Innovation Strategy and Firm Performance - An empirical study of publicly listed firms

Nanja Strecker

Innovation Strategy and Firm Performance An empirical study of publicly listed firms

With a foreword by Prof. Dr. Søren Salomo

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Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.

Dissertation Karl-Franzens-Universität Graz, Österreich, November 2007

1st Edition 2009 All rights reserved © Gabler | GWV Fachverlage GmbH, Wiesbaden 2009 Editorial Office: Claudia Jeske / Sabine Schöller Gabler is part of the specialist publishing group Springer Science+Business Media. www.gabler.de No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder. Registered and/or industrial names, trade names, trade descriptions etc. cited in this publication are part of the law for trade-mark protection and may not be used free in any form or by any means even if this is not specifically marked. Cover design: Regine Zimmer, Dipl.-Designerin, Frankfurt/Main Printed on acid-free paper Printed in Germany ISBN 978-3-8349-1755-3

Foreword In practically oriented studies, innovation strategy is consistently identified as a key success factor for innovation performance as well as sustainable, overall firm performance. Empirical research has, in comparison to other aspects of innovation management, not devoted much attention to this topic. In addition to this lack of empirically validated results, there is a deficit of clear definition in terms of what is actually meant by innovation strategy. Nanja Strecker’s dissertation starts there and concentrates on three central questions: What characterizes innovation strategy of companies? To what extend does a relationship between innovation strategy and a firm’s performance exist and which critical conditions need to be considered for this relationship? Nanja identifies and answers these practically as well as scientifically highly relevant and interesting research questions. The research was conducted in a highly competent manner. Particularly worth emphasizing is the coherent deduction of the conceptual framework as well as the hypotheses, considering prior empirical evidence in a very comprehensive manner. The good conceptual part is even exceeded by the sophisticated empirical study. Impressively, Nanja proves her methodological competence and diligence when analyzing her data. The presented results are very interesting and, to a large extend, support the formulated hypotheses. Moreover, the present study states meaningful suggestions for future research in innovation management. For management practice in particular, Nanja’s research provides interesting advice on how to improve firms’ innovation management. Those who face the task to define a firm’s innovation strategy can find highly relevant information on the dimensions characterizing this strategy, which together are critical for a firm’s overall performance.

Dr. Søren Salomo, Professor for Innovation Management, Danish Technical University, DTU Copenhagen, March 2009

Preface Innovation has become a prime source of gaining and sustaining a competitive advantage in the market. While a large research body addresses the question of individual innovation projects, a firm´s overall innovation strategy has received much less attention. Should innovations be driven by market orientation and a close relation to customers or rather a strong technology orientation with a world leading R&D department behind? Should a company focus on radically new products or rather a steady stream of incremental ones? Is a diverse portfolio of unrelated innovation projects beneficial or should a firm focus its innovation projects in deliberately chosen innovation fields? These strategic questions form part of innovation strategy. The objective of this dissertation is to understand innovation strategy better and its relation with a firm´s overall performance. First ideas to this dissertation topic came up during my time as a consultant at The Monitor Group. Particularly the concept of innovation fields had raised our attention. There was anecdotal evidence, but no systematic one. For the purpose of this study, innovation field orientation was put into a broader context of innovation strategy. First of all, I want to thank my PhD advisor, Prof. Dr. Søren Salomo (Danish Technical University Copenhagen) for his competent advice and generous oversight. Another thank you goes to the 138 company representatives who participated in the empirical study, and to Gregor Einetter, who successfully chased after them. A very cordial thank you goes to my PhD mate, Antje Lutz. She not only served as a critical discussion partner, but we shared all the challenges of the PhD programme. My partner, Kevin, was another very valuable discussion partner. He ensured that the theoretical part of this work stayed linked to the reality of companies. Last but not least, Steffen Gackstatter and Iason Onassis, former colleagues from Monitor, not only helped to crack the one or other content question, but also offered their broad network for the empirical study. My father, Günther Strecker, did not get tired to make me consider a PhD at all. Him and Gitta Abele, I also wanted to thank for all their life long support and lessons, not taught at any school. Kevin was the key moral

VIII

Preface

support. Thank you for always being behind me during these sometimes challenging times. Last but not least, I wanted to thank my sister, Jutta Strecker, and several other people from the medical field who helped me get through typing a dissertation thesis, even though my body is not built for it.

Nanja Strecker

Table of Contents Table of Contents ........................................................................... IX Tables ..........................................................................................XV Figures ...................................................................................... XVII Abbreviations ........ ..................................................................... XIX Symbols .......................................................................................XX Chapter I

Introduction .................................................................1

1 Research Problem and Relevance ....................................................1 1.1 On the Importance of Innovation ..............................................1 1.2 How to be successful through innovation? ..................................3 2 Objectives of Study .......................................................................5 3 Structure of Document...................................................................9 Chapter II

Literature Review........................................................ 11

1 Innovation Strategy..................................................................... 11 1.1 Definitions ........................................................................... 11 1.1.1 Definition of Strategy .................................................. 11 1.1.2 Definition of Innovation ............................................... 13 1.1.3 Definition of Innovation Strategy................................... 16 1.2 Innovation Strategy Typologies and Dimensions........................ 18 1.2.1 Methodical considerations ............................................ 18 1.2.2 Innovation Strategy Typologies and Dimensions .............. 19 1.2.3 Innovation Strategy Dimensions of This Study ................ 23 1.2.3.1 Innovativeness ...................................................... 29 1.2.3.2 Distance to Core Business ....................................... 30 1.2.3.3 Driver of Innovation ............................................... 32 1.2.3.4 Innovation Field Orientation..................................... 35 1.2.3.5 Overview of Four Innovation Strategy Dimensions ...... 39 2 Innovation Strategy and Performance ............................................ 40 2.1 Innovation Performance and Firm Performance ......................... 40 2.1.1 Innovation Performance ............................................... 40

X

Table of Contents

2.1.2 Innovation and Firm Performance.................................. 41 2.2 Innovation Strategy and Performance...................................... 42 2.2.1 Innovation Strategy and Performance ............................ 42 2.2.2 Innovativeness and Performance................................... 46 2.2.3 Distance to Core Business and Performance.................... 50 2.2.4 Driver of Innovation and Performance............................ 52 2.2.5 Innovation Field Orientation and Performance ................. 55 3 Innovation Strategy and Firm Context............................................ 58 3.1.1 Business Strategy as Contingency ................................. 61 3.1.2 Environmental Uncertainty as Contingency ..................... 64 Chapter III

Research Hypotheses .................................................. 69

1 Conceptual Framework ................................................................ 69 2 Hypotheses ................................................................................ 70 2.1 Success Hypotheses.............................................................. 70 2.1.1 Hypothesis 1: Innovativeness and Performance............... 70 2.1.2 Hypothesis 2: Distance to Core Business and Performance 73 2.1.3 Hypothesis 3: Driver of Innovation and Performance........ 76 2.1.4 Hypothesis 4: Innovation Field Orientation & Performance 80 2.2 Contingency Hypotheses........................................................ 85 2.2.1 Business Strategy as Contingency ................................. 85 2.2.1.1 Hypothesis 5a: Innovativeness, Business Strategy, and Performance.................................................... 86 2.2.1.2 Hypothesis 5b: Distance to Core Business, Business Strategy, and Performance ...................................... 87 2.2.1.3 Hypothesis 5c: Driver of Innovation, Business Strategy, and Performance ...................................... 88 2.2.1.4 Hypothesis 5d: Innovation Field Orientation, Business Strategy, and Performance ...................................... 89 2.2.2 Environmental Uncertainty as Contingency ..................... 90 2.2.2.1 Hypothesis 6a: Innovativeness, Environmental Uncertainty, and Performance .................................. 90 2.2.2.2 Hypothesis 6b: Distance to Core Business, Environmental Uncertainty, and Performance ............. 92 2.2.2.3 Hypothesis 6c: Driver of Innovation, Environmental Uncertainty, and Performance .................................. 93 2.2.2.4 Hypothesis 6d: Innovation Field Orientation, Environmental Uncertainty, and Performance ............. 97 2.3 Summary of Hypotheses........................................................ 98

Table of Contents

Chapter IV

XI

Empirical Study ........................................................ 101

1 Research Design ....................................................................... 101 2 Sample and Data ...................................................................... 105 2.1 Sample Frame.................................................................... 105 2.2 Quantitative Survey ............................................................ 106 2.3 Document Analysis ............................................................. 109 2.3.1 Methodology ............................................................ 109 2.3.2 Comparison with Survey Results and Outcome .............. 112 2.4 Financial Databases ............................................................ 115 3 Operationalization of Constructs .................................................. 117 3.1 Methodical Considerations.................................................... 117 3.2 Operationalization of Innovation Strategy Variables ................. 120 3.2.1 Measure for Innovativeness........................................ 120 3.2.2 Measure for Distance to Core Business......................... 122 3.2.3 Measure for Driver of Innovation ................................. 124 3.2.4 Measure for Innovation Field Orientation ...................... 126 3.3 Operationalization of Performance Variables ........................... 130 3.3.1 Measure for Innovation Performance............................ 130 3.3.2 Measures for Firm Performance................................... 131 3.3.2.1 Tobin’s q............................................................. 132 3.3.2.2 Total Return on Share Relative to Industry .............. 133 3.4 Operationalization of Contingency Variables............................ 134 3.4.1 Measure for Business Strategy .................................... 134 3.4.2 Measure for Environmental Uncertainty ........................ 135 3.5 Operationalization of Control Variables................................... 137 4 Results .................................................................................... 138 4.1 Descriptive Statistics ........................................................... 138 4.1.1 Demographic Characteristics ...................................... 138 4.1.1.1 Firm Size ............................................................ 138 4.1.1.2 Industry Sector.................................................... 140 4.1.1.3 R&D Expenditure.................................................. 141 4.1.2 Descriptive Results for Innovation Strategy Variables ..... 143 4.1.2.1 Descriptive Results for Innovativeness .................... 143 4.1.2.2 Descriptive Results for Distance to Core Business ..... 146 4.1.2.3 Descriptive Results for Driver of Innovation ............. 148 4.1.2.4 Descriptive Results for Innovation Field Orientation... 150 4.1.3 Descriptive Results for Performance Variables ............... 154 4.1.4 Innovation Strategy – Performance Correlations............ 155

XII

Table of Contents

4.1.5 Descriptive Results for Contingency Variables ............... 156 4.2 Validation of Measures ........................................................ 159 4.2.1 Methodical Considerations .......................................... 159 4.2.2 Validation of Innovation Strategy Variables................... 161 4.2.2.1 Validation of Innovativeness .................................. 161 4.2.2.2 Validation of Distance to Core Business ................... 162 4.2.2.3 Validation of Driver of Innovation ........................... 164 4.2.2.4 Validation of Innovation Field Orientation ................ 165 4.2.3 Validation of Innovation Performance........................... 167 4.2.4 Validation of Contingency Variables ............................. 169 4.2.4.1 Validation of Business Strategy .............................. 169 4.2.4.2 Validation of Environmental Uncertainty .................. 170 4.2.5 Validation of First-Order Constructs ............................. 171 4.2.5.1 Validation of Innovation Strategy (1st Order)............ 171 4.2.5.2 Validation of Innovation Performance (1st Order) ...... 172 4.2.5.3 Validation of Contingency Factors (1st Order) ........... 173 4.2.6 Validation of Second-Order Constructs ......................... 174 4.2.6.1 Validation Innovation Strategy (2nd Order)............... 174 4.3 Testing of Hypotheses ......................................................... 174 4.3.1 Methodical Considerations .......................................... 175 4.3.2 Innovation Strategy – Performance Relationship............ 176 4.3.2.1 Second Order Innovation Strategy Dimensions ......... 176 4.3.2.2 First Order Innovation Strategy Dimension .............. 179 4.3.3 Contingencies........................................................... 181 4.3.3.1 Moderated Regression Results for Business Strategy . 181 4.3.3.2 Moderated Regression Results for Environmental Uncertainty ......................................................... 183 4.3.4 Summary of Hypotheses Testing ................................. 185 4.4 Innovation Field Orientation ................................................. 186 Chapter V

Discussion & Implications........................................... 191

1 Discussion of Results ................................................................. 191 1.1 Which dimensions charaterize innovation strategy? ................. 191 1.2 To what extent does a relationship exist between innovation strategy and firm-level performance? .................................... 193 1.2.1 Does a relationship exist between innovativeness and performance? ........................................................... 194 1.2.2 Does a relationship exist between distance to core business and performance? ........................................ 195 1.2.3 Does a relationship exist between driver of innovation and performance? ..................................................... 197 1.2.4 Does a relationship exist between innovation field orientation and performance? ..................................... 199

Table of Contents

XIII

1.3 How do contingencies influence the relationship between innovation strategy and firm-level performance? ..................... 201 1.3.1 Does business strategy moderate the relationship between innovation strategy and performance?............. 201 1.3.2 Does environmental uncertainty moderate the relationship between innovation strategy and performance?............. 204 2 Implications ............................................................................. 206 2.1 Implications for Innovation Research ..................................... 206 2.2 Managerial Implications ....................................................... 210 3 Limitations & Outlook ................................................................ 213 Appendix:

Cover Letter and Questionnaire................................... 217

Bibliography ................................................................................ 225

Tables Table II-1:

Definitions of Strategy .................................................. 12

Table II-2:

Definitions of Innovation Strategy .................................. 16

Table II-3:

Overview of Research on Innovation Strategy .................. 20

Table II-4:

Overview of Innovation Strategy Dimensions ................... 25

Table II-5:

Research on Innovation Strategy & its Performance Effect . 43

Table II-6:

Research on Innovation Activity and its Performance Effect 45

Table II-7:

Recent Studies on Innovativeness and Performance .......... 47

Table II-8:

Studies on Distance to Core Business and Performance ..... 52

Table II-9:

Studies on Drivers of Innovation and Performance ............ 54

Table II-10: Studies on Innovation Field Orientation and Performance... 57 Table II-11: Research on Innovation Strategy and Contingencies ......... 60 Table II-12: Contingencies Studied in Innovation Research.................. 60 Table II-13: Environmental Uncertainty in Innovation Research............ 66 Table III-1: Summary of Hypotheses ............................................... 99 Table IV-1: Overview of Indicators from Financial Databases ............ 116 Table IV-2: Operationalization of Innovativeness............................. 122 Table IV-3: Operationalization of Distance to Core Business.............. 124 Table IV-4: Operationalization of Driver of Innovation...................... 126 Table IV-5: Operationalization of Innovation Field Orientation ........... 129 Table IV-6: Operationalization of Innovation Performance ................ 131 Table IV-7: Operationalization of Business Strategy......................... 135 Table IV-8: Operationalization of Market Uncertainty ....................... 136 Table IV-9: Operationalization of Technological Uncertainty .............. 136 Table IV-10: Employees in 2005 - by Sector and Continent ................ 139 Table IV-11: Total Sales 2005 (thousand €) - by Sector and Continent 140 Table IV-12: Sectors Represented in Sample.................................... 141 Table IV-13: Innovativeness – Descriptive Statistics.......................... 143 Table IV-14: Innovativeness – Quotes from Document Analysis .......... 145 Table IV-15: Distance to Core Business – Descriptive Statistics........... 146 Table IV-16: Distance to Core Business – Document Analysis ............. 147 Table IV-17: Driver of Innovation – Descriptive Statistics................... 148 Table IV-18: Driver of Innovation – Quotes from Document Analysis ... 150 Table IV-19: Innovation Field Orientation – Descriptive Statistics ........ 151 Table IV-20: Innovation Field Orientation – Document Analysis........... 153

Tables

XVI

Table IV-21: Performance Variables – Descriptive Statistics................ 154 Table IV-22: Correlations: Innovation Strategy, Performance Variables 155 Table IV-23: Contingency Variables – Descriptive Statistics ................ 157 Table IV-24: Business Strategy – Quotes from Document Analysis ...... 158 Table IV-25: Innovativeness – Collinearity Statistics ......................... 161 Table IV-26: Distance to Core Business – Collinearity Statistics .......... 163 Table IV-27: Driver of Innovation – Collinearity Statistics................... 164 Table IV-28: Innovation Field Orientation – Collinearity Statistics ........ 166 Table IV-29: Innovation Performance – Collinearity Statistics ............. 168 Table IV-30: Business Strategy – Collinearity Statistics...................... 170 Table IV-31: Environmental Uncertainty – Collinearity Statistics.......... 171 Table IV-32: Innovation Strategy, 1st Order Constructs – Collinearity... 172 Table IV-33: Innovation Performance, 1st Order – Collinearity............. 173 Table IV-34: Business Strategy, 1st Order – Collinearity ..................... 173 Table IV-35: Environmental Uncertainty, 1st Order – Collinearity ......... 173 Table IV-36: Innovation Strategy, 2nd Order Constructs – Collinearity. 174 Table IV-37: Innovation Strategy (2nd) and Performance – Regression . 176 Table IV-38: Innovation Strategy (1st) and Performance – Regression . 180 Table IV-39: Business Strategy – Moderated Regression .................... 181 Table IV-40: Environmental Uncertainty – Moderated Regression ........ 184 Table IV-41: Summary of Results from Hypotheses Testing................ 185 Table IV-42: Innovation Field Orientation – Split Group Analysis ......... 186 Table IV-43: Selected Innovation Field Orientation Items – Means ...... 188

Figures Figure I-1:

Structure of Document.................................................. 10

Figure II-1: Innovation Strategy vs. Firm and Functional Strategies ..... 17 Figure II-2: Four Innovation Strategy Dimensions of This Study .......... 39 Figure III-1: Conceptual Framework ................................................. 70 Figure IV-1: Overview of Research Design....................................... 104 Figure IV-2: Response Process....................................................... 108 Figure IV-3: Overview of Sample Composition (n=122)..................... 108 Figure IV-4: Procedure of Qualitative Content Analysis ...................... 111 Figure IV-5: Results: Document Analysis vs. Quantitative Survey ....... 114 Figure IV-6: Reflective vs. Formative Measurement Model ................. 118 Figure IV-7: R&D Rates per Sector ................................................. 142

Abbreviations BU

Business Unit

DAX

Deutscher Aktien Index (top 30 German firms)

CEO

Chief Executive Officer

FTSE

Financial Times Stock Exchange 100 stock index

IFO

Innovation Field Orientation

Dow Jones Dow Jones Industrial Average Index N

Sample size

NASDAQ

NASDAQ stock market (index)

NPD

New Product Development

NPV

Net Present Value

Pro

Proactive (market orientation)

R&D

Research and Development

Re

Reactive (market orientation)

ROA

Return on Assets

SD

Standard Deviation

SIC

Standard Industry Classification

SPSS

Statistical Package for the Social Sciences

TecDax

Deutscher Technologie Index (top 30 technology firms)

TPDP

Technology Push and Demand Pull

TRS

Total Return on Share

VIF

Variance Inflation Factor

Symbols F

F-test

r R

Correlation coefficient 2

Coefficient of determination

Xmax

Maxium value

Xmean

Mean

Xmin

Minimum value

+

Positive relationship

-

Negative relationship

O

No relationship

8

U-shaped relationship

U

Inverted U-shaped relationship

Statistical Significance ***

Very significant, at the p<0.01 level (two-tailed test)

**

Significant, at the p<0.05 level (two-tailed test)

*

Low significance, at the p<0.1 level (two-tailed test)

n.s.

Not significant (relationship)

Chapter I

Introduction

GARY HAMEL, a thought leader in the field of innovation, recently stated: “There are all kinds of alternatives to innovation. In the short term, you can cut costs, make acquisitions and buy back your own shares. But in the medium to long term there is no alternative [to innovation].”1 This research makes suggestions as to how firms can positively influence their innovation and firm performance through making careful choices with regard to their innovation strategy. As part of this introduction the relevance of the topic is discussed, the objectives of this research are outlined and the structure of the document is presented.

1

Research Problem and Relevance

1.1

On the Importance of Innovation

Rarely does the business world agree as broadly as on the question about the most innovative companies at present. Several recent surveys rank Apple and Google in first and second place.2 Both are also extremely successful. Since launching its iPod in 2001 Apple’s share price soared by more than 600%.3 Solely in the past two years it achieved a share price increase of 163%.4 Apple’s success is predominantely driven by the iPod which offers an unrivaled user experience thanks to its easy-to-use click wheel, the interlinked iTunes software platform and its elegant design. Moreover, the iPod has helped Apple to increase its share in the PC mar-

1

See HAMEL (2005).

2

See e.g. “The world’s most innovative companies”, BUSINESSWEEK, April 24th, 2006, p.63-74; “Innovation Leaders, 2006/2007 Analysis”, by INNOVARO.

3

See e.g. www.finance.yahoo.com.

4

See e.g. INNOVARO (2006/7), p.2.

2

Introduction

ket through selling more Macs.5 Google started with a technically superior Internet search engine and a clean image. A clever advertising-based business model followed and many other neat applications, like Google Earth, continue to emerge. Google focuses on simplicity and the customer in all it does.6 Its share price more than quadrupled in the last five years and increased by 139% in 2005 and 2006 alone.7 One might argue that these are merely two examples and the insight is not generalizable. However, various parties measure performance indicators of so-called ‘innovation portfolios’ - composed of innovative companies. The observations support the finding from the two examples that innovative companies are more successful. The 2005 R&D Scoreboard, a study regularly issued by the British Department of Trade and Industry (dti), observed that the most active innovators grew by 63% over the last eight years compared to 7% for the FTSE 100 Index.8 INNOVARO, a consulting firm specialized on innovation, periodically publishes the Innovation Leaders analysis.9 The share price of their Innovation Leaders portfolio rose by 49% in 2005 and 2006 – significantly better than that of the FTSE, NASDAQ or Dow Jones.10 Firms have recognized the importance of innovation. Over 70% of senior executives name innovation as one of their top three priorities.11 At the same time almost half of top level decision makers are dissatisfied with the returns on innovation.12 This means that firms are conscious about the importance of innovation; however, a significant portion is not satisfied with their innovation performance.

5

In 2005, Apple’s market share for PCs increased by 3-4%. Surveys suggest that 1020% of PC users who buy an iPod subsequently go on to buy a Mac. THE ECONOMIST, January 14th, 2007, p.64.

6

See e.g. BUSINESSWEEK (2006), p.65.

7

See e.g. www.finance.yahoo.com and INNOVARO (2006/7), p.2.

8

See, The 2005 R&D Scoreboard (by The Deparment of Trade and Industry, UK), p.80.

9

See http://www.innovaro.com/Flyerpdfs/InnovationLeader2007.pdf

10

See INNOVARO (2006/7), p.2.

11

Based on a recent (2006), worldwide study BUSINESSWEEK and THE BOSTON CONSULTING GROUP stated a number of 72%. RIGBY and ZOOK (2002), and BOOZ ALLEN HAMILTON (2004) even claim the number to be beyond 80%.

12

See BUSINESSWEEK (2006), p.66. The number is in line with JONASH and SOMMERLATTE’S earlier observation of above 49% of firms who reported a considerable gap in effectiveness of current innovation practices (1999, p.XII).

Research Problem and Relevance

1.2

3

How to be successful through innovation?

Large parts of the business world seem convinced that the level of spending on innovation, also termed R&D rate,13 is key for success through innovation.14 The underlying assumption is that the more a firm spends on innovation, the more innovative and hence the more successful it is. This hypothesis has received significant attention. Several older as well as recent studies observe a positive effect of R&D spending on firm performance.15 Most recently, HO

ET AL.

(2006) state that R&D investment retains

a significant positive impact on a firm's growth opportunities, measured by a combined accounting and capital market based firm performance metric.16 Studies observing no or a negative impact of R&D spending on firm performance exist likewise. LEIFER

ET AL.

(2006) recently conducted an ex-

tensive investigation on the relation between R&D investment, innovativeness and organizational success.17 They found either no or even a negative relation between R&D rate and firm success - depending on the success measure and the time lag allowed for. Moreover they observed no relation between R&D investment and innovativeness. “It is necessary to invest in competencies to play the innovation game, but not to win it”, they conclude their findings. In line with the results from LEIFER

ET AL.,

BOOZ

ALLEN HAMILTON found no significant statistical relation between R&D spending and financial and corporate success - for two consecutive years, 2004 and 2005.18 “Money simply cannot buy effective innovation”, they

13

R&D rate is defined as the spending on R&D over sales. Given firms report R&D spending, but not innovation spending, the R&D rate continues as the best proxy for innovation spending based on publicly available information. Hence, for this section, the terms R&D and innovation are used interchangeably.

14

See BOOZ ALLEN HAMILTON (2005), p.55-56 for several quotes from well known newspapers and business magazines.

15

See e.g. CHAUVIN/HIRSHEY (1993), p.128-129, also for further references; and The 2005 R&D Scoreboard, p.80.

16

See HO/TJAHJAPRANATA/YAP (2006), p.866. Their firm performance measure, MBASS, is very similar to Tobin’s q, the firm performance measure used in this study.

17

Also for the following, see LEIFER/KASTHURIRANGAN/ROBESON (2006). They measure three firm performance indicators (revenue growth, short term earnings, stockholder return) at 1, 3 and 5 years after R&D investment.

18

See BOOZ ALLEN HAMILTON (2006). Since 2004, they annually conduct an analysis of 1,000 firms with the world’s largest corporate R&D budgets.

4

Introduction

summarize their results.19 Concluding, it is far from clear that an increase in R&D spending does result in better firm performance. Hence it remains to be answered how a firm can positively influence firm performance through innovation. The question is which other major decisions regarding innovation a firm needs to make beside the level of spending. The sum of major choices concerning innovation is termed innovation strategy.20 The success story of Apple, for example, attributes to a clearly defined innovation strategy. Their new products consistently exhibit a high degree of newness with regard to technical aspects (e.g. iPod’s click-wheel, iPhone’s multi-touch technology) as well as the fulfilment of customer needs (e.g. user-friendliness of the iPod-iTunes platform).21 Apple’s highly innovative products are driven by a balanced combination of cutting-edge engineer work and an extraordinary sense for the market and customers’ preferences. Surprisingly, the majority of firms have not defined any innovation strategy nor is the topic much investigated as part of innovation research. In a recent North American study, COOPER

ET AL.

(2004) found that innovation strategy is more myth than re-

ality for many firms and identified having one as best practice.22 In line, a new German study (2006) observed that 57% of participating firms had no explicit innovation strategy.23 Researchers from the KELLOGG SCHOOL

OF

BUSINESS agree and claim that most firms’ innovation strategies tend to be the result of simple inertia (“this is what we've always innovated on”) or industry convention (“this is how everyone innovates”).24 Based on this finding one might infer that it is impossible to investigate innovation strategy, because the mayority of firms have not defined any. This is not the

19

See BOOZ ALLEN HAMILTON (2006), p.3.

20

This understanding of innovation strategy follows MINTZBERG’S (1976) and MILES and SNOW’S (1978) view about strategy. For a more detailed definition see chapter II1.1.3.

21

Apple’s new products tend to be considered revolutionary compared to evolutionary new products by competitors. Also, the newly presented iPhone was characterized as “an impressive device that transcends the description mobile phone”. See e.g. THE ECONOMIST, 13.01.2007, p.53.

22

See COOPER/EDGETT/KLEINSCHMIDT (2004), p.50f. In their paper, innovation strategy is termed new produt strategy, however, with the same meaning.

23

See PRICEWATERHOUSECOOPER/EBS UNIVERSITY/DLR (2006), p.36.

24

See SAWHNEY/WOLCOTT/ARRONIZ (2006), p.81.

Objectives of Study

5

case, given strategy is not only about intended, ex-ante defined strategies, but also about realized strategies.25 In the latter sense a firm’s strategy is derived from its behaviour, which is the predominant approach used in overall business strategy and innovation strategy research. In the field of innovation research only 16 empirical studies investigate innovation strategy with the majority dating back to the 1980s and 1990s.26 Moreover, only six out of these studies investigate a performance effect and only three use firm performance as success measure, as done in this study. The two most important pieces on the relation between innovation strategy and performance, by COOPER and ZAHRA/COVIN, were published in 1984 and 1993 respectively.27 Hence, from a research perspective there is clearly a need for more and more up-to-date investigation on innovation strategy and its performance effect. Concluding, the relatively disregarded topic of innovation strategy and its effect on firm performance is more relevant than ever - for innovation research as well as business practice.

2

Objectives of Study

A review of the literature on innovation strategy reveals a lack of consensus concerning theoretical concepts, construct definition, and measurement. Existing innovation strategy typologies and their underlying dimensions are very heterogeneous and little established – with two exceptions. First, the typology originated by ANSOFF and STEWART (1967) which focuses on the aspect of timing of market entry (first-to-market, follow-theleader, me-too, etc.) is widely known, accepted and examined.28 Second, COOPER’S (1984) new product strategy typology (technologically driven, balanced strategy, etc.) is also frequently referred to.29 The conducted

25

See MINTZBERG (1978).

26

For the underlying meta-analysis see chapter II-1.2.2.

27

See COOPER (1984) and ZAHRA/COVIN (1993).

28

See ANSOFF/STEWART (1967) and BARCZAK (1995), p.226 for a similar assessment and literature review on this typology.

29

See COOPER (1984). BARCZAK (1995), p. 226, and FIRTH/NARAYANAN (1996), p. 342, are two examples referring to COOPER’S typology and its high level of acceptance.

6

Introduction

meta-analysis shows that almost every study on innovation strategy uses a different conceptualization. Moreover, 25 different underlying dimensions were counted characterizing innovation strategy.30 Surprisingly, the four MILES and SNOW strategy types (Prospector, Defender, Analyzer, and Reactor) are also applied to investigate innovation strategy,31 even though the four types are intended to characterize overall firm strategy and not innovation strategy.32 The present study taps into this research gap by suggesting a comprehensive, multi-dimensional concept for innovation strategy along four dimensions: innovativeness, distance to core business, driver of innovation and innovation field orientation. ƒInnovativeness stands for the degree of newness of a firm’s new product portfolio from the perspective of the marketplace, that is to say customers. Innovativeness is the dimension most investigated to date as part of innovation strategy.33 However, most studies mix or combine innovativeness from a market and from a firm perspective.34 In line with DANNEELS and KLEINSCHMIDT, this study investigates the two perspectives of newness separately,35 because they can yield different results.36 ƒDistance to core business: Distance to core business measures how close or distant a firm’s new products are positioned from the core business. Thereby, innovation from the firm perspective is termed distance to core business. The chosen name conveys the conviction that a product new to a firm is not necessarily new to the marketplace, the ultimate arbiter of innovativeness.37 ƒDriver of innovation captures the driving forces behind a firm’s innovations, e.g. market orientation, technology orientation and

30

For the analysis see chapter II-1.2.2 and Table II-3 and II-1.2.3 and Table II-4.

31

See e.g. DYER/SONG (1998), p.508 and MARKHAM/GRIFFIN (1998), p.442.

32

See MILES/SNOW (1978), p.28ff.

33

For the underlying meta-analysis see chapter II-1.2.3 and Table II-4.

34

See DANNEELS/KLEINSCHMIDT (2001), p.359.

35

See DANNEELS/KLEINSCHMIDT (2001), p.357ff and GARCIA/CALANTONE (2002), p. 112f.

36

See DANNEELS/KLEINSCHMIDT (2001), p.357 and SCHLAAK (1999), p.107ff.

37

For a similar view, see e.g. SAWHNEY/WOLCOTT/ARRONIZ (2006), p.76.

Objectives of Study

7

competitive response. Driver of innovation is the third most researched dimension as part of innovation strategy.38 However, most often only one or two drivers are investigated, not three like in this research.39 ƒInnovation field orientation: The fourth dimension, innovation field orientation, is a new phenomenon in innovation research, prevailing in practice.40 Innovation field orientation describes whether a firm focuses its innovation activities along pre-defined fields. Innovation fields facilitate continuous streams of innovations, capable to close the growth gaps many firms face.41 This study is the first one to conceptualize and empirically assess it as a multi-dimensional construct. Summarizing, innovation strategy is conceptualized through three established and one new dimension. Characterizing innovation strategies is important on its own, but decision makers will immediately ask for the most successful innovation strategy.42 Prior research on the performance effect of innovation strategy is limited and little up-to-date. Only six studies could be identified with the most recent one dating back to 1998.43 The majority found a positive relation between innovation strategy and performance; however, these results need to be looked at carefully, because the underlying conceptualizations of in-

38

See chapter II-1.2.3 and Table II-4. The second most researched dimension, timing of market entry, is not included in this research, because there is an extensive, separate literature stream on it.

39

GATIGNON/XUEREB (1997), p.78, and ETTLIE/SUBRAMANIAM (2004), p.99, constitute exceptions. They also investigated three drivers of investigation, as done here.

