional blocks and concepts is that their interfaces match or can be easily adapted to the current or future operational environment in the business unit. The modules coming out of the in-house development of a telco will serve their purpose well if they can be recombined with each other and with functionalities that are or will be available from external sources.

Cross-over application of enterprise architecture in telco R&D Modularization is combined with concepts of EA. In this way a resource efficient and flexible framework for telco R&D and innovation in technologically uncertain environment is defined. It describes the innovative concepts, the related modules, and the relations and variations amongst them4. The framework is applied at Deutsche Telekom Laboratories. The cross-over application of EA (CEA) is described through system, tools, and results. The system of cross-over application of the enterprise architecture is (CEA) = EA + Modularization + Technology roadmapping. First, on the enterprise level the referring objectives and business unit interfaces (organizing departments) are identified and decomposed into architectural layers. Second, the target architecture is created from layers and identified entities. The R&D results are modularized and classified (e.g., as software, business, or research concept module). Third, technology roadmapping is used for modules that match within multiple development tracks (processes with different speed/output). Often parallel development tracks are influenced by a different output focus and time perspective. For example, product management in the pre-market launch phase is short-term oriented and focused on a few but good products. On the other hand, the product development and related research is long-term oriented with a wider focus on many modules, reusable, and can be matched with more future product strategies. Hence, good knowledge of EA and internal process related experience is necessary.

Cross-over Application of Enterprise Architecture and Modularization

127

The CEA tools are applied in following way: While the enterprise architecture principles are used to identify the right placement of R&D modules, technology roadmapping helps to plan and match R&D topics and projects to market and business units’ needs. The topics are identified through the modularization approach. In addition, the software tools could be used. For instance at Telekom Laboratories the software application called enabler DB tool is used for both R&D results modularization and matching to various enterprise architecture entities. It serves as simple knowledge management database as well – facilitating future-oriented innovation and overall R&D strategy. The results of CEA are better matching of R&D outputs with new product requirements, the ability to handle multi-parallel development tracks, and a conceptual prerequisite for thorough enterprise architecture management. As such it helps in strategic alignment between R&D and business units as well as in resource planning and task prioritization. CEA has the following impacts on Deutsche Telekom R&D strategy: (a) maximize customer value; (b) ensure and enabling unique offering for Deutsche Telekom; (c) contribute to key Deutsche Telekom innovation areas.

The case-study on Telekom Laboratories CEA The following is taken from Arnold and Dunaj (2007). A related conceptual mentality has already been established at Deutsche Telekom Laboratories in order to better focus on white spots or possibilities for valuable differentiation left by the other contributors of the innovation ecosystem. At Telekom Laboratories, a CEA approach improves the delivery of R&D results in the form of “scalable” prototype functions and the probability of addressing the emerging web and telecommunication service needs, and pre-empt external technological change. Telekom Laboratories follows the idea of a layered and modular overall architecture by segmenting the technology stack into 5 distinct innovation spaces (see Figure 1).

128

D. The Early Stages of New Product Development

Adapted corporate innovation categories Home 5i Intuitive Usability

Integrative Service Components

Intelligent Access

Infrastructure Development

Personal & Social Networking

Mobile Timeline to broader market appearance

Topic 1

Future R&D options Topic 5

Topic 2

Overarchiving Topics (FMC)

Topic a Topic b

Topic 3

Topic c Topic d

Topic 4

Topic 6

Inherent Security

Figure 1 One of the possible simplified Telekom Laboratories enterprise architecture views. The user interface layer is governed by the quest for usability. The application and enabler domain has three major blocks: integrated communication (or better integrative service components), multi-access IP service components and network centric enablers, as well as identity management functions (such as AAA). Broadband and wireless access looks for progress in “pervasiveness” and the management of heterogeneous networks. Security is investigated from a network centric view. Together with concepts for metro and core networks they form the foundation of the architectural framework. These five innovation domains form the top level by providing links between the requirements in the business areas of the connected life and work topics (e.g., in home, mobile, personal social networks (PSN), enabling infrastructure, and overarching topics) Each one of the categories can be layered down from the business and technology domain to the system or production layer and its components.

Conclusion According to Michael Raynor, author of the book The Strategy Paradox, putting all your eggs in one basket might also mean loosing them all if core business happens to be in crisis. A modularization combined with the EA is therefore introduced as an underlying principle for applied R&D work at telcos, which enables the addition of value in an environment of technological uncertainty.

Cross-over Application of Enterprise Architecture and Modularization

129

EA has its roots in information systems, but is ready to make its way to interdisciplinary teams of applied R&D expert constellations in telcos. Especially important features of EA, alongside the stringent modularization, are those that allow telcos to stay both flexible and focused in the early innovation stage. The module reusability aspects help to increase the number of services and the possibility of differentiation on the module level, and to make increases in permutations possible. The CEA is suggested as a combination of EA modularization and technology roadmapping approaches and is proposed for the domain of telco R&D. Thereby, in-house Telco R&D can maximize the applicability of its results, maximize focus, and thus increase value contribution to operating businesses in times of technological uncertainty. Moreover, it makes it possible to embrace more opportunities that come along with the architectural innovation that large telcos face. As such this method helps to manage the complexity. However, the idea is not only to produce a few but good products through managing modularization and parallel processes. The EA has to be thoroughly implemented from the strategic to the operational level. This is the responsibility of operators who want to be successful in the future.

References Arnold, Heinrich M. and Michal Dunaj. 2007. Enterprise Architecture and Modularization in Telco R&D as a Response to an Environment of Technological Uncertainty. ICIN, Bordeaux, France. Arnold, H. M. and Schläffer, C. 2007. Media and networks innovation – technological paths, customer needs and business logic. In e&i “Digitales Fernsehen.” October 2007. Baldwin, C. and Clark, K. 1997 ‘Managing in an age of modularity’, Harvard Business Review, Vol. 75, No. 5, pp.84–93. Baldwin, C. Y., & Clark, K. B. 2000. Design Rules. Volume 1: The Power of Modularity. Cambridge, Massachusetts: MIT Press. Belzowski, B.M., Flynn, M.S., Richardson, B.C. and Sims, M.K. 2003 ‘Harnessing knowledge: the next challenge to inter-firm cooperation in the North American auto industry’, International Journal of Automotive Technology and Management, Vol. 3, Nos. 1/2, pp.9–29. Bernus, P, Nemes, L, Schmidt, G. 2003: Handbook on Enterprise Architecture (International Handbooks on Information Systems), Publisher: Springer-Verlag; November 1, 2003 Brockhoff, K. (1999): „Forschung und Entwicklung: Planung und Kontrolle“, 5. Aufl., München et al.: Oldenbourg. Chesbrough, H. (2003), Open innovation: The new imperative for creating and profiting from technology, Harvard Business School Press: Harvard, MA. Christensen, C. and Raynor, M. (2003): The Innovator’s Solution, Boston: HBS Press.

130

D. The Early Stages of New Product Development

Cooper, R.G. 1988 The Mew Product Process: A Decision Guide for Management, Journal of Marketing Management i,1988, 3, No. 3. 23B-255 Cullen, A., Orlov, L., Radjou, N., Hoppermann, J., Peyret, H. and Sessions, L. 2006. Enterprise Architecture‘s Role In IT-Enabled Business Innovation.“EA’s Role In Innovation” series. Forrester Research. Cusumano, M. and Nobeoka, K. (1998) Thinking Beyond Lean, New York: Free Press. Erner, M and Presse, V. 2007. A Modular based approach to reduce uncertainty in R&D. The R&D Management Conference 2007, Risk and Uncertainty in R&D Management, 4–6 July, in Bremen, Germany. Graziadio, T. and Zilbovicius, M. (2003) ‘Knowledge transfer through the supply system: does modularity make it easier?’, International Journal of Automotive Technology and Management, Vol. 3, Nos. 1/3, pp.47–60. Henderson, Rebecca M. and Kim B. Clark. 1990. Architectural Innovating: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms. Administrative science Quarterly 35. von Hippel, Eric (1994) “Sticky Information” and the Locus of Problem Solving: Implications for Innovation” Management Science 40, no.4 (April): 429-439 von Hippel, Eric (1986) Lead Users: A Source of Novel Product Concepts MANAGEMENT SCIENCE 1986 32: 791-805 Johnson, P., Lagerstrom, R., Narman, P. and Simonsson, M. 2006. Extended Influence Diagrams for Enterprise Architecture Analysis. edoc ’. pp.3-12, 10th IEEE International Enterprise Distributed Object Computing Conference (EDOC’06), 2006 Kodama, F. (2004). Measuring emerging categories of innovation: Modularity and business model. Technological Forecasting and Social Change, 71, pp. 623-633. Kotha, S. (1995) ‘Mass customization: Implementing the emerging paradigm for competitive advantage’, Strategic Management Journal, Vol. 16, pp.21–43. Lampel, J. and Mintzberg, H. 1996. Customizing Customization. Sloan Management Review 37: 21–30. Lankhorst, M. 2005. Enterprise Architecture at Work: Modelling, Communication and Analysis. Springer. Matthes, F. and Wittenburg, A. 2004. Softwarekartographie: Visualisierung von Anwendungslandschaften und ihrer Schnittstellen. Informatik 2004 – Jahrestagung der GI, in Ulm, Germany. Max, R. (1997) ‘The modular consortium in VW in Brazil: new forms of assembler and suppliers relationship’, International Journal of Manufacturing Technology Management, Vol. 8, No. 5, pp.292–298. O’Grady, P. 1999. The Age of Modularity - Using the new world of modular products to revolutionize your corporation: Adams and Steele Publishing. Probst, G. J. B., Raub, S. and Romhardt, K. 1999. Wissen managen: Wie Unternehmen ihre wertvollste Ressource optimal nutzen. Frankfurt am Main: Frankfurter Allg. Zeitung für Deutschland. Pulkkinen, M. 2006. Systemic Management of Architectural Decisions in Enterprise Architecture Planning. Four Dimensions and Three Abstraction Levels. In The Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS), January 2006, ed. R. H. Sprague Jr., 179. Ross, J. W. 2003. Creating a Strategic IT Architecture Competency: Learning in Stages. Working paper number 4314-03, MIT Sloan School of Management. Ross, J. W., Weill, P. and Robertson, D. 2006. Enterprise Architecture As Strategy: Creating a Foundation for Business Execution. Harvard Business Review Press. Salerno, M.S. 2001 ‘The characteristics and the role of modularity in the automotive business’, International Journal of Automotive Technology and Management, Vol. 1, No. 1, pp.92–107.

Cross-over Application of Enterprise Architecture and Modularization

131

Sanchez, R., & Mahoney, J. T. 1996. Modularity, Flexibility, and Knowledge Management in Product and Organization Design. Strategic Management Journal, 17 (Winter Special Issue): 63–76. Schekkerman, Jaap. 2003. How to Survive in the Jungle of Enterprise Architecture Frameworks: Creating or Choosing an Enterprise Architecture Framework. Trafford Publishing. See also http://www.trafford.com/4dcgi/view-item?item=4668&14262344-16421aaa and www.enterprise-architecture.info Schläffer, C. 2007. Convergent Media and Networks. Proc. of German-Japanese Symposium. Münchner Kreis. Schönherr, M. 2004. Connecting EAI Domains via SOA. In: Conference Proceedings: IFIP ICEIMT’04, International Conference on Enterprise Integration Modelling and Technology. Bernus, P. (Hrsg.), Toronto. Telecom Industry Value Opportunities for Private Equity. Episode 2, TheTelecom Enabler, October 3, 2006. See also web: http://www.consultmerlin.com/latest-thought-papers/ the-telecom-enabler.html Vasconcelos, A., A. Caetano, J. Neves, P. Sinogas, R. Mendes, e J. Tribolet 2001. A Framework for Modeling Strategy, Business Processes and Information Systems, 5th International Enterprise Distributed Object Computing Conference EDOC, Seatle, EUA, September 2001 Winter, R. 2003: An Architecture Model for Supporting Application Integration Decisions, Proceedings of the 11th European Conference on Information Systems. Worren, N., Moore, K., & Cardona, P. 2002. Modularity, Strategic Flexibility, and Firm Performance: A Study of the Home Appliance Industry. Strategic Management Journal, 23: 1123–1140. http:www.zifa.com Zachman institute for Framework Architecture, see also: http://www. zachmaninternational.com/index.php/the-zachman-framework

Endnotes 1 2 3

4

Pioneering works include John Zachman’s activities at IBM or HP’s adaptive enterprise concept. Lampel/Mintzberg identified more than 2000 articles written on mass customization in 1996 (Lampel and Mintzberg 1996). The other aspects of modularization are considered to be strategic management and supply chain management. The strategic aspects of modularization are described through the identification of its potential in new coordinating technologies and knowledge management processes based on modularity concepts (Sanchez and Mahoney 1996). Supply chain management aspects are recognized in the integration of supply chain (Becker and Zirpoli 2003; Filho et al. 2003; Fujimoto 2001; Graziadio and Zilbovicius 2003). According to the Henderson–Clark model, the technological knowledge behind innovation can be divided in two dimensions: knowledge of the components and knowledge of the linkage between them, referred to as architectural knowledge (Henderson and Clark 1990).

132

D. The Early Stages of New Product Development

Enterprise Architecture in Innovation Implementation

A core challenge in technology-oriented innovation is the correct focus of innovation implementation in highly complex environments, including a fragmented value chain. This section presents a method that helps innovation departments steer innovation implementation in order to decrease timeto-market and improve the quality and alignment of the technology artifacts developed.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_12, © Springer-Verlag Berlin Heidelberg 2010

Enterprise Architecture in Innovation Implementation

133

Introduction The type and source of innovation in the telecommunications industry have undergone major changes in recent years. As the industry has begun to focus more on services rather than traditional network provision, the development of innovation has changed accordingly. Whereas innovation in telecommunications used to be driven by the suppliers of the telecommunications providers, innovation in services is mainly being driven by the providers themselves. Telecommunication network providers originally offered a limited number of different services, where the main differences among providers were driven by non-functional criteria such as price, quality of service, and support services. The differences between new services, such as IPTV or mobile Internet, are shaped far more by functional criteria. Therefore telcos are forced to innovate in order to differentiate themselves from their competitors. This new innovation value chain creates two major challenges when implementing innovative new services: complexity and time-to-market. For service innovations, time-to-market is a huge challenge. Whereas the innovation cycle in classic network services was rather long-term, service innovations need to be brought to market much more quickly, and they become outdated much faster. The complexity of innovation is another challenge. Telcos already have very complex IT and network infrastructures, although these have been built up using a limited number of suppliers. Opening up these systems to add many short-term innovations with multiple partners is taking the complexity of innovation to a new level. New methods in innovation implementation are required in order to cope with these challenges. These methods should support the implementation of innovation to keep the complexity manageable and shorten time-to-market. Therefore the method goals are twofold: focus and quality. The implementation aspect of innovations needs to be closely aligned with the innovation of the suppliers as well as with platform development in the operational systems. Only well-focused and aligned implementations have a manageable level of complexity and therefore can be turned into successful services. Time-to-market, especially with respect to the challenges in complexity described, requires comprehensive quality management. The special focus of the method presented in this section is to reduce the natural gap between the innovation developer and those responsible for the operation of the innovative service. While innovation developers focus on the functional criteria of the new service, non-functional aspects become more relevant in the operation of a new service. This often leads to the need for complete re-

134

D. The Early Stages of New Product Development

development when the innovation is handed over to form a new service on the market. The method presented here takes well-understood methods from the enterprise architecture and software engineering domains and adapts them to the special focus of innovation implementation.

Conceptual basics Enterprise architecture

According to the ANSI/IEEE Standard 1471–2000, architecture is defined as “the fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution” (IEEE 2000). Based on this definition, enterprise architecture (EA) represents the fundamental organization of the entire enterprise. The main objective of EA is to model the most important entities within an organization and bring these entities into relation with each other. The modeling methodologies allow these entities to be brought to a higher level of abstraction. The method term is defined as a systematic approach, which enables the enterprise architecture or even parts of the overarching architecture to be analyzed. Entities in these models range from strategic aspects to technical aspects. This means that entities such as customers and suppliers and their relationships are modeled on the strategic level, whereas the technical infrastructure is modeled as a part of the technical aspects of the model (Winter and Fischer 2007). These models are an integral representation of the entire enterprise. Having modeled the enterprise in this way, many different operational scenarios can be supported (Niemann 2005), yet each of these scenarios contains an integral aspect between different entities. Enterprise architecture research has led to a variety of different metamodels that help the modeler to design a concrete model of the enterprise in question. Project type differentiation

As enterprise architecture models cover a broad range of different aspects as well as different company situations, creating a “one-size-fits-all” method has not led to the modeling precision that is needed. This has led to situationally aware methods that are discussed as “situational method construction”

Enterprise Architecture in Innovation Implementation

135

(Baumöl 2005; Bucher et al. 2007; Harmsen et al. 1994; Kumar 1992; Van Slooten and Hodes 1996). A situation is defined as a specific set of contextual parameters that influence the way a method should be conducted. Therefore, for each of these situations a specific method is constructed from method fragments, which can be assembled (Bucher et al. 2007). As the situational context is derived from real business situations, the situational methods should reflect practical experiences and be improved through continuous development (Baumöl 2005). Bucher et al. showed that not only the known factors for the situation itself influence an efficient and effective application of the method, but also that factors not directly controlled may have a strong impact (Baumöl 2005).