40

See JONASH/SOMMERLATTE (1999), p.26, and LAURIE/DOZ/SHEER (2006), p.82ff. Note that given innovation field orientation is a new phenomenon the names used to describe it still vary. LAURIE/DOZ and SHEER, for example, term innovation fields new growth platforms.

41

See LAURIE/DOZ/SHEER (2006), p.90. Also, both JONASH/SOMMERLATTE (1999) and LAURIE/DOZ/SHEER (2006) show via case studies that innovation field orientation can positively influence performance.

42

For the importance of success factor research in economics as well as a critical discussion, see e.g. ALBERS/HILDEBRANDT (2005).

43

See chapter II-2.2.1 and Table II-5.

8

Introduction

novation strategy vary significantly.44 This study contributes to the scarce and relatively old literature on the relationship between innovation strategy and firm-level performance by providing a new, robust empirical assessment, based on a quantitiative study of 122 firms. The performance effect is measured using two indicators, innovation and firm performance.45 Firm performance is measured using capital market data. Thereby the study responds to calls for more research in marketing and innovation which uses performance metrics directly related to shareholder value.46 It is conceivable that some innovation strategies are poised to be more successful than others.47 However, one might counter that this depends on the individual firm and its environment. The contingency view accounts for these contextual factors. It claims that a certain strategy is not per se better or worse than another, but that ‘it depends’.48 The contingency approach has become of central importance in business strategy research.49 Even though spreading in innovation research overall, only three studies on innovation strategy account for contingencies.50 Hence the third main contribution of this study is an extension of the contingency approach to the realm of innovation strategy and its relation with performance. Two contingencies are investigated: business strategy and environmental uncertainty, of a firm. Overall, the objective of this study is to answer the following three research questions:

44

See chapter II-1.2.2 and Table II-3.

45

From now on, the term firm-level performance is used, when both performance indicators, innovation performance and firm performance, are meant. Both are investigated at the aggregation level of the firm.

46

See e.g. VENKATRAMAN/RAMANUJAM (1986), p.801ff; DAY/FAHEY (1988), SRIVASTAVA/SHERVANI/FAHEY (1999), p.177; and LEHMANN (2004), p.73ff.

47

COOPER (1984, p.158f), for example, showed that his balanced strategy type outperforms all other types along almost all performance measures.

48

See e.g. ZEITHAMEL/VARADARAJAN/ZEITHAMEL (1988), p.37, MILES/SNOW (1978), p.251, and chapter II-3.

49

See e.g. HAMBRICK/LEI (1985), MILLER (1988) and VENKATRAMAN/PRESCOTT (1990).

50

See MILLER/FRIESEN (1982), COOPER (1984) and ZAHRA/COVIN (1993) and chapter II-3, Table II-11.

p.53ff;

Structure of Document

9

ƒWhich dimensions characterize innovation strategy? ƒTo what extent does a relationship exist between innovation strategy and firm-level performance?51 ƒHow do contingencies influence the relationship between innovation strategy and firm-level performance?

3

Structure of Document

This document is divided into four chapters. In chapter I the research problem and its relevance were explained. Moreover, it was demonstrated how this research fits into the existing literature and what its main contributions are. The key objectives were summarized in three, overall research questions. Chapter II lays out the theoretical basis of this research. Definitions are undertaken and the relevant literature is reviewed. First, the key concepts – innovation, strategy and innovation strategy – are defined. Moroever, innovation strategy is described as a four-dimensional construct. Subsequently, prior research is discussed for innovation strategy, its performance effect as well as the contingencies. Chapter III provides the conceptual framework and research hypotheses. For the research hypotheses, chapter III reverts to chapter II in terms of prior empirical evidence. In addition, conceptual arguments are discussed – where possible deduced from underlying, economic theories. The hypotheses are derived by balancing prior empirical evidence and conceptual arguments. Chapter III ends with an overview of all research hypotheses. Chapter IV contains the empirical assessment of the research hypotheses. The chapter starts with an introduction to the overall research design. Subsequently, sample and data are explained. Before presenting the results, the operationalization of the different constructs is demonstrated. Then, descriptive statistics are stated characterizing the sample. Outcomes of the regression analyses follow for the different performance and

51

Firm-level performance stands for the two performance indicators, innovation and firm performance, both measured at the firm level.

10

Introduction

contingency effects. The chapter closes with an overview of all results and a short excursus on the empirical insights for the new phenomenon, innovation field orientation. As part of chapter V, the results are discussed against the background of the existing literature. Moreover, implications for research and practice are stated and limitations are made explicit. The chapter ends with suggestions for future research. The overall structure of the document is outlined in Figure I-1. Figure I-1: Structure of Document Chapter

I II III IV V

Name

Selective Content

INTRODUCTION

LITERATURE REVIEW

RESEARCH HYPOTHESES

EMPIRICAL STUDY

DISCUSSION & IMPLICATIONS

• Research problem and relevance • Objectives • Structure of document

• Conceptualization of innovation strategy • Detailed literature review for all concepts, relationships and contingencies

• Conceptual framework • Underlying economic theories • Research hypotheses: performance effects and contingencies • • • •

Research design Operationalization Test of hypotheses: results Excursus: Innovation field orientation

• Discussion • Implications • Limitations & furture research

Source: Own illustration

Chapter II

Literature Review

Chapter II introduces the central concepts of this study: innovation strategy and its relationship with firm performance. The chapter starts with definitions for strategy, innovation and innovation strategy. Moreover, exisiting innovation strategy typologies are reviewed and the conceptualization of innovation strategy for this study is derived. Subsequently prior studies on the innovation strategy – performance relationship are discussed. Last, the investigated contingencies are introduced together with the relevant literature. Overall this chapter provides the theoretical foundation for the conceptual framework (chapter III) and the empirical study (chapter IV).

1

Innovation Strategy

1.1

Definitions

1.1.1 Definition of Strategy Strategy has become a buzz word of modern management. The origins of the term lie in Greek (strategós) where strategy stands for the art of leading an army.52 Game theoresits, NEUMANN and MORGENSTERN,53 introduced the term to business studies.54 Numerous definitions emerged since then. Table IV-1 gives an overview of selected definitions by renowned strategy researchers.

52

See e.g. Der Brockhaus (2004), http://en.wikipedia.org/wiki/Strategy.

53

See NEUMANN/MORGENSTERN (1953).

54

See e.g. VAHS/BURMESTER (2002), p.97.

12

Literature Review

Table II-1: Definitions of Strategy Author NEUMANN / MORGENSTERN (1953) CHANDLER (1962) ANSOFF (1965)

Definition of Strategy

z

Sequence of individual decisions geared towards a certain goal

z

Determination of long-term goals and objectives, the adoption of courses of action and the allocation of resources necessary for reaching these goals

z

Measures to secure long-term firm success

z

In Mintzberg’s view, and in ours, strategy is more of a pattern or stream of major and minor decisions about an organization’s possible future domains. Further these decisions take on meaning only as they are implemented… in other words, an organization’s strategy can best be inferred from its behavior

z

Competitive strategy is a combination of the ends (goals) for which the firm is striving and the means (policies) by which it is seeking to get there (1980) Competitive strategy is about deliberately choosing a different set of activities to deliver a unique mix of value (1996)

MILES / SNOW (1978)

PORTER (1980 & 1996)

z

Source: Own illustration

The key differences between these strategy definitions are briefly discussed in order to derive an understanding of strategy for this study: ƒ‘Means’ versus ‘means and ends’: Some authors prefer to separate strategy (means) from goals (ends) whereas others take a comprehensive approach encompassing goals and means.55 Whereas NEUMANN/MORGENSTERN, ANSOFF and MILES/SNOW purely consider means, CHANDLER explicitly states the inclusion of ends. PORTER’S view is less clear. In his seminal book, Competitive Strategy, he views strategy as means and ends, whereas later statements imply a definition more oriented towards means. This study separates strategy from goals following VENKATRAMAN’S suggestion.56 ƒHierarchical level: Three hierarchical levels of strategy are typically distinguished: corporate, business and functional.57 Corporate strategy encompasses the entire firm and focuses on the question in which set of businesses a firm is engaged in. Business strategy relates to the business unit or division level and addresses the

55

For a more detailed discussion see VENKATRAMAN (1989), p.945ff.

56

See VENKATRAMAN (1989), p.946. VENKATRAMAN recommends to measure strategy without including firm goals or ends.

57

See VENKATRAMAN (1989), p.946f.

Innovation Strategy

13

question of how a firm competes. PORTER’S work on competitive strategy falls into this category.58 Functional strategy focuses on the maximization of resource productivity within specified functions, e.g. production, marketing, finance. This study adopts a business level perspective. However, given the focus of the empirical study is on single and dominant business firms,59 the distinction between corporate and business strategy is blurred. ƒIntentions versus realization: MINTZBERG distinguishes between intended

and

realized

strategies.60

MINTZBERG,

MILES/SNOW

and

VENKATRAMAN strongly recommend studying realized strategies by observing firms’ behaviour, as done in this research. DEFINITION: In a business context, strategy is defined as a stream or pattern of major decisions to achieve a firm’s long-term goals. 1.1.2 Definition of Innovation SCHUMPETER (1984) is widely acknowledged as the founder of research in innovation. According to SCHUMPETER, innovation is the implementation of new factor combinations (e.g. new good, new production method).61 All factor combinations are eventually replaced by new ones which lead to significant improvements. Numerous definitions for innovation emerged since then.62 This study builds on HAUSCHILDT’s understanding of innovation: Inovations are qualitatively new products or processes, which distinguish themselves significantly from previous ones.63 This definition implies different dimensions of innovation:

58

See PORTER (1980).

59

See RUMELT (1974), p.11, for definitions of single and dominant business firms, and chapter IV-2.1 for details about the sample frame.

60

See e.g. MINTZBERG (1978).

61

See SCHUMPETER (1934), p.100ff, and HAUSCHILDT (2004), p.8.New factor combinations encompass five cases: new goods or quality of goods, new production methods, new markets, new resources, new organizations, e.g. in the sense of creating or breaking a monopol position.

62

For an extensive overview and discussion of definitions for innovation see HAUSCHILDT (2004), p.3ff.

63

See HAUSCHILDT (2004), p.7.

14

Literature Review

ƒContent dimension:

What is new? And how new?

ƒSubjective dimension: New to whom? ƒProcedural dimension: Where does innovation start and end? ƒNormative dimension: Does new equal success? The content and subjective dimensions are particularly important for this study. Content dimension: What is new and how new? Product and process innovations are typically distinguished as the two classical types of innovations (what is new?).64 Product innovations are new or improved products offering new benefits which allow the user to serve new needs or old needs in a new way. Thereby innovations need to penetrate the market. This aspect clearly distinguishes innovation from invention. Innovation equals invention plus exploitation, which implies that innovations create some form of economic value.65 Process innovations are most often limited to the realm of a firm and thereby improve the efficiency of some process or method. Product and process innovations can happen independently or at the same time. Particularly radically new products often involve a combination of product and process innovations, e.g. a radical product innovation requiring a new production method.66 This study focuses on product innovations. The second aspect of the content dimension (how new?) addresses the degree of innovativeness. Innovativeness is generally defined along a continuum from incremental to radical.67 A new generation of diesel engines, for example, represents an incremental innovation. A hydrogen based engine, which incurs changes in the entire automobile industry, would be categorized as a radical innovation. Object of this investigation is a firm’s entire innovation portfolio, encompassing all innovation projects of a firm.

64

See HAUSCHILDT (2004), p.8.

65

See HAUSCHILDT (2004), p.5, and the further literature references mentioned there.

66

For a more detailed analysis and discussion see HAUSCHILDT (2004), p.11f.

67

See e.g. GARCIA/CALANTONE (2002), p.121

Innovation Strategy

15

Given firms tend to pursue projects with different levels of innovativeness at the same time,68 incremental and radical new products are considered. Subjective dimension: New to whom? Two levels of addressees are distinguished: the firm and the market.69 From a firm perspective a product is innovative if it is new to the firm and was not produced or sold by this firm before.70 From a market perspective a product is innovative if it offers new value adding benefits to customers (e.g. new feature, better ease of use), not provided by alternative products.71 It is important to highlight that one perspective does not necessarily imply the other. If a product is new to a firm it is not necessarily new to the market. However, a product new to the market is always new to the firm which introduces it. The two perspectives are often combined or mixed

in

innovation

research.72

This

study

follows

DANNEELS

and

KLEINSCHMIDT’S call to investigate the two perspectives of innovation separately.73 The market perspective is called innovativeness, paying tribute to the fact that the market is the ultimate arbiter for innovativeness.74 Newness to the firm is termed distance to core business, to clearly distinguish it from the market perspective and to indicate that newness to the firm does not necessarily imply innovativeness in the marketplace. DEFINITION: Product innovations are qualitatively new products, which distinguish themselves significantly from previous ones. Thereby the market, that is to say customers, makes this judgement.

68

See CRAWFORD (1980), p.8, and COOPER (2005).

69

See e.g. DANNEELS/KLEINSCHMIDT (2001), p.357ff; HAUSCHILDT (2004), p.24

70

See DANNEELS/KLEINSCHMIDT (2001), p.360f.

71

See DANNEELS/KLEINSCHMIDT (2001), p.360.

72

See DANNEELS/KLEINSCHMIDT (2001), p.358/59 for a detailed analysis.

73

See DANNEELS/KLEINSCHMIDT (2001), p.370/71.

74

For a similar view, see e.g. SAWHNEY/WOLCOTT/ARRONIZ (2006), p.76.

16

Literature Review

1.1.3 Definition of Innovation Strategy Definitions for innovation strategy are rare. Most often the term is used without prior definition.75 Table II-2 shows the few identified definitions for innovation and new product strategy. Given this research focuses on product innovations, the two terms are used interchangeably. GILBERT’S and HAUSCHILDT’S definitions start with the fundamental question whether a firm wants to innovate at all. If this question is answered positively a firm can move on to the question how it intends to innovate, that is to say by which means. GILBERT, HAUSCHILDT and VAHS’ definition include some detail on means (e.g. degree of innovativeness, stand-alone innovation or in cooperation). In addition to means, VAHS also includes innovation goals (ends) in his definition. In contrast to the other authors, FIRTH and NARAYANAN focus their definition on product innovations and take a purely results oriented perspective. For them a firm’s innovation strategy is fully reflected by the new products introduced over time. Table II-2: Definitions of Innovation Strategy Author

Definition of Innovation Strategy

z

Innovation strategy determines to what degree and in what way a firm attempts to use innovation to execute its business strategy

FIRTH/ NARAYANAN (1996)

z

We focus our analysis on realized strategies at the firm level, and hence define a firm’s new product strategy as the aggregate pattern of product introductions that emerge from the firm over time

DYER/SONG (1998)

z

Innovation strategy, defined as the new product and market development plans of the firm…

z

Innovation strategy contains the strategic goals and activities for the product and process innovations aimed at. [Subsequently Vahs discusses:] – Innovation strategy as functional or meta strategy – Innovation strategy as market entry or timing strategy.

z

Innovation strategies [formulated via questions]: – Aim at own innovations at all? Imitate or not innovate at all? – Innovation on own or in cooperation with others? – Innovation as permanent task or one off project(s)? – Innovation as core task of the firm?

GILBERT (1994)

VAHS (2002)

HAUSCHILDT (2004)

Source: Own illustration

75

See e.g. MILLER/FRIESEN (1982), p.1; GRIFFIN/PAGE (1996), p.478; and ANTHONY/EYRING/ GIBSON (2006), p.104.

Innovation Strategy

17

Summarizing, no consistent definition or understanding of innovation strategy exists. The observation is reinforced when looking at innovation strategy typologies and dimensions, in the following chapter (II-1.2). Innovation Strategy vs. Firm and Functional Strategies Defining something also means delineating it from something else. Figure II-1 shows how innovation strategy fits between other strategies of a firm, be it firm wide or functional ones. There is agreement in the literature that innovation goes beyond research & development (R&D) and that a more holistic and firm-wide approach to innovation is beneficial.76 Hence innovation strategy is viewed as a firm wide policy – crossing functional areas. Such a meta-perspective allows tapping into the knowledge of other functional areas for innovation and contributes to realize synergies between functions.77 Figure II-1: Innovation Strategy vs. Firm and Functional Strategies

Firm Strategy originally drives

to continuously align

Finance

Human Resources

Production

Sales

Marketing

Research & Development (R&D)

Innovation Strategy

Source: Own illustration following VAHS/BURMESTER (2002), p.107.

76

See e.g. VAHS/BURMESTER (2002), p.107f, and HAUSCHILDT (2004), p.101ff.

77

See VAHS/BURMESTER (2002), p.108.

18

Literature Review

Ideally, the innovation strategy of a firm should originally be derived from the overall firm strategy78 and the two should subsequently be aligned on an on-going basis.79 Depending on the output and success, a firm’s innovation activities can also change the overall firm strategy driven by socalled emergent strategies.80 Moreover, innovation and functional strategies (e.g. marketing, production) should be continuously aligned. Building on these remarks and in line with the general strategy definition earlier,81 the following understanding of innovation strategy was chosen: DEFINITION: Innovation strategy is defined as the sum of strategic choices a firm makes regarding its innovation activity. Innovation goals (ends) are not included - only means. Innovation strategy is considered a firm-wide, cross-functional meta-strategy. 1.2

Innovation Strategy Typologies and Dimensions

1.2.1 Methodical considerations According to VENKATRAMAN, there are three approaches to research and measure strategy:82 ƒNarrative approach: The narrative approach has its origin in the case-based tradition of business research and consists of holistic, verbal descriptions of individual strategies. While this approach is useful for conceptual developments it has its limits for testing theories. ƒClassificatory approach: The classificatory approach categorizes strategies in different types. Prominent examples include the MILES and SNOW typology (prospecter, analyzer, defender, and reactor) and PORTER’S three strategies (differentiation, cost leadership, and

78

See e.g. MILLER/FRIESEN (1982), p.17.

79

See e.g. MAIDIQUE/PATCH (1982), p.285.

80

Emergent strategies are unintended, intuitive strategies which have not been planned. For details see MINTZBERG (1995), p.30ff.

81

See chapter II-1.1.1.

82

Also for the following, see VENKATRAMAN (1989), p.943-944.

Innovation Strategy

19

focus).83 Typologies are best known for their conceptual elegance; however, the arbitrariness with which the underlying classificatory dimensions are chosen is an inherent weakness. Also the classificatory approach does not reflect within-group differences along the underlying dimensions. ƒComparative approach: The comparative approach measures and describes strategies along key dimensions. Collectively, these dimensions describe the strategy construct. MILLER/FRIESEN, DESS/ DAVIS and HAMBRICK are renowned researchers who applied this approach.84 The advantage of the comparative approach lies in its ability to decompose strategy variations into more fine-grained differences along the underlying dimensions. Research on innovation strategy either follows the comparative approach85 or a combination of the comparative and classificatory approach86 – further illustrated in the next chapter. This research follows VENKATRAMAN’S recommendation and applies the comparative approach. 1.2.2 Innovation Strategy Typologies and Dimensions The purpose of this section is to give an overview of the research on innovation strategy until today. The review also serves as a basis to derive the dimensions characterizing innovation strategy in this study. Table II-3 includes all research - conceptual or empirical - in which innovation strategy plays an integral part. Especially up to the 1980s the term innovation strategy was rarely used. Therefore, the table also includes studies wherthe object of investigation was termed differently at the time, but which are nowadays considered part of innovation strategy research.87

83

See MILES/SNOW (1978) and PORTER (1980).

84

See MILLER/FRIESEN (1984), DESS/DAVIS (1980) and HAMBRICK (1984).

85

See e.g. MILLER/FRIESEN (1982), ZAHRA/COVIN (1993), VEUGELERS/CASSIMAN (1999), and ETTLIE/SUBRAMANIAM (2004).

86

See e.g. COOPER (1984), FIRTH/NARAYANAN (1996), and MOTOHASHI (1998).

87

For example, ANSOFF and STEWART’S marketing strategy typology (1967), or ZAHRA and COVIN’S (1993) technology policy typology.

20

Literature Review

Table II-3: Overview of Research on Innovation Strategy Author

ANSOFF/ STEWART (1967)

Denotation by Author

Typology 4 Types: First-to-Market z Follow-the-Leader z Application Engineering z Me-Too z

Marketing strategy

MILES/SNOW (1978)

Types of organizational adaptation

CRAWFORD (1980)

Product innovation charter

4 Types: z Prospector z Analyzer z Defender z Reactor

Underlying Dimensions – – –

Firm

Conceptual

Firm

Conceptual

Target business arenas Driver of innovation Sources (internal / external) Innovativeness

Firm

Empirical

Timing of market entry Sources (internal / external) Investment level Technology selection/specialization Level of competence Organization

Firm

Conceptual

15 variables, innovation specific: Driver of innovation – Innovativeness – Risk taking

Firm

Empirical

19 dimensions – 4 blocks: – Nature of products developed, e.g. innovativeness – Nature of markets targeted, e.g. distance to core market – Nature of product and production technology employed, e.g. distance to core technology – Orientation and nature of the process, e.g. driver of innovation

Firm

Empirical

Innovation goals Innovativeness / risk Timing of market entry

Firm

Empirical

Production method Product sophistication Rate of innovation (in industry)

Firm

Empirical

Timing of market entry Innovativeness Type of innovation (product, process)

Firm

Empirical

Innovativeness Timing of market entry

Firm

Conceptual

–

–

Timing of market entry

Firm

Empirical

– – –

Innovation specific: Innovation posture (e.g. aggressive, cautious)

–

– – – –

MAIDIQUE/ PATCH (1982)

4 Types: First-to-Market z Second-to-Market z Late-to-Market or Cost Minimization z Market Segmentation z

Technology policy

– – – –

– –

Models of product innovation

2 Types: z Conservative model z Entrepreneurial model

COOPER (1984)

New product strategy

5 Types: z Technologically driven z Balanced strategy z Defensive, focused, technologically deficient z Low-budget, conservative z High-budget diverse

RAMANUJAM/ MENSCH (1985)

Major strategic choices regarding innovation

MILLER/ FRIESEN (1982)

MILLER (1988)

Technologic types

ZAHRA/COVIN (1993)

Technology policy

GILBERT (1994)

Innovation strategy

BARCZAK

New product strategy

–

– – –

6 Types: z Established batch z Innovative batch z Flexible line z Fixed line z Unaltered process z Modified process

– – –

–

(1995)

– –

2 Types: z Proactive z Reactive

4 Ansoff/Stewart types

Timing of market entry Research vs. development mix Cross-functional cooperation Product life cycle Investment level Proximity to state of the art

Aggregation Conceptual/ Level Empirical

–

Innovation Strategy

21

5 Types: z The innovators z Investors in technology z Searching for new markets z Business as usual z Middle-of-the-road

FIRTH/ NARAYANAN (1996)

New product strategy

GRIFFIN/ PAGE (1996)

Project strategy

MUFFATTO/ PANIZZOLLO (1996)

Innovation strategy

DYER/SONG (1998)

Innovation strategy

4 Miles & Snow types

MARKHAM/ GRIFFIN (1998)

Innovation strategy

4 Miles & Snow types

MOTOHASHI (1998)

Innovation strategy

5 Types: z High-tech and inhouse R&D z High-tech, open R&D z Technology dependent z Intermediary z Traditional industry

MEYERKRAHMER/ REGER (1999)

Innovation strategy

VEUGELERS/ CASSIMAN (1999)

Innovation strategy

ETTLIE/ SUBRAMANIAM (2004)

New product development strategies and tactics

HAUSCHILDT (2004)

Innovation strategies

Firm - based on products

Empirical

Product

Empirical

Firm

Empirical

Firm

Empirical

Firm

Empirical

Sources (external/internal) Investment level

Firm

Empirical

Internationalization Investment level Innovativeness

Firm

Empirical

–

–

Sources (external/internal)

Firm

Empirical

–

Driver of innovation Innovativeness Distance to core business / skills

Product

Empirical (case studies)

Sources (external/internal)

Firm

Conceptual

–

Innovativeness

–

Innovativeness Distance to core business

–

– – –

Measured by items – innovation specific: – Innovativeness – Driver of innovation

– –

– –

– –

2 Types: z Outsourcing of innovation z Innovating in-house

Innovativeness Product design Timing of market entry

–

Source: Own illustration

For each study, typologies are given, where existent, together with the underlying dimensions. Not all dimensions in Table II-3 are stated with their original name. Very similar dimensions were changed into the same name to facilitate comparison between studies. However, this does not mean that dimensions with the same name were conceptualized and/or measured in the same way. Construct definitions and measures vary significantly across innovation strategy studies. For ANSOFF/STEWART and

22

Literature Review

MAIDIQUE/PATCH the ‘timing of market entry’ dimension is highlighted, because their typologies are predominantly based on this one dimension.88 The other dimensions are used to describe the resultant types. COOPER investigated a total of 19 dimensions, aggregated into four blocks.89 For reasons of scope, not all 19 dimensions are listed in the table but only selected ones most relevant to this study. Moreover, the aggregation level is stated for each study, which is to say whether the research was undertaken on the firm or product/project level. In the last column, studies are classified into conceptual or empirical. The studies are ordered chronologically. Overall the research on innovation strategy is limited and little up-todate. A total of 16 empirical and 5 conceptual studies could be identified where innovation strategy plays an important role - be it as an independent variable or a moderator. In contrast, there are over 40 empirical studies on the the degree of innovativeness, alone.90 Moreover, the majority of research on innovation strategy dates back to the 1980s and 1990s.91 Most studies focus on product innovation; only few consider product and process innovations (e.g. MILLER and ZAHRA/COVIN). With regard to innovation strategy typologies some were conceptually developed, but the majority is based on empirical investigation. Moreover, the typologies are heterogeneous and little established - with two exceptions. First, the typology by ANSOFF and STEWART (first-to-market, follow-the-leader, etc.) is widely known, accepted and examined.92 Second, COOPER’S innovation strategy typology (technologically driven, balanced strategy, etc.) is also fairly well known.93 In contrast to ANSOFF and STEWART’S typology, COOPER’S is based on numerous dimensions and was empirically derived. Hence it is less memorable - like the majority of empirically derived typologies.94 The

88

See ANSOFF/STEWART (1967) and MAIDIQUE/PATCH (1982).

89

See COOPER (1984).

90

See HAUSCHILDT/SALOMO (2005), p.5.

91

See ETTLIE/SUBRAMANIAM (2004).

92

See BARCZAK (1995), p.226, for a similar assessment and further literature review on this typology.

93

See COOPER (1984).

94

See VENKATRAMAN (1989, p.943.

Innovation Strategy

23

MILES and SNOW typology is also used to investigate innovation strategy (e.g. DYER/SONG, MARKHAM/GRIFFIN). Even though the four MILES and SNOW types stand for different attitudes towards innovation,95 this typology was originally intended to characterize overall organizational and firm strategy types96 and continues to be used and examined in this sense.97 Hence its use to research innovation strategy is questionable, because it is not specific enough. Concluding, the literature review on innovation strategy reveals a lack of consensus concerning theoretical concepts, construct definition and measurement. Moreover, little up-to-date research exists which also considers new developments in innovation research or practice. This study taps into this research gap by suggesting a comprehensive, multi-dimensional concept for innovation strategy along four dimensions – which are derived in the following chapter. 1.2.3 Innovation Strategy Dimensions of This Study To choose the innovation strategy dimensions investigated in this study, the following criteria were applied: ƒAction oriented dimensions: For each dimension it should be possible to associate concrete actions of a firm undertaken in the realm of innovation. This means that more abstract dimensions - e.g. futurity, aggressiveness - are not considered.98 Theses dimensions or traits might provide a more holistic perspective,99 however, action oriented dimensions can be more easily and validly measured by asking or observing concrete actions. ƒAdoption of established dimensions: Established dimensions in innovation strategy research should be considered, if this research promises to add new insights. The ‘timing of market entry’ dimen-

95

See e.g. DYER/SONG (1998), p.508.

96

See MILES/SNOW (1978), p.28ff.

97

See e.g. SLATER/OLSON (2000) or DESARBO/DIBENEDETTO/SONG/SINHA (2005) for recent studies.

98

See e.g. VENKATRAMAN (1989), p.948f.

99

See e.g. VENKATRAMAN (1989), p.948f and ZAHRA/COVIN (1993), p.456.

24

Literature Review

sion of ANSOFF and STEWART’s typology (first-to-market, follow-theleader, etc.), for example, has been extensively researched on its own.100 Investigating it again and in a broader setting of several dimensions does not promise to add significant new insight. ƒNew aspects and dimensions: New trends in innovation research or practice should also be reflected. This does not only mean adding entirely new dimensions, but also amending and extending existing dimensions. ƒMeaningful at firm level: The aggregation level of this research is the firm. With regard to innovation, this means that the entire innovation project portfolio is considered, not only single projects. Only dimensions are included for which a portfolio perspective makes sense and promises to yield differentiated results. ƒLimited scope: The more dimensions included, the less profound insights tend to be. Moreover, questionnaires – one research approach of this study - need to stay within a certain scope in order to motivate firm representatives to participate. Hence the maximum number of innovation strategy dimensions was set to three to four.101 As a starting base the innovation strategy dimensions researched to date were analyzed. Table II-4 shows which dimensions were investigated at which frequency in the context of innovation strategy. The information is based on the content presented in Table II-2. For this analysis all of COOPER'S 19 underlying dimensions were considered, not only the selected ones listed in Table II- 2.

100

See e.g. BARCZAK (1995), p.226.

101

Similar studies also limited the dimensions investigated, e.g. ZAHRA/COVIN (1993): 3 dimensions; ETTLIE/SUBRAMANIAM (2004): 3 dimensions.

Innovation Strategy

25

Table II-4: Overview of Innovation Strategy Dimensions No.

Dimension

#

Authors

Comment

Numerous

• Many studies combine innovativeness for the firm and for the market within this dimension • Includes ANSOFF’S ‘proximity- to state-of-the-art’ dimension

ANSOFF/STEWART (1967), MAIDIQUE/PATCH (1982), RAMANUJAM/MENSCH (1985), ZAHRA/COVIN (1993), GILBERT (1994), BARCZAK (1995), etc.

• Does not include all studies – separate stream of literature, see e.g. BARCZAK (1995)

6

CRAWFORD (1980), MILLER/FRIESEN (1982), COOPER (1984), GATIGNON/XUEREB (1997), DYER/SONG (1998), ETTLIE/SUBRAMANIAM (2004)

• Called strategic orientation by GATIGNON/XUEREB (1997) • New: pro-active vs. re-active market orientation (2004/5: by NARVER, SLATER, etc.)

Sources of innovation (external, internal)

5

CRAWFORD (1980), MAIDIQUE/PATCH (1982), MOTOHASHI (1998), VEUGELERS/CASSIMAN (1999), HAUSCHILDT (2004)

• Studied as single dimension by VEUGELERS/CASSIMAN • Large, stock-market listed firms expected to do both

5

Investment level

5

ANSOFF/STEWART (1967), MAIDIQUE/PATCH (1982), COOPER (1984), MOTOHASHI (1998), MEYER-KRAHMER/REGER (1999)

• Included as control variable

6

Distance to core business

4+

MAIDIQUE/PATCH (1982), COOPER (1984), GRIFFIN/PAGE (1996), ETTLIE/SUBRAMANIAM (2004)

• Often included in innovativeness

7

Innovation posture (aggressive, cautious, etc.)

2

MILES/SNOW (1978), COOPER (1984)

8

Target business arenas / program focus

2

CRAWFORD (1980), COOPER (1984)

9

Risk taking

2

MILLER/FRIESEN (1982), RAMANUJAM/MENSCH (1985)

10

Research vs. development mix

1

ANSOFF/STEWART (1967)

11

Cross-functional cooperation

1

ANSOFF/STEWART (1967)

12

Product life cycle

1

ANSOFF/STEWART (1967)

13

Technology selection / specialization

1

MAIDIQUE/PATCH (1982)

14

Innovation goals

1

RAMANUJAM/MENSCH (1985)

15

Production method

1

MILLER (1988)

16

Production sophistication

1

MILLER (1988)

1

Innovativeness

12

2

Timing of market entry

7+ *

3

Driver of innovation

4

• E.g. productivity or efficiency increase, cost reduction, customer value

26

Literature Review

17

Type of innovation (product, process)

1

ZAHRA/COVIN (1993)

18

Product design

1

MUFFATTO/PANIZZOLLO (1996)

19

Internationalization

1

MEYER-KRAHMER/REGER (1999)

20

Export orientation

1

COOPER (1984)

21

Premium priced products

1

COOPER (1984)

22

Product customness

1

COOPER (1984)

23

Market potential, size and growth

1

COOPER (1984)

24

Market competitiveness

1

COOPER (1984)

25

Competitive dominance

1

COOPER (1984)

• Rather firm strategy dimensions, e.g. similar to items for PORTER’s firm strategy types, see e.g. DESS/DAVIS (1984)

• Rather contingency variables

# = frequency with which dimension was researched as part of innovation strategy *) Frequency higher, given separate stream of literature exists

Source: Own illustration

25 different dimensions characterizing innovation strategy were identified in the 21 research pieces included in Table II-4. This confirms again the lack of consensus concerning research in innovation strategy. Two dimensions stand out which were investigated in numerous studies: innovativeness and timing of market entry. Innovativeness is included in this research. It characterizes how innovative firms’ new products are to the market. The timing of market entry dimension was left out of this study given it has been extensively researched on its own and there is a separate stream of literature on it.102 Hence, this study was not expected to yield new insights for this dimension. Another four dimensions are fairly established, having been researched in multiple studies: driver of innovation, sources of innovation, investment

102

See e.g. BARCZAK (1995), p.226, for an initial literature overview.

Innovation Strategy

27

level and distance to core business. Driver of innovation encompasses different forces behind firms’ innovation activities; market orientation, technology orientation and competitive response represent the most prominent drivers. Driver of innovation is included as a second dimension in this study. On the one hand more than one or two drivers have rarely been investigated together;103 on the other hand the distinction of different types of market orientation (proactive vs. reactive market orientation) is recent.104 Even though more relevant to innovation than reactive market orientation, proactive market orientation has not yet been researched as an element of innovation strategy and only to a limited extent in innovation research overall.105 Sources of innovation addresses whether innovations originate internally and/or externally. VEUGELERS and CASSIMAN’S research focuses solely on this aspect and they found that larger firms tend to combine make and buy strategies.106 This research investigates stock market firms107 which are, by nature, larger in size. Moreover, large companies already communicate publicly that they source innovations internally as well as externally.108 Hence, including this dimension did not promise to add further insights to what VEUGELERS and CASSIMAN had already found out and was left out of this research. Investment level, that means R&D rate, is included as a control variable, however not as a dimension of innovation strategy. As illustrated in the introduction chapter, the underlying assumption of this research is that it is less important how much a firm spends on innovation, but rather on what and how it spends it. However, given the on-going debate about its importance and the fact that several studies show a positive correlation with importantce, its consideration as control variable is self-evident. Distance to core business is also termed innovativeness to the firm.109 Innovativeness to the market-

103

See GATIGNON/XUEREB (1997), p.78; ETTLIE/SUBRAMANIAM (2004), p.101; AUH/MENGUE (2005), p.337.

104

See ATUAHENE-GIMA/SLATER/OLSON (2005), p.464ff and NARVER/SLATER/MACLACHLAN (2004), p.334ff.

105

See ATUAHENE-GIMA/SLATER/OLSON (2005) and NARVER/SLATER/MACLACHLAN (2004).

106

See VEUGELERS/CASSIMAN (1999).

107

For details on the sample frame see chapter IV-2.1.

108

See e.g. INDUSTRY WEEK (2004), p.30f and www.pgconnectdevelop.com.

109

As to the choice of the name distance to core business, see chapter I-2.

28

Literature Review

place and to the firm are often mixed or combined.110 By separating the two

perspectives

of

newness,

this

study

follows

DANNEELS

and

KLEINSCHMIDT’S suggestion.111 A separation of the two different perspectives is recommendable, because they can yield different results.112 Moreover, as part of one and the same study, distance to core business was rarely conceptualized and measured separately beside innovativeness.113 Therefore, distance to core business is included as a dimension to characterize innovation strategy in this study. All other dimensions in Table II-4 were only investigated once or twice so far and are therefore not considered established dimensions of innovation strategy. Beside the above mentioned three well-established dimensions, a fourth, new dimension is investigated in this research, which is termed ‘innovation field orientation’. Innovation field orientation describes whether a firm focuses its innovation activities along predefined fields.114 This new dimension is included to characterize innovation strategy, because casestudy based evidence suggests that the approach is increasingly applied by firms and that it positively influences performance.115 Moreover, innovation field orientation has not yet been conceptualized and empirically investigated as part of a quantitative study. Summarizing innovation strategy is characterized along four dimensions, innovativeness, distance to core business, driver of innovation, and innovation field orientation. All four dimensions are studied at the firm level considering a firm’s entire new product portfolio. The four innovation strategy dimensions are described in more detail in the following chapters.

110

See DANNEELS/KLEINSCHMIDT (2001), p.357ff, and GARCIA/CALANTONE (2002), p.112.

111

See DANNEELS/KLEINSCHMIDT (2001).

112

See Schlaak 1999, p.107ff.

113

See DANNEELS/KLEINSCHMIDT (2001), p.359.

114

See LAURIE/DOZ/SHEER (2006), p.82ff. They call innovation fields: new growth platforms. Given the concept is new, no single name exists so far.

115

See JONASH/SOMMERLATTE (1999) and LAURIE/DOZ/SHEER (2006).

Innovation Strategy

1.2.3.1

29

Innovativeness

Innovativeness is typically defined as a measure of the degree of newness or discontinuity of a product.116 Highly or radically innovative products exhibit a high degree of newness and incrementally innovative products a low degree of newness. Incremental innovations are mainly modifications or improvement of existing products. Characteristics of radical innovations are, for example, that they fulfill so far unmet customer needs, offer entirely new performance features, improve known performance features dramatically or transform relevant markets.117 In line with the literature on innovativeness, the degree of newness is measured along two main sub-dimensions: market and technology.118 Market innovativeness addresses the degree of newness from a marketing perspective. It includes, for example, if a certain innovation addresses new customer benefits and if it changes established attitude and behaviour.119 Technology innovativeness is defined as the degree of newness from a technological perspective and investigates how new the applied technology is. Whether the innovation uses a different core technology and if it permits quantum leaps in performance are just two examples of aspects included in technology innovativeness.120 Moreover and in accordance with several recent publications121 this study considers the marketplace as the reference point for the level of innovativeness, not the firm. For this reason innovativeness is defined from the perspective of customers and relative to competitiors’ offers. The distinction of market vs. firm perspective is important, because the choice of perspective can yield different results.122 Moreover, a high level of innovativeness to a certain firm does not necessarily mean

116

See e.g. GARCIA/CALANTONE (2002), p.112, HAUSCHILDT (2004), p.3ff as well as chapter II-1.1.2.

117

See e.g. LEIFER ET AL., (2000) p.5.

118

See e.g. GARCIA/CALANTONE (2002), p.112f.

119

For the entire list of aspects included in market innovativeness, see IV-3.2.1.

120

For the entire list of aspects included in technology innovativeness, see IV-3.2.1.

121

See FIRTH/NARAYANAN (1996), p.338; LIEFER ET. AL (2000), p.6; SONG/MONTOYA-WEISS (2001), p.79; GATIGNON ET AL. (2002), p.1112; DANNEELS/SETHI (2005), p.8; SAWHNEY/WOCOTT/ARRONIZ (2006), p.77.

122

See DANNEELS/KLEINSCHMIDT (2001), p.357; SCHLAAK (1999), p.107ff.

30

Literature Review

that the product is highly innovative from a market perspective. This depends on the firm and competitors’ offerings. On the single product or project level, innovativeness has been highly researched.123 This study investigates innovativeness to the marketplace at the firm level by considering the innovativeness of a company’s entire new product portfolio - rarely done so far.124 DEFINITION: Innovativeness stands for the degree of products’ newness from the perspective of the marketplace.125 It is modeled along two main sub-dimensions: market innovativeness and technology innovativeness. Innovativeness is measured at the firm level, considering the entire new product portfolio of a firm. 1.2.3.2

Distance to Core Business

Distance to core business represents the degree of newness from a firm perspective. To clearly discriminate it from innovativeness to the marketplace, innovativeness to the firm is termed distance to core business. Distance to core business encompasses two aspects named ‚fit’ and ‚familiarity’ in DANNEELS and KLEINSCHMIDT’S seminal paper on the topic.126 ƒFit refers to the extent to which a firm’s resources fit with the requirements of an innovation.127 In other words, fit measures to what degree a firm can build on existing resources for a new product or whether it needs to develop or acquire new ones. Resources include tangible and intangible assets of a firm.128 Relevant resources in the context of new product development are, e.g. R&D

123

See GARCIA/CALANTONE (2002), p.113.

124

Even though several authors measure innovativeness as part of innovation strategy and at the firm level (see Table II-3), only few measure innovativeness to the market separately and at the firm level: COOPER (1984), p.152ff; FIRTH/NARAYANAN 1996, p.337. In another context and independent of innovation strategy, COOPER and DANNEELS/SETHI recently also researched innovativeness to the marketplace and at the firm level: COOPER ET AL. (2004), p.50ff; DANNEELS/SETHI (2005), p.22 and 53.

125

This means that the firm perspective is excluded from innovativeness and measured separately in this study under the dimension distance to core business.

126

See DANNEELS/KLEINSCHMIDT (2001), p.366.

127

See SALOMO (2003), p.5.

128

See WERNERFELDT (1984), p.172.

Innovation Strategy

31

expertise, market research skills, and production facilities.129 Fit was already measured as part of innovativeness from a firm perspective.130 Furthermore, fit is sometimes investigated as an independent aspect - then termed ‘synergy’ or ‘appropriateness of skills’.131 ƒFamiliarity refers to the relationship between an organization and its environment.132 Familiarity measures to what extent a firm is familiar with the market and customers targeted by an innovation. In other words, familiarity addresses the degree to which an innovation falls within the technology and market domains of an existing business or outside.133 The familiarity aspect has been researched as an aspect of innovativeness to the firm.134 For both sub-dimensions, fit and familiarity, technological and market factors are considered.135 At the individual project level, distance to core business was studied before even though not always separately from innovativeness (to the marketplace).136 This study investigates distance to core business at the firm level by considering a company’s entire new product portfolio.137 DEFINITION: Distance to core business addresses the degree of newness from a firm perspective. It measures how close/distant a firm’s product innovations are positioned from the core business. It considers innovations’ alignment with a firm’s current market and technology domains

129

See DANNEELS/KLEINSCHMIDT (2001), p.361.

130

See e.g. DANNEELS/KLEINSCHMIDT (2001), p.366; SALOMO (2003), p.14. Also see DANNEELS/KLEINSCHMIDT for a further literature review.

131

See e.g. COOPER/DEBRENTANI (1991), p.89; SOUDER/JENSSEN (1999), HENARD/SZYMANSKI (2001), p.364; SONG/MONTOYA-WEISS (2001), p.127.

132

See DANNEELS/KLEINSCHMIDT (2001), p.360.

133

See e.g. LEIFER ET AL. (2000), p.6f, and DANNEELS/KLEINSCHMIDT (2001), p.361.

134

See e.g. COOPER (1984), p.153; DANNEELS/KLEINSCHMIDT (2001), p.366 and 361 for an overview; GARCIA/CALANTONE (2002), p.118 and 126 for an overview.

135

See DANNEELS/KLEINSCHMIDT (2001), p.361 and GARCIA/CALANTONE (2002), p.112f.

136

See DANNEELS/KLEINSCHMIDT (2001), p. 359; SONG/MONTOYA-WEISS (2001), p.127; and GARCIA/CALANTONE (2002), p.113.

137

See e.g. COOPER (1984), p.153; FIRTH/NARAYANAN 1996, p.337f; and COOPER/EDGETT/ KLEINSCHMIDT (2004), p.53.

p.185f;

32

Literature Review

(familiarity) as well as their fit with a firm’s resources (fit). For both subdimensions, familiarity and fit, market and technological factors are included. Distance to core business is measured at the firm level considering the entire product innovation portfolio of a firm. 1.2.3.3

Driver of Innovation

Driver of innovation relates to the major forces behind innovation in a company, i.e. whether corporate innovating activity originates from a marketing or technology perspective or whether innovations are generated as a response to competitor activities.138 GATIGNON and XUEREB show that the role of a fim’s strategic orientation (market, technology or competitor) on new product development is essential for the performance of the firm.139 Two streams of literature address drivers of innovation. First, the technology push and demand pull (=TPDP) literature distinguishes between innovations being driven by scientists looking for new technologies and scientific breakthrough (technology push) and market oriented managers directing scientists and/or marketing people into what appear to be exciting markets with attractive demand (demand pull).140 Major studies were undertaken from the 1960s through 1980s to investigate the TPDP question.141 The results are inconclusive and differ with regard to research objectives and construct definitions.142 Moreover the applicability of these relatively old results to today’s situation is questionable, because circumstances were different at the time.143 The second stream of literature terms the different forces driving innovation ‘strategic orientation’.144 Three strategic orientations are typically distinguished: market or cus-

138

See e.g. GATIGNON/XUEREB (1997), p.77ff; NARVER/SLATER/MACLACHLAN (2004), p.334ff.

139

See e.g. GATIGNON/XUEREB (1997), p.88.

140

See e.g. BURGELMAN/SAYLES (1986), p.44 and HERSTATT/LETTL (2004), p.156.

141

See e.g. SCHMOOKLER (1963 and 1966), UTTERBACK (1974), MOWERY/ROSENBERG (1979), SCHERER (1982), BURGELMAN/SAYLES (1986).

142

For a silimar opinion and an extensive review of the technology-push demand-pull literature see e.g. CHIDAMBER/KON (1994) and HOWELLS (1997).

143

See CHIDAMBER/KON (1994), p.111. “The 1950s and 1960s […] were a period of heavy investment in R&D, so innovation activity in the 1970s may have been skewed towards market-oriented incremental innovations…”

144

See e.g. GATIGNON/XUEREB (1997).

Innovation Strategy

33

tomer orientation, technology orientation and competitive response.145 This stream of literature is more recent with conceptual papers and empirical studies starting in the early 1990s up to today. Earlier publications investigate the strategic orientation of the entire firm,146 whereas recent papers research strategic orientation specifically in the context of innovation.147 The conceptualization of strategic orientation is still under development. Technology orientation is at times called ‘innovative orientation’ containing additional aspects like timing.148 This study follows the second, more recent stream of literature and investigates the three innovation drivers: market orientation, technology orientation and competitive response. ƒMarket orientation refers to a firm’s ability to generate and disseminate market intelligence and to respond to it with appropriate products.149 NARVER, SLATER and MACLACHLAN (2004) recently enhanced the concept by distinguishing between reactive and proactive market orientation.150 Reactive market orientation tries to understand and serve customers’ expressed needs. Thus products driven by reactive market orientation can be more easily imitated.151 Proactive market orientation goes beyond customers’ expressed needs and addresses unarticulated, latent needs. To disclose latent needs alternative approaches are needed e.g. observing customers, working with lead users.152 Proactive market orientation is central to new product development, but has been little studied

145

See e.g. COOPER (1984), p. 153; GATIGNON/XUEREB (1997), p.78; ETTLIE/SUBRAMANIAN (2004), p.101; AUH/MENGUE (2005), p.340.

146

See e.g. NARVER/SLATER (1990), JAWORSKI/KOHLI (1990), and DESHPANDE/FARLEY (1998).

147

See e.g. GATIGNON/XUEREB (1997), BERTHON/HULBERT/PITT (1999), ETTLIE/SUBRAMANIAN (2004), NARVER/SLATER/MACLACHLAN (2004), and ATUAHENE-GIMA/SLATER/OLSON (2005).

148

See e.g. NARVER/SLATER/MACLACHLAN (2004), p.346.

149

See e.g. KOHLI/JAWORSKI (1990).

150

See NARVER/SLATER/MACLACHLAN (2004).

151

See NARVER/SLATER/MACLACHLAN (2004), p. 344.

152

See NARVER/SLATER/MACLACHLAN (2004), p.336; specifically on the lead user concept: VON HIPPEL (1986), p.791ff.

34

Literature Review

to date.153 Therefore and in order to manage the scope of this study this research focuses on proactive market orientation. ƒTechnology orientation describes a firm’s ability and will to acquire substantial technological knowledge and to use the expertise in the development of new products.154 Technology orientation has been researched before,155 but to a less significant extent than market orientation.156 ƒCompetitive response relates to a firm’s approach to innovation characterized by observing, analyzing and responding to competitors’ new products.157 Competitive response has been studied before, either as a separate driver of innovation158 or as part of market orientation.159 This study follows GATIGNON and XUEREB who argue that competitive reponse is central for the commercial success of innovations and investigates competitive response as a separate driver of innovation.160 A firm’s innovation activities can be driven by any one of these three drivers of innovation. However, more realistic and empirically observed are

153

The two empirical studies to date distinguishing between reactive and proactive market orientation are NARVER/SLATER/MACLACHLAN (2004) and ATUAHENE-GIMA/SLATER/ OLSON (2005). CHANDY/TELLIS (1998) investigated a construct similar to proactive market orientation which they called future market focus. In conceptual papers, DANNEELS (2004) and SLATER/MOHR (2006) argue in favour of the distinction of proactive and responsive market orientation and that proactive market orientation is more relevant to new product development and success.

154

See e.g. GATIGNON/XUEREB (1997), p.78.

155

See e.g. COOPER (1984), p.184; GATIGNON/XUEREB (1997), p.78f; BERTHON/HULBERT/PITT (1999) p.37ff; ZHOU/YIM/TSE (2005), p.45f.

156

For a similar opinion see ZHOU/YIM/TSE (2005), p.43.

157

See e.g. GATIGNON/XUEREB (1997), p.78.

158

See e.g. GATIGNON/XUEREB (1997), p.77ff; JAN/KIM/KIM (2001), p.1ff; ETTLIE/ SUBRAMANIAM (2004), p.101; PALMBERG (2004), p.188; and AUH/MENGUE (2005), p.337.

159

See e.g. NARVER/SLATER (1990), p.23; ZHOU/YIM/TSE (2005), p.56; HULT/KETCHEN/ SLATER (2005), p.1174.

160

See e.g. GATIGNON/XUEREB (1997), p.78.

Innovation Strategy

35

certain combinations of the three factors.161 Hence combinations of drivers are also allowed for. DEFINITION: Driver of innovation captures the different force(s) behind a firm’s innovations. Three drivers – market and technology orientation and competitive response – are considered. Moreover, combinations of the three drivers are allowed for. As for all other innovation strategy dimensions driver of innovation is measured at the firm level, considering the entire new product portfolio of a firm. 1.2.3.4

Innovation Field Orientation

Innovation field orientation describes whether a firm focuses its innovation activities along pre-defined fields. An innovation field consists of multiple, thematically related innovation projects, thus, facilitating synergies between them. The thematic relation between the projects can be based on a combination of aspects, e.g. customer need or group, technology or core competence. ‘Comfort enhancement’ is an example for an innovation field at Valeo, an automobile supplier.162 It comprises different potential solutions (e.g. keyless vehicle access system, ergonomic controls and comfortable, pleasant cabin) to an overarching customer need – more comfort when driving. Typically projects within innovation fields are mightier in size and of a mid-to-long-term time horizon. Several leading innovators and high growth firms like 3M, Canon, Procter & Gamble and Medtronic do not only innovate along existing product lines or have a portfolio of unrelated innovation projects, but instead focus their innovation activities along pre-defined innovation fields.163 The concept of innovation field orientation is new to innovation research and driven by practice.164 Given it is not yet established in the literature

161

See COOPER (1984), p.156; GATIGNON/XUEREB (1997), p.87; ETTLIE/SUBRAMANIAM (2004), p.105; PALMBERG (2004), p.192ff; DESARBO/DIBENEDETTO/SONG/SINHA (2005), p.63.

162

See www.valeo.com/automotive-supplier/Jahia/lang/en/pid/48. Valeo calls its innovation fields innovation domains. They have three and one is Comfort Enhancement.

163

See JONASH/SOMMERLATTE (1999), p.26, and LAURIE/DOZ/SHEER (2006), p.80ff.

164

LAURIE/DOZ/SHEER (2006) are the first ones to elaborate on the topic in more detail and as part of a scientific journal (Harvard Business Review).

36

Literature Review

different authors name the phenomenon differently. The following gives an overview of the key articles touching the concept. To date, LAURIE, DOZ and SHEER elaborate on innovation fields in most detail.165 They call innovation fields new growth platforms however, with the same meaning: “New growth platforms […] are families of strategic opportunities […] on which a firm can build families of products, services and businesses and extend their capabilities into multiple new domains […]. They are not small, fledging ventures that might be funded by a business unit or an encouraging executive. The scale of platforms is strategic and material to the corporation […]. New growth platform units are both independent from and highly dependent on the corporation’s existing businesses […]. They have to be independent, because looking for new growth platform opportunities requires a longer performance horizon.” The quote highlights the key characteristics of an innovation field: ƒGroup of multiple, related innovation projects under common theme ƒMighty in size ƒMid- to long-term time and performance horizon ƒSome organizational formality and level of independence ƒPotential of synergies between related projects. The last characteristic, realization of synergies between related projects, which constitutes at the same time an objective of innovation field orientation, is not mentioned in the quote above. Innovation fields are not termed new growth platforms in this study in order to avoid the misleading association with product and technology platforms which originated in the car industry.166 Innovation fields go beyond the meaning of platforms and are not necessarily associated with common technologies and a stream of highly related products that are produced

165

Also for the following, see LAURIE/DOZ/SHEER (2006), p.80ff.

166

See MCGRATH (1995); JONASH/SOMMERLATTE (1999), p.26; and BOWMAN (2004), p.14.

Innovation Strategy

37

based on a common technology.167 In fact, DANNEELS and HALMAN/HOFER /VAN VUUREN both argue for the product platform concept to be extended. DANNEELS asks that technological competences that underlie highly related product families (product platforms) should be extended to “more extreme cases of technology leveraging”.168 A car maker’s competence in gasoline powered engines, for example, could be leveraged for lawn movers. HALMAN, HOFER and

VAN

VUUREN require platforms to be built on a multidi-

mensional core of assets that includes capabilities and assets from the whole value chain, not only technological competences. Subsequently they distinguish process, customer, brand and global platforms.169 Back in 1980, CRAWFORD was the first one to mention a similar concept as part of his product innovation charter: target business arenas that product innovation is to take the firm into or keep in.170 As part of their work on radical innovation competency, O’CONNOR and AYERS identify four models for radical innovation of which two set up their innovation activities along innovation fields.171 The strategy-driven model defines technology-market domains that are emerging as new business arenas. The execution-driven model forms growth platforms based on independent initiatives already under way and builds up these growth platforms to be very large businesses. Last, COOPER, EDGETT and KLEINSCHMIDT name the phenomenon strategic arenas or areas of strategic focus on which to concentrate NPD efforts. 172 It remains to be explained why innovation field orientation is beneficial for a firm. Innovation fields facilitate a continuous stream of innovations ca-

167

BOWMAN (2004) defines platforms as “a strategic set of technologies and capabilities along with an integrating architecture that form the basis for a group of related products” (p.14). The common power trains and electrical systems that Volkswagen and Audi cars (VW Golf, Audi TT and VW Beetle) share are two examples for platforms.

168

See DANNEELS (2002), p.1115.

169

See HALMAN/HOFER/VAN VUUREN (2003), P.101.

170

See CRAWFORD (1980), p.4f.

171

For this and the following see O’CONNOR/AYERS (2004), p.29ff.

172

See COOPER/EDGETT/KLEINSCHMIDT (2004), p.51.

38

Literature Review

pable to close the growth gap many companies are facing.173 This is not possible through periodic innovation activities or incremental innovations at the one-product or single business level.174 Moreover, through innovation field orientation firms enhance the effectiveness of their innovation activities by providing focus175 and enabling the realization of synergies between related projects.176 With regard to synergies, HENDERSON and COCKBURN found that firms with larger research efforts benefit from internal and external knowledge spillovers between related projects within a particular therapeutic area.177 In other words, HENDERSON and COCKBURN argue to have groups of related research projects – here called innovation fields - in order to realize synergies between related projects. Also using the pharmaceutical industry as example, BLAU ET AL. demonstrate the value of synergies between related new product candidates.178 DEFINITION: Innovation field orientation characterizes the set up of a firm’s innovation activities along pre-defined fields. Innovation fields can be defined along a combination of aspects, e.g. customer need or group, technology or core competence. An innovation field consists of multiple, thematically related and mighty innovation projects with a mid-to-long term time and performance horizon. Typically innovation fields are structured and supported by some level of organizational formality.

173

BLAU/PEKNY/VARMA/BUNCH (2004), p.227, also stress the importance and challenge of maintaining a continuous stream of new products.

174

For a similar opinion see LAURIE/DOZ/SHEER (2006), p. 90.

175

See COOPER (1984), p.156f and COOPER/EDGETT/KLEINSCHMIDT (2004), p.51.

176

Synergies between (complementary) activities can be than the sum of the parts. See e.g. STIEGLITZ/HEINE based on MILGROM/ROBERTS (1995, p.181) definition are complements if doing more of any one of them more of the others”.

177

See HENDERSON/COCKBURN (1996), p.32ff. Their research concentrates on the pharmaceutical industry. They therefore speak about therapeutic areas.

178

See BLAU/PEKNY/VARMA/BUNCH (2004), p.227ff. They propose a new portfolio management approach which incorporates project dependencies. Illustrated by a case study they show that their methodology promises to yield a significantly higher return (+28%) at almost the same level of risk.

defined as the total being more (2007), p.3. Their definition is of complementarity: “activities increases the returns to doing

Innovation Strategy

1.2.3.5

39

Overview of Four Innovation Strategy Dimensions

Figure II-2 gives an overview of the four innovation strategy dimensions investigated as part of this research. Figure II-2: Four Innovation Strategy Dimensions of This Study z

Innovativeness

Distance to Core Business

Degree of newness from the perspective of the marketplace (= customers and competitors)

z

Two sub-dimensions: market innovativeness and technology innovativeness

z

Measured at the firm level considering the entire new product portfolio of a firm

z

Degree of newness from a firm perspective: how close/distant are innovations from core business?

z

Two sub-dimensions: familiarity and fit – both measured along market and technology factors

z

Measured at the firm level considering the entire new product portfolio of a firm

Driver of Innovation

z

Driving force(s) behind a firm’s innovations

z

Three drivers considered: proactive market orientation, technology orientation and competitive response

z

Combination of drivers allowed for

z

Measured at the firm level

z

Set up of innovation activities along pre-defined fields

z

Innovation field: multiple, thematically related, mighty and longer term innovation projects - supported by some level of organizational formality

z

Potential for synergies among related projects

z

Measured at the firm level

Innovation Field Orientation

Source: Own illustration

The past chapter was dedicated to the concept of innovation strategy, independent of any performance effect. The following chapter reviews the literature on the relationship between innovation strategy and firm-level performance, the central research question of this study.

40

Literature Review

2

Innovation Strategy and Performance

2.1

Innovation Performance and Firm Performance

2.1.1 Innovation Performance Innovations are neither an end in itself nor a lottery.179 Even though they are uncertain, innovations need to perform and contribute positively to a firm. Innovation performance is most frequently used as performance indicator in innovation research.180 Innovation performance is predominantly defined and measured at the level of individual projects.181 For individual innovations HAUSCHILDT defines innovation performance as the extent to which the return on the invested capital corresponds to the original objective or firm return level.182 Innovations are investments under uncertainty and should contribute to firm performance. HAUSCHILDT’S perspective stresses the financial dimension of innovation performance. However, innovation performance is typically operationalized in a broader sense, as a multidimensional construct.183 At the project level, the innovation performance construct comprises the following sub-dimensions: financial, technical, customer-based and process aspects. Financial performance includes criteria like sales, profits, and market share. Technical success addresses the technical performance of the innovation. Customer-based performance covers factors like customer satisfaction and image improvement. Process performance relates to project criteria, i.e. whether a project meets timeline and cost targets. Innovation performance is then aggregated to a latent construct based on these sub-dimensions. Thereby

179

See e.g. HAUSCHIDLT (1991), p. 452.

180

See e.g. GRIFFIN/PAGE (1996) and ATUAHENE-GIMA/LI (2004).

181

See e.g. HAUSCHILDT (1991), p.16 and HAUSCHILDT/SALOMO (2005), p.16 – also both papers for a more in-depth review on the status quo and shortcomings of measuring innovation performance.

182

See HAUSCHILDT (1991), p.2 and 23, and HAUSCHILDT (2004), p.498-99.

183

Also for the following see e.g. GRIFFIN/PAGE (1996), p.486/7, ALTMANN (2003), p.16ff, and HAUSCHILDT (2004), p.502ff.

Innovation Strategy and Performance

41

the different dimensions are all included with the same weight.184 This means that all dimensions are considered to be of same importance. Some authors question this approach and argue that financial performance is more important than the other dimensions.185 ALTMANN goes even further and takes a two-stage approach; he considers financial success as the overall innovation performance and technical and process performance as antecedents of financial success.186 The aggregation level of this research is the firm, and not the project. Innovation performance at the firm level can be defined as the sum of the successes of the individual innovations.187 This means that the entire innovation portfolio of the firm needs to be considered. Similar to the project level, innovation performance at the firm level encompasses financial, technical, customer-based and process measures.188 2.1.2 Innovation and Firm Performance In line with HAUSCHILDT and ALTMANN this study supports the view that financial performance is the ultimate arbiter of innovation performance. GRIFFIN and PAGE argue similarly, when requesting that financial success measures need to be included in any innovation performance metric, because firms’ ultimate objective is financial success. Financial measures provide the necessary link between product development and firm performance.189 Considering financial success as the ultimate goal of innovation, one should expect firms’ innovations to have an impact on firm performance. Therefore, this study also investigates the relationship of innovation strategy with firm performance. Concluding and in line with previous studies190

184

See e.g. HAUSCHILDT (1991), p.19.

185

See e.g. HAUSCHILDT (1991), p.452 und (2004), p.498; ALTMANN (2003), p.20f.

186

See ALTMANN (2003), p.16ff.

187

See HAUSCHILDT/SALOMO (2005), p.16.

188

See e.g. GRIFFIN/PAGE (1996), P.491 for their suggestion of firm-level innovation performance measures.

189

See e.g. GRIFFIN/PAGE (1996), P.492.

190

See e.g. SLATER/NARVER (1994) and ZHOU/YIM/TSE (2005).

42

Literature Review

two indicators are included to measure a performance effect: innovation performance and firm performance. Apart from these conceptual considerations measuring firm performance bears the advantage of being able to access objective, publicly available performance data – be it accounting or stock market based. It thereby avoids classical challenges of subjective performance metrics.191 Finally, by investigating objective, stock-market based performance effects, this study responds to the call for more research in marketing and innovation that uses performance metrics directly related to shareholder value.192 2.2

Innovation Strategy and Performance

Whether innovation strategy has an effect on performance is the key research question of this study. In this chapter the related literature is reviewed, which means that prior empirical findings are presented and analyzed. The conceptual discussion is not included in this chapter, but done separately and subsequently, in chapter III. First, those studies from Table II-3 are discussed which investigate a relationship with performance; in addition, related research is examined which analyzes the performance effect of selected innovation activity variables. Second, literature investigating the performance effect of each of the four individual innovation strategy dimensions is revisited. 2.2.1 Innovation Strategy and Performance Only six out of the 16 empirical studies shown in Table II-3 investigate a performance effect. Moreover, these studies are little up-to-date. The most recent piece is from 1998, with COOPER’S seminal research dating back to 1984. In addition, the majority of these studies use subjective performance

measures

based

on

surveys.

Only

ZAHRA/COVIN

and

FIRTH/NARAYANAN apply objective success metrices.193 Furthermore, FIRTH

191

For details on methodical challenges see chapter IV-1.

192

See e.g. VENKATRAMAN/RAMANUJAM (1986), p.801ff; DAY/FAHEY (1988), SRIVASTAVA/SHERVANI/FAHEY (1999), p.177; and LEHMANN (2004), p.73ff.