Constructed method: a specific challenge Managing innovation in complex environments like the telecommunications industry requires integrated steering instruments (Arnold and Dunaj 2007). When introducing new product innovation in productive environments many aspects need to be taken into consideration. The enterprise architecture models are often the only consistent documentation of all the aspects that need to be examined. Therefore, enterprise architecture methods can support an evaluation and recommendation method for innovation projects. EA can help to introduce non-functional aspects earlier on, during the course of the project itself. These aspects can induce changes both inside and outside of the project. Examples of these internal aspects are impact analysis on certain levels of the architecture as well as conformance with certain architectural definitions. The external aspects can lead to changes in the architectures that will be affected by the results of the innovation project. These analyses can lead to a project redesign or add new aspects to the innovation project. Although the EA methods seem to fit the focus of innovation projects well, existing research tends to focus on the evaluation and design of the EA itself (Johnson et al. 2006; Simonsson et al. 2006; Vasconcelos et al. 2007). A special view of the relationship between project architectures and EA is presented by Foorthuis and Brinkkemper (2007) in which they concede that different project types require specific methods. The remainder of this section develops a situational method based on the Deutsche Telekom Laboratories innovation process. Firstly, a classification of different projects is developed which is then followed by a set of

136

D. The Early Stages of New Product Development

possible method fragments that cover different areas of the overarching EA models. Finally, a process model for the newly developed situational method based on the Deutsche Telekom Laboratories innovation process is explained. A situational model based on the telekom laboratories innovation process Project classification

Classification of different projects is the first step to developing a situationally specific method. A cluster approach has been used to classify the different types of innovation projects: 116 innovation projects from the Telekom Laboratories portfolio have been analyzed and assigned to clusters. The criteria used for this classification are shown in Table 1. Criterion

Description

Occurrences

Duration

Overall project time

Number of months

Budget

Overall budget in thousand EUR

Pb <= 250

Size of consortium

Number of partners involved

Number

External funding

Classification if project Yes was supported by public funds (e.g., EU)

Size of possible production scenarios

Number of units that would like to use the project results in business practice

0 to 5

Result type

Main deliverables of the project

Knowledge

Software component

Classification if Yes software development was part of the project

Table 1.

250 < Pb <= 750

Pb > 750

No

Concept/study

Prototype

No

Criteria for project type classification.

Using these criteria, a cluster analysis could be conducted on a sample of innovation development projects that allowed homogenous groups of projects to be identified within the heterogeneous set of projects being analyzed in total.

Enterprise Architecture in Innovation Implementation

137

This segmentation then could be used to construct a situational method for each of these clusters. The two-step algorithm was used for the clustering (Bacher et al. 2004). This analysis led to four distinct clusters, which are shown in Table 2. Cluster criterion Duration

Cluster 1 (n=33)

Cluster 2 (n=23)

Cluster 3 (n=42)

Cluster 4 (n=18)

21.3

10.6

12.3

20.4

Size of possible production scenarios (x)

1.8

1.7

1.2

1.5

Size of consortium (x)

5.0

1.2

2.1

3.3

Budget

50% > 750

68% < 250

55% < 250

750 > 50% > 250

External funding

40.5

2.7

40.5

16.2

Result type: concept/ study

53.2

37.1

9.7

0.0

0.0

10.4

62.7

26.9

Result type: prototype

55.0

0.0

0.0

45.0

Software development

51.7

0.0

18.3

30.0

Result type: knowledge

Table 2.

Project type by cluster.

The clusters represent four different types of Telekom Laboratories projects: Strategy Implementation, Proof-of-Concept, External Co-funded Basic Research and Coordinated Applied Research. Strategy Implementation projects have a rather long duration with a bigger consortium and also a rather high number of possible production scenarios. These projects require a careful design that makes them fit into several environments from a technical and business perspective. Furthermore, due to consortium size, the project leaders have to take the diverse stakeholder interests into consideration. Proof-of-Concept projects are characterized by a rather short duration, small consortium, but nevertheless a higher number of possible production scenarios. Additionally, implementation is normally not part of these projects, which in turn means that only non-technical aspects need to be reviewed. External Co-funded Basic Research projects focus on a single production scenario and primarily focus on delivering knowledge. Additionally, the external funding and the documentation requirements involved discourage the project teams from including internal company knowledge in the project.

138

D. The Early Stages of New Product Development

Coordinated Applied Research projects are characterized by a focus on a single production scenario and are developed by a rather small consortium. Nevertheless, similar to projects from Cluster 1, they have a rather long duration. This leads to significant challenges because of the fast-changing environments in business practice. Also, as these projects deliver prototypes, the full EA scope must be reviewed. Method fragments for innovation implementation

As described, EA allows analysis to be carried out and deductions to be made for innovation projects at all levels of the enterprise. Placing the focus on innovation implementation restricts this review to projects that contain certain aspects of implementation only. Nevertheless, due to the close interaction between many aspects, it does not restrict the review to purely technical questions. EA research has developed a variety of methods to evaluate and guide the design of technical and non-technical artifacts (Niemann 2005; Riege et al. 2008; Winter et al. 2007). These methods allow a clear view into dependencies between different layers and between single items. This in turn helps to define constraints on different layers of the projects. Figure 1 depicts generic questions which can be developed from the EA perspective. The table shows these questions sorted by a meta-model as described by Winter (Winter and Fischer 2007). Design issues

Analysis

Strategy

Products, Market Segments, Partner, Strategic Projects

Organization

Business Processes, Business Functions, Roles, Organizational Entities

Integration

Domains, Applications, Business Services

Dependency- and Impact-Analysis

Coverage-Analysis

Conformity-Analysis

Profitability-Analysis

4

7 1

9

2 5

10

Data

Software Components Functional Hierarchies

8 3

Infrastructure

Plattforms, Hardware- and Network-Components

6

Figure 1 Analytical questions based on the EA model (Aier et al. 2009).

Enterprise Architecture in Innovation Implementation

139

Question key: 1. Is the IT delivery of a product based on the reorganization of services possible? 2. Is the organization capable of transforming the innovation into a product? 3. Is the innovation based on existing infrastructure components? 4. Does the innovation support the strategic focus? 5. Are the organizational processes capable of delivering the innovation? 6. Are the existing platforms capable of delivering the innovation? 7. What effort in process reengineering is required to deliver the innovation? 8. Are the necessary technical modifications achievable during project runtime? 9. Is the business case as expected, when using existing organizations and processes? 10. Does the innovation touch overarching aspects? Process model

The current innovation processes have been analyzed in order to develop a process model for the situational method. Innovation processes, in general, are characterized by putting as little process overhead on the project administration as possible (Cooper 2003). Therefore, based on the aspects described in Table 3 a checklist has been developed that defines the necessary documentation requirements. Each of these questions can be supported by different method fragments. Figure 2 shows the simplified innovation stage-gate process as implemented by Telekom Laboratories. Three gates are required to align the innovation with all stakeholders and to substantiate the proposal. Each project is then divided in several sub-phases that deliver revisable results. The fourth gate, at the end of the project life cycle, decides on the further steps in the innovation process, which can either be a dedicated transfer project or direct productization. During the project runtime, i.e., after Gate 3, the method proposed in this section is implemented in order to reduce up-front effort for the project team.

140

D. The Early Stages of New Product Development

Gate 4

Project-evaluation concerning Enterprise Architecture levels

Idea Proposal

Project Scheme

P&I internal

Gate 3

Gate 2

Gate 1

R&D Panel

Kick-off

...

Project Plan

Productization with BusinessUnits

Milestone 1

Milestone 2

Transfer Project

Innovation Board

Innovation Board

Figure 2 The innovation process at Telekom Laboratories.

The fragments are shown in Table 3. For each of these activities, cluster relevance has been defined as well as a preconditioned activity. With this information for each project, an individual process can be built to ensure the alignment of the innovation with the target environment, both on a technical and on an organizational basis. ID Activity

Description of the activity

Precondition

Clusters

A1

Construct individual process

Gate 3

CL1, CL2, CL3, CL4

A2

Evaluate external preconditions

A1

CL1, CL3

A3

Define relevant criteria

A1, A2

CL1, CL2, CL3, CL4

A4

Analyze relevant aspects with respect to strategy layer

A3

CL1, CL4

A5

Analyze relevant aspects with respect to organizational layer

A3

CL1, CL2

A6

Analyze relevant aspects with respect to integration layer

A3

CL1, CL2, CL3, CL4

A7

Analyze relevant aspects with respect to software layer

A3

CL1, CL3, CL4

A8

Analyze relevant aspects with respect to infrastructure layer

A3

CL1, CL4

A9

Develop concrete steering tools for project life cycle

A4–A8

all

Table 3.

Procedure model.

Enterprise Architecture in Innovation Implementation

141

Case study An innovation project (Prosero) in the area of service-oriented architecture (SOA) tools and methods will be used to demonstrate the approach described above. Firstly, the innovation project must be classified according to the cluster logic presented above. Table 4 shows the classification of the project as a Coordinated Applied Research project. Criterion

Values

Duration

24 months

Budget

Pb >750

Size of consortium

3

Number of possible production scenarios

1

Result type

Prototype

Software component

Yes

Table 4.

Example classification.

The individual processes for the reviewed project would therefore include activities A1, A3, A4, A6, A7, A8, and A9. In each activity the question can be answered using a variety of different methods. The following example simplifies the scenario and shows how software layer questions, as described in A7, can be examined in more detail using known methods. As shown in the above table Prosero is a prototype delivering project. Hence in the case of a productization of the projects software deliverables technical decisions need to be supported. Prosero software deliverables are mainly on the application layer (see Figure 3). The necessary infrastructure as hardware, BPEL engine, web service container, application server, web server, network etc. has not been in the focus of the innovation project. Due to easy access open source infrastructure has been used. To choose the most suitable infrastructure is one of the relevant issues for productization planning. The case study differentiates an open source infrastructure and a well-known commercial SOA infrastructure vendor and focuses on the overall system quality considering the application and the infrastructure layer. Närman et al. defined system quality as shown in Figure 3 (Närmann et al. 2008). They propose a method building upon Bayesian networks to calculate system quality considering availability, accuracy, confidentiality and integrity within the shown application and infrastructure layers. The system quality model has been derived from the ArchiMate (Jonkers 2006) system model. To derive a concrete system quality meas-

142

D. The Early Stages of New Product Development

ure, the model has been instantiated using the values that were acquired from running Prosero application layer artefacts on open source and commercial SOA infrastructure. To receive realistic values an industrial scenario from the power generating industry has been implemented on both testing environments.

Application Service Availability Accuracy

Data Object

Application Function/Interaction

Availability

Availability

Accuracy

Accuracy

Confidentiality

Confidentiality

Integrity

Integrity

Application Component Availability

Application Technical Infrastructure Service Availability Confidentiality Integrity

Node

System Software

Communication Path

Availability

Availability

Confidentiality

Confidentiality

Integrity

Integrity

Device

Network

Availability

Availability

Availability

Confidentiality

Confidentiality

Confidentiality

Integrity

Integrity

Integrity

Figure 3 System quality as developed by Närmann et al. 2008

Enterprise Architecture in Innovation Implementation

143

Figure 4 shows the cumulated values derived from the Bayesian computing comparing both scenarios. Especially within availability the Open Source scenario is loosing ground and therefore a commercial SOA vendor has been used for productization of Prosero artefacts that are being used from DTAG customers today already. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Commercial SOA infrastructure scenario

ity Int eg r

Co nfi de nti ali ty

Ac cu rac y

Av ail

ab

ilit

y

Open Source SOA infrastructure scenario

Figure 4 Analysis of results.

Conclusion The proposed approach allows innovation projects to better align the implementation of innovation with the overall enterprise architecture. A situational method allows a huge variety of different innovation projects to be covered. Using the method allows an individual process to be constructed that guides the project team to the most important questions that need to be evaluated, and proposes the appropriate method fragments. Future investigations should evaluate more method fragments and assign them to the individual research questions.

References Aier, S., Riege, C.; Schönherr, M.; Bub, Udo 2009. Situative Methodenkonstruktion für die Projektbewertung aus Unternehmensarchitekturperspektive. In: Business Services: Konzepte, Technologien, Anwendungen. Hansen, H.R. et al (eds), S. 109–118.

144

D. The Early Stages of New Product Development

Arnold, H. M., Dunaj, M., Enterprise Architecture and Modularization in Telco R&D as a Response to an Environment of Technological Uncertainty. Presented at ICIN 2007, October 26–29, in Bordeaux, France. Bacher, J., Wenzig, K., Vogler, M. 2004. SPSS TwoStep Clustering – A First Evaluation. Presented at the Sixth International Conference on Logic and Social Science Methodology, August 16–20, in Amsterdam, Netherlands. Baumöl, U. 2005. Strategic Agility through Situational Method Construction. Presented at the European Academy of Management Annual Conference, May 4–7, in Munich, Germany. Bucher, T., Klesse, M., Kurpjuweit, S., Winter, R. 2007. Situational Method Engineering – On the Differentiation of “Context” and “Project Type”. Presented at IFIP WG8.1 Working Conference on Situational Method Engineering – Fundamentals and Experiences (ME07), September 12–14, in Boston, USA. Cooper, R. G. 2003. Best Practices in Product Innovation: What Distinguishes Top Performers. Ancaster: Stage-Gate.Foorthuis, R. M., Brinkkemper. 2007. A Framework for Project Architecture in the Context of Enterprise Architecture. Presented at the Second Workshop on Trends in Enterprise Architecture Research (TEAR 2007), June 6, in St. Gallen, Switzerland. Harmsen, A. F., Brinkkemper, S., Oei, H. 1994. Situational Method Engineering for Information System Project Approaches. Presented at IFIP 8.1 Working Conference on Methods and Associated Tools for the Information Systems Life Cycle, September 26–28, in Amsterdam, Netherlands. IEEE. 2000. IEEE Recommended Practice for Architectural Description of Software Intensive Systems. IEEE Std 1471–2000. New York: IEEE. Johnson, P., Lagerström, R., Närman, P., Simonsson, M. 2006. Extended Influence Diagrams for Enterprise Architecture Analysis. Presented at the Tenth IEEE International EDOC Enterprise Computing Conference, October 16–20, in Los Alamitos, CA, USA. Jonkers, H. 2006. Architecture Language Reference Manual v4.1. Netherlands: Telematica Instituut/Archimate Consortium. Kumar, K., Welke, R.J. 1992. Methodology Engineering – A Proposal for Situation-specific Methodology Construction. In Challenges and Strategies for Research in Systems Development, ed. W. Cotterman and J. A. Senn, 257–69. New York:John Wiley & Sons. Närmann, P., Schönherr, M., Johnson, P., Ekstedt, M., Chenine, M. 2008. Using Enterprise Architecture Models for System Quality Analysis. Presented at Enterprise Distributed Object Conference, September 15–19, in Munich, Germany. Niemann, K. D. 2005. Von der Unternehmensarchitektur zur IT-Governance: Leitfaden für effizientes und effektives IT-Management. Wiesbaden: Vieweg. Simonsson, M., Linström, Å., Johson, P., Nordström, L., Grundbäck, L., Wijnbladh, O. 2006. Scenario-Based Evaluation of Enterprise Architecture – A Top-Down Approach for CIO Decision-Making. Presented at the International Conference on Enterprise Information Systems, May 23–27, in Paphos, Greece. Van Slooten, K., Hodes, B. 1996. Characterizing IS Development Projects. Presented at IFIP TC8, WG8.1/8.2 Working Conference on Method Engineering, Springer, Berlin et al 1996, 29–44, in Berlin, Germany. Vasconcelos, A., Sousa, P., Tribolet, J. 2007. Information System Architecture Metrics: an Enterprise Engineering Evaluation Approach. The Electronic Journal Information Systems Evaluation (EJISE) 10:91–122. Winter, R., Bucher, T., Fischer, R., Kurpjuweit, S. 2007. Analysis and Application Scenarios of Enterprise Architecture – An Exploratory Study. Journal of Enterprise Architecture 3:33– 43. Winter, R., Fischer, R. 2007. Essential Layers, Artifacts, and Dependencies of Enterprise Architecture. Enterprise Architecture 3:7–18.

D. The Early Stages of New Product Development

145

Managing Technology Push and Market Pull within Pre-Product Development

Modularization is one approach taken from the manufacturing industries and applied to the early stages of new product development in order to be able to react and adapt to a fast changing and heterogeneous environment of market, competition, and company internal interfaces. With many of the telecommunication service innovations consisting of software, where modularity is common practice, the extension of modularity to the earlier stages of innovation is a logical next step. This concept helps define an interdisciplinary meta-language as an important ingredient in practically combining the forces of innovation (technology push and market pull).

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_13, © Springer-Verlag Berlin Heidelberg 2010

146

D. The Early Stages of New Product Development

Introduction Innovation needs a high level of individual freedom, thus the task of innovation management is on the one hand to establish and assure this freedom within an organization while on the other to frame the development of innovations in a planned, controllable, and structured process. Innovation management can be defined as the structured organization of the innovation processes (Hauschildt 2004). This includes the definition of strategies and goals, decision-making, the determination and influencing of information flows, the creation and shaping of social relationships, and the implementation of decisions made (Hauschildt 2004). The sources of innovation are numerous and matter of considerable debate. One phenomenon seems to be of dedicated interest: Innovations can be either the result of a technical change/invention (technology push) or they can be induced by a rising and unsatisfied market/user need (market/demand pull). Considering these two concepts, the following section examines (new) product development from the perspective of the market- and technologydriven design of innovation processes for new telecommunication services. Innovations in telecommunication services are usually software developments in a dynamic, complex, and in most cases diverse environment and as such subject to a high degree of uncertainty and risk. A specific approach to methodically support the early phase of new product development (NPD) for both technology push and market pull innovations is discussed below.

New product development Product development as a central component of innovation management

Pressure on revenues on the one hand and the current technological frenzy on the other ultimately lead to the individual market players’ immense need for innovation. The most notable external challenge is the considerable uncertainty in the newly emerging and converging markets with regard to the technologies in use and the relevant customer requirements. Internally, the generic innovation value chain comprises three consecutive phases: the frontend of innovation, new product and process development, and the final commercialization phase (Koen et al. 2001). Among them, new product and process development is one of the most crucial issues.