193

See ZAHRA/COVIN (1993) and FIRTH/NARAYANAN (1996).

p.53ff;

Innovation Strategy and Performance

43

and NARAYANAN are the only ones to date investigating the innovation strategy - firm performance relationship based on capital market measures, like done in this study. However, due to analyzing 18 firms only, they explicitly position their research as an exploratory analysis.194 Irrespective of these methodical considerations five out of the six studies in Table II-5 find a positive relationship between innovation strategy and performance. However, it needs to be highlighted that the comparison of these studies among themselves and with this study is problematic. Most importantly, the conceptualization of innovation strategy varies widely - as shown in Table II-3. Taking into account the conceptualization of innovation strategy as well as the type of performance measure used (subjective vs. objective), the work of COOPER, ZAHRA/COVIN and FIRTH/NARAYANAN is most comparable to this research. All three find positive performance effects. Table II-5: Research on Innovation Strategy and its Performance Effect Relationship Studied Author

Success Measure

Independent Variable

Dependent Variable

COOPER (1984)

New product strategy

Innovation performance

Subjective

ZAHRA/COVIN (1993)

Technology policy

Firm performance

Objective: accountingbased

New product strategy

Innovation performance

Subjective

BARCZAK (1995)

Key Finding

 New product strategy and new

FIRTH/ NARAYANAN (1996)

New product strategy

Firm performance

Objective: accounting- & capital market based

MARKHAM/ GRIFFIN (1998)

Innovation strategy

Innovation performance

Subjective

MOTOHASHI (1998)

Innovation strategy

Innovation and Firm Performance

Subjective

product performance (=innovation performance) intimately linked

 Technology policy viable means to promote company performance

R No single NPD strategy more successful

 More innovative firms enjoy higher returns

 Innovation strategy positively related to firm level success

 Small, positive association

‘+’ Indicates a positive relationship and ‘o’ no relationship NPD = new product development Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

194

See FIRTH/NARAYANAN (1996), p.334.

44

Literature Review

Given the limited number of studies directly analyzing the performance effect of innovation strategy (see Table II-5), further, related studies were consulted (see Table II-6). These studies analyze the performance effect of some specific aspect of innovation activity. The first six pieces of research shown in Table II-6 investigate firm performance effects of individual innovation introductions. All apply event study methodology with the more recent studies focussing on specific industries. It is worth pointing out that all six studies – older ones from the early 1990s as well as recent ones - measure firm performance (and not only innovation performance) and use objective metrics. Five analyze the effect of individual innovations on firms’ stock market value and one on accounting-based performance metrics. All six studies find a positive relationship. The bottom part of Table II-6 lists empirical research investigating the performance effect of further new product development variables ranging from the number of innovations introduced to different new product development activities. All but one study apply objective firm performance measures. Five of the seven studies find a positive relationship with firm performance. Concluding this literature review not only shows how limited and little upto-date the research on the innovation strategy - firm performance relationship is, but also that other streams of innovation research already use objective performance measures – like done in this study. Finally, even though the mentioned studies are not fully comparable, a clear majority found a positive performance effect. Hence the empirical evidence indicates a positive relationship between innovation strategy and performance. Given innovation strategy is for this research defined along four dimensions, the following four chapters look at the relevant literature for each individual dimension. In other words, the following chapters take a decomposed view at the relevant literature in addition to the integral perspective in this past chapter.

Innovation Strategy and Performance

45

Table II-6: Research on Innovation Activity and its Performance Effect Relationship Studied Author

Success Measure

Independent Variable

Dependent Variable

WOOLRIDGE/ SNOW (1990)

Strategic investment announcements, i.e. R&D projects

Firm performance

Objective: capital market based

CHANEY/ DEVINNEY/ WINER (1991)

New product introductions

Firm performance

Objective: capital market based

BAYUS/ ERICKSON/ JACOBSON (2003)

New product introductions (in PC industry)

Firm performance

Objective: accounting based

SORESCU/ CHANDY/ PRABHU (2003)

New product introductions (in pharmaceuticals)

Firm performance

Objective: capital market based

PAUWELS ET AL. (2004)

New product introductions (in auto industry)

Firm performance

Objective: accounting- & capital market based

SHARMA/LACEY (2004)

New product introductions (in pharmaceuticals)

Firm performance

Objective: capital market based

GEROSKI/ MACHIN/ VAN REENEN (1993)

Innovation (number of innovations produced)

Firm performance

Objective: accounting based

CALANTONE/ VICKERY/ DRÖGE (1995)

NPD activities (e.g. customization, NDP cycle time

Firm performance

Objective: accounting based

BLUNDELL/ GRIFFITH/ VAN REENEN (1999)

Technological innovations

Firm performance

Objective: capital market based

MIZIK/ JACOBSON (2003)

Value creation (i.e. innovation) vs. value appropriation (i.e. advertising)

Firm performance

Objective: capital market based

HERTENSTEIN/ PLATT/VERYZER (2005)

Industrial design effectiveness

Firm performance

Objective: accounting- & capital market based

LIN/CHEN (2005)

Corporate technology portfolio

Innovation and Firm Performance

LÖÖF/ HESHMATI (2006)

Innovation (intensity)

Firm Performance

Key Finding

 Stock market reacts quickly and positively to investment decisions

 Original new product introductions dominate reformulations or repositionings in terms of value

 New product introductions influence profit rate and size…

R … however, not profit persistence  Radical innovations valued significantly more than either technology or market breakthroughs

 New product introductions increase long-term financial performance and firm value

 Market valuations respond strongly and clearly to success and failure of NPD efforts

 Number of innovations produced with positive impact on firm profitability, but effect is modest

 Emphasis on innovation does raise overall business performance

 Direct effect of innovation on stock market value

Stock market reacts favorably when emphasis on value appropriation relative to value creation

 Good industrial design related to firm performance

Objective: +/- Technology portfolio compositions accounting cum and technology concentration affect capital market performance measures in different based ways

/o Positive or no relationship, Subjective

depending on the performance measure

‘+’ Indicates a positive relationship, ‘-’ a negative relationship and ‘o’ no relationship NPD = new product development Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

46

Literature Review

2.2.2 Innovativeness and Performance This chapter reviews the literature on the first innovation strategy dimension, innovativeness, and its effect on performance. Innovativeness is defined as the degree of newness from the perspective of the marketplace.195 Past research on this relation is extensive. In the most recent and largest meta-analysis HENARD and SZYMANSKI analyze 41 studies.196 HENARD and SZYMANSKI find a positive impact, overall; however, correlation coefficients vary significantly, from –.62 to .81.197 HAUSCHILDT and SALOMO claim that the empirical evidence for the performance effect of innovativeness is inconclusive.198 In a review of the key empirical (meta-) studies through 2002 they find more negative than positive relationships. This may, however, be driven by methodological deficiencies. Where positive relationships exist, they are modest in size. However, they also observe that more recent studies tend to yield more positive results than older ones. Table II-7 shows recent studies not included in the meta-analysis and literature review mentioned above. FIRTH and NARAYANAN’S study from 1997 is also included, because it was not included in the meta-analysis, but is highly relevant for this research. In line with HAUSCHILDT and SALOMO’S observation, positive performance effects dominate among the recent studies listed in Table II-7.

195

See chapter II-1.2.3.1 for more details on the definition of innovativeness in this study.

196

See HENARD/SZYMANSKI (2001), p.363.

197

See HENARD/SZYMANSKI (2001), p.365.

198

See HAUSCHILDT/SALOMO (2005), p.6-7.

Innovation Strategy and Performance

47

Table II-7: Recent Studies on Innovativeness and Performance Independent Variable

Dependent Variable

Author

Key Finding Name

Perspective

Level

Level

Measure

SORESCU/ CHANDY/ PRABHU (2003)

Types of product innovations (incremental, breakthrough, radical)

Market

Product

Firm performance

Objective: capital market

COOPER/ EDGETT/ KLEINSCHMIDT (2004)

Project type (incremental, new-to-business, new-to-world)

Market & Firm (combined)

Firm

Innovation performance

Subjective

LANGERAK/ HULTINK/ ROBBEN (2004)

Product advantage

Market

Product

Innovation & firm performance

Subjective

 Almost linear relation with shortterm revenue impact Objective: 8 U-shaped relation for long-term Firm accounting revenues and stock market performance & capital performance, but with preference market for new market entries over minor up-dates (for automobile industry)

 Radical innovations are valued significantly more than either technological or market breakthroughs (for pharmaceutical industry)

 Best performers do more innovative and riskier projects (new-to-world) than worst performers

 Product advantage is positively related to new product performance, which is itself positively related to firm performance

PAUWELS ET AL. (2004)

Level of innovation

Market & Firm (combined)

Product

DANNEELS/ SETHI (2005)

Level of exploration

Market

Firm

Innovation performance

CHO/PUCIK (2005)

Innovativeness

(no info)

Firm

Objective:  Innovativeness with positive Firm accounting impact on growth; and growth on performance & capital market value market

FIRTH/ NARAYANAN (1996)

Innovativeness in the market

Market

Firm

Objective:  Firms that emphasize market Firm accounting innovations enjoy higher returns performance & capital than less innovative firms market

 Explorative new products are Subjective

positively correlated with innovation performance and play major role in renewal of firm‘s revenue and growth

‘+’ Indicates a positive relationship and ‘U’ a U-shaped relationship Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

The majority of these investigations are, however, only to a limited extent comparable with this research - for several reasons: ƒPerspective: Older studies until the mid 1990s tend to take a firm or mixed firm-market perspective regarding innovativeness.199 This study measures the two views – market and firm - separately and defines innovativeness purely from a marketplace perspective.200 SCHLAAK observes that depending on the perspective the relation of

199

See GARCIA/CALANTONE (2002), p.112, and for an example, KLEINSCHMIDT/COOPER (1991), p.243.

200

For an argumentation why the two perspectives – market and firm – are conceptualized and measured separately, see chapter II-1.2.3.1.

48

Literature Review

innovativeness with performance differs.201 Analyzing 35 studies, he finds a positive impact of innovativeness in the marketplace on performance. In contrast, for innovativeness from a firm perspective, the relation reverses. The majority of recent studies shown in Table II-7 adopt a pure market perspective – as done in this study. ƒLevel of aggregation: Overall, most studies investigating the level of innovativeness are undertaken at the project or product level,202 and not at the firm level considering the entire new product portfolio. HAUSCHILDT and SALOMO argue that conclusions based on the individual project level need to be qualified and handled with care for the firm level.203 Remarkably, four out of the seven recent studies in Table II-7 investigate innovativeness at the firm level – as done in this research. ƒConceptualization: Conceptualizations of innovativeness continue to vary substantially.204 Each of the studies in Table II-7, for example, is based on a different conceptualization of innovativeness, already indicated by the different names. Generally market and/or technology innovativeness are considered.205 Even though not statistically demonstrated, one can argue that different conceptualizations which emphasize varying aspects of innovativeness can lead to different performance associations. ƒDimensionality: In earlier studies innovativeness was often measured via a single item or dimension.206 Empirical research supports the view that innovativeness should be measured as a multidimensional construct,207 as done in this study. A multi-dimensional approach allows for a broader and more differentiated coverage of in-

201

See SCHLAAK (1999), p.107ff.

202

See e.g. GARCIA/CALANTONE (2002), p.113 and HAUSCHILDT/SALOMO (2005), p.16.

203

See HAUSCHILDT/SALOMO (2005), p.17.

204

See e.g. GARCIA/CALANTONE (2002), p.110f.

205

See e.g. GARCIA/CALANTONE (2002), p.112 and DANNEELS/KLEINSCHMIDT (2001), p.359.

206

See e.g. HAUSCHILDT/SALOMO (2005), p.10.

207

See e.g. GREEN/GAVIN/AIMAN-SMITH (1995), p.207ff, GARCIA/CALANTONE (2002), p.113ff, SALOMO (2003), p.399ff.

Innovation Strategy and Performance

49

novativeness208 and can therefore lead to performance associations that differ from those grounded in data gathered from a singledimensional scale.209 ƒPerformance measurement: Different performance measurement methods may also lead to varying performance effects.210 First, there is an obvious and significant difference between innovation or new product success and overall firm performance. Recent studies on the innovativeness – performance relation (Table II-7) also measure the effect on firm performace. Second, even though subjective and objective performance measures have been proven to correlate,211 some authors continue to argue that they may result in different outcomes.212 Three out of the six most recent studies in Table II-7 continue to use subjective, perceptual performance measures. Third, given the time lag between the launch of an innovation and its performance effect the results can differ for shortand long-term performance metrics.213 Recent findings of PAUWELS ET AL.

and LEIFER ET AL. support this point.214

The discussion above highlights the challenges when inferring indications on the innovativeness - performance relation based on previous research. The studies from FIRTH/NARAYANAN and CHO/PUCIK resemble this study most when it comes to the measurement of innovativeness and its performance effect. Both measure innovativeness purely from a market perspective and at the firm level. Moreover they investigate the effect on firm performance and use objective performance metrices. Both studies find a significant positive impact of innovativeness on firm performance. In other words,

208

For a similar opinion see e.g. GARCIA/CALANTONE (2002), p.120f, and HAUSCHIDLT/ SALOMO (2005), p.10.

209

For a similar argumentation regarding multi-item vs. single-item measures see HENARD/SZYMANSKI (2001), p.366.

210

See HENARD/SZYMANSKI (2001), p.366.

211

See e.g. DESS/ROBINSON (1984).

212

See e.g. HENARD/SZYMANSKI (2001), p.366.

213

See e.g. HENARD/SZYMANSKI (2001), p.366.

214

See PAUWELS ET AL. (2004), p.142ff, and LEIFER ET AL. (2006), p.27f. Both studies find varying results depending on the time lag allowed for and/or the type of performance measure used.

50

Literature Review

they show that firms with a more innovative new product portfolio are overall more successful. Summarizing and despite the issue of comparability, one would expect a positive relationship between innovativeness and performance - based on prior empirical evidence. 2.2.3 Distance to Core Business and Performance Distance to core business addresses the degree of newness from the firm perspective. It measures how close or distant firms’ product innovations are positioned from the core business. Two sub-dimensions are distinguished: familiarity and fit. Familiarity considers innovations’ alignment with the current market and technology domains. Fit relates to how well a firm’s resources and competencies fit with the new product portfolio.215 One stream of literature concerning distance to core business and its performance effect was discussed in the previous chapter - for the innovativeness dimension. When measuring innovativeness, the two perspectives, market and firm, are often mixed.216 HAUSCHILDT and SALOMO conclude that the empirical evidence on the innovativeness - performance relation is equivocal.217 SCHLAAK finds that when separating the firm from the market perspective negative relations dominate.218 For distance to core business and its performance effect no specific meta-analyses exist. However, DANNEELS/KLEINSCHMIDT and SONG/MONTOYA-WEISS published two seminal papers on the topic.219 Whereas DANNEELS and KLEINSCHMIDT investigate both sub-dimensions, familiarity and fit, SONG and MONTOYA-WEISS concentrate on the fit dimension. For fit, DANNEELS and KLEINSCHMIDT observe that prior research shows a positive impact on performance. This means that the better the fit of an innovation with existing resources and

215

For more details and a literature review see chapter II-1.2.3.3.

216

Innovativeness from the firm perspective is for this study termed distance to core business, in order to clearly distinguish the two perspectives.

217

See HAUSCHILDT/SALOMO (2005), p.6f.

218

See SCHLAAK (1999), p.107ff.

219

See DANNEELS/KLEINSCHMIDT (2001) and SONG/MONTOYA-WEISS (2001). Please note that SONG/MONTOYA-WEISS call fit synergy, but with the same meaning.

Innovation Strategy and Performance

51

competencies, the more positive for performance.220 Similarly, SONG and MONTOYA-WEISS state that in numerous studies fit enhances new product outcome.221 In their analysis of new product success antecedents HENARD and SZYMANSKI also investigate marketing and technology fit. They find a modest but significant positive relation between fit and new product success.222 With regard to the second sub-dimension, familiarity, prior research and literature reviews are scarcer. DANNEELS and KLEINSCHMIDT conclude that the empirical evidence on the performance effect of familiarity is equivocal.223 For their own research they hypothesized a positive effect, but could not confirm it. To illustrate and complement the literature review the key empirical papers investigating the distance to core business - performance relation are compiled in Table II-8. In contrast to innovativeness from a market perspective (see Table II-7) no very recent studies exist. Out of the ten studies shown eight test fit and six of them familiarity. For fit, five studies find a positive relationship and three studies no significant association. For familiarity, only two studies observe a positive relationship and four no association. Similar as for the innovativeness dimension earlier, not all listed studies are fully comparable with this research.224 Only two studies in Table II-8 undertake research at the firm level (COOPER 1984, and FIRTH/NARAYANAN 1996) considering the entire new product portfolio. Moreover, only FIRTH and NARAYANAN measure firm and not only innovation performance and use objective success metrics.225 Both studies focus on the familiarity aspect and find a positive and no effect, respectively. Summarizing and combining the empirical evidence for the two subdimensions, fit and familiarity, prior research indicates a positive or no relationship between distance to core business and performance. In other words, based on prior studies it is not clear whether close-to-home inno-

220

See DANNEELS/KLEINSCHMIDT (2001), p.364.

221

See SONG/MONTOYA-WEISS (2001), p.66.

222

See HENARD/SZYMANSKI (2001), p.365 and 369.

223

See DANNEELS/KLEINSCHMIDT (2001), p.363ff.

224

For a more detailed discussion of the challenges one faces when comparing empirical studies on innovativeness (and also distance to core business), see chapter II-2.2.2.

225

See FIRTH/NARAYANAN (1996), p.338.

52

Literature Review

vations promise more success than new products further away from the core business. Table II-8: Studies on Distance to Core Business and Performance Independent Variable

Dependent Variable

Author

Key Finding Name

Fit / Familiarity

Knowledge (familiarity) and synergy (fit)

Familiarity and fit

COOPER (1984)

Product fit

Familiarity

Firm

Innovation performance

Subjective

ZIRGER/ MAIDIQUE (1990)

Synergy with existing competencies

Fit

Product

Innovation performance

Subjective

COOPER/ DE BRENTANI (1991)

Newness to the firm (familiarity) and synergy (fit)

Familiarity and fit

Product

Innovation performance

COOPER/ KLEINSCHMIDT (1993)

Familiarity and synergy (fit)

Familiarity and fit

Product

Innovation performance

FIRTH/ NARAYANAN (1996)

Newness to the firm

Familiarity

Firm

SONG/PARRY (1997)

Synergy

Fit

Product

Innovation performance

Subjective

DANNEELS/ KLEINSCHMIDT (2001)

Familiarity and fit

Familiarity and fit

Product

Innovation performance

 Fit: measures of fit are related to product performance Subjective o Familiarity: no significant association with product performance

SONG/ MONTOYAWEISS (2001)

Synergy

Fit

Product

Innovation performance

Subjective

GATIGNON/ TUSHMAN/ SMITH/ ANDERSON (2002)

New competence acquisition and competenceenhancement

Fit

Product

Innovation performance

Subjective

Level

Level

Product

Innovation performance

Measure

 Familiarity: market familiarity COOPER (1979)

Subjective

second most important new product success predictor

 Fit: technical fit third most important predictor, marketing fit 7th (out of 11 predictors)

 Familiarity: best performing firms with highest degree of familiarity, e.g. similar end-use and/or product class as existing products

 Fit: synergy with the firm‘s existing competences affects new product outcome

 Fit: synergy with firm‘s resources, skills and experiences is strongest predictor of success Subjective o Familiarity: close-to-home products marginally more successful than new-to-the firm (for financial services)

o The impact of synergy (fit) and Subjective

familiarity on new product success was less than expected (in chemicals industry)

Objective: o Familiarity: no relation between Firm accounting new product portfolio‘s newness to performance & capital the firm and firm performance market

 Fit: marketing and technical

synergy have indirect, positive effect on new product success – via product advantage

o Fit: technical synergy with no significant effect on performance (performance effect of marketing synergy not tested)

o Fit: most successful innovations build on existing competencies and acquire new competencies

‘+’ Indicates a positive relationship and ‘o’ no relationship Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

2.2.4 Driver of Innovation and Performance Driver of innovation captures the forces behind a firm’s innovation. Three drivers - market and technology orientation and competitive response –

Innovation Strategy and Performance

53

are investigated. Individual drivers and combinations are allowed for. Prior studies investigating multiple drivers at the same time and their performance effect are limited. Many studies look at one or two drivers only or do not consider any performance effect.226 Of the three drivers studied in this research the literature concentrates on market orientation. Thereby the vast majority of research suggests that being market oriented is associated with superior performance.227 The market orientation concept was recently refined and extended. Responsive and proactive market orientation are now distinguished.228 Most studies to date investigate responsive market orientation. Only three papers analyze proactive market orientation – as done in this study.229 Two studies observe a positive effect, one an inverted-U shaped relation. Table II-9 concentrates on studies investigating several drivers of innovation. In addition the overview includes recent, relevant studies, e.g. distinguishing responsive and proactive market orientation. Overall, there are few studies to date investigating several drivers of innovation at the same time, of which the work by GATIGNON and XUEREB is most renowned. In contrast, for market orientation as a stand-alone driver of innovation significant prior research exists.230 Furthermore, some studies do not differentiate between market and competitor orientation, as suggested by GATIGNON and XUEREB.231 Moreover, no relevant study to date uses objective performance measures.

226

Also see e.g. BERTHON/HULBERT/PITT (1999) beside the studies shown in Table II-8.

227

See e.g. NARVER/SLATER/MACHLACHLAN (2004), p.335, ZHOU/YIM/TSE (2005), p.42.

228

See NARVER/SLATER/MACLACHLAN (2004) and ATUAHENE-GIMA/SLATER/OLSON (2005) and chapter II-1.2.3.3 for a more detailed discussion of the differences.

229

NARVER/SLATER/MACLACHLAN (2004) and ATUAHENE-GIMA/SLATER/OLSON (2005) undertook empirical studies on proactive (vs. responsive) market orientation. CHANDY/TELLIS (1998) investigated future market focus, a concept very similar to proactive market orientation.

230

Table II-9 does not include all studies on market orientation in the realm of innovation, but concentrates on the ones most relevant for t his study. For a more complete overview on empirical research on market orientation and innovation see e.g. LANGERAK/HULTINK/ROBBEN (2004), p.81, and ZHOU/YIM/TSE (2005) p.45.

231

See GATIGNON/XUEREB (1997), p.78. GATIGNON and XUEREB’S research would be most comparable to this study, however, they do not report overall results, but split in four types of markets.

54

Literature Review

Table II-9: Studies on Drivers of Innovation and Performance Independent Variable

Dependent Variable

Author

Key Finding Market Technology Competitor Orientation Orientation Orientation

Level

Measure

COOPER (1984)

X

X

-

Innovation Subjective performance

BURGELMANN/ SAYLES (1986)

X

X

-

Innovation performance

Not clear (case studies)

 Balanced: most successful are firms which balance technological prowess with a strong market orientation

 Balanced: most successful ventures appear to be those with ‘doublelinking’ – combination of technology push and need pull

Recommend different orientations depending on market characteristics: •

GATIGNON/ XUEREB (1997)

X

Re

X

X

Innovation Subjective performance

•

•

•

CHANDY/ TELLIS (1998)

X

Pro

 Pro-active market orientation with

-

-

Radical product innovation

Subjective

Product newness

Subjective

ETTLIE/ SUBRAMANIAM (2004)

X

X

X

LANGERAK/ HULTINK/ ROBBEN (2004)

X Re *including competitor orientation

-

-*

PALMBERG (2004)

X

X

-

-

-

Demand uncertainty high: combin. of market and technology orientation Demand uncertainty low: competitor orientation Market growth high: competitor and technology orientation Market growth low: technology orient. strong positive indirect effect – via willingness to cannibalize

 Balanced: newer products are likely to be driven by a combination of market and technology forces

Innovation & o Market orientation without direct Subjective firm relation to new product and performance organizational performance

 Balance: mastering the diversity of -

different sources of innovation appears to be crucial – market and technology factors seem especially important

-

 Proactive market orientation

NARVER/ SLATER/ MACLACHLAN (2005)

X

ATUAHENEGIMA/SLATER /OLSON (2005)

X

Pro & Re

significantly related to new product success

Innovation Subjective performance

R Responsive market orientation not U Pro & Re

-

-

Innovation Subjective performance

Proactive market orientation with inverted U-shaped relation with new product program performance 8 Responsive market orientation shows U-shaped relation with performance Indirect effect:

X Re *including competitor orientation

X

-*

U

ZHOU/YIM/ TSE (2005)

1.) Effect on types of innovation:  Market orientation has positive effect on technology-based innovation 1.)  Technology orientation has positive Type of effect on technology-based innovation innovation o Technology orientation with no effect Subjective 2.) on market-based innovations Innovation & – Market orientation with negative effect firm on market-based innovation performance 2.) Effect of innovation on performance:  Both types of innovation have positive effect on performance – technologybased innovations stronger than market-based ones

‘+’ Indicates a positive relationship, ‘-’ a negative relation, ‘o’ no relation, ‘U’ a U-shaped relation and ‘ ‘ an inverted U-shaped relation Re = responsive market orientation, Pro = proactive market orientation Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

Innovation Strategy and Performance

55

Hence, few and only to a limited extend comparable studies exist from which to derive inferences for this study. Nevertheless, by analyzing the studies listed in Table II-9 one can reveal tendencies. Seven out of all ten studies find a positive relation with performance – for one or a combination of drivers. Only ZHOU, YIM and TSE identify one negative relation, among several positive and non-significant ones. Three studies find no association with performance – be it for technology, market or competitor orientation. Finally and noteworthy, out of six studies analyzing multiple drivers four find that a balanced market and technology orientation is most beneficial in terms of performance. Even though prior research is limited and only partially comparable, empirical evidence suggests that driver of innovation should overall have a positive impact on performance and that a combination of market and technology orientation promises to yield best results. 2.2.5 Innovation Field Orientation and Performance Innovation field orientation characterizes the set up of a firm's innovation activities along pre-defined fields. Innovation field orientation is a new phenomenon in innovation research.232 Hence very limited empirical evidence exists - on the concept overall as well as its performance effect. Table II-10 includes studies dealing with innovation field orientation overall as well as research touching certain aspects of it. CRAWFORD was the first one to mention a concept very similar to innovation fields which he termed ‘target business arenas’.233 However, he did not investigate any performance effect. Based on case study evidence, JONASH/SOMMERLATTE and LAURIE/DOZ/SHEER both claim that having and creating innovation fields has a positive impact on performance.234 COOPER, EDGETT and KLEINSCHMIDT are the first and only ones to date investigating innovation fields and their performance as part of a large scale study.235 They observe that focusing

232

See chapter II-1.2.3.4 for more details.

233

See CRAWFORD (1980), p.4f.

234

See JONASH/SOMMERLATTE (1999), p.25ff and LAURIE/DOZ/SHEER (2006), p.82.

235

See COOPER/EDGETT/KLEINSCHMIDT (2004), p.51. Their study is based on evidence from 105 US firms.

56

Literature Review

new product development activities within strategic arenas236 is strongly correlated with NPD performance. Beside these studies which analyze innovation field orientation overall, additional pieces of research are listed in Table II-10 investigating key characteristics of innovation field orientation. First and with regard to the aspect that innovation field orientation enhances focus, three studies find that focus in new product development increases return and reduces riskiness.237 However, HENDERSON and COCKBURN notice that focus only increases productivity to a certain point.238 In other words some focus is good, but too much can backfire. Second, new product projects which form part of innovation fields tend to have a longer time horizon. HERRMANN, GASSMANN and EISERT observe that a long-term orientation is negatively or not associated with organizational innovative capability even though they hypothesized a positive relationship.239 They explain their finding with the fact that organizational inertia can be associated with long-term orientation and that inertia is counter-productive for innovation. Third, innovation fields tend to be supported by some sort and level of organizational formality. Various organizational means are conceivable like formalization or separate organizational units. Concerning formalization and its effect on performance prior empirical evidence is ambiguous - ranging from positive to negative effects. This is consistent with CARDINAL’S observation that the empirical evidence of the impact of formalization on innovation is mixed.240 For informal networks RICE

ET AL.

and JANSEN/VAN DEN BOSCH/VOLBERDA conclude that informal networks and connectedness of employees bears benefits for incremental and radical innovation.241 Moreover, HERRMANN, GASSMANN and EISERT242 show that creat-

236

Strategic arenas are their terminology for innovation fields.

237

See COOPER (1984), p.156f; HENDERSON/COCKBURN (1996), p.50f; and FIRTH/NARAYANAN (1996), p.343.

238

See HENDERSON/COCKBURN (1996), p.50-51.

239

See HERRMANN/GASSMANN/EISERT (2005), p.9 and 15.

240

See CARDINAL (2001). Also see PERSAUD (2005), p.416 for a more detailed review on the effect of formalization on innovation.

241

See RICE ET AL. (1998), p.52ff and JANSEN/VAN DEN BOSCH/VOLBERDA (2006), p.19.

242

See HERRMANN/GASSMANN/EISERT (2005), p.15.

Innovation Strategy and Performance

57

ing independent organizational units for innovation programs has an indirect positive effect on firms’ innovation output. Table II-10: Studies on Innovation Field Orientation and Performance Author

Central Topic of Research

Independent Variable (related to IFO)

Dependent Variable Key Finding Level

Measure

Innovation Field Orientation (IFO) - Overall CRAWFORD (1980)

Product innovation charter

Target business arena

-

-

First one to mention a concept very similar to innovation fields

JONASH/ SOMMERLATTE (1999)

Premiums for innovation

Growth platforms

Firm performance

Objective: capital market (case studies)

COOPER/ EDGETT/ KLEINSCHMIDT (2004)

Benchmarking best NPD practices

Strategic arenas or areas of strategic focus

Innovation performance

Subjective

LAURIE/DOZ/ SHEER (2006)

New growth platforms

New growth platforms

Firm performance

COOPER (1984)

New product strategies

Program focus (how related are new products)

Innovation performance

Subjective

HENDERSON/ COCKBURN (1996)

Research productivity

Focus of research portfolio

Innovation performance

Objective: patent output

FIRTH/ NARAYANAN (1996)

New product strategies

Focus

Firm performance

Objective:  Absence of strong focus in new accounting & product strategies enhances capital market riskiness of firm (return & risk)

 Show that growth platforms are one element of innovative firms which enjoy a premium at capital markets

 Best performers define strategic arenas more than worst performers; this strategy element is strongly correlated with NPD performance

Objective:  All 24 studied companies which achieved significant organic organic growth growth, grew by creating new growth platforms (case studies)

Focus

 Best performing firms with high score on program focus Substantial return to focus, but only up to certain point (Æ appropriately focused)

U

Time Horizon of Projects

– Long-term orientation with

HERRMANN/ GASSMANN/ EISERT (2005)

Antecedents for radical product innovation

RICE/ O‘CONNOR/ PETERS/ MORONE (1998)

Management practices and discontinuous innovation

Informal networks

-

-

PERSAUD (2005)

Antecedents of synergistic innovative capability

Formalization

Innovative capability

Subjective

HERRMANN/ GASSMANN/ EISERT (2005)

Antecedents for radical product innovation

Independent organizational units

1.) Innovative capability 2.) Innovation performance

JANSEN/VAN DEN BOSCH/ VOLBERDA (2006)

Antecedents for exploratory and exploitative innovation

Formalization & informal networks

Innovativeness

Long-term orientation

Innovative capability

Subjective

negative effect on capability to transform competencies o … and with no effect on capability to transform markets

Organizational Formality

 Informal networks: Found that for all 11 radical innovation projects studied (case studies) informal networks played important role

– Formalization: High levels of

Subjective

formalization negatively impact synergistic innovative capability Indirect effect:  1.) Independent organizational units with positive effect on innovative capability  2.) Innovative capability with positive effect on innovation performance O/+

Subjective

Formalization: positive effect on exploitative (incremental) innovation and no effect on exploratory (radical) innovation  Informal networks: Positive effect on exploitative (incremental) and exploratory (radical) innovation

58

Literature Review

Synergies Research productivity

Economies of scope in research

Innovation performance

Objective: patent output

BLAU/PEKNY/ VARMA/ BUNCH (2004)

Portfolio management

Dependencies (between projects)

Innovation performance

Objective: project NPVs (n=9)

U

HENDERSON/ COCKBURN (1996)

Find inverted-U shaped relationship between scope and research productivity (Æ appropriately diverse)

 Accounting for project dependencies within a NPD portfolio increases expected NPV per project significantly (+28%)

U

‘+’ Indicates a positive relationship, ‘-’ a negative relation, ‘o’ no relation, and ‘ ‘ an inverted U-shaped relation IFO = innovation field orientation, NPD = new product development Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

Fourth and last, prior research on the realization of synergies between related innovation projects suggests that leveraging synergies has a positive effect on performance. HENDERSON and COCKBURN state that having related innovation projects has a positive effect on performance, however, only up to a certain level.243 They recommend an appropriately related, but still diverse portfolio of projects. BLAU

ET AL.

investigate the effect of project in-

terdependencies on project NPV and conclude that considering dependencies between projects when assorting a portfolio can significantly enhance individual projects’ performance.244 Combining and integrating these few pieces of empirical evidence on innovation field orientation, prior research indicates that innovation field orientation should overall have a positive impact on performance, even though the performance effect of certain aspects of innovation field orientation, e.g. formalization, is less clear. 3

Innovation Strategy and Firm Context

The past chapters reviewed prior empirical evidence on the performance effect of innovation strategy. Assuming there is a relation between innovation strategy and firm-level performance, does it hold true for any firm under any circumstances? This chapter addresses this question and revisits prior research on contingencies in innovation research.

243

See HENDERSON/COCKBURN (1996), p.47.

244

See BLAU/PEKNY/VARMA/BUNCH (2004), p.227ff.

Innovation Strategy and Firm Context

59

The contingency view has become of central importance for business strategy research.245 A certain management approach is not per se better than another, but depends on the situation and context of the organization.246 Essentially, the contingency theory argues that ‘it depends’.247 In other words, the contingency view questions that there is one single best way to manage. With regard to strategy research, the contingency theory makes two important assumptions.248 First, it says that no strategy is universally superior or inferior. Consistent with this axiom, MILES/SNOW and PORTER claim that - except for Reactors - no other strategy type is superior or inferior.249 Second, contingency theory states that a certain strategy is not equally effective under different environmental or organizational contexts. This means that the outcome and success of a strategy depends on the conditions under which it operates. Based on contingency theory, it is assumed in this study that the relation between innovation strategy and performance is influenced by contextual factors. To measure the contingency effect, a moderation perspective is adopted. The moderation approach assumes that the impact a predictor variable (here innovation strategy) has on the criterion variable (here performance) is dependent on the level of a third variable, called moderator. The fit between the predictor and the moderator is the primary determinant of the criterion variable.250 Within business strategy research, the contingency approach is well established. For innovation strategy only three out of the 21 studies in Table II3 investigate contingencies. Their findings - shown in Table II-11 - suggest that contingencies may influence the relation between innovation strategy and performance.

245

See e.g. HAMBRICK/LEI (1985); MILLER (1988); VENKATRAMAN/PRESCOTT (1990).

246

See ZEITHAMEL/VARADARAJAN/ZEITHAMEL (1988), p.37.

247

See MILES/SNOW (1978), p.251.

248

See CALANTONE/GARCIA/DROEGE (2003), p.92.

249

See MILES/SNOW (1978), and PORTER (1980), p.34f.

250

See VENKATRAMAN (1989), p.424.

60

Literature Review

Table II-11: Research on Innovation Strategy and Contingencies Relationship Studied Author

Independent Variable

MILLER/ FRIESEN (1982)

Models of product innovation

Contingencies

Dependent Variable

-

COOPER (1984)

New product strategy

Innovation performance

ZAHRA/COVIN (1993)

Technology policy

Firm performance

Firm level: • Heterogeneity of firm Market level: • Dynamic and hostile environment Firm level: • Export orientation Market level: • Market potential • Market competitiveness • Competitive dominance

Firm level: • Business strategy

Key Finding

o Hypothesize that contingencies have positive effect on two models of innovation, but find only very weak support

 Best performers choose high potential, high-growth, noncompetitive markets (descriptive, no moderation effect tested)

 Strength of relationship between technology policy and performance varies across firms with different business strategy configurations

‘+’ Indicates a positive moderation and ‘o’ indicates no or very weak moderation effect Performance terms standardized to innovation performance and firm performance; original names might differ

Source: Own illustration

In innovation research overall, contingency research is more prevalent.251 Table II-12 lists the contingency factors studied to date in the context of innovation, in alphabetical order. Typically two levels are distinguished: organizational characteristics at the firm level and external environmental factors at the market level.252 Table II-12: Contingencies Studied in Innovation Research Organizational Characteristics (firm level)

• Business strategy

External Environmental Factors (market level)

• Concentration / competitive intensity / competitive

• Dominance / incumbency / market share

dominance

• Competitive response intensity

• Export orientation

• Environmental dynamism

• Firm heterogeneity

• Environmental hostility

• Geography

• Likelihood of competitive response

• Import intensity

• Market growth

• Industry

• Market size

• R&D rate

• Market uncertainty (= market turbulence)

• Size

• Technological uncertainty (= technological turbulence)

• Unionization

Source: Own illustration

251

For a similar opinion, see e.g. JANSEN/VAN DEN BOSCH/VOLBERDA (2006), p.8.

252

See VENKATRAMAN/PRESCOTT (1990), p.1.

Innovation Strategy and Firm Context

61

Two contingency variables are investigated in this study, one for each aggregation level: business strategy (firm level) and environmental uncertainty (market level). Business strategy was selected, because ZAHRA and COVIN show that it moderates the relation between innovation strategy and firm performance.253 Moreover, several other authors emphasize that strategic choices regarding innovation need to be aligned to business strategy.254 Environmental uncertainty – encompassing market and technology uncertainty - was chosen because it has received most attention in the business strategy literature255, is well established in the field of marketing256 and is diffusing in innovation research.257 Furthermore, GATIGNON and XUEREB show that the success of an innovation is not independent of the market in which the firm functions.258 To the author’s best knowledge the moderation effect of environmental uncertainty on the innovation strategy – performance relation has not been studied before. Hence, this research contributes to the literature by extending the contingency approach to the topic of innovation strategy and its performance effect. In addition to the two contingency factors mentioned above, firm size, industry, geography, R&D rate and ROA are included in the analysis as control variables. 3.1.1 Business Strategy as Contingency Business strategy is characterized as the manner in which a firm decides to compete.259 This includes the pursuit, achievement and maintenance of

253

See ZAHRA/COVIN (1993), p.470. Note that they term innovation strategy technology policy.

254

See BARCZAK (1995), p.224; COOPER/EDGETT/KLEINSCHMIDT (2004), p.51; LIN/CHEN (2005), p.567/8; SLATER/MOHR (2006), p.26.

255

See e.g. Miles/Snow (1978), p.254; DESARBO/DI BENEDETTO/SONG/SINHA (2005), p.47; SONG/DRÖGE/HANVANICH/CALANTONE (2005), p.259.

256

See e.g. JAWORSKI/KOHLI (1993), SLATER/OLSON (1994) and KIRCA/JAYACHANDRAN/ BEARDEN (2005), p.36f – the latter research also for an extensive literature review on environmental uncertainty as a moderator.

257

See e.g. CALANTONE/GARCIA/DRÖGE (2003), DANNEELS/SETHI (2003), BSTIELER (2005), ZHOU/YIM/TSE (2005).

258

See GATIGNON/XUEREB (1997), p.80.

259

See e.g. WALKER/RUEKERT (1987), p.16.

62

Literature Review

competitive advantage in an industry.260 The typologies of MILES and SNOW and PORTER continue as the most prevalent frameworks for business strategy.261 Moreover, in both frameworks the role of technology and innovation is explicitly recognized.262 PORTER’S typology is applied in this study given its external focus on competitive advantage263 and its clear distinction from innovation strategy.264 PORTER distinguishes between three types of strategy: cost leadership, differentiation and focus.265 ƒCost leaders: Cost leaders achieve lower costs than the competition. Through internal efficiencies they enjoy higher profits from lower costs and/or offer lower prices to customers. Potential for cost reduction can be found along the entire value chain, e.g. purchases of raw materials, production, research and development, service, sales or communication.266 It is worth emphasizing that cost leaders cannot fully ignore differentiation (e.g. quality, service) otherwise they might be forced to discount prices and into price wars. Low cost business models in the airline (e.g. Southwest and EasyJet) or telecommunications industry (Tele2) are examples for this strategy type. ƒDifferentiators: A differentiation strategy is based on building the customer perception of a superior product.267 It requires the firm to create something unique and thus permitting to ask for price premiums.268 Uniqueness can be achieved through various means e.g. brand image, technology, service, product properties. Product innovation is one option to differentiate and create something unique.269

260

See MORGAN/STRONG (2003), p.164.

261

See WALKER/RUEKERT (1987), p.16, and HAMBRICK (2003), p.115f.

262

See VAZQUES/SANTOS/ALVAREZ (2001), p.75.

263

See OLSON/SLATER/HULT (2005), p.51.

264

MILES and SNOw’s typology was already (ab-) used to assess innovation strategy. See e.g. GRIFFIN/PAGE (1996), p.482, and MARKHAM/GRIFFIN (1998), p.442 and chapter II1.2.2, Table II-3.

265

Also for the following characterizations, see PORTER (1980), p.34ff.

266

See VAZQUES/SANTOS/ALVAREZ (2001), p.75f.

267

Also see WALKER/RUEKERT (1987), p.16.

268

Also see DESS/DAVIS (1984), p.469.

269

Also see VAZQUES/SANTOS/ALVAREZ (2001), p.75f.

Innovation Strategy and Firm Context

63

However, a differentiator cannot neglect its cost position either otherwise its price premium will be nullified. Apple, for example, pursues a differentiator strategy combining different unique aspects like product innovation (e.g. iPod and iTunes), design and brand image. ƒFocus: Focus strategy is the third of PORTER’S strategies. Its definition is based on a different criterion. Whereas cost leadership and differentiation vary with regard to how to achieve a competitive advantage, a focus strategy distinguishes itself through its competitive scope. Any focus strategy concentrates on a particular segment – be it customer, geography or product line.270 Premium car makers like Porsche and Ferrari are examples for focus strategy. Prior research suggests that a firm’s innovation strategy needs to be aligned with its overall business strategy. MILLER and FRIESEN note that “determinants of product innovation in firms are to a very great extent a function of the strategy that is being pursued.”271 They not only demand alignment between innovation and business strategy, but that a firm’s innovation strategy should be derived from the overall strategy. Similarly, MAIDIQUE and PATCH call for marketing and technology strategies to be defined so that they are mutually consistent and in support of the overall business strategy.272 In their empirical study ZAHRA and COVIN confirm that business strategy moderates the relationship between technology policy and firm performance.273 BARCZAK finds for the telecommunications industry that no single NPD strategy is superior but that the outcome depends on the fit between NPD strategy and corporate goals and capabilities.274 Most recently, COOPER, EDGETT and KLEINSCHMIDT ask for NPD goals to be linked to overall business goals, not only for performance reasons, but also that the role of NPD in achieving business goals is clearly articu-

270

Also see DESS/DAVIS (1984), p.469.

271

See MILLER/FRIESEN (1982), p.17.

272

See MAIDIQUE/PATCH (1982), p.285.

273

See Zahra/Covin (1993), p.470.

274

See BARCZAK (1995), p.224.

64

Literature Review

lated.275 Empirically they observe that ‘the role of NPD in achieving the overall business goals’ is the element of NPD strategy most strongly correlated with NPD performance. Finally, SLATER and MOHR claim in a recent conceptual paper that successful development and commercialization of innovation depends on the interaction between a firm’s overall and innovation strategy. Moreover, they call for more research to look at this interaction.276 The present study addresses this research gap by investigating differentiation, cost leader and focus strategies as moderators of the innovation strategy – performance relation. Prior research indicates that business strategy influences this relation. 3.1.2 Environmental Uncertainty as Contingency Environmental uncertainty refers to the degree and unpredictability of change in an organization’s environment.277 Environmental uncertainty is typically defined along two dimensions: market and technological uncertainty.278 Market uncertainty is characterized by changes in customers, their preferences, and competitors. The more unpredictable, frequent and significant these changes the higher the market uncertainty and the more difficult the ability to forecast any market developments.279 Technological uncertainty refers to the frequency and significance of technological advances within an industry.280 Market and technological uncertainty are also termed market and technological turbulence.281 Moreover, market and technological uncertainty are at times investigated in a combined manner, and then termed environmental uncertainty.282 Even though less researched than in the context of business strategy, environmental uncertainty is the most investigated contingency in innovation

275

See COOPER/EDGETT/KLEINSCHMIDT (2004), p.51.

276

See SLATER/MOHR (2006), p.26.

277

See e.g. DANNEELS/SETHI (2003), p.3.

278

See e.g. JAWORSKI/KOHLI (1993), p.53ff, and NARVER/SLATER (1994), p.46ff.

279

See e.g. CALANTONE/GARCIA/DROEGE (2003), p.91f.

280

See e.g. ZHOU/YIM/TSE (2005), p.47.

281

See e.g. CALANTONE/GARCIA/DROEGE (2003), DANNEELS/SETHI (2003) and KIRCA/ JAYACHANDRAN/BEARDEN (2005).

282

See e.g. CALANTONE/GARCIA/DROEGE (2003).

Innovation Strategy and Firm Context

65

research.283 Nevertheless, it has not yet been analyzed in the context of innovation strategy. For innovation research overall, BSTIELER compiled a literature review on the moderating role of environmental uncertainty for new product development variables.284 He concludes that the findings indicate mixed results in regard to any impact of environmental uncertainty on the relationship between new product development variables and outcome.285 He identifies some moderations; however, the majority of effects are insignificant. KIRCA, JAYACHANDRAN and BEARDEN conducted a metaanalysis of the impact of moderators on the market orientation – performance relation. In line with BSTIELER they observe insufficient empirical evidence that market uncertainty moderates the market orientation – performance relation. 286 They identify five studies with a positive, two with a negative and seven with no significant effect. For technological uncertainty, the empirical evidence is even more limited. The majority of studies show no significant effect.287 To complement the meta-analyses of BSTIELER and KIRCA/JAYACHANDRAN/ BEARDEN, additional empirical work is listed in Table II-13, not included in their literature reviews. As a main effect, environmental uncertainty was only investigated in two studies. Even so, they tend to suggest that environmental uncertainty is a relevant contingency. With regard to a moderating effect, the picture is similarly equivocal as observed by BSTIELER and KIRCA/JAYACHANDRAN/BEARDEN. Technical uncertainty shows positive and insignificant effects, but no negative ones. Customer, demand and competitor uncertainty (elements of market uncertainty) and overall market uncertainty exhibit predominantely no significant effects and if so more negative than positive ones. For environmental uncertainty, the combination

or

market

and

technological

uncertainty,

MILLER/FRIESEN

and

CALANTONE/GARCIA/DROEGE find predominantely positive associations and moderations with innovativeness.

283

Also see chapter II-3, including literature references.

284

See BSTIELER (2005), p.269-70.

285

See BSTIELER (2005), p.268.

286

See KIRCA, JAYACHANDRAN and BEARDEN (2005), p.26.

287

See KIRCA, JAYACHANDRAN and BEARDEN (2005), p.36.

66

Literature Review

Table II-13: Environmental Uncertainty in Innovation Research Author

Independent Variable

Dependent Variable

MILLER/ FRIESEN (1982)

Innovation / Innovativeness

-

z

z

z

 Environmental dynamism

Environmental dynamism

importantly related with innovation

Innovation performance

z

Technology orientation  Demand uncertainty

Demand uncertainty

Competitor orientation – Demand uncertainty

1. NPD speed & Innovativeness corporate strategy Market planning orientation 2. Innovation Risk taking performance

z z

z z

z

Customer orientation: o Market turbulence  Technological turbulence

Market turbulence Technological turbulence

Market turbulence Technological turbulence Environmental turbulence (= combination of first two)

Competitor orientation: Market turbulence  Technological turbulence

 Technological turbulence impacts program performance o Market and environmental turbulence with no effect

o/+

CALANTONE /GARCIA /DRÖGE (2003)

Moderating Effects

Customer orientation:  Demand uncertainty

1. InnovativeHAN/KIM/ ness SRIVASTAVA Market orientation 2. Firm (1998) performance

z

Main Effects

o/+

GATIGNON/ XUEREB (1997)

Strategic orientation: z Customer orientation z Technology orientation z Competitor orientation

Contingencies

Environmental turbulence moderates two out of four expected paths positively; others n.s.

Source: Own illustration

z

DANNEELS/ SETHI (2005)

z

Future oriented market scanning* Willingness to cannibalize

Canadian sub-sample (82) o No moderations z

Time efficiency

z

Market uncertainty Technological uncertainty

Australian sub-sample (100):

o Market uncertainty: no effect on any path Technological uncertainty: 2 paths positive, 2 n.s.

o/+

BSTIELER (2005)

Various NPD: z Marketing proficiency z Technical proficiency z Project team z Process compression

z

Innovativeness

z

z

Future oriented market sc.: – Customer turbulence – Competitor turbulence  Technological turbulence

Customer turbulence Competitor turbulence Technological turbulence

Willingness to cannibalize:

o Customer turbulence

 Competitor turbulence o Technological turbulence

ZHOU/YIM/ TSE (2005)

Strategic orient., Market forces: 1. Innovativez Demand ness uncertainty 2. Innovation z Technological and firm turbulence performance z Competitive intensity

 Demand See independent variables

uncertainty impacts innovativeness  Technological turbulence impacts innovativeness

‘+’ Indicates a positive moderation effect, ‘-’ a negative effect, and ‘o’ no effect; n.s. = not significant *) Future oriented market scanning is similar to proactive market orientation Performance terms standardized to innovativeness, innovation performance and firm performance; original names might differ

Source: Own illustration

Innovation Strategy and Firm Context

67

Summarizing, it first needs to be stressed that there is to date no directly comparable, prior research on the moderating effect of environmental uncertainty on the innovation strategy – performance relation. This study addresses this research gap by investigating market, technological and environmental uncertainty as moderators for the innovation strategy performance relation.288 Related empirical evidence from the broader field of innovation research shows mixed results. If at all, technological uncertainty and environmental uncertainty (the combination of technological and market uncertainty) seem more influential than market uncertainty. In the past chapter prior empirical evidence for the innovation strategy – performance relation as well as for potential contingencies was reviewed. In the following chapter III, the conceptual framework is introduced and the research hypotheses are derived. To develop the hypotheses the empirical evidence from chapter II is complemented with conceptual arguments. In other words, when generating the hypotheses in chapter III, the empirical support presented and discussed in chapter II will be referred to.

288

Environmental uncertainty is the combination of market and technological uncertainty. CALANTONE/GARCIA/DROEGE (2003) also investigated this combination.

Chapter III

Research Hypotheses

Chapter III introduces the conceptual framework and the research hypotheses. First, success hypotheses are derived for the four innovation strategy dimensions: innovativeness, distance to core business, driver of innovation, and innovation field orientation. Subsequently, moderation hypotheses are developed for the two contingency variables, business strategy and environmental uncertainty. The chapter ends with an overview of all hypotheses tested in the empirical study.

1

Conceptual Framework

Based on the literature review in chapter II it is assumed that a relationship exists between innovation strategy and firm-level performance. Innovation strategy is conceptualized along four dimensions: innovativeness, distance to core business, driver of innovation and innovation field orientation. For all four dimensions, performance effects are presumed – even though of varying intensity and direction. Furthermore it is expected that business strategy influences these success relationships depending on the fit between innovation strategy and a firm’s overall business strategy. In addition, environmental uncertainty (market and technological uncertainty) is expected to affect the relation between innovation strategy and performance. Based on these assumptions, which are elaborated and argued in detail in the following chapters, the conceptual framework is spanned – shown in Figure III-1. In addition to the relations outlined above, five control variables are included in the model: firm size, industry, geography, R&D rate and ROA. The conceptual framework serves as a measurement model for the empirical analysis. In addition, it is used to structure the following chapters. In chapter III-2.1 the four success hypotheses (H1-H4) are derived for the four innovation strategy dimensions. Chapter III-2.2 deals with all moderation hypotheses (H5a-d, H6a-d). Chapter III-2.3 concludes with a summary of all hypotheses.

70

Research Hypotheses

Figure III-1: Conceptual Framework

Contingencies Environmental Uncertainty

Business Strategy

H5a-d

Innovation Strategy

H6a-d

Firm-Level Performance

Innovativeness

H1

X

Distance to Core Business

H2

X

Innovation Performance

Driver of Innovation

H3

X

Firm Performance

Innovation Field Orientation

H4

X

Control Variables • Firm size • Industry • Geography • R&D rate • ROA H = Hypothesis

Source: Own illustration

2

Hypotheses

2.1

Success Hypotheses

2.1.1 Hypothesis 1: Innovativeness and Performance One of the main theories which supports the assumption of a positive relationship between innovativeness and performance is the resource-based view.289 The fundamental principle of the resource-based view is that the basis for a competitive advantage of a firm lies in the application of a

289

For resource-based view, see e.g. PENROSE (1959) and WERNERFELT (1984). For its application in innovation see e.g. KLEINSCHMIDT/DE BRENTANI/SALOMO (2004), p. 3f, and CHO/PUCIK (2005), p.556.

Hypotheses

71

bundle of valuable resources.290 WERNERFELDT defines resources as “those tangible and intangible assets which are tied semi-permanently to the firm.”291 Strong brands, technological know-how and the reputation of a firm are some examples of intangible assets. According to the resourcebased view a firm can achieve and maintain a sustainable competitive advantage through the inimitability, rarity and non-tradeability of unique assets.292 The products and services that arise from a firm’s unique set of resources are likewise unique.293 The same holds true for new products and services, that is innovations. This means that innovations can contribute to a firm’s competitive advantage.294 According to ARROW new products provide a transitory, competitive advantage that allows firms to obtain private returns on innovation,295 e.g. higher sales and firm growth.296 The more innovative new products the more significant and longer lasting this competitive advantage should be.297 Second, especially more radical innovations, cause indirect financial and non-financial spillover effects,298 e.g. on brand, image and reputation, which can indirectly influence growth and profitability. Moreover, new products can transform a firm’s capabilities and thus facilitate a more sustainable competitive advantage over time.299 GEROSKI, MACHIN and

VAN

REENEN claim that these indirect effects may be

much larger than the direct ones of new products.300 Third, the more innovative a product or technology the easier it may be to patent. Through patenting the competitive advantage of radical innovations and the associated financial returns may become more sustainable.301

290

See WERNERFELT (1984) and BARNEY (1991).

291

See WERNERFELT (1984), p.172.

292

See BARNEY (1991 and 1997).

293

See WERNERFELT (2005).

294

See COOPER/KLEINSCHMIDT (1991), p.250.

295

See ARROW (1962).

296

See BAYUS/ERICKSON/JACOBSON (2003), p.198.

297

See GATIGNON/XUEREB (1997), p.80.

298

Also for the following argumentation see AVLONITIS/PAPASTATHOPOULOU/GOUNARIS (2001), p.333ff, and PAUWELS/SILVA-RISSO/SRINIVASAN/HANSSENS (2004), p.151.

299

See BAYUS/ERICKSON/JACOBSON (2003), p.198.

300

See GEROSKI/MACHIN/VAN REENEN (1993), p.198ff.

301

See AVLONITIS/PAPASTATHOPOULOU/GOUNARIS (2001), p.329.

72

Research Hypotheses

Higher risks and costs are the main drawback of a higher level of innovativeness. First and foremost, the more radical an innovation the riskier it tends to be.302 LEIFER

ET AL.

distinguish between four types of risks: techni-

cal, market, organizational and resource-related risks.303 To manage and mitigate these risks costs time and money.304 Furthermore, the success rate of radical innovation projects is lower due to higher uncertainties. A low success rate results in innovative returns not materializing, which can impact firm performance.305 Second, radical innovation projects require a different, more demanding type of management in order to succeed. This implies more firm resources and can have a negative influence on performance.306 KOTZBAUER and HAUSCHILDT/SALOMO argue that the risks and costs associated with more radical (individual) innovations increase disproportionately more than returns. They believe that the relation between innovativeness and profitability is inverted-U shaped.307 However, in contrast to the vast majority of studies on innovativeness and performance to date308 this study does not take an individual project but a firm perspective. A firm’s entire new product portfolio is taken into account. At the portfolio level risks and the associated costs are spread out and managed across all projects. Also a portfolio tends to include a mixture of more and less radical innovations.309 Hence at the portfolio level higher risks and costs of some more radical innovations carry less weight overall. FIRTH and NARAYANAN measured innovativeness at the firm level and found that risk does not increase with the level of innovativeness.310 Overall, the direct and indirect financial benefits a more innovative new product portfolio provides seem to outweigh the somewhat higher risk and costs, especially, because the higher risks of more innovative products are

302

See HAUSCHILDT/SALOMO (2005), p.6-7.

303

See LEIFER ET AL. (2002), p.22.

304

See HAUSCHILDT/SALOMO (2005), p.7.

305

See e.g. HAUSCHILDT/SALOMO (2005), p.7.

306

See O’CONNOR (1998), p. 151ff; VERYZER (1998), p.304ff; DANNEELS/KLEINSCHMIDT (2001), p.357 and LEIFER ET AL. (2002), p.55ff.

307

See KOTZBAUER (1992), p.123 and HAUSCHILDT/SALOMO (2005), p.7.

308

See chapter II-2.2.2 for more details and literature references.

309

See e.g. CRAWFORD (1980), p.8 and COOPER (2005).

310

See FIRTH/NARAYANAN (1996), P.334.

Hypotheses

73

mitigated when the performance of the entire portfolio is evaluated. This is in line with prior empirical evidence (see II-2.2.2), which observes more positive than negative or non-significant relations between innovativeness (to the market) and performance. Hence a positive relationship between the innovativeness of a firm’s new product portfolio and firm-level performance is expected. Stated formally: Hypothesis 1:

The innovativeness of a firm’s new product portfolio positively influences firm-level performance.

2.1.2 Hypothesis 2: Distance to Core Business and Performance Two underlying theories are typically applied to discuss the relationship between distance to core business and performance.311 The resourcebased view of the firm312 suggests that better synergy between a firm’s skills and resources and a project’s requirements should enhance project outcome.313 Typically technical and marketing synergies are distinguished. Technical synergy refers to a project’s fit with existent R&D, engineering and production skills and resources. Marketing synergy relates to a project’s fit with existent market research, sales force, distribution, advertising and promotion skills and resources.314 Skills and resources are fungible. They can be used for more than one product or task - also for new products. Knowing, for instance, one market well and enjoying good access to certain customer groups can be leveraged for new products.315 Moreover, for new products close-to-home, the quality of implementation of certain marketing and technical tasks is higher, because employees can build on relevant experience.316 In contrast, entering new markets or using new technologies, with which a firm is not familiar, requires an organization to develop new competencies, a process that can be riskier and

311

See e.g. SONG/MONTOYA-WEISS (2001), p.66.

312

For literature references see chapter II-2.1.1.

313

See PRAHALAD/HAMEL (1990), p.81.

314

See COOPER/KLEINSCHMIDT (1991), p.247 and SONG/MONTOYA-WEISS (2001), p.66.

315

See DANNEELS/KLEINSCHMIDT (2001), p.361.

316

See SONG/PARRY (1997), p.71.

74

Research Hypotheses

more time consuming and hence more expensive than building upon an existing base.317 Second, information processing theory is accessed to discuss the distance to core business - performance relation. Information processing theory views new product development as a series of information acquisition and utilization activities designed to reduce uncertainty.318 For innovations close-to-home less of an information gap exists between the knowledge required for the development of new products and the information already possessed.319 Furthermore, entering new markets and technologies often requires the establishment of new connections to customers, suppliers, etc. This means that established communication paths and networks as well coordination mechanisms can be significantly disrupted, when innovations are further away from the core business320 Last, when innovations are close-to-home perception and interpretation of environmental signals from the organization's present domain are easier to discern and to handle.321 Although there are various reasons which imply that innovating close to home is beneficial for success, one key argument exists in favor of exploring a new product portfolio further away from the core business. Innovations further away from home can open up new windows of opportunity.322 Opportunities for growth driven by new products further away from the core business may be larger than for close-to-home innovations.323 To date no specific empirical evidence exists on the relative importance of this last argument. Balancing the arguments for a closer-to-home innovation portfolio vs. one further away from the core business, the points in favor of a close-tohome portfolio are considered to carry more weight. Therefore a portfolio

317

See MEYER/ROBERTS (1986), p. 812, and ZIRGER/MAIDIQUE (1990), p.873.

318

See MOENAERT/SOUDER (1990), p.92 and SONG/MONTOYA-WEISS (2001), p.66.

319

See GALBRAITH (1973) and SONG/MONTOYA-WEISS (2001), p.66.

320

See TUSHMAN/ROMANELLI (1985), p.171f and ZIRGER/MAIDIQUE (1990), p.873

321

See DANNEELS/KLEINSCHMIDT (2002), p.364.

322

See e.g. MEYER/ROBERTS (1986), p.812 and KLEINSCHMIDT/DE BRENTANI/SALOMO (2005), p.25.

323

See MEYER/ROBERTS (1986), p.812.

Hypotheses

75

of innovations close-to-home is expected to be more successful than a portfolio of new products distant from the core business. Empirical evidence on the subject is equivocal – as discussed in chapter II-2.2.3. Prior studies find some positive relations between fit and familiarity, the two sub-dimensions of distance to core business, and performance, but also several non significant relations. In contrast to prior studies this research defines newness to the firm as distance to core business – in the inverted sense to fit and familiarity.324 This definition has an important advantage. Distance to core business is defined in the same direction as innovativeness,325 which facilitates comparison and distinction of the two innovation strategy dimensions - a key aspiration of this study. Newness to the marketplace (innovativeness) and newness to the firm (distance to core business) are two distinct variables which are expected to have directionally different impacts on performance – as SCHLAAK observed before.326 Whereas innovativeness is expected to show a positive relation with firm-level performance (the more innovative in the market the better for performance) distance to core business is claimed to have a negative relation with performance (the further away from home the worse for performance). The traditional definition based on the two sub-dimensions, fit and familiarity, does not facilitate this direct comparison with innovativeness and is therefore not adopted.327

324

Fit and familiarity are typically used as sub-dimensions to characterize and operationalize newness to the firm. See e.g. DANNEELS/KLEINSCHMIDT (2001).

325

Innovativeness indicates an increasing level of newness to the market, distance to core business an increasing level of newness to the firm. In contrast, the terms fit and familiarity imply a decreasing level of newness to the firm, in other words an increasing closeness to the firm. A positive relation between familiarity/fit and performance might at first appear in parallel to a positive relation between innovativeness and performance. However, this is not the case, given a positive relation between familiarity/fit actually implies a negative relation of distance to core business with performance. Only the definition chosen for this study can clearly verify SCHLAAK’S point.

326

See SCHLAAK (1999), p.107ff.

327

The relation between fit/familiarity and performance would revert to a positive one (the closer to home the better), which would seem similar to the positive innovativeness – performance relation, even though the underlying definitions are contrary.

76

Research Hypotheses

Overall, a negative relation is expected between distance to core business and firm-level performance – in line with recent work by DANNEELS/ KLEINSCHMIDT and SONG/MONTOYA-WEISS.328 Stated formally: Hypothesis 2:

The distance of a firm’s new product portfolio from its core business negatively influences firm level performance (=the further away the worse).329

2.1.3 Hypothesis 3: Driver of Innovation and Performance Three drivers of innovation are investigated: (proactive) market orientation, technology orientation and competitive response. First the single drivers and their effects on performance are discussed. Subsequently, a combination of drivers is considered and the hypothesis derived. Market Orientation and Performance By definition, innovations driven by a strong market orientation are closely aligned with customer needs. Typically, new products, which address customers’ needs well, are valued higher by customers.330 Moreover, COOPER argues that market oriented firms are more sensitive in choosing lucrative markets for their new products.331 Both effects should have a positive implication on performance. In addition, proactive market orientation allows firms to anticipate and respond to latent and emerging needs earlier and better than less or responsively market oriented firms.332 Furthermore, proactive market orientation alerts firms to new market and technological developments.333 This and the previous point enable firms to introduce new products with a higher degree of innovativeness and earlier than

328

See DANNEELS/KLEINSCHMIDT (2001) p.364 and SONG/MONTOYA-WEISS (2001) p. 67. Note: both hypothesize a positive relation using fit, familiarity and synergy definitions. As explained in detail above, this translates into a negative relation when applying the distance to core business definition instead.

329

In reversed sense this is equal to ‘the closer to home the better’.

330

See e.g. KOHLI/JAWORSKI (1990), NARVER/SLATER (1990) and ATUAHENE-GIMA/SLATER/ OLSON (2005), p.464.

331

See COOPER (1984), p.156.

332

See e.g. ATUAHENE-GIMA/SLATER/OLSON (2005), p.465.

333

See e.g. ATUAHENE-GIMA/SLATER/OLSON (2005), p.467.