Managing Technology Push and Market Pull within Pre-Product Development

147

The process of new product development in the area of tension between technology push and market pull

As shown in Figure 1, there are basically two complementary innovation concepts. On the one hand, technological progress can lead to new potential solutions or break-through innovations (Trommsdorff and Steinhoff 2006), for which customer requirements must first be identified or even new markets developed. On the other, unsolved customer problems may prompt the development of new solutions. Generally, the term market pull refers to market-induced innovations and technology push to technology-driven innovations (Chidamber and Kon 1994). These two innovation concepts create the context for new product development, so to speak, and can be further differentiated into effort, degree of innovation, market and technology uncertainty, as well as timeframe. Whereas market pull innovations are seeking a technical solution and therefore stimulating technical developments, technology push innovations need market developments and the right timing for the market launch in order to be successful. While market pull innovations are often incremental improvements of existing products and customer preferences and needs are important considerations (Lender 1991), studies have shown their high degree of commercial success (Cooper 1982; Cooper and Kleinschmidt 1987) in contrast to technology push innovations. These are often characterized by radical improvements and therefore often have a much higher business impact but need a longer timeframe to be commercialized. In practice, technology push and market pull usually exist in parallel (Meffert 2000) and overlap at the new product development stage in particular. Consequently, NPD has to be aware of both forms and has to support these different types in this overlapping area.

Technology-Push

Idea generation

Market-Pull

NPD

Commercialization Building Block Approach

Figure 1 Interface between technology push and market pull innovations

148

D. The Early Stages of New Product Development

Building blocks: a method of early new product development Building block requirements

To ensure that these crucial tasks are successfully achieved several requirements have to be fulfilled. The first requirement originates from the large number and the wide variety of disciplines involved in new product development. While initially engineers are primarily entrusted with product development, people with a non-technical background are increasingly involved in the process, as mentioned above. As a result, technicians must explain the opportunities and innovations of the development to product managers, in the same way as product managers must notify technicians of market-relevant requirements. Project results must therefore be available from both perspectives – the technology and marketing sides – and must be interpretable and processable. This is facilitated and promoted by a common uniform language. While a common terminology simplifies the communication between the different stakeholders, additional requirements come from the uncertain and dynamic nature of the market. Furthermore, a long time to market (common for technology push innovations) is a critical factor in the case of innovation projects, as they are often defined and launched with such a long time to market that the precise market objective – in particular the market potential and possible applications – is largely unexplored. However, companies are forced to initiate and drive forward the innovation process at a correspondingly early stage, even without the precise knowledge of market performance or chances of success, if they want to play a leading role in innovation topics and establish a sustaining source for growth. This applies particularly to technology push innovations. In this context, modularization is primarily a method of promoting flexibility in the product development process, the project design, and the organization of project results. The flexible organization of project results achieved due to the modularization also helps to isolate and analyze project results in order to reduce and manage the complexity of the desired solution. In this context, a module is defined as a detailed, independent project outcome extracted from the overall project logic. A module could be a part of software, a conceptual document, or lines of code. The identification of modules serves to neutrally isolate individual project outcomes and departs from the problem- or solution-driven organization of knowledge that is otherwise common to projects (Probst et al. 1999). As a result, modularization can make the innovation and especially the product development process flexible, and therefore reduce complexity and

Managing Technology Push and Market Pull within Pre-Product Development

149

uncertainty (Arnold et al. 2007; Erner and Presse 2007). Additionally, it assists the flexible selection and further processing of project results in particular. In the same way as the modularization requirement is structuring project results, the restructuring and meta-aggregation of these isolated modules has to be ensured: First, in order to give a market-driven structure to the modules that are often taken from technical environments; and second, in order to consolidate the infinite variety of emerging modules in an easily and generally comprehensible manner (Hawes 1991). This requirement is especially essential for technology push innovations as their business impact is often generated out of the huge variety of possible applications. Building block approach

The “building block method” was developed as a result of the above-mentioned requirements for methodically supporting the process of new product development. As a solution, the approach proposes a flexible, modularized meta-language in order to organize and process project results and is implemented in two steps: First, the project results are removed from their technical and project-specific environment; and second, they are transferred to a new meta-aggregation. The procedure described in detail below and presented in Figure 2 is used to achieve these steps.

Figure 2 Basic idea of the building block approach

150

D. The Early Stages of New Product Development

The first step is designed to achieve transparency with regard to project content and results. The goal of projects is typically to solve specific problems. The project results are therefore organized and structured in light of the issue to be resolved. This means that the project results must initially be identified, structured, and codified, so as to organize them to be reused in another form. The goal is to prepare a complete overview of all technical and non-technical project results generated during the project and therefore to ensure their “survival” (Probst et al. 1999). This also includes knowledge content that is required to achieve a project result, but whose capture is not absolutely necessary for the successful completion of a project. Precisely this knowledge often represents a precious resource for other projects. As part of an innovation project for mobile shopping applications, for example, a location and privacy platform was developed. This technology platform was also used in a parallel project developing services for the automotive market. If this knowledge had not been explicitly recorded, it could not have been used for other projects – including areas with different topics – and redundant work and costs could not have been avoided. Therefore a specific recording of the project content and outcome at a simple and manageable level have to be done. To achieve this, the project content and results must initially be described at the module level. This is not a matter of recording process knowledge, such as information on project plan, work breakdown structures, or budget plans, but of recording and presenting knowledge modules as a content-based result of project work. The second step entails the allocation of all modules to a building block. Building blocks are defined as industry-specific, overarching terms for specialist areas, such as “service quality,” “localization,” or “personalization,” which are generally recognized as specialist terms and are understood and used by both technical and non-technical staff. The goal of allocation is to identify the specialist areas in which the module concerned is able to add value and further to allow a first restructuring for potential reuse of the modules.

Management of early product development with the building block-approach As mentioned above, new product development is a central task within innovation management; therefore, the following section presents the building block approach with regard to its contribution to the management of product development.

Managing Technology Push and Market Pull within Pre-Product Development

151

These tasks, derived from the general management approach, are used to determine the key aspects of product development. According to this approach, management has to take care of: • • • •

strategic planning; structuring; leading; controlling.

The following section analyzes product development in the context of these basic management tasks and discusses the use of the building block method to perform the tasks. Two practical applications of technology push and market pull innovations can be presented by way of example. Strategic planning

Building blocks are highly relevant to strategic planning, primarily when determining innovation strategy. For example, the breakdown of project outcomes into modules and their allocation to building blocks enables the analysis of a portfolio’s strengths and weaknesses (Porter 1998) for all innovation activities, and therefore serves as a strategic management tool. The operationalization of the innovation strategy through the initialization and definition of innovation projects has a significant effect on strategic product development. On the one hand, technology push innovations organized into building blocks develop a market-relevant structure, which makes it easier to market them. On the other, product development can identify potential innovation gaps by comparing existing technological expertise with expected market and customer trends, and therefore stimulate market pull innovations. In addition to building blocks, enablers1 serve as a management tool for product development and can influence the entire development process through appropriate design and adjustment. Structuring function

With regard to the structuring function, product development must generate market information, implement it in product requirements, and include it in the product development process in order to fulfill a company’s marketdriven strategy. On the other hand, new product development has to identify and select key technologies, analyze their potential value, and promote them through the entire process (technology push).

152

D. The Early Stages of New Product Development

Particularly in the case of market pull innovations, product managers often already have clear ideas about the required product solution due to recently performed market studies and the short time to market, and are merely looking for a suitable opportunity to technically implement them. A product manager developing an online video rental service must firstly identify and define the necessary general and specific customer needs and functionalities. In the case mentioned, these would be functionalities such as the existence of a user interface, authentication and payment mechanisms, and functionalities to see or consume the video (see Figure 3). In the next step, the product manager must determine how to provide these functionalities. In addition to the option to buy or develop in-house, the product manager can also use technical solutions from innovation projects that are already completed. By structuring the project outcomes into building blocks and modules, the product manager is able to easily search for and therefore quickly obtain the information or functionalities that are relevant for the product. This applies not only to the development of new products and services, product managers can additionally improve existing services by browsing through the module library. Use Case: Video on Demand

Access to system

Select video

Functions

Select video

Play video

Billing and accounting

MPEG-21 Digital Item Declaration

MPEG-21 DID Database

Digital Rights Management

Billing and accounting

Functions Play video

Modules

Access to system

Billing and accounting

Integrated management supervisor

Access to system

Select video

Play video

Billing and accounting

Functions

Modules

Access to system

Select video

Functions Play video

Integrated management supervisor

Modules

Billing and accounting

Digital Item Declaration Charging System

Modules Terminal Device Manager

QoS Monotoring System

Figure 3 Link between technology and market

Digital Rights Management

Account Manager

Managing Technology Push and Market Pull within Pre-Product Development

153

Technology driven projects are often developed on the basis of use cases, which describe the functionalities required for use from the user’s perspective and the corresponding technical solution modules (Kruchten 1999). Allocating modules to use cases over and above building blocks therefore offers additional advantages when identifying project outcomes. In the manner described here, the building block approach structures with regard to both the coordination between technology and marketing and between different projects. It therefore represents a method of supporting the interaction between technicians and marketing staff along the innovation value chain. With regard to their structuring function, building blocks are particularly important in product development in their capacity as a cross-functional metalanguage and in terms of their complexity-reducing function. This supports product development in bridging between and managing the technology- and the market-driven forces along the entire innovation process. Leading (doing) function

One way of countering uncertainty with regard to market and customer requirements and product complexity is flexibilization, which can be achieved using modularization. Above and beyond the structuring function described above, the concept serves as a building block for a complex overall solution in product development (Meyer and Lehnerd 1997). The building blocks comprise individual modules that, as illustrated in Figure 4, can be used to build product solutions. Modules can be regarded as functional elements. While modules in market pull innovations are often merely identified and integrated in an overall solution, the role of product development in the case of technology push innovations is in particular to influence and shape the design of modules and building blocks from a market perspective. Furthermore, product development is responsible for defining or initiating the development of suitable modules and building blocks on the basis of market trends and latent customer needs. Modules can also be aggregated directly to form product and service functionalities with a view to product-driven use. Nevertheless, they often merely represent knowledge content or logical functional units that require further processing. The flexibilization and modularization of the project outcomes is also helpful within product development, as products often need to be adapted towards specific needs of different customer segments. Therefore, the building block approach provides precisely the flexibility required for segment-specific product development. This enables slightly different variants of a service to be developed using modular logic; functionalities can

154

D. The Early Stages of New Product Development

then simply be added to or left out of the service. This type of product variation is widespread in the telecommunications industry (Badke-Schaub and Frankenberger 2003). For example, premium and business products usually have a greater number of product features that are more powerful. The explanations above demonstrate that the building block method supports product development for both technology push and market pull innovations in the context of product design, in particular through the modularization and recombination of the modules for a specific purpose. Step 3 Step 1

Step 2

Building Block 1 Product 1 Project A

Product 2

Product 3

Project ...

Building Block 2

Building Block 3

Building Block ...

Product ...

Figure 4 Methodical procedure for recombining modules

Controlling function

In addition to the planning, structuring and leading functions, the controlling function is essential to check whether the new product development is achieving the desired goals. Therefore the modules designed in the innovation projects should match the identified customer needs and vice-versa the product management should seek new markets using the newly developed modules. By keeping track of the maturity and quality of each module, prod-

Managing Technology Push and Market Pull within Pre-Product Development

155

uct managers as well as technicians can control and if necessary react to certain developments.

Conclusion New product development has to manage, cope with, and negotiate between the two forces of innovation (technology push and market pull). Especially in fast changing environments, the ability of an organization to adapt and transform new technologies into new products suiting the relevant customer needs within an environment characterized by high uncertainty remains the critical challenge. The building block method can help organizations to solve this sophisticated task by establishing a common unified language, structuring the R&D results, recombining them as part of product development, and finally supporting strategic analysis by comparing market requirements with technological expertise. As a result, the method enables faster, more efficient and effective product development and increases the innovative capability of the whole innovation organization. Furthermore, the approach takes both technology push and market pull innovations into account and addresses the key management functions.

References Arnold, H. and Dunaj, M. 2007. Enterprise Architecture and Modularization in Telco R&D as a Response to an Environment of Technological Uncertainty. Paper presented at the ICIN, Bordeaux. Badke-Schaub, P. and Frankenberger, E. 2003. Management kritischer Situationen: Produktentwicklung erfolgreich gestalten. Berlin: Springer. Chidamber, S. R. and Kon, H. B. 1994. A research retrospective of innovation inception and success: the technology-push, demand-pull question. International Journal of Technology Management 9(1): 94–112. Cooper, R. G. 1982. Market-Push Strategy Inhibits Industrial, High-Tech Innovation. Marketing News 15(26): 1–3. Cooper, R. G. and Kleinschmidt, E. J. 1987. New Products: What Separates Winners from Losers? Journal of Product Innovation Management 4: 169–184. Erner, M., and Presse, V. 2007. A Modular based Approach to Reduce Uncertainty in R&D. R&D Management Conference 2007. Bremen, Germany, 4–6 July 2007. Hauschildt, J. 2004. Innovationsmanagement. Munich: Vahlen. Hawes, L. C. 1991. Organizing Narratives/Codes/Poetics. Journal of Organizational Change Management 4(3): 45–51.

156

D. The Early Stages of New Product Development

Koen, P., Ajamian, G., Burkart, R., Clamen, A., Davidson, J., D’Amore, R. et al. 2001. Providing Clarity and a Common Language to the “Fuzzy Front End”. Research Technology Management 44(2): 46–55. Kruchten, P. 1999. Der rational Unified process.: Eine Einführung. Pearson Education Deutschland. Lender, F. 1991. Innovatives Technologiemarketing: Grenzen der “konventionellen” Marktforschungskonzepte und Ansätze zur methodischen Neugestaltung. Vandenhoeck & Ruprecht. Meffert, H. 2000. Marketing: Grundlagen marktorientierter Unternehmensführung; Konzepte, Instrumente, Praxisbeispiele; mit neuer Fallstudie VW Golf. Wiesbaden: Gabler. Meyer, M. H. and Lehnerd, A. P. 1997. The Power of Product Platforms. New York: Free Press. Porter, M. E. 1998. Competitive advantage: Creating and sustaining superior performance. New York: Free Press. Probst, G. J. B., Raub, S. and Romhardt, K. 1999. Wissen managen: Wie Unternehmen ihre wertvollste Ressource optimal nutzen. Frankfurt am Main: Frankfurter Allg. Zeitung für Deutschland et al. Trommsdorff, V. and Steinhoff, F. 2006. Innovationsmarketing. 1st ed. Munich: Vahlen.

D. The Early Stages of New Product Development

157

Design Research in University-Industry Collaborative Innovation: Experiences and Perspectives

Design has been considered a driver of innovation for quite some time (Kelly 2003; Utterback et al. 2007; Center for Design Innovation 2007). Today, it is nothing new for many companies with leading innovation centers, such as Sony and Philips Research Labs, to have a design department. Design Research, however, is less widely practiced in innovation centers. The Design Research Lab within the Strategic Research-arm of Deutsche Telekom Laboratories acts as a partner driver in a university-industry collaborative research and innovation setting. Its subject matter, approaches, and methods are discussed in this section.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_14, © Springer-Verlag Berlin Heidelberg 2010

158

D. The Early Stages of New Product Development

Introduction The concept of university and industry (UI) research collaboration is not an invention of the 21st century, but has existed since the 1800s in Europe and since the industrial revolution in the United States. These partnerships have, however, increased and intensified over the past decade and have received much public and institutional attention (Jones 2008). The growth of UI research collaboration is due to various factors. More effective and efficient knowledge transfer for the benefits of the industry and more funding opportunities for the benefits of academic researchers are two key factors. Furthermore, the industry partner benefits from direct access to state-of-the-art research, can potentially influence the research agenda, and can experience a positive effect on the overall corporate culture as a result of the collaboration. At the same time, the university partner benefits from the opportunity to apply research results to market products, from access to good facilities, and from gathering first-hand information about the state of the market. It stands to reason, therefore, that UI research collaborations will continue to grow in number, making it relatively safe to say that it is a very promising model for innovation.

Design Research Design Research is still a young discipline seeking broader acceptance from both academia and industry – although the results prove its competencies (Hänsch 2007). One might ask what the difference is between Design and its academic arm, Design Research. From certain perspectives, Design Research can (or should) include practical design processes – e.g., one output of a Design Research project could be an artifact – but it must go far beyond that. “Design Research is a systematic search for and acquisition of knowledge related to general human ecology, considered from a ‘designerly’ (i.e., project-oriented) perspective” (Findeli 2008). Design Research is a rigorous inquiry aiming for original knowledge while improving current situations. As in other academic disciplines, it includes reflection on and theories of its methods. It produces knowledge that is transferable to other contexts and presents this knowledge to be peer-reviewed by the community in conference as well as through publications. These practices are not necessarily performed by even the most innovative of professional design groups. With these differences, Design Research in the UI innovation context is a rather new phenomenon. In summary:

Design Research in University-Industry Collaborative Innovation

159

1. The subject matter of Design Research is concerned with general human ecology. 2. The approach of Design Research is research through design (Findeli 2008). 3. The methodology of Design Research is to analyze, project, and synthesize (Chow et al. 2008). 4. The goal of Design Research is the acquisition of theoretical and practical knowledge to inform practice and guide further research. Furthermore, Design Research is concerned with problems that are located in the real world – not with somewhat abstract “academic” phenomena. Design Research always has an empirical aspect because it aims to develop solutions for real-world problems – and therefore needs to be familiar with real-world conditions and with the everyday life of people in their environments. Viewed from this perspective, Design Research is clearly related to the social sciences and their empirical research methods. On the other hand, Design Research often works with prototyping, exploring the interactions between humans and technology. In this respect, it is related to the engineering sciences, and also applies their methods of prototyping and evaluation. What is different about the Design Research approach is that it focuses on practice: Research results are closely tied to their actual application to practical problems and research questions are derived from the field of practice. Moreover, Design Research is always concerned with projection activities, in particular projecting future scenarios to improve the current situation and environment. Subject fields

At the Design Research Lab (DRL), three main project fields are covered, ranging from a focus on technology, to a focus on people, and from theoretical to more practical aspects. Humanizing Technology explores new interaction paradigms for technological innovations in ICT, such as multitouch interfaces or sensor technology. Mediating People takes the user and his or her needs and behavior as a starting point for research projects, for example, on gender-specific design. Finally, Conceiving Design, the most abstract field, deals with theories and methods of Design Research, such as design transfer and the rhetorical framework of audiovisual products. The fourth field shown in the diagram, Basic Technology Research, is beyond the scope of Design Research, but forms the interface to the more technically-oriented research areas at Telekom Laboratories. The DRL is also involved in design and development projects for the strategic business units of Deutsche Tele-

D. The Early Stages of New Product Development

160

kom, including business unit mobile communication, broadband/fixed line and Innovation Marketing. practice

Basic Technology Research* (SR Usability)

Mediating People Design & Irritation Gender & Design MAPS EcoMobile

people

technology

Humanizing Technology Ambient Life Multitouch Tactile Interaction

Conceiving Design Design as Rhetoric Transferability Design Research Network

theory

Figure 1 The project fields covered by the Design Research Lab.