Hypotheses

77

competitors. Through a more innovative new product portfolio proactive market orientation can also indirectly impact firm-level performance.334 However, proactive market orientation is also associated with risks. Foremost, market orientation may lead to incremental innovations, because it does not encourage a sufficient willingness to take risks.335 For this effect HAMEL and PRAHALAD coined the term ‘tyranny of the served market’.336 True innovations may be hindered by seeing the world primarily through customers’ eyes. CHRISTENSEN and BOWER made a similar statement noting that markt orientated firms listen too carefully to their customers.337 Moreover, market orientation may lead to looking at needs which are easily identified but with minor potential. At the same time more fundamental long-term trends are neglected.338 Moreover, a market oriented firm can risk to overlook ideas from other sources beside customers like, for example, firms in different industries and threats from new, nontraditional competitors.339 Last, market-driven innovation projects typically lack a champion and true believer and risk being killed by conventional wisdom.340 Summarizing, there are important risks associated with market orientation. However - except for the missing champion argument - these are potential risks and mistakes which a firm might commit, but need not. Firms may well be conscious of the challenges of a strong market orientation and manage them accordingly. Therefore and in line with prior empirical evidence (see II-2.2.4) it is expected that proactive market orientation has a positive effect on firm-level performance. Technology Orientation and Performance

334

Some authors (e.g. GATIGNON/XUEREB (1997), p.87; HAN/KIM/SRIVASTAVA (1998), p.32) suggest that the effect of driver of innovation is also mediated by the degree of innovativeness. This means that driver of innovation has an effect on the innovativeness of new products and innovativeness then affects performance.

335

See e.g. DANNEELS (2003), p.559 and (2004), p.255; ZHOU/YIM/TSE (2004), p.42; ATUAHENE-GIMA/SLATER/OLSON (2005), p.467.

336

See HAMEL/PRAHALAD (1991), p.83.

337

See CHRISTENSEN/BOWER (1996), p.198.

338

See e.g. BURGELMAN/SAYLES (1986), p.43.

339

See e.g. ACHROL (1991), p.77f and ZHOU/YIM/TSE (2005), p.45

340

See e.g. BURGELMAN/SAYLES (1986), p.43.

78

Research Hypotheses

Technology orientation provides alternative sources of ideas and can complement market orientation’s customer view with a technical perspective.341 Moreoever, technology orientation drives more radical innovations and these tend to have a more positive effect on performance than incremental innovations.342 New technologies have the potential to create new markets and customers.343 The Internet, mobile telecommunications and the MP3 format are just a few recent examples for new markets facilitated through new technologies. Furthermore, a technology oriented organization tends to foster creativity and invention. Hence crazy ideas are more tolerated which can lead to more radical innovations.344 Last, technology driven innovation projects have a champion and true believer, most often a scientist, and therefore run a lower risk to be killed.345 The challenges associated with technology orientation are mainly related to the protagonists of technology orientation, the scientists. First, scientists may get locked into one technical solution of which they are convinced of and may not be open for other solutions.346 Second, scientists may start with what is easily researched and evaluated but the real potential might be elsewhere. Third, innovations driven by technology groups may address the need of atypical, very technology interested and literate users. Even though these are valid challenges of technology orientation the arguments in favor of a technology orientation are expected to outweigh the negative aspects. Moreover the advantages of a technology orientation have been proven empirically.347 Hence it is expected that technology orientation has a positive impact on firm-level performance.

341

See e.g. ZHOU/YIM/TSE (2005), p.45.

342

See e.g. GATIGNON/XUEREB (1997), p.87; LEIFER ET AL. (2000), ETTLIE/SUBRAMANIAM (2004), p.104 – also for further literature references.

343

See BERTHON/HULBERT/PITT (1999), p.42.

344

See ZHOU/YIM/TSE (2005), p.46.

345

See BURGELMAN/SAYLES (1986), p.43.

346

See BURGELMAN/SAYLES (1986), p.43 – also for the following critical arguments for technology orientation.

347

See e.g. GATIGNON/XUEREB (1997), p.87 and ETTLIE/SUBRAMANIAM (2004), p.104 – also for references to further empirical studies.

p.20

and

Hypotheses

79

Competitive Response and Performance Innovations which serve customer needs best require a strong market and competitor orientation. Serving customers best also means serving them better than the competition. Therefore successful innovations are probably also driven by a certain level of competitor orientation. In addition, a strong competitor orientation may lead to cost competitive innovations.348 By responding to competitors’ new product introductions a firm can identify new customer needs and technological trends. Spotting trends this way is certainly cheaper than operating an own trend scout team. However, competitive orientation is also associated with important risk. By definition, innovations driven by competitive response should be similar to competitors’ new products. Moreover, these new products are not only similar, but enter the market after the competition. Hence, unless a firm introduces a significantly cheaper version of a competitor innovation or adds some clearly differentiating feature to its follower-type innovation, pure competitive response is a risky driver of innovation. Little prior research exists on competitive response as an independent driver of innovation. Hence deriving a hypothesis based on prior empirical evidence is impossible. In addition, it is far from clear which of the conceptual arguments above are most important. There are clear benefits to a competitor orientation, but also serious risks. Furthermore, it is assumed that the vast majority of large firms which form the sample of this study undertake some sort of competitive orientation, anyway. This means that competitive orientation may not differ much between firms. Therefore competitive response is only included as a variable in the empirical study, but no hypothesis is formulated. Market and Technology Orientation and Performance In line with the empirical findings (see II-2.2.4) it is not only argued that (proactive) market orientation and technology orientation both have a positive impact on firm-level performance, but that a balanced combination of both drivers of innovtion is most beneficial for a firm. Such a combination does not only combine the advantages of both drivers, but limits

348

See e.g. GATIGNON/XUEREB (1997), p.80.

80

Research Hypotheses

or even neutralizes the risks of the two individual orientations. For instance, innovations driven by a balanced market and technology orientation probably have a champion and therefore run less of a risk to be killed by conventional wisdom. Moreover, technologically driven innovations of a firm which is also strongly market oriented do not run the risk of targeting a small atypical user group. Overall, a balanced combination of market and technology orientation provides more benefits than the sum of its individual parts. Stated formally: Hypothesis 3:

As driver of innovation, a combined market and technology orientation positively influences firm-level performance.

2.1.4 Hypothesis 4: Innovation Field Orientation and Performance Innovation field orientation characterizes the set up of a firm's innovation activities along predefined fields. First, the individual characteristics of innovation field orientation and their relation with firm-level performance are discussed. Then the findings are integrated and an overall hypothesis is formulated. Focus and Performance Innovation field orientation means that a firm’s innovation activity is focused and conducted within pre-defined fields. Having set-up innovation activities along focus areas, means that a firm made choices on what to concentrate in new product development. Companies often pursue new product projects without considering whether a project adds value to the firm.349 Thus, deliberately choosing focus areas for innovation should enhance new product development success, because it forces firms to select innovation areas and projects most beneficial for them.350 HENDERSON and

349

COOPER/EDGETT/KLEINSCHMIDT (2004a, p.44) argue that even though the majority of firms nowadays has a new product development process, it is not satisfactory. Critical is how the process is implemented which also includes well-defined decision criteria at each gate of the process. With respect to process’ implementation (incl. decision criteria for project selection and gates) best and worst performers differ significantly.

350

COOPER/EDGETT/KLEINSCHMIDT make a related point by stating that “having a product innovation strategy is clearly identified as a best practice” (2004, p.52). They show

Hypotheses

81

COCKBURN argue and show that focus is important for new product development. However, they hypothesize and observe that the positive effect of focus on innovation productivity drops again after a certain point. They point out that too much focus limits diversity. Little diversity impedes economies of scope and knowledge spillover effects which both have a positive impact on NPD productivity.351 Moreover, having several projects within an innovation field focusing on one or related topics (versus one project per topic) increases the probability of new product success. Combining the conceptual discussion with the empirical findings of COOPER and FIRTH/NARAYANAN,352 who both observe a positive relation between focus in NPD and performance, it is expected that focus in innovation, as a subconstruct of innovation field orientation, has either a positive or an inverted-U shaped relation with firm-level performance. Mightiness of Projects and Performance Innovation field orientation implies353 that several innovation projects work on related topics within an innovation field. Thanks to encompassing multiple projects, an innovation field should have sufficient resources at its disposal. Deficiently endowed innovation projects pose a problem in NPD.354 HENDERSON and COCKBURN found that there exists a critical mass in terms of full-time scientists and financial resources for an innovation project to generate significant output.355 Given innovation fields contribute to achieve this critical mass, mightier projects should as an aspect of innovation field orientation, positively influence performance. Time Horizon and Performance Innovation fields have a mid- to long-term time horizon. More radical innovation projects typically also have a significantly longer time horizon

that many firms do not have an innovation strategy, in other words do not choose what to focus on in innovation and how to implement it. 351

See HENDERSON/COCKBURN (1996), p.41.

352

See chapter II-2.2.5 for more details and literature references on these and other empirical studies related to innovation field orientation.

353

See detailed definition in chapter II-2.2.5.

354

See e.g. COOPER/EDGETT/KLEINSCHMIDT (2004), p. 56/7.

355

See HENDERSON/COCKBURN (1996), p.42.

82

Research Hypotheses

than incremental innovations.356 Assuming that a more radical innovation portfolio is more beneficial in terms of performance than an incrementally innovative one,357 may imply that a company needs to be more long-term oriented.358 However, long-lasting innovation projects are not necessarily of radical nature. Innovation projects are not always managed as projects (e.g. with clear deadlines) and pass stage gates, but linger on for a long time without being finished or stopped.359 In this sense, a long-term orientation is clearly detrimental for performance. Hence it is not clear whether a long-term orientation has a positive impact on performance or not, which is in line with HERRMANN/GASSMANN/EISERT’S ambiguous results.360 Concluding, the direction of the relation between time horizon and performance is not clear. Organizational Formality and Performance Innovation fields are supported by some kind and level of organizational formality. Organizational formality can assume a variety of shapes, e.g. the formalization of processes, informal social relations or independent organizational units. Formalization is considered beneficial for incremental innovations and disadvantageous for radical innovations.361 On the one hand, formalization reduces the variance of how new product development is done, and codifies best NPD practices.362 Both aspects are advantageous for incremental innovations. On the other hand, formalization hampers experimentation due to setting boundaries and restricting behaviour. Formalization hinders deviating from existing knowledge and behavioral patterns – important

356

See e.g. LEIFER ET AL. (2000), p.19, and LAURIE/DOZ/SHEER (2006), p.87.

357

See chapters II-2.2.1 and III-2.1.1 for details on the hypothesis that ‘the more innovative a firm’s new product portfolio the better for firm-level performance’.

358

See e.g. GASSMANN/HERRMANN/EISERT (2005), p.87.

359

See COOPER/EDGET/KLEINSCHMIDT (2004), p.54, who make the point that metrices, evaluation and feedback is missing for most NPD projects. This means that they are not properly handled as projects.

360

See HERRMANN/GASSMANN/EISERT (2005), p.15 and chapter II-2.2.5.

361

See e.g. JANSEN/VAN DEN BOSCH/VOLBERDA (2006), p.7.

362

See e.g. BENNER/TUSHMAN (2003), p.245 and ZANDER/KOGUT (1995), p.78.

Hypotheses

83

antecedents for radical innovations.363 Based on the hypothesis (H1) that radical innovations are more beneficial for performance than incremental ones and assuming that formalization is rather beneficial for incremental innovations, formalization is expected to have either a negative or no significant relation with performance. This is in-line with the equivocal empirical findings discussed in chapter II-2.2.5. In contrast, informal social networks – also termed connectedness of employees - should positively influence innovativeness and hence indirectly performance.364 Informal social networks increase the opportunity for informal hall talk and thus the accessibility of knowledge.365 Also, informal social networks help combining and developing new knowledge.366 Finally, large networks facilitate to establish legitimacy of new ideas and thus the adoption of innovations.367 However, beyond a certain level, informal social networks may limit access to disparate perspectives and drive the proliferation of informal norms. This might again restrict deviant behavior which fosters creativity.368 Nevertheless the points arguing that informal social networks are benefical for innovativeness and thus for firm-level performance are considered more important. Moreover, the empirical findings - presented in chapter II-2.2.5 - demonstrate that informal social networks have a positive effect on performance.369 Independent organizational units are another vehicle used to implement organizational formality of innovation fields.370 Independent units can be advantageous for more radical and long-term innovation projects, because these special units are not subject to business units’ short-term results orientation, bureaucracy, norms and rules.371 On the other hand, most in-

363

See e.g. JANSEN/VAN DEN BOSCH/VOLBERDA (2006), p.7.

364

See e.g. JANSEN/VAN DEN BOSCH/VOLBERDA (2006), p.7f.

365

See JAWORSKI/KOHLI (1993), p.56.

366

See ATUAHENE-GIMA (2003), p.362.

367

See SUBRAMANIAM/YOUNDT (2005), p.454.

368

See e.g. JANSEN/VAN DEN BOSCH/VOLBERDA (2006), p.7-8.

369

Also see LEIFER ET AL. (2000), p.67, with regard to the importance of informal networks for radical innovations.

370

See e.g. DEGUSSA’S project houses: www.degussa.com/degussa/en/innovations/creavis/project_houses

371

See LAURIE/DOZ/SHEER (2006), p.87.

84

Research Hypotheses

novations need at some point interaction with the mainstream organization e.g. for accessing specific knowledge.372 Having innovation fields organized in independent units might make this interaction more challenging than it already is.373 Overall, some level of organizational independence has proven to be a prerequisite for more radical innovations,374 which are expected to be more beneficial for performance than incremental ones. HERRMANN/GASSMANN/EISERT also found an indirect positive effect of independent organizational units on performance.375 Based on the argumentation and empirical findings for the three subconstructs (formalization, informal social networks and independent organizational units), organizational formality is either expected to have a positive or no significant impact on firm-level performance. Synergies and Performance One key benefit of innovation field orientation is that synergies may materialize between related innovation projects within an area of focus.376 Synergies increase innovation productivity,377 because economies of scope can occur,378 when there are several related projects within an innovation field. Economies of scope exist, when multiple innovation projects share inputs at no additional cost. Hence innovation costs per project decrease and overall performance should increase. HENDERSON and COCKBURN illustrate the idea with the following example: “Economies of scope exist if the work of a group of peptide chemists is potentially relevant to a wide range of applications, and can be utilized in any of them without diminishing its usefulness to the others.” Second, and in addition to economies of scope, internal knowledge spillovers can occur between related projects. These spillovers can affect innovation output irrespective of expenditure. Again, HENDERSON and COCKBURN explain the idea with an example:”Benefits of di-

372

See LEIFER ET AL. (2000), p.66.

373

See LEIFER ET AL. (2000), p.67.

374

See LEIFER ET AL. (2000), p.20.

375

Indirect effect through innovation capability, see chapter II-2.2.5 and Table II-10.

376

See definition of innovation field orientation in chapter II-1.2.3.4.

377

See e.g. COOPER (1984), p.156f, HENDERSON/COCKBURN (1996), p.35f and BLAU/PEKNY/ VARMA/BUNCH (2004), p.21ff.

378

Also for the following, see HENDERSON/COCKBURN (1996), p.35.

Hypotheses

85

versity may also arise if discoveries made in one program stimulate the output of another through cross-fertilization of ideas or other forms of knowledge spillovers.” Considering these arguments and that both COOPER and BLAU/PEKNEY/VARMA/BUNCH empirically observed a positive relationship,379 synergies are expected to have a positive impact on firm-level performance. The majority of the five aspects characterizing innovation field orientation – focus, mightiness of projects, time horizon, organizational formality and synergies – are expected to positively influence firm-level performance. Moreover, JONASH/SOMMERLATTE, COOPER/EDGETT/KLEINSCHMIDT and LAURIE/ DOZ/SHEER, who investigated innovation field orientation in a holistic manner, all claim positive associations with success.380 Hence it is expected that innovation field orientation positively influences firm-level performance, even though some first order constructs (e.g. organizational formality) may exhibit no or a negative relation. Stated formally: Hypothesis 4:

2.2

Innovation field orientation positively influences firmlevel performance.

Contingency Hypotheses

2.2.1 Business Strategy as Contingency In chapter II-3.1.1 it was argued that the relation between innovation strategy and firm-level performance is contingent upon the fit between innovation and business strategy. That is, the influence of innovation strategy on performance may be stronger or weaker depending on the business strategy a firm pursues. In this chapter hypotheses are derived how business strategy influences the relation between the four innovation strategy dimensions and firm-level performance.

379

See chapter II-2.2.5 and Table II-10. Note: HENDERSON and COCKBURN did not investigate the direct relation between synergies and productivity, but included the effects of synergies in their construct ‘scope’. Hence the author does not consider their study as directly comparable as the other pieces of research mentioned.

380

See Table II-10 in chapter II-2.2.5.

86

Research Hypotheses

2.2.1.1

Hypothesis 5a: Innovativeness, Business Strategy, and Performance

Earlier, a positive relationship between the level of innovativeness of a firm’s new product portfolio and performance was claimed (H1). Does the strength of this relationship depend on whether firms pursue differentiation or cost leadership strategies? New product development and innovations are an important element for differentiators.381 Using the MILES and SNOW typology, GRIFFIN and PAGE show that the level of innovativeness of a firm’s project portfolio varies significantly by strategy type. Prospectors have a higher proportion of new-to-the-world innovation projects in their portfolio than Analyzers and Defenders.382 MILES and SNOW’S Prospector strategy type comes closest to PORTER’S differentiator.383 Hence GRIFFIN and PAGE’S finding indicates a positive correlation between innovativeness and differentiation. Second, DESS and DAVIS show that intensive advertising, innovative marketing techniques and strong brand identification are additional characteristics of a differentiator strategy.384 New products and their introduction in the market can benefit from intensive and innovative marketing, because it helps innovations to become more quickly known in the marketplace and/or to reduce barriers of adoption.385 ZAHRA and COVIN observe empirically that marketing intensity is positively correlated with new product development - another indication for an association between innovativeness and differentiation.386 Summarizing, it is expected that a differentiation strategy positively affects the relationship between innovativeness and firm-level performance. With regard to cost leaders the situation is different. Saving costs along the entire value chain is the imperative for a cost leader.387 Expenses for

381

See VAZQUEZ/SANTOS/ALVAREZ (2001), p.76.

382

See GRIFFIN/PAGE (1996), p.485.

383

See e.g. MILLER (1988), p.283.

384

See DESS/DAVIS (1993), p.463.

385

See ZAHRA/COVIN (1993), p.458.

386

See ZAHRA/COVIN (1993), p.463.

387

See chapter II-3.1.1.

Hypotheses

87

R&D and new product development can be significant cost drivers.388 Even though no direct correlation can be claimed between the expenses on innovation389 and innovativeness,390 it is widely acknowledged that more radically new products are more expensive to develop and to launch than incremental innovations.391 Hence a cost leader should, by definition, be less inclined to develop highly innovative new products. Cost leaders improve their existing products rather than create entirely new ones.392 This means that the level of innovativeness should be lower for cost leaders. Contradictory to their expectations ZAHRA and COVIN find a positive relationship between cost leadership and new product development.393 However, their finding is not comparable, as their scale of new product development is about the overall intensity at which a company undertakes innovation and not about the level of innovativeness of new products.394 Hence no moderation effect is expected for cost leaders. Stated formally: Hypothesis 5a: The positive relationship between innovativeness and firm-level performance is stronger for differentiators. 2.2.1.2

Hypothesis 5b: Distance to Core Business, Business Strategy, and Performance

For distance of a firm’s new product portfolio to its core business and firmlevel performance a negative relation was claimed, that is to say the further away from home the worse (H2). The question to be answered is whether this relationship is moderated by type of strategy. GRIFFIN and PAGE found that Prospectors possess project mixes with the highest level of newness to the firm. That is, their new products are further away from the core business. In constrast, Analyzers and Defenders develop new products closer to home.395 GRIFFIN and PAGE’S findings indicate that dis-

388

See e.g. chapter IV-6.1.1.3 and SMITH (2005), p.157, for an overview of average R&D spending per industry.

389

Typically termed R&D rate, see chapter I, introduction.

390

See e.g. LEIFER/KASTHURIRANGAN/ROBESON (2006), also for a review of the literature.

391

See e.g. LEIFER ET AL. (2000), p.19ff.

392

See ZAHRA/COVIN (1993), p.458.

393

See ZAHRA/COVIN (1993), p.464.

394

See ZAHRA/COVIN (1993), p.478.

395

See GRIFFIN/PAGE (1996), p.485.

88

Research Hypotheses

tance to core business varies by type of business strategy. The MILES and SNOW Prospector type comes closest to PORTER’S differentiators.396 Hence, developing new products further away from the core business seems a way to differentiate. This means that differentiation may moderate the distance to core business – performance relation. In contrast, the motivation for cost leaders to develop new products further away from their core business is questionable. Knowledge and capability building for unfamiliar domains is more difficult and expensive. Higher costs for market launch may add on top. Hence, there is no reason to expect that cost leadership moderates the distance to core business – performance relation. Stated formally: Hypothesis 5b: The negative relationship between distance to core business and firm-level performance is weaker for differentiators. 2.2.1.3

Hypothesis 5c: Driver of Innovation, Business Strategy, and Performance

As to the third innovation strategy dimension, driver of innovation, and its effect on firm-level performance it is expected that both technology and market orientation have a positive impact, however, that a combination of market and technology orientation would be even more beneficial (H3).397 How does business strategy influence the effect these drivers have on performance? Prior research shows that more innovative new products are predominantely driven by technology orientation.398 Given that differentiators are – in contrast to cost leaders – more inclined to develop and offer new products with a higher level of innovativeness,399 the relation between technology orientation and firm-level performance may be moderated by a differentiator strategy. A market oriented firm is more predestined to generate incremental innovations.400 Incremental innovations are

396

See VAZQUEZ/SANTOS/ALVAREZ (2001), p.76.

397

See chapter III-2.1.3.

398

See e.g. GATIGNON/XUEREB (1997), p.87; LEIFER ET AL. (2000), ETTLIE/SUBRAMANIAM (2004), p.104 – also for further literature references.

399

See chapter III-2.2.1.1.

400

See chapter III-2.1.3.

p.20

and

Hypotheses

89

important for any firm. Hence, neither differentiators nor cost leaders are expected to moderate the market orientation – performance relation. Similar to a technology orientation, a combined market and technology orientation should drive more significant innovations.401 Hence the relation with firm-level performance should be moderated for differentiators. Applied to the main effect (Hypothesis 3) and stated formally: Hypothesis 5c: The positive relationship between a combined market and technology orientation and firm-level performance, is stronger for differentiators. 2.2.1.4

Hypothesis 5d: Innovation Field Orientation, Business Strategy, and Performance

As a main effect, it is expected that innovation field orientation has a positive impact on firm-level performance (H4).402 Does the type of business strategy have an impact on this relation? An innovation field orientation should be more beneficial to firms with a differentiation strategy than for cost leaders. By definition, innovation fields are of long term nature with the intent to facilitate streams of significant innovations.403 Significant innovations are rather in the interest of differentiators, because new products provide an opportunity to distinguish from the competition.404 Hence the relationship between innovation field orientation and firm-level performance should be positively influenced by differentiation strategy. Cost leaders should, by definition, be more inclined towards incremental product innovations, because their development incurs fewer costs.405 Therefore, it is doubtful whether a long-term oriented innovation field orientation which fosters more radical innovation, but also incurs higher costs, is a sensible approach for cost leaders.406 Hence no moderation is expected for cost leaders. Stated formally:

401

See chapter III-2.1.3.

402

See chapter III-2.1.4.

403

See LAURIE/DOZ/SHEER (2006), p.80ff and chapter II-1.2.3.4.

404

Also see chapter III-2.2.1.1.

405

Also see chapter III-2.2.1.1.

406

See DESS/DAVIS (1984), p.476.

90

Research Hypotheses

Hypothesis 5d: The positive relationship between innovation field orientation and firm-level performance is stronger for differentiators. 2.2.2 Environmental Uncertainty as Contingency Environmental uncertainty refers to the degree and unpredictability of change in an organization’s environment407 and is defined along two dimensions: market and technological uncertainty.408 In chapter II-3.1.2 it was argued that the relation between innovation strategy and firm-level performance is influenced by the degree of environmental uncertainty. The effect of innovation strategy on performance may be stronger or weaker depending on how uncertain the environment is. In this chapter hypotheses are derived how environmental uncertainty impacts the relations between the four innovation strategy dimensions and firm-level performance. 2.2.2.1

Hypothesis 6a: Innovativeness, Environmental Uncertainty, and Performance

In an environment of high uncertainty customers and their needs as well as competitors change often. Also, technologies advance at higher speed. To cope with the high level of change firms need to modify and enhance their products and services more often.409 If firms miss an important development they might be squeezed out of the market. Moreover, time-tomarket is important. Within a dynamic and changing environment being first-to-market can mean a significant advantage and profits.410 The importance of timing is reinforced by the fact that windows of opportunity are shorter in an environment of high uncertainty due to abbreviated product life cycles.411 Last, an uncertain and dynamic environment en-

407

See e.g. DANNEELS/SETHI (2003), p.3.

408

See e.g. JAWORSKI/KOHLI (1993), p.53ff and NARVER/SLATEr (1994), p.46ff. For more details on the definition of environmental uncertainty, see chapter II-3.1.2.

409

See ZHOU/YIM/TSE (2005), p.47.

410

See CALANTONE/GARCIA/DROEGE (2003), p.100.

411

See CALANTONE/GARCIA/DROEGE (2003), p.100, and ZHOU/YIM/TSE (2005), p.47.

Hypotheses

91

courages firms to take riskier decisions, make riskier NPD investments412 and to cannibalize existing revenues.413 Taking these arguments together an uncertain environment should – by pure necessity - enhance the development of innovations. Hence, based on these conceptual arguments one would expect the relation between innovativeness and firm-level performance to be moderated by environmental uncertainty. Overall, prior research on the moderating role of environmental uncertainty on new product development found equivocal results.414 Of the studies shown in Table II-13 research by MILLER/FRIESEN and CALANTONE/ GARCIA/DROEGE comes closest to the moderation effect discussed here, as they investigate environmental uncertainty with innovativeness as an independent variable. Moreover, both authors study environmental uncertainty - that is the combination of market and technological uncertainty like done in this study. MILLER and FRIESEN find that environmental uncertainty is related to innovation.415 CALANTONE, GARCIA and DROEGE observe that the path from innovativeness to corporate strategy planning is stronger the more uncertain the environment.416 In addition, ZHOU, YIM and TSE investigate the main effect of demand and technological uncertainty on innovations and find three out of four positive relations with the fourth being not significant.417 Combining prior empirical evidence with the conceptual discussion environmental uncertainty is expected to positively influence the innovativeness – firm-level performance relation. Stated formally: Hypothesis 6a: The higher the level of environmental uncertainty the stronger the positive effect of innovativeness on firmlevel performance.

412

See GATIGNON/XUEREB (1997), p.90.

413

See DANNEELS/SETHI (2003), p.3.

414

See chapter II-3.1.2 and Table II-13.

415

See MILLER/FRIESEN (1982), p.11.

416

See CALANTONEGARCIA/DROEGE (2003), p.98.

417

See ZHOU/YIM/TSE (2005), p.50. Specifically, they investigate the impact of demand and technological uncertainty on market-based and technology-based innovations. Hence, the four combinations and relations.

92

Research Hypotheses

2.2.2.2

Hypothesis 6b: Distance to Core Business, Environmental Uncertainty, and Performance

In an uncertain and dynamic environment where customers, their preferences and competitors change often418 it may be beneficial to develop part of the new product portfolio further away from the core business. First, it is recommendable in a turbulent environment to develop several new products at the same time in order to be ready with alternatives.419 If these alternatives are all close to the current core business they are - by definition - similar to each other and to existing products. This approach can negatively influence the success rate of innovations, because in a turbulent environment, similar alternatives close to the current business promise less success than varying alternatives also further away from the core business. Second, an uncertain market environment can mean that markets cease to exist. Pursuing close-to-home innovations for a dying market is highly questionable. Hence, in order to replace revenues of markets ceasing to exist it may be advantageous to develop innovations further away from the core business. Third, taking a broader perspective with regard to innovation and looking beyond the current core business may help to notice and prepare for the entry of new competitors, e.g. from other industries, a likely occurence in an uncertain environment. No prior, directly relevant research exists on the moderating effect of environmental uncertainty on the distance to core business – firm performance relationship. None of the studies in Table II-13 come close to this question. Therefore, driven by the conceptual arguments above it is claimed that environmental uncertainty moderates the relationship between distance to core business and firm-level performance. Stated formally: Hypothesis 6b: The higher the level of environmental uncertainty the weaker the negative effect of distance to core business on firm-level performance.

418

See e.g. CALANTONE/GARCIA/DROEGE (2003), p.91f, and chapter II-3.1.2.

419

See DANNEELS/SETHI (2005), p.16.

Hypotheses

2.2.2.3

93

Hypothesis 6c: Driver of Innovation, Environmental Uncertainty, and Performance

Driver of innovation investigates the forces behind a firm’s innovations. Three drivers are studied: market orientation, technology orientation and competitive response. This chapter discusses the question if and how the relation between driver of innovation and firm-level performance (H3) is moderated by environmental uncertainty. Thereby the focus is on market and technology orientation, given a main effect was only formulated for these two drivers and not for competitive response.420 Chapter II-3.1.2 lists prior research on environmental uncertainty as a contingency in innovation research. Table II-13 includes several studies related to driver of innovation and environmental uncertainty. Their findings will be discussed in more detail. Moreover, the empirical evidence will be qualified by conceptual arguments. Both, the more specific literature review and the conceptual discussions, are done on a single driver basis, that is to say discussing market orientation and technology orientation separately. At the end, the findings are integrated into one overall hypothesis on how environmental uncertainty influences the driver of innovation - performance relation. Market Orientation and Environmental Uncertainty KIRKA, JAYACHANDRAN and BEARDEN undertook a meta-analysis on how environmental uncertainty moderates the market orientation - performance relation.421 Overall, they find insufficient evidence that any moderation effect exists. Nevertheless, for market uncertainty, they observe more significant and positive moderations than for technology uncertainty. In line, CALANTONE, GARCIA and DROEGE state that the effect of market and technology uncertainty on market orientation has received mixed conclusions.422 KIRKA

ET AL.’S

analysis includes studies until 2002, and one from 2003.

Hence, Table II-13 in chapter II-3.1.2 is to be considered as an amendment to their analysis. Table II-13 includes more recent research as well

420

See chapter III-2.1.3.

421

See KIRKA/JAYACHANDRAN/BEARDEN (2005), p.35-36.

422

See CALANTONE/GARCIA/DROEGE (2003), p.95.

94

Research Hypotheses

as two highly relevant studies from the late 1990s, which KIRKA

ET AL.

did

not consider. Some studies in Table II-13 investigate driver of innovation in relation to innovativeness as the dependent variable, instead of performance. These studies are also relevant, given GATIGNON and XUEREB claim that the effect of driver of innovation on innovation performance can be mediated by innovation characteristics like radicalness or differentiation.423 Concerning market uncertainty and market orientation, the studies in Table II-13 show mixed results, in line with prior meta-analyses. However, regarding technological uncertainty the studies in Table II-13 show more positive and significant effects than KIRKA

ET AL.

found. Last,

CALANTONE, GARCIA and DROEGE also studied the effect of the combined market and technology uncertainty (environmental uncertainty) on market orientation. They find two positive and two not significant effects.424 Summarizing, prior research on the impact of environmental uncertainty on the market orientation - performance relation does neither provide strong indication for a positive nor a negative moderation effect. Nevertheless, more recent studies show a slight tendency towards a positive effect. With regard to the conceptual discussion of the moderating effect of market uncertainty on market orientation, the following argument clearly dominates the literature. If customers and their preferences are stable (low market uncertainty), market orientation is less effective, because products and services need little adjustment and modification.425 In contrast, within a context of high market uncertainty a strong market orientation can be advantageous, because it facilitates to learn about constantly changing customer needs and buying incentives.426 Knowing which customers exhibit which preferences helps to modify products and develop innovations – esstential in a highly dynamic environment.427 Hence, the

423

See GATIGNON/XUEREB (1997), p.87 and also chapter II-2.2.4.

424

See CALANTONE/GARCIA/DROEGE (2003), p.99.

425

See e.g. JAWORSKI/KOHLI (1990), p.14, and (1993), p.57; SLATER/NARVER (1994), p.48; KIRKA/JAYACHANDRAN/BEARDEN (2005), p.35; ZHOU/YIM/TSE (2005), p.47.

426

See e.g. GATIGNON/XUEREB (1997), p.81.

427

Also see the discussion about the moderating effect of environmental uncertainty on innovativeness – performance relation in the previous chapter, III-2.2.1.