Approach

At the DRL, research projects are structured and guided by “research through design.” This means that the practical design project is an inherent part of research and that the scientific knowledge gained throughout the process is derived through design action and tools. Therefore, a design-practice project is a central part of research through design (Findeli 2008). The model behind the idea is the following: An initial design question can be reframed in a broader research question. For example, one can start the research process by asking what a mobile device for female users should look like and what functionalities it should offer. This would then be expanded into a broader

Design Research in University-Industry Collaborative Innovation

161

research question that could address different issues, such as the different needs of people of different genders in terms of Information and Communication Technologies (ICT). It is also true that a broad research question can be recast as one or more specific design questions. Starting from the overall question of whether there are gender-specific differences in ICT behavior, one can also derive specific design tasks to understand the question and evaluate it on an empirical basis using prototypes. For both approaches, there are potential design answers that can take one of many forms (e.g., verbal, artifactual, visual, narrative). These possible design answers may, in turn, be helpful in answering the research questions posed at the beginning of the project. It is through this model that the outcomes of design research fulfill the needs of both academia and industry. Research-Through-Design-Project

Output

Research Question

Design Question

Design Answer

Artifacts

Knowledge

Industry Public

Peers Education

Research Answer

Figure 2 Research model for the Design Research Lab.

Academia expects researchers to produce new knowledge and understanding, while industry is interested in the immediate application of research results. Fundamental understanding and immediate application are two conflicting demands that must be addressed in UI collaboration. In Design Research processes, the outcome often includes both: artifacts that propose applicable solutions for real-world problems and knowledge that is a communicable result (e.g., papers, book publications, visualizations in a research context, etc.) that fit the requirements of academic discourse. At the DRL, the Transferability and Women’s Phones projects illustrate how research is conducted in such a way as to fulfill the two demands at the same time.

162

D. The Early Stages of New Product Development

In the Transferability project, research (Chow 2008) focused on the case transfer method. Case transfer was designed, tested, and demonstrated to hold promise in supporting design projection. Three different types of transfer are proposed: local, regional, and long-distance. In local transfer, knowledge is taken within same domain; in regional, across similar domains; and in long-distance, across different domains. The research shows that regional transfer is the most productive. Telekom Laboratories gives a high degree of freedom for investigation of any topic. In principle, these research results have been sufficient because the quality, as well as the quantity, of academic publications are the measures of research output. However, the subject matter of transfer – mobile communication devices – was chosen intentionally to yield immediate results for the interests of Deutsche Telekom. In the study, two designers collected and analyzed mobile phones (local), mobile objects (regional), and avant-garde objects (long-distance) and used them to conceive new mobile communication devices. Through studying the case transfer method, more than 80 new concepts of mobile communication devices were produced. While these concepts could be considered mere byproducts from a perspective of academic investigation, they were quite valuable for the innovation development process within Telekom Laboratories and Deutsche Telekom. These concepts could be immediately applied to the development or improvement of new products at Deutsche Telekom, ranging from ways to solve the problem of undesired data on mobile phones to ways to share music within a small group intuitively. In this specific case, it is, funnily enough, not just the fundamental knowledge but these “byproducts” that communicate the value of design research to Deutsche Telekom. The Women’s Phone project was quite different from the case transfer study. The project started with the initial hypothesis that the current offer of mobile phone devices on the market did not reflect gender-specific differences in wishes, needs, and habits. The popular attitude toward women and technology is that they are less interested and less skilful than men. Consequently, the formal design of “women’s phones” uses smooth forms, pastel colors, and floral decorative elements to conceal the technical character of the phone. In short, mobile phone design for female users seems to follow the established paradigm “shrink it and pink it.” While this strategy may be adequate for a portion of the female customers, it still fails to mirror the variety of needs and lifestyles among women. They have a right to be perceived and be taken seriously in their needs as product consumers. At this early stage, the project group started with a design question: What should mobile devices for user groups of different gender be like? A fundamental question that already touched the broader research context im-

Design Research in University-Industry Collaborative Innovation

163

mediately occurred: How could one overcome the traditional gender clichés in designing for female or male user groups? Furthermore, how can gender issues be addressed in research and development projects dealing with ICT? After evaluating different methods and experiences from other research projects, the project team decided to actually involve real users in the entire process – from the very beginning. To this end, a research project using inquiry methods from participatory design was conducted. Together with prospective female users, prototypes for future devices were developed that reflected the symbolic and emotional relevance of communication activities rather than technical features or styling. The cultural probe tool (Gaver et al. 1999) was applied to gain deeper insight into the relevance and quality of communication in the everyday life of the group of eight female users. The users were involved in an intensive research phase lasting four weeks, during which they were asked to document their communication habits with a research toolkit (the cultural probe) that included a disposable camera, a diary, postcards with different tasks and questions, and a social map used to describe their personal social network. The initial results were promising: the project team did indeed glean deep insights into the everyday life of the participants. For example, they expressed their emotional state towards their mobile phone in different situations – was it a way of connecting with the most important people in their lives, or was it a demanding communication device considered to be an annoyance in times of stress? The participants were also tasked with creating their own prototypes for a future communication device that addressed their ideas of appearance, feel, and functionality. The results were astonishing – the participants made prototypes that resembled puppets, that used free forms and bright colors, and that did not look at all like today’s average mobile phone. These early results led to a collaboration with the business unit mobile communication on this topic. As female users are seen as an important target group for future services and devices, further research is continuing with female user groups of different ages and backgrounds. On the one hand, the insights into the life of the user group and the objects developed in the participatory design process provide knowledge and inspiration for the industrial development and design process. On the other hand, the method of addressing gender and diversity aspects is reflected in Design Research processes in academic papers – and produces knowledge about appropriate research processes that aim to avoid gender clichés in design development.

164

D. The Early Stages of New Product Development

Methodology

Being concerned with general human ecology, Design Research is often confronted with what are called “wicked problems” (Rittel 1973). These problems are characterized by a high level of complexity combined with a high degree of uncertainty, and often involve stakeholders with radically different world views. Furthermore, wicked problems can have numerous possible intervention points, consequences that are difficult to imagine, and there is also no “stopping rule” in the form of a single solution. This implies a methodology that makes it possible to deal with uncertainty, ambiguity, and complexity in real-life situations. To this end, an electronic tool called MAPS (Matching Analysis, Projection and Synthesis, see www.design-research-lab.org/ MAPS) was developed to fulfill these needs (Chow et al. 2008).

Figure 3 Screenshot MAPS

MAPS is underpinned by a theoretical model that takes different epistemological domains into account. It is comprehensive, distinguishing and addressing the situation, process, method, and tool. It is also generic, applicable to different kinds of innovation. MAPS guides the users by means of a questionnaire. Users are made aware of the different dimensions of the project that need to be taken into account for project planning.

Design Research in University-Industry Collaborative Innovation

165

• System: scope of contextual factors to be considered: market, society, environment, etc. (degree of complexity) • Research: scientific standard to be considered (degree of scientific knowledge input) • Future: projective time space to be considered (degree of uncertainty) • Implementation: executive opportunities (degree of realization) When users are finished with the questions, MAPS provides them with a diagnosis of the project and recommendation of potential methods. Users can then discuss and plan their projects accordingly. MAPS stores over 150 methods for various stages of innovation development that can be consulted at any time. MAPS serves as a communication tool for interdisciplinary teams. In other words, MAPS integrates Design Research with other disciplines for innovation. MAPS acts from a design research perspective and is based on the assumption that this perspective encompasses technological, market-oriented research and development and innovation processes. MAPS assists design researchers and their collaborators and clients in: 1. specifying and/or categorizing (problem) situations; 2. matching process patterns to the specified situation and identifying the role of design researchers in the process; 3. selecting methods and/or tools related to the process. MAPS helps decrease complexity and uncertainty during problem solving and research. It also helps increase efficiency and effectiveness when collaborating with partners and clients. Outcomes and Values

Design Research plays an important role in developing innovations at Telekom Laboratories. Three major assets can be summarized: 1. Design Research puts its own research questions and hypotheses forward to address the wicked problems of everyday life. 2. Design Research makes research results tangible through prototyping and visualizations within multidisciplinary teams. 3. Design Research develops and applies design methods for analysis, synthesis, and projection. As indicated above, in order to achieve its aim of creating new knowledge, Design Research must pose critical questions and entertain likely hypotheses. However, the questions posed are different from those in technolo-

166

D. The Early Stages of New Product Development

gy and business, and here lies the contribution of Design Research. Generally speaking, because Design Research questions address problems and opportunities in everyday life, they are necessarily interdisciplinary or undisciplined. Knowledge from engineering, business, psychology, sociology, anthropology, communication, and philosophy must be adopted, but more importantly it must be synthesized and applied to make new design propositions. For example, in the project 50+, people aged between 50 and 80 were recruited to express their wishes, needs, and behavior related to Information Communication Technologies (ICT). Sociological and ethical questions must be taken into account so as not to address this user group as “old” – meaning unable to use “normal” technology. Psychological knowledge was employed to ask questions in the right “language.” Drawing on the knowledge of all these disciplines enabled the researchers to work successfully with this group of users and develop new design propositions. In order to make new design propositions, design researchers employ prototyping and visualization skills to synthesize all forms of knowledge. It is known that these design competencies, when used to envision the future, greatly improve the communication between and the imagination of interdisciplinary teams. The project in which the new multitouch technology was explored with technical engineers can serve as a good example. The technological framework for multitouch and multiuser interaction was quickly set up, but the potential for innovative products within ICT was uncertain. Through primary visualization with video prototypes, some scenarios for application were projected without much effort. Questions addressed include: • What kind of interface supports the interaction of multiple users at the same time? • What kind of application can be enriched by multitouch interaction? • How do you combine the visual, tactile, and auditory feedback? The scenarios dealing with home entertainment as well as public space applications were quite helpful in further developments. Ultimately, an installation for multiple users to manipulate sound sources within a three-dimensional sound-space was developed. This output was a vision of how true multimedia and multiuser interaction could look and feel in future – and was only made possible by combining the technical skills of the engineers with the projective skills of design researchers.

Design Research in University-Industry Collaborative Innovation

167

Conclusion The subject matter, approach, and methodology of Design Research are an integral part of corporate research and innovation. At an early stage of innovation, Design Research contributes to producing successful user-centered marketable products as well as fundamental knowledge. The Design Research Lab has helped increase awareness of the design aspect of innovation both at the more technical units at Telekom Laboratories and throughout the entire organization. It can be expected that Design Research and the related approaches will continue to grow and will play more and more of role in both industry and academia.

References Centre for Design Innovation. 2007. The design difference: A survey of design and innovation amongst Ireland’s SMEs. http://www.designinnovation.ie/downloads/TheDesignDifference 2007.pdf (accessed on August 11, 2008). Chow, R. 2008. Case Transfer vs. Case Study: An Evaluation of Case Study as a Method for Design Research. Paper presented at Focused, the 2008 Symposium of the Swiss Design Network, May 30–31, in Bern, Switzerland. Chow, R., and Jonas, W. 2008. Beyond Dualisms in Methodology: An Integrative Design Research Medium “MAPS” and some Reflections. Paper presented at Undisciplined, the 2008 Conference of the Design Research Society, July 16-19, in Sheffield, UK. Findeli, A. 2008. Searching for Design Research Questions. Presented at the Question & Hypotheses Conference, October 25-26, in Berlin, Germany. Gaver, B., Dunne T., and Pacenti, E. 1999. Design: Cultural probes. ACM 21–29. Hänsch, T., ed. 2007. 100 Produkte der Zukunft, 140–141. Berlin: Econ. Jones, L. M. 2008. University-Industry Research Collaboration: Advantages of the collaborative relationship and disadvantages of the collaborative relationship. http://education. stateuniversity.com/pages/2519/University-Industrial-Research-Collaboration.html (accessed on January 16, 2008). Kelly, T. 2003. The Art of Innovation. London; Profile Books. Rittel, H., and Webber, M. 1973. Dilemmas in a General Theory of Planning. Policy Sciences 4:155–169. Amsterdam: Elsevier Scientific Publishing Company, Inc. [Reprinted in N. Cross, ed. 1984. Developments in Design Methodology, 135–144. Chichester: J. Wiley & Sons.] Utterback, J. et al. 2007. Design-inspired Innovation.. Singapore; World Scientific Publishing Company.

168

E. Transfer of Results and Exploitation

Transferring Technology Innovations to Operating Business Units

One of the prominent goals with the establishment of Deutsche Telekom Laboratories in 2004/2005 was to strengthen the take-up of research and development results in the operating units. Since then, the success of Deutsche Telekom Laboratories has not only been measurable through the numbers of patents and scientific publications produced, but also in the project results transferred to operations and commercialized in products. The purpose of this selection is to examine barriers to and corresponding approaches for the demanding business of transferring technologies, knowledge, and business models from a corporate R&D department to the decentralized product marketing departments of Deutsche Telekom’s business units.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_15, © Springer-Verlag Berlin Heidelberg 2010

Transferring Technology Innovations to Operating Business Units

169

Introduction The competitive strength and innovativeness of a company is often measured in research and development expenditure, but the money spent is not necessarily a significant measure of effectiveness. The company’s capabilities to translate their research and development activities into new products that are subsequently accepted by customers are a better quantification of its competitiveness (Iansiti and West 1997). For this reason, the process of transferring technology from the R&D to the product marketing departments is an essential closing step within the innovation process. Steele says that “the transfer of technology from creator to applier is frequently the point at which the system breaks down.” Research has found that successful technology transfer has already been prepared for within the initiation stage of the project (Steele 1989). The sooner R&D managers are aware of the requirements and conditions of the operational systems where the newly developed technologies are to be implemented, the fewer adjustments are necessary during the late phases of technology development and respectively during the transfer activities. Furthermore, addressing the customer requirements for the relevant market segment early on is considered to be a supporting factor for later commercialization success (Iansiti 1995). One could therefore conclude that it is already crucial to reduce the degrees of uncertainty in the initialization of technology development projects. The success of Telekom Laboratories so far stems from overcoming barriers to innovation. This section will therefore examine to which degree the factor of uncertainty can be reduced and which additional barriers can be taken into account; it will also characterize the Telekom Laboratories research projects and corresponding transfer methods to overcome the barriers.

Uncertainty and degree of innovativeness It is first necessary to understand the role and research contract of Telekom Laboratories. The core practical element of the strategic focus is not to compete with but rather to complement existing innovation efforts within Deutsche Telekom. Aligned with this aspect, the innovation mission of Telekom Laboratories is to cover cross-strategic business unit and mid- to longterm innovation topics. This also includes a certain amount of “academic” or fundamental research with the aim of positioning Deutsche Telekom as a preeminent innovation leader in the telecommunications industry.

170

E. Transfer of Results and Exploitation

Several parameters are used to categorize a project portfolio. For a further discussion of the categorization by degree of uncertainty in market and technology aspects, refer to the appropriate model from Lynn and Akgun (1998). Figure 1 below outlines how the degree of uncertainty in technology and the market characterize the project categories as defined by Arnold (2003), which are the categories applied in subsequent examinations in this section. The so-called “incremental” projects have also been included to give a complete picture of the entire responsibilities of Telekom Laboratories’ R&D activities. These, however, will not be further considered as projects characterized by low uncertainty in the market and technology. They are indicated by different shading. The premise of high certainty concerning the future market and the minimized requirements for technological innovativeness characterized by this project type is more within the core remit of the business development departments of the particular strategic business units of Deutsche Telekom. knowledge about new/ alternative markets

y int rta e c n et u ark m g sin rea c n I

Radical Innovation

Architectural Innovation

nt tai

Modular Innovation

log

ing

knowledge of existing markets

Incremental Innovation

operational system know-how

y

r nce

yu

o chn

te

as cre

In

analytical/ experimental technological know-how

Figure 1 Types of R&D projects at Deutsche Telekom.

The remaining three categories of the Telekom Laboratories portfolio are described below.