Hypotheses

95

more uncertain a market environment is, the stronger the market orientation - performance relation should be. DANNEELS and SETHI, however, highlight that a strong market orientation can also have its limits when market uncertainty is high. The usual process of first analyzing the market and then taking the time to develop new products may be too slow and therefore ineffective. Moreover, identifying future customer needs and competitors may simply be impossible.428 Concerning technological uncertainty, a strong market orientation may also be beneficial, because it helps to choose and exploit emerging technologies and link technologies to future customer needs.429 On the other hand, if technology changes rapidly and often, major innovations are rather developed by R&D and outside the industry, and not through market orientation.430 In their seminal studies on the topic, both JAWORSKI/KOHLI (1990) and SLATER/NARVER (1994) found that the market orientation – performance relation was robust to the level of environmental uncertainty. They observe no moderation effects.431 SLATER and NARVER explain their finding as follows: “With its external focus and commitment to innovation, a marketoriented business should be prepared to achieve and sustain competitive advantage in any environmental situation. Indeed, a substantially marketoriented business should find more opportunities in any environment than its less market-oriented competitors.”432 Becoming and remaining market oriented means continuously striving for additional value for existing or new customers.433 This is a key success factor for any firm and under any environment. Concluding, the conceptual discussion also provides mixed indications with regard to environmental uncertainty and its impact on the market orientation - performance relation. However, in-line with other recent publica-

428

See DANNEELS/SETHI (2005), p.16, who also refer to BROWN/EISENHARDT (1998).

429

See DANNEELS/SETHI (2005), p.17.

430

See SLATER/NARVER (1994), p.48.

431

See JAWORSKI/KOHLI (1990), p.64 and SLATER/NARVER (1994), p.52.

432

See SLATER/NARVER (1994), p.53.

433

See SLATER/NARVER (1994), p.53.

96

Research Hypotheses

tions,434 the arguments stating that market orientation is essential in a highly uncertain environment are assigned more weight. Hence, environmental uncertainty is expected to positively moderate the market orientation - performance relation, even though the effect might be weak. Technology Orientation and Environmental Uncertainty For the second driver of innovation, technology orientation, prior research on the moderating effect of environmental uncertainty is almost nonexistent. GATIGNON and XUEREB are the only ones investigating the impact of demand uncertainty on the technology orientation - performance relation. They observe a positive effect.435 GATIGNON/XUEREB and DANNEELS/SETHI both argue that in a highly uncertain market environment it is difficult or even impossible to predict what consumers want.436 Therefore it may be more promising to develop several technologically driven alternatives and be ready when more information becomes available.437 Furthermore, in a highly changing environment more significant innovations are necessary to succeed, which are more frequently driven by technology orientation than by market orientation.438 Therefore, the more uncertain the market environment the better it is for the technology orientation – performance relation. With regard to high technological uncertainty, one can argue that technology orientation helps a firm to be up to date in terms of the changing technological landscape. In contrast, being technology oriented under a highly uncertain technological environment may contribute to neglect customers and their preferences. A firm may risk dedicating too much effort and resources to the ‘technology game’ and thereby loosing the necessary contact to the market. Combining the conceptual discussion and the scarce empirical evidence there is slightly more support for a positive

434

See e.g. HAN/KIM/SRIVASTAVA (1998), p.35; CALANTONE/GARCIA/DROEGE (2003), p.94; KIRKA/JAYACHANDRAN/BEARDEN (2005), p.35.

435

See GATIGNON/XUEREB (1997), p.86.

436

See GATIGNON/XUEREB (1997), p.81 and DANNEELS/SETHI (2005), p.16.

437

See GATIGNON/XUEREB (1997), p.81 who also state WORKMAN (1993), p.405ff, for a similar argumentation.

438

See e.g. GATIGNON/XUEREB (1997), p.86 or ETTLIE/SUBRAMANIAM (2004), p.104. Also see chapter III-2.1.2 for a detailed discussion as well as literature references.

Hypotheses

97

effect of environmental uncertainty on the technology orientation - performance relation. Summarizing, for both drivers, market and technology orientation, conceptual considerations and prior empirical evidence suggest that envirionmental uncertainty positively moderates the relation with firm-level performance. However, the effect is expected to be rather weak. Hence, in-line with GATIGNON and XUEREB,439 it is expected that the main effect between a market and technology orientation and firm-level performance is particularly advantageous in an environment of high uncertainty. Stated formally: Hypothesis 6c: The higher the level of environmental uncertainty the stronger the positive effect of a combined market and technology orientation on firm-level performance. 2.2.2.4

Hypothesis 6d: Innovation Field Orientation, Environmental Uncertainty, and Performance

Given innovation field orientation is a new concept in innovation research440 no previous studies exist investigating how environmental uncertainty may influence the relation with firm-level performance. Innovation fields have a mid- to longterm time horizon and are intended to facilitate streams of more significant innovations. As argued in III-2.2.2.1, a higher degree of novelty is more important under a more turbulent environment, because customers and their preferences change frequently and significantly.441 Under low environmental uncertainty, where incremental innovations are often enough for a firm to maintain a competitive advantage, the costs incurred by setting up and operating innovation fields may be higher than the value extracted. Therefore, when the environment is stable and incremental innovations are sufficient to succeed, it is probably advisable to undertake new product development along existing product lines, instead of defining innovation fields. Consequently, innovation field

439

See GATIGNON/XUEREB (1997), p.81.

440

See II-1.2.3.4.

441

See chapter III-2.2.2.1 for a more detailed argumentation.

98

Research Hypotheses

orientation should be more advantageous in an environment of high uncertainty than in one of low uncertainty. Stated formally: Hypothesis 6d: The higher the level of environmental uncertainty, the stronger the positive effect of innovation field orientation on firm-level performance. 2.3

Summary of Hypotheses

Chapter III was dedicated to develop all research hypotheses to be tested in the empirical study. Four main effects (H1-H4) of innovation strategy dimensions on firm-level performance were hypothesized. In addition, eight moderation hypotheses (H5a-d and H6a-d) were derived – for the influence of business strategy and environmental uncertainty on the performance effects. All hypotheses are summarized in Figure III-1.

Hypotheses

99

Table III-1: Summary of Hypotheses

Success Hypotheses (main effects)

H1

The innovativeness of a firm‘s new product portfolio positively influences firm-level performance.

H2

The distance of a firm‘s new product portfolio from its core business negatively influences firm-level performance (= the further away the worse).

H3

As driver of innovation, a combined market and technology orientation positively influences firm-level performance.

H4

Innovation field orientation positively influences firm-level performance.

H5a

The positive relationship between innovativeness and firm-level performance is stronger for differentiators.

H5b

The negative relationship between distance to core business and firm-level performance is weaker for differentiators.

H5c

The positive relationship between a combined market and technology orientation and firm-level performance, is stronger for differentiators.

H5d

The positive relationship between innovation field orientation and firm-level performance is stronger for differentiators.

H6a

The higher the level of environmental uncertainty, the stronger the positive effect of innovativeness on firm-level performance.

H6b

The higher the level of environmental uncertainty the weaker the negative effect of distance to core business on firm-level performance.

H6c

The higher the level of environmental uncertainty the stronger the positive effect of a combined market and technology orientation on firm-level performance.

H6d

The higher the level of environmental uncertainty, the stronger the positive effect of innovation field orientation on firm-level performance.

Business Strategy

Contingency Hypotheses (moderating effects)

Environmental Uncertainty

Chapter IV

Empirical Study

In chapter III research hypotheses were developed for the relationship between innovation strategy and firm-level performance, based on existing theory and empirical evidence in innovation research. To test these hypotheses a quantitative study was undertaken. The results of this study are presented in this chapter as well as the process leading to the outcome. The chapter starts with an overview of the research design.

1

Research Design

The key research question of this study is whether a relationship between innovation strategy and firm-level performance exists. Information about innovation strategy is of strategic nature to firms and therefore often kept confidential. Besides, this kind of information may simply be too detailed for a firm to include in publicly available documents. Concluding, it is difficult to find meaningful and concrete information on innovation strategy in secondary information. Therefore a mail survey was chosen as the key inquiry tool to collect data about firms’ innovation strategies. Surveys, however, face the challenge of validity, that is to say how good they are at measuring what they are supposed to measure.442 To meet this challenge several actions were taken. To start with, key respondents were preidentified through telephone calls. In addition, the answers from the quantitative survey were complemented and validated by a qualitative document analysis. Publicly available secondary data (e.g. annual reports, web sites) was reviewed for a subset of the sample, where sufficient information on innovation strategy was available. Moreover, reversed items443 were included in the questionnaire in order to avoid halo-effects.444

442

See e.g. BORTZ/DOERING (2003), p.199.

443

Reversed items are formulated in the opposite sense, e.g. instead of ‘Your house is big’ it would be formulated as ‘Your house is small’.

444

Halo effect refers to the fact that the assessment of one item is indiscriminately transferred to other items. See e.g. BORTZ/DOERING (2003), p.182.

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Empirical Study

A typical shortcoming of studies testing success relationships is common method and common source bias.445 Both biases occur when the dependent variable (e.g. performance) and the independent variable (e.g. innovation strategy) are measured using the same method (e.g. mail survey) and by asking the same source (e.g. one key informant per firm). To meet this methodological challenge, the dependent variable, firm-level performance, was not only measured via perceptual performance indicators in the survey, but also based on objective capital market data from stock exchanges. Besides avoiding common source and common method bias, capital market data follow the call for more objective and investor related success mesures.446 Another methodological challenge to overcome is the time lag between the implementation of a firm’s innovation strategy and its success.447 Some authors resolve this problem by waiting several years after completion of the survey before retrieving firm performance data.448 This study chose a different approach. By using capital market data as firm performance measure instead of accounting data the challenge dissolves given capital market data account for the future of a firm.449 The value of a firm is defined as the discounted value of present and future cash flows.450 The capital market data were retrieved from financial databases. Partial models, as the one in this study, face the challenge of not considering all factors impacting e.g. a success relationship. Besides innovation strategy there are other important factors influencing a firm’s performance. To account for some of these factors, several contingency and control variables were included concerning a firm’s environment (e.g. industry

445

See e.g. ERNST (2001), p.195ff; ERNST (2003b), p.1249ff; LEE/GREWAL (2004), p.169; HAUSCHILDT/SALOMO (2005), p.13.

446

See e.g. VENKATRAMAN/RAMANUJAM (1986), p.801ff; DAY/FAHEY SRIVASTAVA/SHERVANI/FAHEY (1999), p.177; LEHMANN (2004), p.73ff.

447

See e.g. HENARD/SZYMANSKI (2001), p.366.

448

See e.g. ZAHRA/COVIN (1993), p.462; FIRTH/NARAYANAN (1996), p.338f.

449

See e.g. ROSS/WESTERFIELD/JAFFE (1996), p.68ff.

450

See e.g. COPELAND/KOLLER/MURRIN (1995), p.22.

(1988),

p.53ff;

Research Design

103

sector, environmental uncertainty) as well as the firm itself (e.g. size, business strategy, R&D rate, ROA).451 Last, the development and use of valid measures is not always respected in strategy research.452 This study builds on well established measures to the maximum extent possible. For new constructs (e.g. innovation field orientation) existing theoretical perspectives served as a basis for the development of new measures. Thereby the approach suggested by BAGOZZI was followed.453 The entire questionnaire – including new measures – was tested during a pilot survey. One purpose of the pilot survey was to verify that the questionnaire was comprehendible, complete, and acceptable. In addition the pilot served to investigate whether the formative indicators sufficiently covered the breadth of the underlying construct.454 For the pilot survey three persons were selected who fell into the relevant sample of firms and respondents. One was a native English speaker, two were nonnatives. The three pilot respondents originated from a technology, chemicals and basic resources firm. Two were heads of innovation or R&D and the third in a leading position within the R&D organization. In addition to these practitioners, three research experts in the field of innovation contributed to the enhancement of the questionnaire.455 Moreover the author sought advice from two experts from leading strategy consulting firms who both have extensive experience in dealing with top level executives of large corporations in the area of innovation/R&D/engineering.

456

In total

eight specialists from academia and practice evaluated the questionnaire in detail and improved it for the quantitative study. The pilot test did not result in major changes of the questionnaire. Some wording changes were

451

For more details about specific contingency and control variables see chapter IV-3.4 and 3.5.

452

See VENKATRAMAN (1989), p.944.

453

See BAGOZZI (1994), p.42.

454

See DIAMANTOPOULOS/WINKLHOFER (2001), p.271f; for more details on the use of formative constructs see chapter IV-3.1.

455

The three innovation experts are members of the institute sponsoring this dissertation, Institute for Technology and Innovation, at the University of Graz/Austria and also include its chair, Prof. Dr. Soeren Salomo.

456

Monitor Group (www.monitor.com) and Booz Allen Hamilton (www.bah.com)

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Empirical Study

undertaken to facilitate the understanding and some indicators were eliminated to reduce the time needed to answer the overall questionnaire. Figure IV-1 gives an overview of the major steps of the empirical study. First, the theoretical constructs were operationalized into measures and tested in a pilot survey. Second, a quantitative mail survey was conducted with publicly listed firms from manufacturing industries. Third, selected results from the quantitative survey were validated through qualitative document analysis. Fourth, capital market data measuring firm performance were retrieved from Thomson Financial’s Datastream database and firms’ web sites. Last, and after validating the used measures, the research hypotheses were tested using multiple and moderated regression. The steps of the empirical study are described in the following chapters as well as the sample frame which guided the selection of the participating companies. Figure IV-1: Overview of Research Design Step

Operationalization

Quantitative Survey

Document Analysis

Financial Databases

Analysis

Detail

Chapter

• Operationalization of constructs • Questionnaire design • Pilot survey

• Written, structured questionnaire • Stock market listed, single- and dominant business firms from manufacturing sectors • Heads of innovation or R&D • Validation of innovation and business strategy constructs • Qualitative content analysis • Publicly accessible, secondary documents

• Firm performance and control variables • Thomson Financial / Datastream databases • Investor relations section of web pages

• Validation of measures • Testing of hypothesis – via multiple and moderated regression

Source: Own illustration

IV-3

IV-2.1/2

IV-2.3

IV-2.4

IV-4

Sample and Data

2

105

Sample and Data

2.1

Sample Frame

The objective of the empirical study was to test the theoretically derived research hypotheses. An intersectoral sample was chosen which contributes to the generalizability of the results.457 To accommodate the study’s focus on product innovation only firms from manufacturing sectors were included. Given firm performance is operationalized via capital market data, participating companies needed to be publicly listed on a stock exchange. To minimize intra-firm heterogeneity and to help to control for potential effects of diversification on company’s strategic choices and financial performance, only dominant and single-product businesses were selected.458 Diversified conglomerates like Siemens and General Electric were excluded, but also firms like Bayer with an almost even split of revenues between chemicals and pharmaceuticals.459 For conglomerates one would need to investigate business units as separate companies in order to get meaningful results; however, capital market data are only available on the corporate level. Moreover, the vast majority of publicly listed companies operate nowadays in international markets. Therefore and to ensure a large enough population after applying all the above mentioned criteria, a cross-national design was chosen - with a focus on Europe and North America. Summarizing participating companies had to meet the following criteria: ƒPublicly listed on stock exchange ƒFrom manufacturing sectors ƒSingle or dominant business firms.

457

See GATIGNON/XUEREB (1997), p.82.

458

For a similar approach see DANNEELS/SETHI (2005), p.19 and ZAHRA/COVIN (1997), p.460. See RUMELT (1974), p.11, for the definition of dominant and single business firms.

459

http://www.investor.bayer.com

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Empirical Study

The initial population was drawn from publicly traded companies on the stock exchanges in Frankfurt, Vienna, Zurich, London, and New York. Thereby, the focus was on prime share companies, who agree to publish more data on a regular basis.460 For these companies the necessary financial data were more likely to be available. Second, manufacturing companies were extracted who belong to the 2000-3999 SIC codes.461 Even though the Standard Industry Classification (SIC) is one of the primary industry classifications used worldwide, it still tends to be better applied for North American firms than elsewhere. Therefore, the SIC codes were validated using the Dow Jones STOXX industry classification as well as companies’ web sites.462 Third, following RUMELT’s suggestion a threshold of 70% of revenues in the main industry was applied to meet the objective of single and dominant business firms which are less diversified.463 Revenue splits were drawn from company websites and annual reports. After this screening 319 firms were approached to provide responses to the survey questionnaire. 2.2

Quantitative Survey

Over the course of four months (February to May 2006) a fully structured, written survey was conducted. Written surveys minimize interviewer influence and allow the respondent an individual timeframe for answering.464 In order to receive valid responses the questionnaire was targeted at heads or leading employees in the area of innovation or R&D, who should have a good overview of their innovation activities and performance. Alternatively, leading employees from adjacent departments (e.g. marketing, business development) were contacted. Through upfront telephone conversations it was ensured that only knowledgeable informants took

460

http://deutsche-boerse.com/ dbag/dispatch/de/kir/gdb_navigation/listing/ 10_Market_Structure

461

www.sec.gov/info/edgar/siccodes.htm

462

www.stoxx.com/indexes/sector_classification.html. STOXX is the European arm of Dow Jones (www.dowjones.com), one of the worldwide leading business and financial news and information companies.

463

See RUMELT (1974), p.11 - also applied by ZAHRA/COVIN (1993), p.460 and FIRTH/ NARAYANAN (1996), p.338.

464

For further advantages of written surveys see e.g. BORTZ/DOERING (2003), p.253ff.

Sample and Data

107

part in the study. The questionnaire was sent via email and could either be filled out online or on paper. Two weeks after sending out the survey follow up telephone calls were started to motivate potential respondents to participate and clarify remaining questions.465 Some potential participants were contacted up to four or five times by the same person. In addition, participants were ensured full confidentiality and promised a results report including an individual section on their company in order to encourage interest and participation. This survey and follow up campaign was undertaken by two persons, very familiar with the study: the author and a research assistant. In total 319 firms were contacted which yielded a total of 138 responses. These 138 responses include nine double-answers where two people from the same company participated, e.g. because the first respondent forwarded the questionnaire to a colleague. With regard to these doubleanswers the following procedure was followed. If one questionnaire was answered by a more senior person in innovation/R&D than another, then the response from the more senior respondent was chosen. If responses were from different divisions or business units, the response from the dominant unit was selected. Where these criteria were not applicable, the average of the two responses was calculated and taken as the response for this firm. In addition, seven firms had to be screened out due to e.g. the company being de-listed in the meantime; missing values or questionable answers.466 Figure IV-2 outlines the response process. The 122 responses come from 10 manufacturing industries and include, for example, automotive parts, health care, industrial goods and technology companies. Participating companies originate from 17 countries, with 79% from Europe and 18% from North America. With regard to the functional background of the responding person 57% belong and/or lead the innovation or R&D area and 19% are from adjacent departments like marketing and business development, which are often involved in innovation. Another 19% of respondents hold general management roles or are CEOs.

465

Also see KANUK/BERENSON (1975), p.441.

466

One firm was excluded due to overly optimistic answers which could not be confirmed through qualitative document analysis. See chapter IV-2.3.2 for details.

108

Empirical Study

This reflects the fact that BU or division heads are often responsible for innovation activities. The CEO responses originate mainly from smaller firms, where CEOs assume multiple responsibilities including innovation. Figure IV-3 gives a first overview of the sample composition. Figure IV-2: Response Process

350

40%

response rate

319 300

250

200

150

138 122

-181

-9

-7

100

50

0

Contacted firms

Rejections

Questionnaires received

Firms with 2 responses

Final screening, e.g. firm de-listed

Sample

Figure IV-3: Overview of Sample Composition (n=122) 100%

Oil & Gas Basic Resources Chemicals

80%

Auto & Parts

60%

Personal & Household Goods

Construction Materials

North America

Food & Beverage

Other Asia

Other CEO / General Management

Marketing & Biz Development

Technology Europe

40%

Innovation / R&D

Industrial Goods 20%

Health Care 0%

Sector Sectors

Continent Continents

Function Function

Sample and Data

2.3

109

Document Analysis

The document analysis was conducted to verify the valid measurement of innovation strategy via a mail survey. Investigations in marketing and organizational research and recently also in innovation show that limiting the response to a survey and one key informant can lead to significant measurement errors.467 The systematic error driving these problems is called informant bias.468 Due to limited expertise and/or diverging opinions informant specific biases occur.469 One option to address this challenge is to have several persons from the same company answering a questionnaire. This multiple informant approach was not chosen for this study given the experienced difficulty of recruiting already one executivelevel respondent from large, publicly listed firms composing the sample.470 Instead the results from the quantitative survey were validated via document analysis. 2.3.1 Methodology The specific methodology applied is qualitative content analysis.471 It is an evaluation procedure to systematically assess text material. Key characteristics of qualitative content analysis constitute: ƒEvaluation procedure to analyze qualitative, non-numeric text material ƒA theory driven category system stands at the center of the methodology and is to be developed in close connection with the text material ƒThe categories function like variables in quantitative evaluation procedures

467

Marketing and organizational research: see e.g. MCGRANAHAN (1976), p.176ff. Innovation research: ERNST (2001), p.97ff and ERNST (2003b), p.34ff.

468

See KUMAR/STERN/ANDERSON (1993), p.1634.

469

See ERNST (2003), p.1268.

470

Especially firms in Germany complained frequently about being over-surveyed. For a similar observation see e.g. KRIEGER (2005), p.181.

471

Also for the following, see e.g. MAYRING (2002), p.114ff; BORTZ/DOERING (2001), p.329ff.

110

Empirical Study

ƒA uniform proceeding is critical to assure inter-subjective traceability. Summarizing, qualitative content analysis is a methodology to systematically analyze text material based on a theory driven category system. The methodology has been used in business strategy research472 and was introduced to the field of new product strategy by FIRTH and NARAYANAN.473 They suggested using it as a complementary instrument to surveys in the field of innovation research474, as done in this study. The text material consisted of publicly available documents from firms primarily annual reports and firms’ web sites. Annual reports were already accessed in prior research to seek evidence for a variety of topics in marketing, organizational, and strategy research.475 Given the Internet facilitates companies to communicate more and in a continuously up-dated manner, information on firms’ web sites was evaluated in addition to annual reports. Overall, the systematic procedure recommended by MAYRING was followed which is shown in Figure IV-4.476 During Step 1 the dimensions from the quantitative survey were chosen which were to be validated: innovation strategy and business strategy. Then the category system had to be developed for these two dimensions. The guiding principle was to stay as close as possible to the constructs and definitions in the quantitative survey to facilitate comparison of the two approaches’ results afterwards.

472

See e.g. MILLER (1987), p.7ff; MINTZBERG/WATERS (1982), p.465ff; NOBLE/SINHA/KUMAR (2002), p.31.

473

See FIRTH and NARAYANAN (1996), p.340.

474

See FIRTH and NARAYANAN (1996), p.345.

475

For an overview see NOBLE/SINHA/KUMAR (2002), p.31. Specifically, BOWMAN (1978, P.64FF) used annual reports to identify corporate strategies.

476

See MAYRING (2002), p.120.

Sample and Data

111

Figure IV-4: Procedure of Qualitative Content Analysis 1

Determination of the structuring dimensions and values (theory driven), development of the category system 2

Formulation of definitions, illustrative examples, coding guide

3

6

1. Review of the material: highlighting relevant sources

Revision of category system and definitions

4

2. Review of material: extracting relevant parts of these sources

5

Preparation of results (in given templates)

Source: Illustration following MAYRING (2002), p.120

As a starting point for the category system the constructs from the questionnaire (see chapter IV-3) were used. This also fulfilled the requirement of a theory driven category system given that all constructs and items from the questionnaire were based on extensive theory and literature review.477 Definitions from the questionnaire were simplified, amended and adapted for a qualitative, text-based analysis. This was done in an iterative process by analyzing the publicly available information of three pilot firms and - in parallel - modifying the category descriptions. During Step 2 illustrative examples from the three pilot firms were included in the category descriptions. In addition, a coding guide was formulated to ensure uniform coding across different coders.478 The coding guide was important given eight coders participated in the analysis beside

477

For details see chapter IV-3.2 (innovation strategy) and IV-3.4.2 (business strategy).

478

It included guidelines like ‘extracted information can be used for more than one category, if applicable’ or ‘extract information about concrete actions instead of vague managerial platitudes. For similar guidelines given to coders see NOBLE/SINHA/KUMAR (2002), p.32.

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Empirical Study

the author.479 Each coder was assigned six or more firms which had also participated in the quantitative survey. To facilitate comparison between firms each coder dealt with firms from one industry only. During a one day workshop broad parameters of the project were explained to the coders. Beside the category system and the coding guide, coders were given further detailed guidance material, e.g. keywords, templates.480 The guidance material ensured that Steps 3 to 5 were done consistently across coders. Step 6 was executed in two ways. First, the category system and definitions were revised and adapted during the iterative category development process, described above. Second, the author met each of the eight coders for an individual review after they had coded their first firms. Agreement was reached on how certain types of statements were to be coded from then on and category definitions were modified if necessary. The coders then proceeded to re-work the initial coding and to code the remaining firms. After coding all assigned firms another extensive discussion with the author followed in form of a one day workshop with all eight coders. This contributed to ensure inter-coder reliability.481 Differences were discussed and resolved during this workshop. Although the agreement level was not formally tracked, there was approximately 90% concordance between the different coders.482 2.3.2 Comparison with Survey Results and Outcome 50 firms (41% of the sample) were included in the qualitative content analysis. A first screening of publicly available information guaranteed that sufficient, relevant text material was available for these companies. For this sub-sample the results of the qualitative document analysis were then compared with the outcome of the quantitative mail survey. To facilitate comparison the simple rating scale (1-3) from the qualitative analysis was

479

The content analysis was undertaken as part of a seminar at the sponsoring Institute of Technology and Innovation, University of Graz.

480

Other information given to coders: basic definitions of four innovation strategy dimensions and prioritized list of documents to review per firm.

481

See e.g. KRIPPENDORF (2004).

482

For a similar approach see e.g. NOBLE/SINHA/KUMAR (2002), p.32.

Sample and Data

113

transformed into the 1-7 scale used in the quantitative survey.483 Then differences between the qualitative and quantitative results were calculated. 65% of the observations484 from the qualitative content analysis were similar to the results from the quantitative survey. Significant differences485 occurred for 35% of the results. The discrepancies were investigated in detail. 89% of the differences could be explained or resolved after revisiting the codifications or doing more research. 11% of the discrepancies could not be explained or understood. A significant portion of these originated from one single firm. As a consequence this firm was subsequently excluded from the quantitative sample, because their very optimistic answers to the questionnaire could not be validated through the qualitative document analysis. Another important portion of these 11% of unexplainable discrepancies related to the construct ‘business strategy’ and specifically the focus strategy type.486 Several responses in the questionnaire relating to ‘focus strategy’ could not be confirmed through the investigation of annual reports, web pages, etc. This could be driven by the fact that it is a very relative judgment whether a firm operates in a niche market or not depending on the informant’s perspective and the industry.487 As a consequence the hypothesis testing focused on the two main strategy types, differentiation and cost leadership.488

483

See chapter IV-3.1 for more details on Likert type scales in the quantitative survey.

484

Category observations in the qualitative content analysis are comparable to item results in the quantitative survey. For the 50 firms qualitatively assessed, a total of 750 category observations about innovation and business strategy were intended; for 484 observations sufficient information existed.

485

Definition of ‘significant difference’: when the qualitative rating was one or more units (on the 1-3 scale) off the results from the quantitative survey. Transformed into a 1-7 scale this meant that the difference between the two needed to be 2.3 points or more.

486

For more details, see chapter III-3.1.1.

487

For definitions of cost leader, differentiator and focus strategy see PORTER (1980), p.34ff.

488

This means that focus strategy continued as a construct in the questionnaire, however, was de-prioritized in the analysis. VAZQUES/SANTOS/ALVAREZ (2001), p.75, also limited to investigate differentiation and cost leadership as PORTER’S two main strategy types.

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Empirical Study

Figure IV-5: Results: Document Analysis vs. Quantitative Survey

N=123 responses from quantitative survey (1)

Qualitatively validated?

Yes

N: number of firms O: number of category observations (comparable to items in survey)

No N=73 firms (59%)

N=50 firms (41%)

Qualitative content analysis O=484 observations

Comparison of results from qualitative document analysis with quantitative survey:

Results in line? No

O=180 (37%)

Differences resolved? No

Yes N=1 firm O=7 unexplained

N=1 firm with O=7 unexplainable observations

Yes O=304 (63%)

Yes O=160 (89%)

O=20 (11%)

Excluded from sample? No

N=49, O=13 unexplained (2)

N=122 firms in final sample

(1)

N=123 after excluding double answers and final cleaning (IV-2.1), except for one firm excluded after this analysis.

(2)

The final 13 not explainable differences are spread among several firms with no more than two unexplained observations per firm. Therefore, only one firm was excluded after this analysis.

Source: Own illustration

Sample and Data

115

Concluding, the qualitative document analysis showed that the measurement of innovation strategy via a written survey is a valid approach, given the vast majority of results from the quantitative and the qualitative analysis coincided. The outcome of the validation is summarized in Figure IV-5. 2.4

Financial Databases

Financial databases were accessed to collect data for the dependent variable, firm performance. Using financial databases for firm performance instead of the mail survey avoids the problem of common method and common source bias.489 Data were primarily retrieved from Thomson Financial’s Datastream database490 and from STOXX, the European arm of Dow Jones. Datastream is a prevalent source in research as well as practice.491 STOXX was used for industry classifications due to its strong European competence and the sample containing 79% European firms. Figure IV-6 gives an overview of the indicators retrieved from financial databases.

492

N stands for the size of the sample (N=122), for which each

indicator could be identified. For the first firm performance indicator, Tobin’s q, all necessary components came from Datastream.493 For missing values information from websites and annual reports were used instead. For the second firm performance indicator, Total Return on Share (TRS) relative to industry, return index494 data for firms and industry indices were also obtained from Datastream. Given the sample consists of 79% European firms the STOXX supersector industry classification was

489

For details on common method and common source bias, see chapter IV-2.

490

http://www.thomson.com/solutions/financial/. The research version of Thomson Financial / Datastream available at the University of Graz was used. For simplicity reasons, and as often done in the literature, Datastream is referred to in the following even though their accounting data do since 2004 come from the Worldscope database. For details see e.g. LARA/OSMA/NOGUER (2006), p.431.

491

For its importance and prevelant usage, especially for non-US and European based research, see e.g. LARA/OSMA/NOGUER (2006), INCE/PORTER (2006).

492

For details on indicators’ definitions see chapter IV-3.2.

493

For Tobin’s q, the proxy formula was used; see chapter IV-3.3.2.

494

Return index is the relevant variable (and not price index), because it includes dividends and capital gains which also influence total return. For detailed definitions see THOMSON FINANCIAL (2003), INCE/PORTER (2006), p.466.

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applied resulting in a sensible number of ten industry sectors. If companies had not been classified by STOXX, their sector was identified triangulating different data sources, e.g. SIC on Datastream and stock exchanges’ industry classifications. With regard to control variables, number of employees and total sales were taken from Datastream. For comparison purposes total sales needed to be converted into Euro. Historic, daily exchange rates from Datastream were used. R&D rate and Return on Assets (ROA) were directly available from Datastream. Table IV-1: Overview of Indicators from Financial Databases Variable Firm Performance

Indicator z

z

Control variables

z

z z z

Tobin‘s q (proxy formula) – Market capitalization plus value of outstanding preferred stock – Book value of short- and long-term debt, net short term assets – Book value of total assets TRS relative to industry – Return index (RI) – per firm – Return index – per sector – Supersector classification Size – Number of employees – Total sales – Exchange rates R&D rate Return on Assets (ROA) (Super-) Sector

Main Source

N

Comment

121 Datastream

z

Market values as of December 31st, 2005

z

Book values: from latest annual report, ideally December 31st, 2005

z

Return index: incl. dividends and capital gains, beside changes in stock price

z

Total sales: converted into Euro

z

Both, R&D rate and ROA directly available from Datastream

122 Datastream Datastream STOXX 122 Datastream

Datastream Datastream STOXX

108 102 122

All measures were retrieved or calculated for the end of 2005 in order to be consistent with the fact that respondents in the questionnaire (which was undertaken from January to May 2006) were asked for average responses over the last three years. Where the information was not available for December 31st 2005, the most recent information was used, e.g. from another date in 2005 or early 2006. When accessing the Datastream database problems arose, especially concerning accounting data from annual reports. First, the information was often incomplete - especially for smaller firms.495 To fill the information

495

Analyzing the quality of the Thomson Datastream database, INCE/PORTER (2006) also observed problems with this database, especially for smaller firms, see p.464-465.