Transferring Technology Innovations to Operating Business Units

171

Modular innovation

Modular innovation projects follow learning-based or technology-based strategies (Lynn and Akgun 1998). The majority of them are performed without any additional project partners other than Telekom Laboratories and the recipient product management departments. The generally medium certainty concerning market aspects may explain the assumption of responsibility for these development activities by the product management departments of Deutsche Telekom. Otherwise the medium uncertainty of the technological aspects requires the technological expertise of Telekom Laboratories. For Telekom Laboratories, a certain number of these projects are follow-up activities for project results that have already been transferred. The resource requirements of manpower, duration, and cost intensity are compared to the other two categories on a lower level. Nevertheless the time-to-market is above the general roadmap development cycles of the operating units and therefore these projects still focus on an overarching corporate R&D framework. Architectural innovation

Project results from architectural R&D projects are rather of platform character and allow project managers to enter new market segments. They also prepare the ground for product and service groups that can be leveraged over a whole decade (Clark and Wheelwright 1993). This requires a relatively high amount of resources and project durations are, in many cases, not shorter than three years. Often, these projects are conducted with other research partners from the industrial network. Examples of these R&D activities are the projects resulting from the participation in the Android™ Open Handset Alliance. The results produced are of high technological novelty, complex and not easy to imitate, and therefore essential contributors to the company’s sustainable marketing strategy (Ensign 1999). On the other hand, this project category is generally characterized by a certain fuzziness with regard to project definitions and project outcomes. Firstly, this is caused by the rather high degree of uncertainty of the market and technology aspects, and secondly by the involvement of several research partners. This fuzziness, however, is accepted due to promising future profit margins.

172

E. Transfer of Results and Exploitation

Radical innovation

Radical innovations form the project category with the highest degree of uncertainty both in terms of the market and technology. In contrast to the other two categories, this project group also faces resource and organizational uncertainties (Rice, Leifer and O’Connor 2002). For Telekom Laboratories, the majority of these radical project ideas are sourced from within the university network. Project organization and duration differ fundamentally from the other two project categories. As these projects are more exploratory by nature, the project teams are provided with a certain degree of freedom and do not have to pass through the normal project process.

Barriers to the technology transfer process In addition to the factors influencing different levels of business knowledge and technical know-how discussed above, further barriers can be found in the interaction between people as well as in the interaction between organizations. Barriers to technology transfer on the level of personal interaction

Barriers to technology transfer on the level of personal interaction might, on the one hand, not be intentional and, on the other hand, be the result of not being able to identify the value of innovative project results. Lack of motivation

A lack of personal motivation to share knowledge on the side of the creator can be excluded because these transfer activities are an essential element of the project manager reward system at Telekom Laboratories. However, the recipient product management team might be characterized by less natural motivation to accept knowledge from “the outside,” a phenomenon better known as “not-invented-here syndrome.” It describes the conscious reluctance of individuals to accept innovations which are not the result of the work of their own department (Katz and Allen 1982). When taking into account that the results of technology projects to be transferred generally include a significant amount of tacit knowledge, the intrinsic motivation of the recipient is essential, given that extrinsic reward systems cannot support the transfer of implicit knowledge (Osterloh and Frey 2000).

Transferring Technology Innovations to Operating Business Units

173

Lack of absorptive capacity

However, even when recipients are highly motivated and look for outside sources of technological knowledge, they might not be able to identify, assimilate, or exploit these sources due to cognitive distance. In the literature, this phenomenon is defined as a lack of absorptive capacity (Cohen and Levinthal 1990). For the purposes of the research approach, absorptive capacity can therefore be treated as another directly interlinked barrier factor. The underlying assumption is that people accumulate, interpret, and assess situations and knowledge according to their cognitive frameworks. These frameworks are influenced by an individual’s national or organizational culture, social value system, educational system, etc. (Wuyts et al. 2005). Figure 2 illustrates the relationship between optimal cognitive distance and absorptive capacity: The more innovative the project is and the more project partners with differing mental assets are involved, the more absorptive capacity is required from them to maximize the success of these projects. In consequence, there is a trade-off between the degree of innovativeness and simplicity in transferring results to the operating units (Nooteboom et al. 2007).

Absoptive capacity Novelty value

Learning

Optimal cognitive distance

Cognitive distance

Figure 2 Optimal cognitive distance. Source: Nooteboom et al. 2007

174

E. Transfer of Results and Exploitation

Barriers to technology transfer on the level of organizational interaction

Organizational barriers arise due to different strategic focuses, reward systems that do not support integration, or different top management focus (Griffin and Hauser 1996). Some important challenges for Telekom Laboratories are briefly presented below. Different reward systems

For Telekom Laboratories, outstanding project performance is honored as part of the present reward system based on the quantification enabled by the project value tracking as introduced in a separate section of this book. It is important that the measurement of success is not limited to scientific publications and patents, but that any future business potential generated is measured within the value tracking process. This measurement of transferred business potential is an essential element in the project manager’s annual review. In contrast, the integration of R&D project results is, in most cases, not specifically rewarded in the recipient operating units. Different time horizons for project execution

A major constraint to technology transfer can be defined as a difference in time orientation. For technology transfer to be successful, the synchronization of product and technology development is essential (Eldred and McGrath 1997). Time roadmaps at Telekom Laboratories go beyond the ones of the operating units by a factor of two to three. However, reducing project duration is a trade-off decision. In particular, the high levels of uncertainty for radical innovation projects are reflected in above-average project duration. Thus a reduction in duration would surely result in immature or less innovative project results, so that project transfer would not be successful in any case (Rice et al. 2002). Geographic distance

For Telekom Laboratories, barriers arising from geographic distance could be of special importance. The obstacles to technology and knowledge transfer mentioned above might be reinforced by the consciously accepted physical distance between Telekom Laboratories and the recipient service business units. Since research has proven that successful new product development requires intense interaction between marketing and R&D, long

Transferring Technology Innovations to Operating Business Units

175

distances might be somewhat of a hindrance, such as those between the corporate R&D department in Berlin and the recipient product managers of the business units’ mobile communication and broadband/fixed line as well as the product and innovation unit in Darmstadt. In the special case of Telekom Laboratories, this aspect is to a certain extent also accepted because the geographical distance between the operating units is balanced by the proximity to the university environment and non-industrial research centers. The short distance between these institutes eases the transfer of highly innovative technological knowledge from these sources. While there is still a large, distinctive cognitive distance between project field managers and researchers at universities, this gap can be bridged by frequent meetings.

Technology transfer at Deutsche Telekom Laboratories Similarly, as there are several factors influencing transfer at Telekom Laboratories, there is more than one transfer approach that can apply to all project types and all kinds of R&D project results from the unit creating the knowledge to the unit applying it. Deutsche Telekom Laboratories’ transfer activities, as indicated in Figure 3, can be categorized into three different approaches: (1) the project results are transferred directly to the operating units with the transfer phase being an explicit element of the R&D project; (2) the R&D project is followed by a separate transfer project conducted by the same people involved in the research project; (3) the R&D project is followed by a separate transfer project conducted by a dedicated transfer team and some of the people involved in the research project. Other members of the newly formed project team are employed to support the project with special skills, such as business development expertise. The project leader and key member is known as the “entrepreneur in residence.” The person has gained significant experience through long career within the company and has a widely distributed network of contacts inside and outside of Deutsche Telekom. Direct transfer to the operating units

For this approach, the project transfer represents the closing phase of the innovation process. Although the transfer of know-how is a permanent element in all phases of these R&D projects, toward the end, the intensity of know-how exchange and the degree of business (non-technical) information available increases. It is not surprising that project results transferred

176

E. Transfer of Results and Exploitation

in this way generally have a medium degree of innovativeness characterizing the modular innovation project activities. As already mentioned, the initiators for these projects are generally product managers from the strategic business units. They contact Telekom Laboratories requesting support for a special problem or with a relatively concrete vision of a future product. A cross-functional team of Telekom Laboratories staff and product managers initiates the R&D project. Progress is regularly revised during project execution by the designated project steering committee, formed by the top management of Telekom Laboratories and the recipient product management departments. The duration of this kind of project is, as far as possible, generally adapted to the planning horizons of the strategic business units involved. Nevertheless, it is important not to consider these activities as contract research or risk-shifting by the product management departments of the strategic business units. These projects indicate a gain of knowledge for both Telekom Laboratories and the product management department. Telekom Laboratories is approached due to its technological expertise, which allows the degree of technological uncertainty to be reduced as much as possible. The product managers, on the other hand, contribute with their detailed knowledge of the market. These projects are essential for trust-building between the R&D teams of Telekom Laboratories and their product manager counterparts, as influencing factors are rather predictable and failure rates can be minimized. In addition, these projects are an essential opportunity for Telekom Laboratories to get closer market insights and detailed information about the technological systems already employed.

Initialization

Development

Gate 1

Gate 2

Gate 3

Gate 1

Gate 2

Gate 3

R&D project

R&D project

Commercialization

Gate 4

Gate 4

Transfer project

Implementation to results in operating unit

Gate 5

Implementation to results in operating unit

Implementation to results in operating unit

Project proposal

Gate 1

R&D project

Gate 2

Transfer project

Gate 3

Spin along

Spin-out

Figure 3 Telekom Laboratories technology transfer approach.

Transferring Technology Innovations to Operating Business Units

177

Transfer through a subsequent transfer project

As already mentioned, the project results from architectural innovation are highly complex and the results affect various strategic business units. Furthermore, in many cases these projects are developed together with different industrial partners and, consequently, these projects are always characterized by a broader project focus. Further specifications are often necessary in order to implement these results in the operating units, as only the uncertainty of the technological aspect would have been reduced in the R&D project. Due to the complexity and innovativeness of this new technology there still remains a certain level of uncertainty about market aspects. Therefore, further concretion and adaptation is required in order to support successful implementation of the results in the recipient business units. In general, these transfer projects last between six and nine months, a time frame that is in line with the planning horizon of the receiving business units. While the transfer project team has always been involved with the initial project, individuals with special business development expertise are now being integrated to address some special topics. Transfer of radical innovation projects

The third approach in all respects entails the greatest efforts of resource assignment for transferring project results to implementation. The majority of project results included in this special methodology are of a radical nature. In contrast, product management activities inside Deutsche Telekom are marked by incremental risk-averse product strategies. In consequence, past experience has shown that project results of radical or disruptive technologies are very difficult and time-consuming to implement, and that final implementation is not necessarily guaranteed. Therefore, the initialization process for these projects is adapted in advance, according to their special character. The management of disruptive innovation has been simplified in order to meet the uncertainties in organization and resources and to increase the likelihood of implementation. As shown in the lower part of Figure 3, project teams are given more autonomy. Only toward the end of the project does the team discuss how to commercialize the results with the product managers of the operating business units. Although it might be considered that this late integration of the operating units decreases the likelihood for successful implementation, the strategic scope and time horizons of radical innovation and the strategic scope of daily business of product managers are so different that a successful dis-

178

E. Transfer of Results and Exploitation

cussion of the results can only be held at the end of the projects, when uncertainties in technology and the market are minimized. If there are aspects indicating that the transfer opportunities are limited, or product managers are too risk averse to implement the R&D results, the entrepreneur in residence joins the project team and leverages their experience in business development and their extensive network of contacts to promote the project results inside the company and foster the transfer discussion. Where product managers of the operating units decide not to implement the R&D outcome, there remain two other options for commercialization: the spin-along and the spin-off approach. Both of these are described in separate sections of this volume.

Conclusion The more knowledge about applied technological modules and future target markets that is taken into account in the early stages of an innovation project, the higher is the likelihood of a successful transfer of results to implementation. Still, these uncertainties can only be reduced to a certain extent, which is dependent on the project characteristics. The decision for a balanced transfer procedure has to be made for each individual project according to these parameters. While for some kinds of transfer the creation of a core transfer project, along with the appropriate team, would absolutely be the correct methodology, the same practice could entail procedural overkill for others. The approaches presented in the latter part of this section are to be understood as exemplary snapshots. They indicate current appropriate answer to maximize the exploitation of R&D project results. Since the transfer process is always dependent on contextual parameters, the transfer methods employed by Telekom Laboratories will be adapted should there be a change in essential key factors.

Transferring Technology Innovations to Operating Business Units

179

References Arnold, H. M. 2003. Technology Shocks. Origins, Managerial Responses, and Firm Performance: Physica-Verlag. Clark, K. B. and Wheelwright, S. C. 1993. Managing new product and process development. Text and cases. New York: Free Press. Cohen, W. M. and Levinthal, D. A. 1990. Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly 35(1): 128–152. Eldred, E. W. and McGrath, M. E. 1997. Commercializing new technology--II. Research Technology Management 40(2): 29-. Ensign P. C. 1999. Innovation in the multinational firm with globally dispersed R&D: Technological knowledge utilization and accumulation. Journal of High Technology Management Research 10(2): 203–221. Griffin A. and Hauser J. R. 1996. Integrating R&D and marketing: A review and analysis of the literature. The Journal of Product Innovation Management 13(3): 191–216. Katz, R. and Allen, T. J. 1982. Investigating the Not Invented Here (NIH) syndrome: A look at the performance, tenure, and communication patterns of 50 R & D Project Groups. R&D Management 12(1): 7–20. Iansiti M. 1995. Technology integration: Managing technological evolution in a complex environment. Research Policy 24(4): 521–543. Iansiti M. and West J. 1997. Technology integration: Turning great research into great products. Harvard Business Review 75(3): 69-79. Lynn G. S. and Akgun A. E. 1998. Innovation strategies under uncertainty: A contingency approach for new product development. Engineering Management Journal 10(3): 11–17. Nooteboom B., Van Haverbeke W., Duysters G., Gilsing V. and van den Oord A. 2007. Optimal cognitive distance and absorptive capacity. Research Policy 36(7): 1016. Osterloh, M. and Frey, B. 2000. Motivation, Knowledge Transfer, and Organizational Forms. Organization Science 11(5): 538–550. Rice, M. P., Leifer, R. and O’Connor, G. C. 2002. Commercializing discontinuous innovations: bridging the gap from discontinuous innovation project to operations. Engineering Management, IEEE Transactions on 49(4): 330–340. Steele, L. W. 1989. Managing technology. The strategic view. New York: McGraw-Hill. Wuyts S., Colombo M.. G., Dutta S. and Nooteboom B. 2005. Empirical tests of optimal cognitive distance. Journal of Economic Behavior & Organization 58(2): 277–302.

Endnotes 1

An enabler is a type of technological product module that enables certain functionalities that are in turn part of an overall product solution. In the convergence example mentioned, this often includes the “one invoice” or “one telephone number” dimensions.

180

E. Transfer of Results and Exploitation

The Project Value Tracking Process at Deutsche Telekom Laboratories

So far, many innovation organizations steer their activities on a cost base due to a lack of quantitative measurements for the business effect of technology transfers. Deutsche Telekom Laboratories has created a method to quantify the economic effect of its research and innovation results. A parallel process focused on the measurement of the value contributions of innovation projects accompanies projects during their execution and transfer phase. The value tracking process makes it possible to obtain an accurate picture not only of the economic value of the successfully transferred results but also of the take-up of every single result of the R&D project portfolio in the operational unit. Value tracking has moved Deutsche Telekom Laboratories even more toward a result-oriented and resource-efficient research and innovation unit.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_16, © Springer-Verlag Berlin Heidelberg 2010

The Project Value Tracking Process at Deutsche Telekom Laboratories

181

Introduction Innovation projects represent major investments; thus, ample instruments have been developed to evaluate their cost base (Bescherer 2006). The most relevant decision criterion, however, is the economic effect of the outcome of the projects. Nevertheless, there is a lack of instruments to constantly monitor the effect over the entire project life cycle and to assess the quantitative effect. The problem is that the expenditures involved in an innovation project accrue long before the revenues (Hauschildt and Salomo 2007). A classification of innovation projects is undertaken in terms of their innovation goals and their alignment with the enterprise strategy (Gälweiler and Schwaninger 2005). The potential strategic options are assessed and the projects are aligned with the product roadmap of the SBU (Strategic Business Unit). Measuring the economic effect is challenging because success is multifaceted and needs to be assessed not only on the basis of individual projects but also on the program (Griffin and Page 1996). To begin with, success can be divided into the direct effects of the project outcome (narrow view) and effects during the whole life cycle of an innovation project (wider view). Important measures to assess a project’s outcome include: (1) sales revenue, (2) market share, (3) customer satisfaction, (4) strategic position against rivals, and (5) customers’ image of the product sold (Garcia-Valderrama and Mulero-Mendigorri 2005). These direct effects have the added problem that they can only be measured after most of project costs have occurred (Hauschildt and Salomo 2007, 548–552). These narrow views can be extended by taking into account the effects of other events during the innovation project life cycle, including announcement on: (1) alliances, (2) funding, (3) expansion, (4) prototypes, (5) patents, (6) pre-announcements, (7) launch, and (8) awards (Zantout and Chaganti 1996; Tellis and Johnson 2007; Sorescu et al. 2007; Pauwels et al. 2004). These events have been shown to have a significant effect on the stock market value of a company; they also yield the benefit that their effect occurs closer to investment than the effects of the outcome in the narrow view (Sood and Tellis 2009; Woolridge and Snow 1990). Companies struggle to establish stable success tracking systems with so many criteria, reporting levels, and stakeholders involved. The challenge is to provide managers with information on success, which neither over nor underestimates the real impact of innovation, while not making the system too complex and thus unusable in the long run. Like in many other companies, project assessments were completed following a specific management request – for example, for reporting purpos-

182

E. Transfer of Results and Exploitation

es – before the Deutsche Telekom Laboratories project value tracking (PVT) process was established. Since no dedicated process existed, only a snapshot of the current situation was possible. For every report, the project team managers had to be contacted individually. Collecting project information after the fact was exceptionally difficult and time-consuming. The difficulties were compounded by the fact that there was no standardized management report to convey the results after the information was retrieved. Consequently, the main reason for establishing the PVT process was to have a continuous overview of all ongoing projects with respect to project result transfers. In designing the process, all of the necessary data concerning the project and transferred results was initially considered. The data requirement was then refined and further improved. In the next step, a standardizing process and a visualization method were developed.

Procedure Assuming an average project duration of two to three years, the project value tracking process starts during the project’s runtime – 12 months after the initiation of the project proposal. During this project execution phase, progress is permanently monitored until the project results are transferred to the corporation’s SBUs. The phases of the PVT process involve: (1) contacting the Project Field Manager; (2) further developing a business plan based on special transfer insights; (3) checking the project result transfers; (4) visualizing the current status; and (5) presenting the results to the management (Figure 1). In Phase 1, the PVT process is initiated as soon as the first concrete transfers of project results to the SBUs are planned by the project field management. In Phase 2, the development of a business plan is imperative to the project’s success. The project field manager and the project manager examine the business logic.1 Another important requirement is that specific key performance indicators (KPIs) in the business plan must be agreed upon with the SBUs in advance. General assumptions (e.g., about customer potential) are applied to all possible scenarios and define the framework for value estimation. The targeted innovation goal – whether for new business development, enhanced business opportunities, and/or possible cost savings – is a key parameter for revenue calculation. The revenue model includes detailed price assumptions for price per click, price per user, price per call, etc. Cost sav-

The Project Value Tracking Process at Deutsche Telekom Laboratories

(Phase5) presentation to the management

(Phase 4) visualization of the current status

183

(Phase 1) contacting the Project Field Manager

Project Value Tracking

(Phase 2) development of a business plan

(Phase 3) checking the project result transfers

Figure 1 Phases of the project value tracking process.

ings include the reduction of resource requirements, such as time, hardware, software, manpower, storage, and process optimization. Other KPIs used to measure value contributions include: OPEX (operational expenditure; e.g., production, infrastructure, service, marketing, sales, personnel, system integration), CAPEX (capital expenditure; which is the cumulative amount invested each year over the project’s life span), EBITDA (earnings before interest, taxes, depreciation, and amortization), and the NPV (net present value). After completing a simplified business case, transfer agreements between project management, project leaders, and the product management of the SBU are reached. Transfer agreements focus primarily on the expected cumulative revenues or cost savings of innovation results over the following five years based on the business case and financial projections. The application and use case scenarios developed during the project are an essential component of the agreements. This phase concludes with a revenue commitment on the part of SBU product management to the project field manager of the Deutsche Telekom Laboratories. Further process improvements include the formal approval of the revenue commitments by the finance department of the respective SBU. At the end of this phase, all relevant data for the quantitative assessment is available.

184

E. Transfer of Results and Exploitation

Phase 3 serves to prepare the qualitative assessments. PVT co-ordinates with the project field management which transfers will be made and what priority will be adopted for the first value tracking analysis. If further transfers are planned, a second project value tracking analysis can be initiated at an appropriate later date. All transfers are separately listed and commented upon. It is particularly important to establish and record the expected impact of the transferred scenario on the respective SBU and the current transfer status. Moreover, the divisions’ impressions and comments are noted here. The business view of the transfer forms the core of the assessment. The technical view of the transfer is analyzed separately and the technological benefit of the transferred scenario is noted. In Phase 4, all available qualitative and quantitative information required to conduct a value tracking analysis is recorded in a standardized management report (see Visualization section below). Phase 5 concludes the current Telekom Laboratories PVT process. It consists of a review session with management and the respective project field manager where the PVT results are discussed in-depth and action items are identified. Measures agreed upon during the review session are subject to continuous monitoring and may involve the initiation of an additional project value tracking analysis. Value tracking reviews take place with the management of the Deutsche Telekom Laboratories at regular intervals during the course of the entire financial year. A value tracking target is anchored within the target of every team leader/project field manager. The target is based on a ratio of value to resources employed. This has made Telekom Laboratories even more of a resource-efficient, target-oriented R&D unit.

Visualization Several management reports are generated to deliver the project value tracking status, which focuses on the key pieces of information. The first report is a management summary of the project (Figure 2). It is comprised of a project description and the motivation for the project. The motivation for the project is a description of a current problem that is solved by the project. The report also explains the expected outcome and/or the results to be delivered. Project results can include demonstrators or product concepts. Additionally, other key data is given such as the duration of the project, the Deutsche Telekom AG business units involved, the relevant contact persons in the project, and any external partners. An extended management report, which includes additional details relating to the status of the project, is provided on a quarterly basis.

The Project Value Tracking Process at Deutsche Telekom Laboratories

Illustr examaptive le

How many times has France participated in a world cup? 11 times!

185

11 times!

Multimodal, multilingual and broadband access to Semantic Web

DT Labs Service Intelligence Expected outcome

Project description and motivation ƒ Creation of new and advanced services and business models ƒ Multimodal and broadband access to mobile services ƒ Modality-specified ƒ From machine readable web content to machine ƒ Development of ƒ Concept and demonstration of intelligent

Key facts

ƒ State-of-the-art study ƒ Specification of the system architecture ƒ Proof-of-concept consortium ƒ Demonstration of new DTAG services

ƒ Duration: 04/2008 – 09/2009 ƒ Involved Divisions: ƒ External Partners:

Figure 2 Sample management summary.

The PVT system includes a central report comprised of four sections covering the qualitative and quantitative assessment of the project. The qualitative assessment gives a detailed description of transferred project results – in terms of transfer scenarios – and records the receiving SBU (Figure 3). Transferred results xxx Adopted by SBU…

 XXX  XXX

Strategic Fit Category 1 Category 1 Category 3

xxx Adopted by SBU…

xxx Adopted by SBU…

 XXX  XXX  XXX  XXX

... ... ... ... iPF aligned Innovation goals Cost saving

Further planned transfers

Figure 3 Qualitative assessment.

 XXX  XXX

Enhanced business New business

186

E. Transfer of Results and Exploitation

Value creation

Analysis

Estimated value creation by R&D [M€]

Comments

R&D expenses [M€]:

XX

NPV of project [M€]:

XX

 XXX

Status value creation - [M€]

Task  XXX

Cumulated revenue/ cost saving

XX 2008/09

2010

2011

2012

2013

forecasted by R&D Revenue/cost saving realized by SBU

0

+Revenue/cost saving committed by SBU2 Remaining potential

Overall rating  XXX XXX XXX

Figure 4 Quantitative assessment.

The project’s place in and alignment with the company’s innovation strategy – with respect to the innovation goals – are also documented. The quantitative assessment (Figure 4) shows the value creation in terms of R&D expenditure and the NPV of the project. The structure of the report is a waterfall diagram visualizing the accumulated revenue or cost savings forecast by the R&D department over a five-year period in relation to SBU expectations. The report also shows the revenues or cost savings already realized by the SBU, the revenues committed by the SBU, and the remaining financial potential of the project. The analysis provided by this report is important for monitoring purposes – particularly in relation to further potential transfers – and optimization via the overall rating mechanism in terms of understanding the status of project results. In the fourth report (Figure 5), the key drivers of the respective innovation goals and their importance – based on the business case – are recorded as additional information with regard to value creation. For example, the ratio of new customers to new services may be correlated for the innovation goal “new business”. The transferred technical results are recorded in a separate report (Figure 6). The technological approach of the solution is described, specifying the components of the technical infrastructure that enables the new product.

The Project Value Tracking Process at Deutsche Telekom Laboratories

KPIs

Innovation goals

Cost savings Enhanced business

New business

NPV

Importance

...

0%

...

0%

...

0%

...

0%

Enhanced services

25%

Churn rate

7%

New customers

1%

New products/services

67%

187

0%

0 Mio.

29%

XXX Mio.

71%

XXX Mio.

Illustr examaptive le

Figure 5 Key drivers.

Transferred technical results xxx Adopted by SBU…

 XXX  XXX

xxx Adopted by SBU…

 XXX  XXX

xxx Adopted by SBU…

 XXX  XXX

Further planned transfers

 XXX  XXX

Figure 6 Technical transferables.

The solution also describes the role of integrators and vendors in the system and the key differentiating factors in relation to competitive offerings.

188

E. Transfer of Results and Exploitation

The evolution of the project value tracking process The project value tracking process has been consciously and continuously enhanced since its introduction. The original rigid weekly review has been more closely aligned with the project milestone plans. Currently, a PVT is run as many as two or three times over the duration of a project. Additional tracking is usually initiated whenever major changes resulting from alterations in business models or planned transfers occur, although other reasons or opportunities may call for an additional tracking process. One reason may be that the initial project scenario proves more suitable for an external commercialization than an internal implementation; in which case this scenario can, for instance, be migrated to a spin-off or licensing option during the PVT process. The PVT provides an additional benefit in the form of a complete and transparent project overview for the Venture and Licensing divisions. While business case estimations were somewhat fluid at the outset, they have become very concrete during the execution of the value tracking process. This is due to the increased integration of the SBUs into the process. As a result, SBU economic expectations and project forecasts are much more closely aligned. A plan to underline the binding character of revenue commitments made by product managers, by including revenue commitments in the product roadmap and by engaging the finance department of the SBUs in the formal process, has already been adopted. Business plans and scenarios where the scope is too broad, however, prove cumbersome and difficult to assess and, consequently, to transfer. Therefore, focused business plans with a closer alignment to SBU transfer feasibility are now being structured. Currently, methods of implementing complete end-to-end monitoring beyond the transfer phase as well as a documentation process to record the long-term impact of innovation results following market introduction are under investigation. The process improvements mentioned above are expected to enhance and facilitate the entire PVT process, which has become an integral part of the innovation process as a whole.

The Project Value Tracking Process at Deutsche Telekom Laboratories

189

Conclusion The data collection process and accompanying management reviews contribute regularly to the generation of new ideas. They frequently identify solutions for planned innovation transfers and uncover potential overlaps among project activities and results. With the support of the value tracking process, innovation success can be more closely monitored and processes significantly optimized. For instance, improvement potential that could be leveraged was identified in the transfer process. Furthermore, the identification of overlaps between individual business scenarios is possible, enabling appropriate adaptation. The process enables the selection of best-of-breed from various technical modules across different transfers and promotes the reuse of results in different application scenarios. By introducing the PVT process, the value contributions of innovation projects in the company have become much more transparent and quantitatively controllable. In addition to providing a precise return on investment analysis for individual projects on the basis of SBU commitment statements, it is useful for comparing the value creation potential of multiple projects in terms of R&D expenditure and revenue potential. Moreover, a more factual examination of the effectiveness of the R&D department is possible. The PVT process has already provided substantial benefit in the financial follow-up of innovation projects and, as such, has achieved its primary goal during its introduction phase.

References Bescherer, F. 2006. Management of Early Innovations with Cost Management Tools, Benchmarking Methods used in Business. Helsinki University of Technology, Report 2005/6. Espoo. Gälweiler A. and Schwaninger M. 2005. Strategische Unternehmensführung, 3rd ed., 235–237. Campus: Frankfurt. Garcia-Valderrama, T. and Mulero-Mendigorri, E. 2005. Content validation of a measure of R&D effectiveness. R&D Management 35(3): 311–331. Griffin, A. and Page, A. L. 1996. PDMA success measurement project: Recommended measures for product development success and failure. Journal of Product Innovation Management 13(6): 478–496. Hauschildt, J. and Salomo, S. 2007. Innovationsmanagement, 4th ed., 48–552. Vahlen: Munich. Pauwels, K. et al. 2004. New products, sales promotions, and firm value: The case of the automobile industry. Journal of Marketing 68 (4): 142–156.

190

E. Transfer of Results and Exploitation

Pinnekamp, Enkel. 2006. EIRMA Conference on “Benchmarking R&D Processes”. European Industrial Research Management Association (EIRMA), Paris, France. RTEC T-Labs Value Tracking Case 8.18.09 from the RESEARCH & TECHNOLOGY EXECUTIVE COUNCIL® of the FINANCE AND STRATEGY PRACTISE, 5–17. Sood, A. and Tellis, G. J. 2009. Innovation does pay off – if you measure correctly. ResearchTechnology Management 52(4): 13–15. Sorescu, A. et al. 2007. New product preannouncements and shareholder value: Don’t make promises you can’t keep. Journal of Marketing Research 44(3): 468–489. Tellis, G. J. and Johnson, J. 2007. The value of quality. Marketing Science 26(6): 758–773. Zantout, Z. and Chaganti, R. 1996. New product introductions, shareholders’ wealth, and firstmover advantages. Journal of financial and strategic decisions 9: 49–61.

Endnotes 1

Business Case Standardization V. 5.0, Deutsche Telekom Laboratories, November 2006.

E. Transfer of Results and Exploitation

191

Venturing for Commercialization of R&D Results

In the current rapidly changing environment of the telecommunications industry – an environment that offers a multitude of prominent directions in technological and market development – it is essential to drive innovation and extend the portfolio of business areas. Deutsche Telekom Laboratories supports the creation of new business fields with a venturing approach that allows for external commercialization of R&D results. In collaboration with a network of external partners, R&D results are spun out, developed externally, and, if successful, spun back in.

H. Arnold et al. (eds.), Applied Technology and Innovation Management, DOI 10.1007/978-3-540-88827-7_17, © Springer-Verlag Berlin Heidelberg 2010

192

E. Transfer of Results and Exploitation

Introduction As mentioned in the section on strategic foresight, three major disruptions have created both an environment of rapid change in the telecommunications market and competition. Firstly, the liberalization of the telecommunications industry opened the markets up to new competitors. In Germany, this took place in 1995, and led to a redistribution of the market, allowing new entrants to capture 52% of the overall revenue in 2007. In addition, a combination of price regulation and competition led to a steady decline in margins and, for the first time in 2006 and 2007, to an overall revenue decrease in the industry. Secondly, the technological change that brought about a horizontalization of service architecture, which in turn allowed small companies to offer services that used to be highly integrated vertical silos. One such service is voice call, which formerly required large networks to be built and maintained by the operators. Today, small software developers such as Skype, a Voice over Internet Protocol (VoIP) provider, can offer voice calls over the Internet with comparably negligible investment and operating costs. Thirdly, a shift in the value distribution in the industry is taking place. Previously, network operators were in a position to demand premium prices and earn a high margin on their services. The threat today is that network operators might be reduced to mere “bit pipes” and price premiums will be captured by device manufactures, such as Apple with the iPhone, or by service providers. Consequently, operators need to carry out research and constantly move into new business areas in order to retain revenue and profit levels. Therefore, most large incumbents today have launched their own devices under their own brand and moved into new services such as Internet Protocol Television (IPTV) or services that allow advertising to be sold, such as Web portals. In order to support the development of new business, Deutsche Telekom Laboratories has created a venturing approach that allows for the external commercialization of R&D results. In collaboration with a network of external partners, R&D results are spun out, developed, and, if successful, spun back in.

Limitations of incumbents to innovate A common paradigm in innovation management states that even though large companies are often based on radical innovations, nowadays they mostly rely on incremental innovations to maintain their competitive posi-

Venturing for Commercialization of R&D Results

193

tion, leaving radical innovations to smaller competitors. Even though it has been shown that this is not true in all cases, there are a number of limitations that prevent large incumbent companies from successfully competing with smaller, more agile competitors (Table 1) (Chandy and Tellis 2000). Field

Barriers

Incumbent curse

Innovations with less than critical mass are neglected Strategy per definition rejects innovations that are too radical or non-core Portfolio management prevents projects with a weak strategic fit from being funded

R&D operational problems Difficult transfer of R&D results to receiving units Lack of business and marketing competence in R&D High-potential employees commercialize promising ideas externally on their own Missed window of opportunity

Wrong timing of innovations Lack of marketability Lack of entrepreneurial push Lack of customer relevance in innovations

Table 1.

Limitations of incumbents in innovation.

Incumbent curse

One of the most important competitive advantages of large companies is their size. By focusing on products with high revenues, they can use economies of scale that allow them to offer products at lower prices or generate larger margins. In consequence, large companies have to focus on products that are large enough, have synergy with other products, or enhance the footprint of the company in its existing markets. In order to ensure that only those products that fit the above criteria are developed and introduced, multiple filter mechanisms are put in place. These mechanisms include portfolio management in new product development (NPD), prioritization in the allocation of marketing budgets, and a refocusing of the existing product portfolios.

194

E. Transfer of Results and Exploitation

The result of such mechanisms can be called “incumbent curse.” Through this filtering, companies reject ideas, concepts, projects, and products that: • have less than – what is considered – critical mass, which is usually the case for new products in new markets; • are too radical or non-core and thus prevent the development of new business; • have a limited strategic fit, e.g., with predefined strategic directions or strategic business fields. This filtering results in the elimination of many promising growth and innovation opportunities. R&D operational problems

As a consequence of the strong division of labor in large companies, R&D results are generally transferred from R&D to market-oriented units for their commercialization. In many companies the market-oriented units are good at marketing and selling existing products but ill-prepared for the commercialization of completely new product types. Another operational problem is that employees might take their innovative idea to market outside their company and the innovation is then lost for the incumbent. Two major reasons exist that fuel this threat: Firstly, the high availability of venture capital to kick-start a new business; and secondly, the possibility of outsourcing any missing corporate functions in the commercialization of new products. One such example is SAP, where five IBM employees took their idea external and used it as the basis to found their own company, which today is the global market leader in enterprise software solutions and the third-largest software company worldwide. Missed window of opportunity

Many innovations in the past have been successfully commercialized – but not by the company that invented them. Instead, a competitor copied the product and entered the market later, when customers where ready to adopt the product. Examples include the video cassette recorder (VCR) and personal digital assistant (PDA). In these cases, the major reason for the lack of success was bad timing. In particular, technology-driven innovations tend to be ahead of customer demand.

Venturing for Commercialization of R&D Results

195

In comparison with small companies, incumbents often miss the window of opportunity because of a lack of entrepreneurial push, a lack of customer relevance, and a lack of marketability.

Corporate venturing to overcome limitations One tool used by large companies to overcome these limitations is corporate venturing. It is generally used to describe the activities of companies aiming to enter new business areas or expand into existing or new markets. This can be done internally by creating dedicated teams or units, or externally by founding start-up companies (Table 2). Characteristics

External venturing

Internal venturing

Idea origin

• Inside the parental organization (Keil 2004)

• Inside the parental organization (Keil 2004)

Idea realization

• Externally (Keil 2004; Sharma and Chrisman 1999)

• Internally (Burgelman 1983a; Burgelman 1983b)

Idea commercialization

• Creation of spin-outs, investing in start-up companies (EIRMA 2003; Tidd and Barnes 2000)

• Creation of teams or units inside the company (Sharma and Chrisman 1999)

Level of autonomy

• High

• Low to medium

Table 2.

Differentiation in internal and external venturing.

In a number of studies, it has been shown that corporate venturing is an important tool for identifying and exploiting new market opportunities (Christensen 2004; Siegel et al. 1988), especially if they are outside the company’s core competencies (Little 2002; Freese 2006; Tidd and Barnes 2000). In addition it was shown that corporate venturing facilitates the identification and integration of external knowledge into the company (Cohen et al. 1987; Schildt et al. 2005). External venturing

According to Sharma and Chrisman (1999), external corporate venturing “refers to corporate venturing activities that result in the creation of semi-autonomous or autonomous organizational units that reside outside the organi-

196

E. Transfer of Results and Exploitation

zational domain,” while Keil (2004) defines external corporate venturing “as a new business creation activity of established organizations, in which the corporation leverages external partners in the process of creating a venture or developing an internal venture.” Keil (2004) argues that external venturing enables the development of new capabilities and the adaptation and recombination of existing capabilities. By sponsoring and investing in start-up companies, either with financial assets or other resources, the main aim of the organization is to gain knowledge, intellectual property, and access to innovation for future sustainable growth (Keil 2004; Sharma and Chrisman 1999). Examples of external corporate ventures are joint ventures, investments in start-up companies, spin-outs, and venture capital activities (EIRMA 2003; Tidd and Barnes 2000). Internal venturing

In contrast to external venturing, internal corporate venturing aims to create teams or units internally. According to Sharma and Chrisman (1999) “corporate venturing activities result in the creation of organizational entities that reside within an existing organizational domain.” Internal venturing activities require the commitment of organizational resources and the commitment of the management. The management enables internal and external communication to take place and stimulates interaction between resources, technologies, and entrepreneurially motivated employees (Burgelman 1983a; Burgelman 1983b; Freese 2006; Sharma and Chrisman 1999). The main driver behind internal corporate venturing is typically top management’s quest for new growth opportunities (Burgelman and Valikangas 2005).

Deutsche Telekom laboratories’ spin-along approach Goals

In the literature, a set of five different goals of corporate venturing have been discussed (an overview is given in Figure 1). A first differentiation can be made between financial and strategic goals. Financial goals include profit generation from opening new businesses or return on investment (ROI) from selling the company for a higher price than the initial investment. In addition, a public offering or management buyout can also result, where the company is either floated on the stock market or

Venturing for Commercialization of R&D Results

197

purchased by the management team (Dushnitsky and Lenox 2005; Ernst et al. 2005). Strategic goals are: fostering innovation, which includes allowing noncore R&D (Gilbert 2002) and buying new technologies or new capabilities; growth, which aims to extend the current business and develop new business; and internal value creation, which aims to enhance current business and develop new business internally (Little 2002; Chesbrough 2000; Maula et al. 2005; Siegel et al. 1988).

Corporate venturing goals

Financial

Strategic

Innovation

Growth

Internal value creation

Profit

ROI

Spin-along goals ! Alternative path for innovations that are non-core or radical ! Driving business model innovations ! Innovation in areas with little synergy with existing business ! Innovating closer to the market

Figure 1 Goals of corporate venturing and the spin-along approach.

For Deutsche Telekom, innovation and growth are the overall aims. More specifically, Deutsche Telekom follows four concrete goals: • Proposing alternative paths for radical and non-core innovations. The spinalong approach allows the development of radical and non-core products through alternative paths that are not subject to the filtering of marketing and product portfolio management. • Driving business model innovations. With the spin-along approach, Deutsche Telekom can experiment with alternative business models without damaging the brand or company image.

198

E. Transfer of Results and Exploitation

• Innovation in areas with little synergy with existing business. Deutsche Telekom uses the spin-along activities to develop new business with little synergy with current business. • Innovation closer to the market. A further goal of spin-along activities is the ability to be closer to the customer. Through the spin-along, the founders are able to interface directly with their customers, obtain deeper insights into their needs, and adapt the product faster to changing needs. Organization

Within Deutsche Telekom, the spin-along activity is run within Telekom Laboratories, the corporate R&D unit. Telekom Laboratories drive R&D with a time horizon that is beyond that of the business units and in areas that affect more than one business unit. Telekom Laboratories is organized as a university-industry research center (UIRC) that is in integral part of both Deutsche Telekom and the Berlin Institute of Technology (Rohrbeck and Arnold 2006). The spin-along initiative was started in 2005 as a possibility for project teams whose projects had not been successfully transferred to business units to take their innovation external by creating a spin-out company. These companies are financed by the R&D budget, corporate venture capital (T-Venture), and external investors, such as venture capital funds, seed capital, or business angels. A corporate venture board within the product and innovation unit supports the entrepreneurially motivated employees in the development of business models, business planning, and the acquisition of investors. Further support might be given by a business unit that is interested in the product and wants to keep a close relationship with the spin-along but is not interested in launching the product under its own brand The first spin-out company, Qiro, was founded in 2006 and is co-financed by the High-Tech Gründerfonds, a seed capital investor. Qiro is a localization-based service that allows users to find points of interest, friends, and public transport through their mobile phone. A second company, Zimory, was founded in 2007, also with investments from the High-Tech Gründerfonds and T-Venture, the corporate venture capitalist of Deutsche Telekom. Zimory launched a platform for trading IT resources. The platform allows companies that have temporarily free server capacity to sell it to a second company that needs more capacity. This scheme is not only capable of reducing the cost for its customer companies, but also contributes to increasing energy efficiency.

Venturing for Commercialization of R&D Results

199

Another company is Yoochoose, founded in 2009 and specializing in providing high-end content recommendation solutions built around a high performant hybrid recommendation approach. Yoochoose also provides its software expertise back to Deutsche Telekom as a customer. Benefits

The spin-along approach combines benefits of the spin-out and spin-in activities. From the analysis, three major benefits of the spin-along approach have been identified (Figure 2). Firstly the spin-along approach at Deutsche Telekom offers the possibility to externalize innovation activities and to facilitate the cooperation with external partners. In consequence, a risk reduction can be achieved by sharing it with the employees (i.e., the founders of the start-up company) and, more importantly, with other investors. Secondly, founding a start-up company to externalize innovation activities would encompass the benefit of the entrepreneurial push that is difficult to stimulate in a large company per se but can enhance the organization’s innovation activities. Having a smaller, more agile entity also enhances the market and customer proximity, and individual motivation. These days, a popular saying at Telekom Laboratories is: “When you have been thrown out of the door as an innovator you need to come back through the window.” Such behavior is needed with every successful innovator and is encouraged even more through the self-dependency in spin-offs.

200

E. Transfer of Results and Exploitation

! Commercializing R&D results ! Outsourcing non-core activities

Spin-out

! Acquiring technologies ! Entering new markets ! Accessing new knowledge

Spin-along

Spin-in

! Externalization of innovation activities ! Enabling entrepreneurial push ! Fostering growth through non-core and radical innovation

Figure 2 Spin-along as a combination of spin-out and spin-in aspects.

Thirdly, the founding of spin-alongs allows the development of non-core and radical innovations, such as in the fields of localization-based services and virtualization of IT infrastructure. This has laid the groundwork for growth that would have been lost if the activities had been forced to prosper within established structures.

Conclusion Although venturing activities carry a high level of risk and uncertainty, many organizations do it well. Venturing is especially strong and effective if business and strategic goals require the development of new businesses and the diversification into new markets. So far, the experience with the spin-along approach at Deutsche Telekom suggests that it is an effective alternative innovation path, and able to increase the innovation capacity for radical innovations. It seems to be a promising way for incumbents to foster innovations in fields with little synergy with existing business.

Venturing for Commercialization of R&D Results

201

The further application of the spin-along approach will provide evidence on possible downsides that have been reported in similar approaches. At Cisco, which has a similar scheme, the two major downsides that have been reported are the difficulty of reintegrating the spin-out management after it has become used to a high level of freedom, and secondly the creation of envy among the employees that remain in the internal innovation management and have financial rewards that are nowhere near the amount received by spin-along founders after they have been acquired back.

References Birkinshaw, J. 1997. Entrepreneurship in multinational corporations, The characteristics of subsidiary initiatives. Strategic Management Journal 18: 207–229. Burgelman, R. A. 1983a. A Model of the Interaction of Strategic Behavior, Corporate Context, and the Concept of Strategy. Academy of Management Review 8: 61–70. Burgelman, R. A. 1983b. A Process Model of Internal Corporate Venturing in the Diversified Major Firms. Administrative Science Quarterly. 28: 223–244. Burgelman, R. A. and Valikangas, L. 2005. Managing internal corporate – Venturing cycles. Mit Sloan Management Review 46: 26-. Carrier, C. 1996. Entrepreneurship in Small Businesses: An Exploratory Study. Entrepreneurship: Theory and Practice 21:5–20. Chandy, R. K. and Tellis, G J. 2000. The Incumbent‘s Curse? Incumbency, Size, and Radical Product Innovation. Journal of Marketing 64: 1–17. Chesbrough, H. 2000. Designing Corporate Ventures in the Shadow of Private Venture Capital. California Management Review 42: 3–49. Christensen, K. S. 2004. A classification of the corporate entrepreneurship umbrella: labels and perspectives. International Journal of Management & Enterprise Development 1: 1–1. Cohen, W. M., Levin, R. C. and Mowery, D. C. 1987. Firm Size and R & D Intensity: A ReExamination. The Journal of Industrial Economics 35: 543–565. Dushnitsky, G. and Lenox, M. J. 2005. When do firms undertake R&D by investing in new ventures? Strategic Management Journal 26: 947–965. EIRMA. 2003. Innovation Through Spinning In and Out. Research Technology Management 46: 63–64. Ernst, H., Witt, P., and Brachtendorf, G. 2005. Corporate venture capital as a strategy for external innovation: an exploratory empirical study. R & D Management 35: 233–242. Freese, B. 2006. Corporate-venture-Capital-Einheiten als Wissensbroker: empirische Untersuchung interorganisationaler Beziehungen zwischen Industrie- und Start-up-Unternehmen. Dt. Univ.-Verlag, Wiesbaden. Gilbert, C., Bower, J.L. 2002. Disruptive Change – When trying harder is part of the problem. Harvard Business Review 80: 95–101. Keil, T. 2004. Building External Corporate Venturing Capability. Journal of Management Studies 41: 799–825. Maula, M., Autio, E. and Murray, G. 2005. Corporate Venture Capitalists and Independent Venture Capitalists: What do they know, Who do They Know and Should Entrepreneurs Care? Venture Capital 7: 3–21. Miles, M. P. and Covin, J. G. 2002. Exploring the Practice of Corporate Venturing: Some Com-

202

E. Transfer of Results and Exploitation

mon Forms and Their Organizational Implications, Entrepreneurship: Theory & Practice 26: 21–40. Rohrbeck, R. and Arnold, H. M. 2006. Making university-industry collaboration work – a case study on the Deutsche Telekom Laboratories contrasted with findings in literature. ISPIM Annual Conference: “Networks for Innovation”, Athens, Greece Schildt, H. A., Maula, M. V. J. and Keil, T. 2005. Explorative and Exploitative Learning from External Corporate Ventures. Entrepreneurship: Theory & Practice 29: 493–515. Sharma, P. and Chrisman, J. J. 1999. Toward a Reconciliation of the Definitional Issues in the Field of Corporate Entrepreneurship. Entrepreneurship: Theory & Practice 23: 11–27. Siegel, R., Siegel, E. and MacMillan, I C. 1988. Corporate Venture Capitalists: Autonomy, Obstacles, and Performance. Journal of Business Venturing 3: 233. Tidd, J. and Barnes, S. 2000. Spin-in or spin-out? Corporate venturing in life sciences. The International Journal of Entrepreneurship and Innovation 1: 109–116. Zahra, S. A. 1993. Environment, Corporate Entrepreneurship, and Financial Performance – a Taxonomic Approach. Journal of Business Venturing 8: 319–340. Zahra, S. A. 1995. Corporate Entrepreneurship and Financial Performance – the Case of Management Leveraged Buyouts. Journal of Business Venturing 10: 225–247. Zahra, S. A. 1996a. Governance, ownership, and corporate entrepreneurship: The moderating impact of industry technological opportunities. Academy of Management Journal 39: 1713–1735. Zahra, S. A. 1996b. Technology strategy and financial performance: Examining the moderating role of the firm‘s competitive environment. Journal of Business Venturing 11: 189–219.

203

List of authors

Ahrens, Maximilian Author of: • Enterprise Architecture in Innovation Implementation Maximilian Ahrens is currently CTO of Zimory, a spin-off of Deutsche Telekom Laboratories. He is an expert in service-oriented architecture and virtualization. Before co-founding Zimory, he served as a project manager and research scientist at the Innovation Development entity of Deutsche Telekom Laboratories. He was responsible for innovative infrastructure and enterprise IT projects spanning multiple divisions of the Deutsche Telekom Group. Before Deutsche Telekom, he worked in several business process re-engineering projects for major German companies.

Arnold, Heinrich Lead editor, and author of: • Deutsche Telekom Laboratories as a Testbed for Modern Technology and Innovation Management • Implementing Open Innovation to Benefit from External Dynamics of Innovation • Options for Customer Integration in the Open Innovation Paradigm at Deutsche Telekom • Cross-over Application of Enterprise Architecture and Modularization in Telco R&D • Venturing for Commercialization of R&D Result Dr. Heinrich Arnold has been heading up Innovation Development since its inception in 2004 and shaping Telekom Laboratories according to the latest findings and insights in innovation research and technology management. In addition, Dr. Arnold is in charge of R&D strategy, the Office for Technol-

204

List of authors

ogy and Market Trends Research, and takes care of the entrepreneurship and spin-off activities at Telekom Laboratories. Before his engagement at Deutsche Telekom AG, he worked for Mercer Management Consulting and as a member of the management team of the German-Chinese research company Bicoll Group. Arnold studied Technical Physics at the Technical University of Munich. He graduated with a Master of Science in Engineering from Stanford University, a Master of Business Research, as well as a Ph.D. in Technology Management from Ludwig Maximilians University, Munich. Dr. Arnold is a lecturer in Engineering Management, author of the scientific book Technology Shocks on the management of radical technological change, and member of the Innovation Leadership Advisory Board of the School of Engineering at the University of Illinois at Urbana-Champaign. He is active in the Scientific Committee of the Münchner Kreis, as a referee for the German Ministry of Education and Research, and represents Deutsche Telekom as a member of the Feldafinger Kreis and in the academic selection committee for new professorships at the Berlin Institute of Technology. Dr. Arnold has recently been invited to the first European Young Leaders Forum as well as the Global Young Leaders Forum of the Quandt Foundation.

Berlin, Marcus Junior editor Marcus Berlin was born in 1981 and studied Industrial Engineering and Management at the Berlin Institute of Technology. He has been working since 2008 in Innovation Development and is an associated researcher at the chair for Information and Communication Management at the Berlin Institute of Technology. Marcus Berlin supports the project field “AutoMobile” and mainly deals with quality management aspects within innovative projects. Before joining Telekom Laboratories he worked as an IT-consultant.

List of authors

205

Bub, Udo Author of: • Enterprise Architecture in Innovation Implementation • Partnering for Research and Development within an Open Innovation Framework Dr. Udo Bub is member of the executive team of DeutscheTelekom Laboratories (since its inception in 2004), where his areas of responsibility include R&D in human-computer interaction, ICT architectures, ICT infrastructures, and ICT security, as well as the activities of Telekom Laboratories at Ben-Gurion University. He is co-heading the Innovation Development Laboratory. In addition to his tasks at Telekom Laboratories he has taken over the CEO position of the European Center for Information and Communication Technologies (EICT) GmbH in 2007, a public private partnership for innovation with the current partners Daimler, Deutsche Telekom, Fraunhofer Society, Opera Software, and the Berlin Institute of Technology. He received both his master’s and doctor’s degrees in Electrical Engineering and Information Technology at Munich University of Technology (Dipl.-Ing., Dr.-Ing.) and had held long-term research appointments at Carnegie Mellon University’s School of Computer Science in Pittsburgh, PA, and at Siemens AG’s Corporate Technology in Munich. His research interests are information systems engineering, software engineering, and human-computer interaction. He is lecturer for ICT Systems Engineering at Berlin Institute of Technology. Before taking up his current positions, he had spent six years as a management and technology consultant in the telecommunications industry.

Chow, Rosan Author of: • Design Research in University-Industry Collaborative Innovation Dr. Rosan Chow works as a research scientist at the Deutsche Telekom Laboratories of the Berlin Institute of Technology. She holds a bachelor’s and a master’s degree in Communication Design from the University of Alberta, Canada. She has studied, practiced, and sessionally taught design in Canada, the United States, and Germany. She obtained her Ph.D. in Design Studies from the University of Arts in Braunschweig in 2006. Her dissertation, entitled “For User Study. The Implication of Design”, employs contempo-

206

List of authors

rary design theories to evaluate practice of user studies and to propose new forms of practice. Her research focuses on design theory and design research methodology. She is a research fellow at the Communication Research Institute in Australia and the project manger of the newly established Design Research Network.

Consmüller, Guido Author of: • The Project Value Tracking Process at Deutsche Telekom Laboratories Guido Consmüller studied Industrial Engineering and Management at the Berlin Institute of Technology. During his studies he gained practical experience in Shanghai, China, and São Paulo, Brazil, and in several consultancy projects. In his diploma thesis, he analyzed the evaluation differences of revenue potentials in Innovation Development projects based on different project cases at Deutsche Telekom Laboratories. Since 2008 he has worked in the tax advising and audit field.

Dörflinger, Tim Author of: • Evaluation Tools to Project Customer Potential Tim Dörflinger is a research scientist for Innovation Development at the Berlin Institute of Technology and Deutsche Telekom AG Laboratories. His main research focus lies in customer behavior and research methods for information and communication technologies. His professional background is in empirical sociology, business administration, and cultural studies (B.A. Hons.). Besides his work as a research scientist, where he mainly deals with evaluating consumer and customer needs in innovation projects, he is conducting a part-time master’s degree (M.A.) in Economics and Culture.

List of authors

207

Dunaj, Michal Author of: • Cross-over Application of Enterprise Architecture and Modularization in Telco R&D Michal Dunaj was born in 1974 and studied International Trade at the University of Economics Bratislava (Slovakia). He switched to telco industry in 2001, when he started with the Deutsche Telekom group in Slovakia, and held various positions in product management and corporate strategy. In 2006 he joined Telekom Laboratories in Berlin, where he has now been appointed to build up the R&D strategy field within Innovation Development. Dunaj is responsible for the strategic cross-project portfolio coordination in Telekom Laboratories, where he also brings together respective Deutsche Telekom group technology strategy departments. His current activities range from the roadmapping of R&D modules and R&D strategy development to the cross-over application of enterprise architecture concepts to early innovation processes. He also believes in balancing process rigor and passion for work, so he experiments with Enterprise 2.0 knowledge management IT tools. Besides his daily business activities, he is also preparing his Ph.D. thesis on the transfer of proper innovation tools and concepts from large enterprises to SMEs.

Elovici, Yuval Author of: • Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute Yuval Elovici is a senior lecturer at the Department of Information Systems Engineering at Ben-Gurion University. He holds B.Sc. and M.Sc. degrees in Computer and Electrical Engineering from the Ben-Gurion University of the Negev and a Ph.D. in Information Systems from Tel-Aviv University. His main research interests are computer and network security, information warfare, machine learning, and information retrieval. Currently he is the director of the Deutsche Telekom Laboratories at Ben-Gurion University.

208

List of authors

Englert, Roman Author of: • Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute Roman Englert studied Computer Science and Statistics of Economics at the University of Bonn until 1994. In 1998 he completed a Ph.D. in Computer Science and Photogrammetry. A post-doctorate was completed at the Institute of Pattern Recognition and Image Processing at the Technical University of Vienna in 1999. In March 2008 he completed his habilitation in Planning and Scheduling at the Technical University of Berlin. In the same month he became an assistant professor at Ben-Gurion University, Israel, and a faculty member of the Information System Engineering Department. Dr. Englert was a product manager at T-Mobile Deutschland GmbH until 2002. Then he was the European System Manager for MMS at T-Mobile International AG until 2004. Subsequently, he became the head of R&D Usability at Innovation Development until 2006. He was responsible for setting up of the research institute Telekom Laboratories Ben Gurion University in Beer-Sheva, Israel, which is now running with more than 100 employees. Till today he is the liaison officer at Telekom Laboratories@Ben-Gurion University in Beer-Sheva and he lectures in the areas of artificial intelligence and usability at Ben Gurion University and the Technical University of Berlin, Germany.

Erner, Michael Editor, and author of: • Implementing Open Innovation to Benefit from External Dynamics of Innovation • Options for Customer Integration in the Open Innovation Paradigm at Deutsche Telekom • Managing Technology Push and Market Pull within Pre-product Development Michael Erner was born in 1963 and studied Business and Economics at the Universities of Cologne, Paris, and Klagenfurt, where he received his doctor’s degree. He joined Deutsche Telekom in 1994, where from 1996 on he held several senior management positions in sales, marketing, and strategy – amongst those 5 years abroad and several years in international business

List of authors

209

development. Currently he is heading the Innovation Development group “Automobile” of Deutsche Telekom Laboratories in Berlin. Furthermore he is lecturing Marketing and International Marketing at the FHTW in Berlin. His research activities are in the area of innovation marketing and management.

Foken, Helga Marion Author of: • The Project Value Tracking Process at Deutsche Telekom Laboratories Marion Foken’s career in innovation management spans 15 years with the Deutsche Telekom corporation. She joined the staff of the executive management board of Deutsche Telekom in 1994, reporting directly to the director of research and development. In 1997 she became staff director for the Innovation and Information Management Division responsible for the development of market-focused scenarios and evaluation techniques for new technologies. Following the formation of Deutsche Telekom Laboratories, Mrs. Foken has led several customer-targeted innovation initiatives in close cooperation with Fortune 500 partners. She implemented a company-wide innovation value tracking analysis in 2006 and assumed responsibility for the Innovation Developments’ strategy review in 2007. Mrs. Foken previously worked with the logistics leader Hapag Lloyd and holds a master’s degree in Economics from the University of Hanover, Germany.

Glezer, Chanan Author of: • Integration of Academic Research into Innovation Projects: The Case of Collaboration with a University Research Institute Dr. Chanan Glezer holds a Ph.D. in Management Information Systems from Texas Tech University (1996). Until 1998 he was a post-doctoral researcher at the Department of Communication Systems Engineering at Ben Gurion University. Between 1998 and 2007, he was a lecturer at the Departments of Industrial Engineering and Management and Information Systems Engineering at Ben Gurion University. Since 2007 he is a research associate at Telekom Laboratories@Ben-Gurion University. He managed the M2M4FM project

210

List of authors

at Telekom Laboratories@Ben-Gurion University and is currently involved in several security-related projects running in the labs.

Henke, Katja Author of: • Evaluation Tools to Project Customer Potential Katja Henke is working as senior project field manager Innovation Development for Intuitive Usability. She is an expert in usability methods and user interfaces with an additional focus on media topics. After studying business administration at the Free University of Berlin majoring in Marketing Management and Macroecomomics, Katja Henke started her professional career in the telecommunication sector and had held different positions as product manager in the telecommunication sector. In 1997, she joined Deutsche Telekom working as marketing manager and later on as senior strategist leading innovation projects. At the beginning of 2004, she joined Deutsche Telekom Laboratories as project field manager for customer behavior & needs. Katja has been working in the telecommunications industry for more than 13 years.

Joost, Gesche Author of: • Design Research in University-Industry Collaborative Innovation Prof. Dr. Gesche Joost is the head of the Design Research Lab within Strategic Research at Deutsche Telekom Laboratories in Berlin, Germany. As senior design researcher, she is in charge of all design-driven innovation activities in R&D for Deutsche Telekom. She develops research projects together with international partners and the TU Berlin, where she also teaches design theory & research. Her current research topics are interface and interaction design, gender & design, as well as user-centric design methods. She holds a Ph.D. in Rhetoric and a Master in Design. She published books on the “Image-Language” of audio-visual media as well as on “Design as Rhetoric”.

List of authors

211

Kapitány, Dávid Author of: • Business (Lead) Customer Involvement in The Innovation Process Dávid Kapitány holds a B.A. in Intercultural Sciences. He studied Social Sciences, Politics, and Law at the University of Frankfurt (Oder), Germany; and Administration, Politics, and Communication at the universities Los Andes, Pontificia Javeriana, and Nacional (all Colombia). Joining Deutsche Telekom Laboratories in April 2007, Dávid became a research scientist at the Berlin Institute of Technology in October 2007 for Innovation Development. His fields of activity include customer behavior, usability and market research, as well as technology assessment. At the moment he is mainly working in the project News4Me on a personalized, digital newspaper.

Linke, Daniela Author of: • Transferring Technology Innovations to Operating Business Units Daniela Linke has been working since 2005 at the Innovation Development Laboratory and is responsible for the management of the project office. She holds a double Diploma in Business Administration from the Viadrina European University in Frankfurt (Oder), Germany, and Ecole Supérieure de Commerce de Reims, France. Since summer 2006 she has been a Ph.D. student at the Chair for Technology and Innovation Management at the Berlin Institute of Technology. Her research topic is the success factors of the intracompany technology transfer.

Lüke, Karl-Heinz Author of: • Business (Lead) Customer Involvement in The Innovation Process Dr. Karl-Heinz Lüke was born in Hildesheim and studied Electrical Engineering and Business Administration at the Braunschweig University of Technology, Germany. He worked as a consultant for a leading IT company for several years and received a Doctor of Economics in 2006 for his work on the “diffusion of telecommunication services with network effects”.

212

List of authors

Since joining Deutsche Telekom Laboratories, he has worked as a project manager at Innovation Development. The scope of his projects includes mobile ticketing, mobile portals, and different strategy projects as well as the calculation of business cases.

Möckel, Peter Editor, and author of: • Deutsche Telekom Laboratories as a Testbed for Modern Technology and Innovation Management Peter Möckel is the head of Deutsche Telekom Laboratories, the corporate research and development department of Deutsche Telekom. When helping to set up Telekom Laboratories in 2004, Peter had founded it very much on the principle of open innovation. Telekom Laboratories focus on technology issues for Deutsche Telekom AG, operating within a time scope of two or more years out. The purpose of Telekom Laboratories is to identify new opportunities at an early stage, to research them and to develop any topics identified as promising to a stage shortly before market launch. Prior to his engagement at Telekom Laboratories, Peter has worked in corporate strategy for Deutsche Telekom, first in managing strategy for the online market, later also for the ICT market, and finally as head of corporate strategy development. In his strategy work Peter has been concentrating on questions of technological change and its impact on telecommunications companies. Before joining Deutsche Telekom, Peter had been a consultant for Booz, Allen & Hamilton. There he worked with telecommunications and media companies on their market facing strategies and with public policy advisors on online and media regulation. Peter Möckel read computer science at Corpus Christi College of Cambridge University, graduating with a B.A. (Hons) and an M.A. He also holds a Diplom in computer science from the technical university of Aachen in Germany.

List of authors

213

Presse, Volker Author of: • Implementing Open Innovation to Benefit from External Dynamics of Innovation • Options for Customer Integration in the Open Innovation Paradigm at Deutsche Telekom • Managing Technology Push and Market Pull within Pre-product Development Volker Presse (Dipl.-Ing.) has been working since 2005 at the Innovation Development Laboratory of Deutsche Telekom and is associate researcher at the Chair for Innovation and Technology Management at Berlin Institute of Technology. He studied Industrial Engineering and Management at the Berlin Institute of Technology, Germany, and the University of Queensland, Australia. Volker Presse supports the project field “AutoMobile” and takes care of the development of innovation management tools and methods. Before joining Telekom Laboratories, Volker worked as a consultant in the automotive as well as the health management industry.

Riege, Christian Author of: • Enterprise Architecture in Innovation Implementation Christian Riege earned a degree in Information Systems at the University of Leipzig in 2005. He has been a project manager and consultant for miscellaneous projects with Commerzbank AG in Frankfurt and Singapore. Amongst others, he was assigned to software development projects for investment banking and the asset management operations. He has been a Ph.D. research assistant at the Institute of Information Management at the University of St. Gallen since January 2006. He is an associate in the Competence Center Integration Factory under the chair of Prof. Dr. R. Winter and Dr. St. Aier. He is a member of the chair’s architecture group working on joint projects with Deutsche Telekom Laboratories.

214

List of authors

Rohrbeck, René Author of: • Strategic Foresight • Venturing for Commercialization of R&D Results • The Project Value Tracking Process at Deutsche Telekom Laboratories René Rohrbeck is the head of innovation management at the European Center for Information and Communication Technology (EICT) and associated researcher at Berlin University of Technology, Chair for Innovation and Technology Management. Before joining EICT he worked for Innovation Development at Deutsche Telekom Laboratories where he coordinated the technology intelligence activities and worked on the improvement of innovation management methods & tools. In 2007 he founded the European Conference on Corporate Foresight an annual forum for foresight professionals. René Rohrbeck serves also as a consultant for various multinational companies in the ICT, Energy, Automobile & Transport and Pulp & Paper industry. His research interests are foresight, open innovation and technology management. His works have been published in several books and in peer reviewed journals such as R&D Management, Technology Analysis & Strategic Management and Global Business and Organizational Excellence.

Schläffer, Christopher Editor, and author of: • The Importance of Innovation Management at Deutsche Telekom – Technological Uncertainty and Open Innovation Christopher Schläffer is Deutsche Telekom’s Chief Product & Innovation Officer and member of the group’s Executive Operating Committee. Christopher Schläffer is in charge of Deutsche Telekom’s product portfolio across strategic business areas and bringing key innovations like IPTV, the mobile internet or new services around “Connected Life & Work” to market. He also takes responsibility for Deutsche Telekom’s Research & Development. From 2002 to 2006 Christopher Schläffer served as Corporate Development Officer (CDO) in charge of Corporate Strategy, Technology & Innovation, Research& Development, IT, Venture Capital as well as Deutsche Te-

List of authors

215

lekom’s Regional Offices in Asia and Europe. In 2000 Christopher Schläffer became Senior Vice President Corporate Strategy at Deutsche Telekom. Prior to joining Deutsche Telekom in 1998 Christopher Schläffer worked with Accenture, the global consulting, technology and outsourcing company. Christopher Schläffer holds a master’s degree from the Vienna University of Economics and has been elected Young Global Leader by the World Economic Forum.

Schoenherr, Marten Author of: • Enterprise Architecture in Innovation Implementation Marten Schoenherr holds a Master of Business Administration and a Ph.D. in Computer Science from the Faculty of Electrical Engineering and Computer Science at the Berlin Institute of Technology. He was in charge for an industry-funded group at Berlin Institute of Technology in the field of information systems research (mainly method construction and systems analysis and integration) before he started working as a senior research scientist at Telekom Laboratories in charge of the Innovation Development project field architecture and quality assurance.

Steinhoff, Fee Author of: • User Driven Innovation at Deutsche Telekom Laboratories Dr. Fee Steinhoff is project field manager for user driven innovation and new media at Deutsche Telekom Laboratories’ Innovation Development in Berlin. She studied Business Administration at the Berlin Institute of Technology, earned a Master of Business & Engineering at the Steinbeis University Berlin, and received her Ph.D. from the Berlin Institute of Technology. Her area of research interests include innovation marketing, customer orientation, and radical innovation – topics which she also teaches in executive programs. She co-authored the book Innovation Marketing together with Prof. Dr. V. Trommsdorff.

216

List of authors

Thom, Nico Author of: • Strategic Foresight Nico Thom was born in 1978 and studied Business Administration at the Berlin Institute of Technology and the Hogeschool-Universiteit Brussel. At Telekom Laboratories’ Innovation Development he is a researcher in the field of technology exploration. He also coordinates the technology intelligence activities for Deutsche Telekom AG. Before joining Telekom Laboratories, Nico worked as a consultant in the automotive industry.

Wogatzky, Mitja Author of: • Options for Customer Integration in the Open Innovation Paradigm at Deutsche Telekom Mitja Wogatzky, Diplomkaufmann (FH), studied Business Administration at the University of Applied Science Berlin, Germany, and at the Universidad de Vigo, Spain. Before and during his studies, he was active in different industrial enterprises and in the management of an international retailer. Currently he holds the position of a scientific researcher at the Berlin Institute of Technology and works for the Innovation Development in the User Driven Innovation Team of Deutsche Telekom Laboratories.

217

Index A Absorptive Capacity 173 Alignment 101, 119, 140 Artificial Neural Networks 32 Assessment • Qualitative 184f. • Quantitative 185f. Attitudes 103, 105, 106 Automatic Signature Generation 32, 34

B Bayesian Networks 141 Building Blocks 148ff. Business • Case 183 • Customer Integration 64

Innovation • Cluster 54 • Goal 181, 186 • Limitations of 192 ff. • Management 73, 146 • Market Research 73 • Process 27, 32, 140 • Projects 75, 136 • System 7, 39 • Toolkits for 96 f. Intrusion • Detection/Prevention 32

K Key Drivers of Innovation Goals 186 f. Knowledge Management 118

L C Collaboration 158 Community, Virtual 95 Consumer, Behavior 101, 103 Cooperation • Models 50 • Pre-Competitive 54 Cross-Over Application 126 Customer • Insights 16, 74, 80 • Integration 91f. • Potential 107

Lead User, Approach 91 ff.

M Malware Cleaning/Detection 29 f. Market • Estimation 82 • Potential 73, 79, 105 ff. • Pull 147 f. Meta-Aggregation 149 Mobile TV 77 ff. Modularization/Modules 124 ff.

N D Decision Trees 32

E

Network Service Provider 30 New Product Development 104, 146

O

Forecast 17

Open Innovation 37f., 60 f., 90 Organization • Knowledge 42 • Structural 41 f. Organizational • Design 41 • Process 65

I

P

Ideas Competition 93

Partnering 49 ff., 62

Enablers 151 End-to-End Monitoring 188 Enterprise Architecture 116, 118, 134ff.

F

218

Index

Process • Integration/Involvement 66 Project • Field Management 182 • Result Transfer 182 f. • Value Tracking Process 182 f. Public-Private Partnership 7, 55 f.

R Radical Technological Change 6 Research and Development • Chain 26 f. • Marketing Interaction 174 Revenue • Commitments 183 • Model 182 Roadmapping 20

S Scenario Planning 20 Segmentation 101 ff. Sinus-Milieus 105 Spin • Along 196 f. • In 200

• Out 200 Strategic Foresight 12 ff.

T Technology • Intelligence 68 • Push 147 ff. • Shocks 13, 37 • Transfer 169 ff., 172 Transferables, Technical 187 Transfer Agreements 183

U Use Case Scenarios 183

V Value Contributions of Innovation Projects 183 Venture Capital • Corporate 198 Venturing • Corporate 195 ff. • External 195 • Internal 195, 196