Operationalization of Constructs

117

gapy annual reports and investor relation sections of firm’s web pages were reviewed. In some cases the investor relations or finance department of a firm was contacted. Second, data integrity issues were revealed: several companies’ records contained the same data for 2006 and 2005; some annual report data had ‘slipped’ from one year to the next year, that is, annual report data for 2005 was partly stated in the line of 2005 and partly in 2006.496 The problems were resolved by manually comparing and correcting the records of the affected firms with the original data from annual reports. This significantly extended the time necessary for accessing the financial databases.497

3

Operationalization of Constructs

3.1

Methodical Considerations

Before describing the operationalization of individual constructs this chapter serves to explain the chosen measurement model. The measurement model defines the relationship between the latent variable (e.g. innovativeness) and the concrete indicators measuring the variable. Two types of relationships between the latent variable and its indicators are distinguished:498 ƒFor reflective indicators the causality goes from the latent variable to its indicators; the indicators are viewed as a reflection of the latent variable. If the latent variable changes, the values of all indicators will change. Reflective indicators should be highly correlated, as they reflect the same latent variable. Hence, individual indicators can be eliminated in order to improve the goodness of the measurement model.

496

INCE/PORTER (2006) also observed data integrity issues as to Thomson Datastream.

497

Further investigating, STOXX personnel confirmed data integrity problems with Datastream and suggested to use Bloomberg instead, to which neither the author nor the University of Graz have access.

498

Also for the following see e.g. FORNELL/BOOKSTEIN (1982), p.441f; BOLLEN (1984), p. 381; BAGOZZI (1994), p.331ff; CHIN (1998), p.313ff; EDWARD/BAGOZZI (2000), p.155ff; JARVIS/MACKENZIE/PODSAKOFF (2003), p.201ff.

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ƒFor formative indicators the latent variable is defined as a function of its indicators. In other words, the latent variable is viewed as an index consisting of a combination of indicators. The individual indicators measure specific aspects of the same variable. The indicators can be correlated, but are not expected to do so, as they measure different aspects. Dropping an indicator from the measurement model may alter the meaning of the construct. Another important difference between reflective and formative measurement models is that for the reflective model measurement errors are taken into account at the indicator level whereas for the formative model these are considered at the construct level. The main differences of the reflective and formative measurement models are summarized in Figure IV-6. Figure IV-6: Reflective vs. Formative Measurement Model

Reflective Model

e1

X1

e2

X2

e3

X3

Formative Model

X1 Reflective Factor

X2

Formative Factor

d

X3

• Direction of causality from construct to measure (xi)

• Direction of causality from measure (xi) to construct

• Measures expected to be correlated (measures should possess internal consistency reliability)

• No reason to expect measures to be correlated (internal consistency is not implied)

• Dropping an indicator (xi) from the measurement model does not alter the meaning of the construct

• Dropping an indicator (xi) from the measurement model may alter the meaning of the construct

• Takes measurement error into account at the item level (e1)

• Takes measurement error into account at the construct level

Source: Own illustration following JARVIS/MACKENZIE/PODSAKOFF (2003), p.201.

The choice of the appropriate measurement model should be based on theoretical considerations regarding the nature of the relevant construct. JARVIS, MACKENZIE and PODSAKOFF defined a set of criteria to decide on the right measurement model.499 The most important aspect is the causal di-

499

See JARVIS/MACKENZIE/PODSAKOFF (2003), p.203.

Operationalization of Constructs

119

rection between the indicators and the construct. If the modification of the construct leads to a change of all indicators, then a reflective model should be applied; otherwise a formative model is adequate.500 For the survey-based constructs501 of this study a formative measurement model was chosen. For a formative factor different aspects can be defined which compose the factor. The innovation strategy dimension innovativeness, for example, is typically defined along two aspects: market and technological innovativeness.502 A more innovative product can either be technically more advanced and/or offer new customer benefits to the market. These two aspects need not be correlated. High technological innovativeness does not necessarily mean that the market innovativeness is higher as well. Consequently a formative measurement model is appropriate. The argumentation is similar for the other three innovation strategy dimensions, the contingency variables and innovation performance. By using formative constructs this study follows the quest of ALBERS and HILDEBRANDT for adequate measurement models in success factor research. They argue that reflective factors are often used in success factor studies, when formative ones would be appropriate. Formative factors allow accounting for distinct facets of a certain construct, which might be neglected using reflective factors. Using the wrong measurement model can lead to different results.503 For the development of indicators established items were reverted to the maximum extent possible. Where necessary, new items were developed following the procedure suggested by DIAMANTOPOULOS and WINKLHOFER:504 ƒContent specification: First, the breadth of the content of a certain construct needs to be thoughtfully determined. For this study, this was ensured by a thorough literature review and linking the chosen constructs to the existing research.

500

See e.g. HERRMANN/HUBER/KRESSMANN (2004), p.13.

501

For the operationalization of constructs based on financial databases, see chapters IV-3.3.2 and IV-3.5.

502

See e.g. GARCIA/CALANTONE (2002), p.112-113.

503

See ALBERS/HILDEBRANDT (2005), p.1ff.

504

See DIAMANTOPOULOS/WINKLHOFER (2001), p.271ff.

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ƒIndicator specification: Second, the formative construct needs to cover the breadth of the content and be deduced from theory. In a pretest it should be tested to what extent target respondents consider the indicators sufficient to indicate a certain construct.505 For details on the pretest of this study see chapter IV-1. For the measurement of all survey-based constructs a 7-point-Likert-scale was used. STADTLER showed that respondents distinguish their judgments sufficiently on a scale with 7 points.506 Moreover, an average judgement for the last three years was requested for all items.507 The intention was to level potential recent changes which have not had any impact yet on the relevant, 2005 performance data. The following chapters list and describe all measures for all constructs. The entire questionnaire can be found in the Appendix. 3.2

Operationalization of Innovation Strategy Variables

3.2.1 Measure for Innovativeness Innovativeness refers to the degree of newness of the product portfolio in the market. The breadth of the innovativeness construct as well as all other constructs were described earlier (II-1.2.3). In this chapter the concrete indicators measuring innovativeness in the written survey are introduced. Innovativeness is an established construct in innovation research.508 It is traditionally treated as a second order construct consisting of two first order factors: market innovativeness and technology innovativeness.509 Both first order factors were operationalized based on established indicators. No new items had to be developed. However, existing indicators needed to be adapted for the firm level given the majority of

505

On the advantages of a pretest assessment of indicators see e.g. ANDERSON/GERBING (1991), p.735ff.

506

See STADTLER (1985), p.3.

507

See introduction phrases of the questionnaire in Appendix I. LEIFER ET AL. (2006) also allowed for a 3 (and 5 year) time lag for the impact of innovation on firm success.

508

See GARCIA/CALANTONE (2002), p.110-132.

509

See GARCIA/CALANTONE (2002), p.112 and 124.

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121

studies on innovativeness take place at the product or project level.510 To account for this difference the items were rephrased from a single new product perspective to the new product portfolio of a firm. In addition, introductory phrases in the questionnaire emphasized that answers should be given considering the entire new product portfolio.511 More specifically, the respondents were asked for a judgement on the majority of a firm’s innovations. This formulation was chosen to prevent from receiving too average and thus similar answers. Asking for a portfolio perspective inherently bears the challenge of average answers. The problem is not fully avoidable. However, asking for an assessment of the majority of innovations proved to result in differentiated answers.512 Concerning individual indicators established items were selected which covered the full breadth of the innovativeness construct and addressed distinct aspects of newness to the market. The latter is important for a formative model.513 Table IV-2 gives an overview of the used items as well as their sources. For market innovativeness the six indicators address two, broad underlying questions: (a) new benefits and/or advantages for the customer – absolutely or compared to the competition; and (b) required changes and/or costs for adoption. To prevent from Halo-effect514 a reversed item was included at the end. For the second factor, technology innovativeness, five established items were used, also including a reversed item at the end. The items address three broad questions: (a) to what extent the technology differs from the presently dominating technology; (b) to what level the performance of innovations is improved due to new technologies; and (c) whether new technologies cause significant change for the entire industry.

510

See GARCIA/CALANTONE (2002), p.113.

511

See questionnaire in appendix I.

512

This was tested in the pilot survey. For details on the pilot survey see chapter IV-1.

513

See chapter IV-3.1 for characteristics of formative measurement models.

514

See chapter IV-1.

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Table IV-2: Operationalization of Innovativeness Factor Market Innovativeness

Technology Innovativeness

R

Indicator

Author

z

The majority of our innovations address completely new customer benefits.

z

Based on COOPER (1985), SALOMO (2003)

z

The majority of our innovations offer customers unique advantages over competitors‘ products.

z

Based on COOPER (1987), GATIGNON/XUEREB (1997)

z

The majority of our innovations require changes in established attitude and behavioral patterns from customers.

z

Based on COOPER (1985), SALOMO (2003),

z

The majority of our innovations require major learning efforts by mainstream customers.

z

Based on ELIASHBERG/ROBERTS. (1988), ZHOU/YIM/TSE (2005)

z

The majority of our innovations involve high switching costs for mainstream customers.

z

Based on ZHOU/YIM/TSE (2005)

z

The majority of our innovations are similar to our main competitors’ products. R

z

Based on GATIGNON/XUEREB (1997)

z

The majority of our innovations are based on substantially different core technology never used in our industry before.

z

Based on SONG/PARRY (1997), SONG/MONTOYA-WEISS (2001)

z

The majority of our innovations involve technology that makes old technologies obsolete.

z

Based on GATIGNON/TUSHMAN/ SMITH/ANDERSON (2002)

z

The majority of our innovations use new technology that permits quantum leaps in performance.

z

Based on SALOMO (2003)

z

The majority of our innovations use technologies that have an impact on or cause significant changes in the whole industry.

z

Based on SONG/MONTOYA-WEISS (1998)

z

The majority of our innovations use technologies which represent minor improvements over previous technologies. R

z

Based on GATIGNON/XUEREB (1997)

= reversed item

3.2.2 Measure for Distance to Core Business Distance to core business describes the degree of newness from a firm perspective, that is, how distant new products are from the core business.515 In their seminal paper DANNEELS and KLEINSCHMIDT distinguish two aspects of distance to core business: familiarity and fit. Familiarity takes an external perspective and measures to what extent a firm is familiar with the customers, competitors, technologies, etc. involved in their new products.516 Fit refers to an internal perspective investigating how well new products fit with a firm’s resources and capabilities.517 For both, familiarity and fit, a further distinction is made between marketing and technology. For reasons of scope, this study emphasizes the familiarity

515

See DANNEELS/KLEINSCHMIDT (2001) for a detailed study of innovativeness to the firm.

516

See DANNEELS/KLEINSCHMIDT (2001), p.360.

517

See DANNEELS/KLEINSCHMIDT (2001), p.361.

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123

aspect. Three first order factors are measured: market familiarity, technological familiarity and internal configuration. Internal configuration includes some aspects of market and technology fit. ƒMarket familiarity: This first order factor is measured through five indicators out of which four were adopted from DANNEELS and KLEINSCHMIDT.518 All items had to be modified to account for the entire new product portfolio, given the majority of research has taken place on a single project level. The indicators include different aspects of the market (e.g. product category, customers) that a firm might be familiar with depending on how close the innovations are to their core business. ƒTechnological familiarity: Three items measure technological familiarity whereas two build on the work of DANNEELS/KLEINSCHMIDT and GATIGNON ET AL.519 The latter distinguish between core and peripheral technologies.520 Core technologies are at the heart of a product (e.g. engine of a car), whereas peripheral technologies are less essential to the product (e.g. lacquering of a car). The third item was newly developed and pays tribute to the increasing relevance of architectural innovations.521 ƒInternal configuration: This factor encompasses various aspects of fit and investigates to what extent a firm’s resources, organization, etc. need to be changed for innovation. Four established indicators were used. For reasons of scope, DANNEELS and KLEINSCHMIDT’S multiitem constructs ‘market fit’ and ‘technology fit’, which usually consist of multiple items,522 were each tested by one summarized item (item 2 and 3). In addition, two items from SALOMO were investigated concerning the necessary adaptation of formal and informal organization.523

518

See DANNEELS/KLEINSCHMIDT (2001), p.366.

519

See DANNEELS/KLEINSCHMIDT (2001) and GATIGNON/TUSHMAN/SMITH/ANDERSON (2002).

520

See GATIGNON/TUSHMAN/SMITH/ANDERSON (2002), p.1106.

521

See HENDERSON/CLARK (1990) and GATIGNON/TUSHMAN/SMITH/ANDERSON (2002).

522

See DANNEELS/KLEINSCHMIDT (2001), p.366.

523

See SALOMO (2003), p.14.

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Table IV-3 shows the indicators for distance to core business with their respective sources. Table IV-3: Operationalization of Distance to Core Business Factor Market Familiarity

Technological Familiarity

Internal Configuration

R

Indicator

Author

z

The majority of our innovations represent new product categories – types of products that our firm had not made and/or sold before.

z

Based on DANNEELS/ KLEINSCHMIDT (2001)

z

The majority of our innovations serve new customer needs – customer needs we had not served before.

z

z

The majority of our innovations generate new customers for our firm – customer we had not sold to before.

Based on DANNEELS/ KLEINSCHMIDT (2001), COOPER (1985)

z

The majority of our innovations take us up against new competitors – competitor firms we have never faced before.

z

The majority of our innovations are improvements of our existing products. R

z

Based on COOPER/DEBRENTANI (1991)

z

For the majority of our innovations, the core technologies (e.g. engine for cars) involved in our innovations are new for our firm.

z

Based on DANNEELS/ KLEINSCHMIDT (2001) and GATIGNON et al. (2002)

z

For the majority of our innovations, the peripheral technologies (e.g. lacquering for cars) involved in our innovations are new for our firm.

z

For the majority of our innovations, different technologies are combined in a new way for our firm.

z

New

z

Overall, our internal configuration (e.g. organization, processes) usually had to be changed significantly in order to develop and introduce our innovations.

z

Based on SALOMO (2003)

z

Our firm’s commercial people/skills/resources (e.g. marketing, sales, distribution, customer service) had to be changed for our innovations.

z

Based on DANNEELS/ KLEINSCHMIDT (2001)

z

Our firm’s technical people/skills/resources (e.g. in R&D, engineering, production) had to be changed for our innovations.

z

Our firm’s informal organization (e.g. culture) had to be changed for our innovations.

z

Based on SALOMO (2003)

= reversed item

3.2.3 Measure for Driver of Innovation Driver of innovation investigates the major forces driving innovation in a company, e.g. whether corporate innovating activity originates from a marketing or technology orientation or whether innovations are generated as a response to competitor activity (competitive response). Strategic orientation of the firm - be it market orientation, technology orientation or competitive response - are established constructs.524 The operationaliza-

524

See NARVER/SLATER (1990) and JAWORSKI/KOHLI (1993) as the two key papers. Also see e.g. GATIGNON/XUEREB (1997), NARVER/SLATER/MACLACHLAN (2004), ATUAHENEGIMA/SLATER/OLSON (2005), and ZHOU/YIM/TSE (2005) for recent applications in innovation research.

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125

tion of driver of innovation follows the recent stream of research and treats the construct as a second-order factor with three first-order factors: ƒMarket orientation: Market orientation is conceptualized and operationalized as proactive market orientation, given it is more innovation specific than reactive market orientation.525 Five indicators measure this first-order factor predominantly building on the work of NARVER, SLATER and MACLACHLAN.526 The items describe different approaches of how a firm can proactively originate innovations from a market perspective, e.g. by collaborating with lead users,527 or by working with trends. ƒTechnology orientation: For the operationalization of technology orientation measures from NARVER/SLATER and GATIGNON/XUEREB were adopted. The four items assess a firm’s activity and approach in using state-of-the-art technologies in new product development.528 ƒCompetitive response: Competitive response has been less investigated with regard to innovation.529 Two measures were adopted from NARVER/SLATER and from JAWORSKI/KOHLI. Two more indicators were newly developed.530 To the author’s best knowledge gaining insight on competitors’ innovation activities through the participation in industry associations and/or joint research programs has not been measured before. Experience indicates that these are broadly applied practices of collecting information about competitors’ innovation.531 Table IV-4 shows the indicators measuring driver of innovation.

525

See e.g. NARVER/SLATER/MACLACHLAN (2004).

526

See NARVER/SLATER/MACLACHLAN (2000 and 2004).

527

See van VON HIPPEL (1986).

528

See e.g. ZHOU/YIM/TSE 2005, p.49.

529

ETTLIE/SUBMARANIAN (2004) treat competitive response as a separate motivator for innovation (beside market and technology orientation). ZHOU/YIM/TSE (2005) consider competitor orientation as part of market orientation.

530

On the procedure of developing new items, see chapter IV-3.1.

531

Based on the author’s experience this is common practice in at least the chemicals, healthcare and media industry sectors.

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Table IV-4: Operationalization of Driver of Innovation Factor Market Orientation

Technology Orientation

Competitive Response

Indicator

Author

z

We continuously try to discover additional needs of our customers of which they are unaware.

z

NARVER/SLATER/MACLACHLAN (2000)

z

We reflect on how customers use our products to discover new customer needs and applications.

z

Based on NARVER / SLATER / MACLACHLAN (2000)

z

We work closely with demanding users in order to recognize customer needs months or even years before the majority of the market recognizes them.

z

We have specifically assigned personnel to extrapolate key trends to gain insight into what users in a current market will need in the future.

z

Individuals from our manufacturing and R&D departments interact directly with customers to learn how to serve them better.

z

Based on JAWORSKI/KOHLI (1993)

z

We systematically scan for new technologies inside and outside the industry.

z

NARVER/SLATER (1990)

z

Our incentive system strongly encourages our R&D personnel to invent.

z

Based on NARVER/SLATER (1990)

z

Technological developments are of highest priority for our top executives.

z

Our new products are always at the leading edge of technology.

z

Based on GATIGNON/XUEREB (1997)

z

In order to gain insight into the innovation activities of our competitors, we are involved in industry associations.

z

New

z

In order to gain insight into the innovation activities of our competitors, we engage in joint research programs with them.

z

New

z

We have assigned staff to monitor competitors’ innovation activities.

z

Based on JAWORSKI/KOHLI (1993)

z

Our sales people share information about competitors (new) products with our innovation staff.

z

Based on NARVER/SLATER (1990)

3.2.4 Measure for Innovation Field Orientation Innovation field orientation describes whether a firm focuses its innovation activities along pre-defined fields or if a firm has a portfolio of unrelated innovation projects. An innovation field consists of multiple, related innovation projects under a common theme. The theme can be based on a combination of aspects, e.g. customer need, customer group, technology. Innovation field orientation has not yet been investigated as a multi-item construct. Nevertheless, the operationalization builds on established conceptual and empirical work from different streams of innovation and organizational research. Innovation field orientation is treated as a second order factor with six first order factors – shown in Table IV-5. ƒFocus areas: Innovation fields are pre-defined areas within which companies focus their innovation activities. The definition occurs

Operationalization of Constructs

127

along certain aspects, e.g. technology, customer group, customer need or core competence, or a combination of these four criteria. Back in 1980 CRAWFORD emphasized the importance of ‘target business arenas’ for successful product innovation. The first three definition criteria (technology, customer group, customer need) build on CRAWFORD.532 The fourth criteria, core competencies, is new given the recently emphasized importance of considering a firm’s core capabilities when defining innovation fields.533 The item builds on the conceptual work of PRAHALAD/HAMEL and PORTER.534 ƒMultitude of projects: By definition an innovation field consists of several innovation projects.535 To test for this characteristic one item asks for the number of innovation projects per focus area whereas a second item investigates how many innovation fields firms have defined for their innovation activities. ƒMightiness of projects and time horizon: Setting up an innovation field can involve significant resources. Therefore innovation fields are rather defined for larger and longer term projects. Both mightiness in terms of expenses or project members, and time horizon in terms of development or payback time investigate the size of innovation projects within focus areas. The two indicators for mightiness are conceptually based on HENDERSON and COCKBURN’s work who showed that a critical size (in $ spending) and mass (in full time employees) is important for innovation projects to generate significant output.536 Concerning the two items for time horizon one was inspired by NARVER and SLATER’s ‘long term orientation’ construct537 and the other is new. ƒOrganizational formality: Organizational formality investigates to what extent and how a firm has formally organized its innovation

532

See CRAWFORD (1980), p.4.

533

LAURIE/DOZ/SHEER (2006), p.84.

534

See PRAHALAD/HAMEL (1990) and PORTER (1996).

535

See chapter II-1.2.3.4 for definitions and descriptions of innovation fields.

536

See HENDERSON/COCKBURN (1996), p.41.

537

See NARVER/SLATER (1990), p.24.

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fields. The first item serves as an introduction understanding if a firm has created new organizational units for their focus areas. A key benefit of innovation fields is the realization of synergies between related innovation projects. This is only possible if the individual projects within an innovation field collaborate. Thus, items two to six under organizational formality analyze different formal approaches to encouraging collaboration. The items build on prior work from various authors. ƒSynergies: The realization of synergies between related innovation projects is a key characteristic and objective of innovation field orientation. The six indicators under the last first order construct, synergies, investigate different types of synergies. Except for one new reversed item the indicators are conceptually following the concepts of HENDERSON and COCKBURN and a recent empirical study of PERSAUD.538 The first, third and sixth item describe different types of synergies in research: economies of scope, economies of scale and internal knowledge spillovers.539 Whereas economies of scope and scale relate to research expenditure, that is to say inputs, knowledge spillovers affect the outcome of research, irrespective of inputs.540 The fourth and fifth items are adopted from PERSAUD and relate to additional output benefits of synergies.541 Table IV-5 shows the indicators for innovation field orientation with their respective sources.

538

See HENDERSON/COCKBURN (1996), p.35-36 and PERSAUD (2005), p.416 and 428.

539

See HENDERSON/COCKBURN (1996), p.35-36.

540

See HENDERSON/COCKBURN (1996), p.35.

541

See PERSAUD (2005), p.428.

Operationalization of Constructs

129

Table IV-5: Operationalization of Innovation Field Orientation Factor Definition Criteria

Multitude of Projects

Mightiness of Projects

Time Horizon of Projects

Organizational Formality

Synergies

R

= reversed item

Indicator

Author

For our innovation activities, we have defined areas of focus along the following criteria: z

Certain technologies

z

Certain customer groups (e.g. pharmacies, doctors, pregnant women)

z

Certain customer needs (e.g. hydration, infection prevention, age defiance)

z

z

Based on CRAWFORD (1980)

Certain core competencies (e.g. Sony‘s competence to miniaturize, 3M‘s with sticky tape, IKEA‘s in modular furniture design

z

New, conceptually following PRAHALAD/HAMEL (1990), PORTER (1996)

z

How many areas of focus do you have in your innovation portfolio?

z

New

z

How many innovation projects are typically within an area of focus?

z

Relative to our industry, our innovation projects are to a large degree characterized as heavy-weight projects in terms of expenses?

z

New, conceptually following HENDERSON/COCKBURN (1996)

z

The vast majority of our innovation projects have 3 or more full time project members (or equivalent)?

z

Within our innovation project portfolio, a significant proportion of our projects (>30%) require a longer development time than average in our industry.

z

New

z

Within our innovation project portfolio, a significant proportion of our projects (>30%) have a longer payback time than average in our industry.

z

Based on NARVER/SLATER (1990)

z

We have organizational units that were created particularly for our focus areas in innovation.

z

Based on HERMANN/ GASSMANN/EISERT (2005)

z

A defined set of rules and policies exist governing the collaboration among innovation projects.

z

Based on NOHRIA/GHOSAL (1997) and PERSAUD (2005)

z

On the status of their collaboration with other innovation projects, project leaders must submit regular and formal progress reports.

z

We have appointed specific liaison personnel to support collaboration between innovation projects.

z

Based on MILLER/DROEGE (1986)

z

Informal networks of personnel across innovation projects exist.

z

New, conceptually following GRIFFIN/HAUSER (1996)

z

Part of the compensation of individual leaders of innovation projects is based on the success of all innovation projects within a given focus area.

z

Based on GOMEZ-MEJIA (1992)

z

Our innovation projects use common resources (e.g. people, equipment) with related innovation projects.

z

Conceptually following HENDERSON/COCKBURN (1996) and PERSAUD (2005)

z

In our firm, collaboration among different innovation projects leads to dilution of focus on individual project objectives. R

z

New

z

In our firm, collaboration among innovation projects leads to more efficient utilization of resources required for innovation.

z

Conceptually following HENDERSON/COCKBURN (1996) and PERSAUD (2005)

z

In our firm, collaboration among our innovation projects leads to improvements in the capabilities of our innovation staff.

z

Following PERSAUD (2005)

z

In our firm, collaboration among innovation projects permits the access to a wider number of new knowledge sources (e.g. scientists, related institutes).

z

Our innovation projects benefit from internal spillovers of knowledge when related innovation projects (e.g. results of text interesting for several projects).

z

New, conceptually following HENDERSON/COCKBURN (1996)

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Empirical Study

Operationalization of Performance Variables

3.3.1 Measure for Innovation Performance At the firm level innovation performance is defined as the sum of the successes of all individual innovations.542 In line with the innovation strategy dimensions, innovation performance is measured at the portfolio level, considering the entire new product portfolio of a firm. Following GRIFFIN and PAGE’s suggestion a multi-item approach was taken.543 Innovation performance is defined and measured along four dimensions: financial, market, technical and process. The financial indicators investigate the economic success of a firm’s innovation activities. The market dimension analyzes how a firm’s new products score against competitors in terms of customer satisfaction, competitive advantage and opening up new markets. The technical dimension refers to the technical performance and quality of a firm’s innovations. Moreover it tests to what extent new products lead a company into new technology fields. Whereas the financial, market, and technical dimension compare a firm’s innovation output with competitors’, the fourth dimension, process, takes an internal perspective. It measures how efficient and effective a firm’s new product development is e.g. in terms of development time or costs. Moreover, a new item on the exploitation of synergies was developed accounting for the importance of synergies with regard to innovation field orientation.544 Last, an overall item on new product development success was added. In line with the measurement of innovation strategy, key informants were asked for their firm’s average innovation performance over the last three years. Table IV6 shows all indicators measuring innovation performance and their respective sources.

542

See HAUSCHILDT/SALOMO (2005), p.16-17.

543

See GRIFFING/PAGE (1996) for recommended measures and approches for new product development (= product innovation) success.

544

See chapter II-1.2.3.4 for innovation field orientation and synergies.

Operationalization of Constructs

131

Table IV-6: Operationalization of Innovation Performance Factor Financial

Market

Technical

Process

Overall

Indicator z

Our sales from new products as percentage of total sales have been high – compared to our key competitors.

z

Our profits from new products as percentage of total profits have been high – compared to our key competitors.

z

Our market share has been high – relative to the competition.

z

Our sales growth has been high – relative to the competition.

z

Our growth in profit has been high – relative to the competition.

z

Our market share growth has been high – relative to the competition.

z

Compared to competitors’ innovations, customers are more satisfied with our product innovations.

z

Compared to the competition, our innovations enhance our competitive advantage more.

z

Author z

Based on GRIFFIN/PAGE (1996), MARKHAM/GRIFFIN (1998)

z

Based on ATUAHENE-GIMA/LI (2004)

z

Based on GRIFFIN/PAGE (1996)

Relative to competitors, our innovations contribute more strongly to a positive image of our company.

z

New

z

Overall, our firm’s innovation portfolio is successful in opening new markets for our firm.

z

Based on KLEINSCHMIDT/ BRENTANI/SALOMO (2004)

z

In terms of quality, our new products are better than new products offered by competitors (e.g. function better, last longer).

z

Based on ATUAHENE-GIMA/LI (2004)

z

In terms of technical performance, our new products are superior to competitors’ new products (e.g. significantly quicker, stronger and/or with additional features).

z

New

z

Overall, our firm’s innovation portfolio is successful in enabling us to get into new technologies for our business.

z

Based on KLEINSCHMIDT/ BRENTANI/SALOMO (2004)

z

Compared to our competitors, our new product development cycle time has been relatively short.

z

Based on DYER/SONG (1998)

z

Compared to competitors, our costs for new product development are low.

z

Compared to competitors, we exploit more input and output synergies across our innovation activities.

z

New

z

Overall, our new product success is good compared to our major competitors.

z

Based on NARVER/SLATER/ MACLACHLAN (2004)

3.3.2 Measures for Firm Performance Firm performance was measured via capital market data, which provide advantages over both perceputal performance measures and accounting data. Using capital market data also avoids common source and method bias and follows the call for more investor related performance measurement.545 Moreover, capital market data are not influenced by varying ac-

545

See LEHMANN (2004), p.73-75, who calls for more objective and investor related performance measurement in marketing and related fields.

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counting standards.546 Most importantly, capital markets are considered the ultimate performance arbiter and market value includes expectations about future cash flows, also for innovation.547 Using forward looking capital market based metrices addresses the time lag between innovation strategy and success.548 As concrete measures, Tobin’s q and a firm’s stock market performance relative to its industry were applied. 3.3.2.1

Tobin’s q

Tobin’s q is a measure of investors’ expectations concerning a firm’s future profit potential. It is defined as the ratio of the market value of a firm to the replacement cost of its assets.549 Tobin’s q is an index commonly used to measure firm value. In the quest for more objective and investor related performance measures550 it was recently applied in several studies in innovation and marketing research.551 The original formula and procedure to obtain Tobin’s q is very complex and cumbersome,552 such that the vast majority of studies apply the simplified, proxy formula by CHUNG and PRUITT.553 The proxy formula only requires basic and readily available financial data. Using the proxy formula Tobin’s q is defined as Q = (MVE + PS + DEBT) / TA, where MVE is the market value of equity, in other words the market capitalization of common shares at year end, PS is the liquidating value of outstanding preferred stocks, DEBT is the value of the firm’s short-term liabilities net of its short-term assets, plus the book

546

See WERNERFELT/MONTGOMERY (1988), p.247 and CHAUVIN/HIRSHEY (1995), p.34, for detailed discussions of the problems with accounting based performance measures.

547

See e.g. COPELAND/KOLLER/MURRIN (2005), p.8.

548

See e.g. SORESCU/CHANDY/PRABHU (2003), p.84, and LEE/GREWAL (2004), p.162, as well as chapter IV-1 for a more detailed discussion of the time lag challenge.

549

See TOBIN/BRAINARD (1968), LINDENBERG/ROSS (1981), BREALEY/MYERS (2003), p.831.

(1995),

p.22

and

HERTENSTEIN/PLATT/VERYZER

550

See LEHMANN (2004), p.75.

551

Innovation studies using Tobin’s q: e.g. FIRTH/NARAYANAN (1996), LEE/GREWAL (2004), CHO/PUCIK (2005), LIN/CHEN (2005). Marketing studies using Tobin’s q: e.g. SIMON/SULLIVAN (1993), ANDERSON/FORNELL/MAZVANCHERYL (2004), RAO/AGARWAL/ DAHLOFF (2004), SHORT/KETCHEN/PALMER/HULT (2007).

552

See e.g. LIN/CHEN (2005), p.161.

553

See CHUNG/PRUITT (1994), p.70ff.

Operationalization of Constructs

133

value of the firm’s long-term debt, and TA is the total assets of the firm.554 Even though commonly used, Tobin’s q suffers the small disadvantage of being a capital market cum accounting measure.555 Therefore, a second, purely capital market based metric was used. 3.3.2.2

Total Return on Share Relative to Industry

Total return on share (TRS) relative to its industry index measures to what extent a firm’s stock market performance exceeds or falls short of its industry average.556 In contrast to Tobin’s q it is purely capital market based. Even though less frequently used, it was previously applied in innovation research.557 To calculate the metric HERTENSTEIN, PLATT and VERYZER’S approach was followed.558 For each firm the total stock market return559 relative to the industry was calculated. Specifically, quarterly returns for 2005 were obtained for all firms and necessary industry sector indices. The two indices, total share return per firm and total index return per industry, were then divided. The resultant ratio reveals if a firm outperfomes its industry index (ratio>1) or not (ratio<1). To facilitate further analysis a single TRS metric was derived by calculating a geometric average of the four quarter return ratios. For European firms European sector indices were applied, whereas for North American firms North American or US indices were used depending

554

See CHUNG/PRUITT (1994), p.70-74, LIN/CHEN (2005), p.161-162, LEE/GREWAL (2004), p.162.

555

For detailed discussions of the problems related to accounting based measures as performance indicators, see e.g. WERNERFELT/MONTGOMERY (1988), p.247; CHAUVIN/HIRSHEY (1993), p.134; HAWAWINI/SUBRAMANIAN/VERDIN (2003), p.3-4.

556

See HERTENSTEIN/PLATT/VERYZER (2005), p.12.

557

For similar, industry-specific approaches, see e.g. HAMBRICK/JACKSON (2000), MIZIK/JACOBSON (2003), HERTENSTEIN/PLATT/ VERYZER (2005). Other (purely) stock return based approaches were applied in the following, innovation studies: CHANEY/ DEVINNEY/WINER (1991), SORESCU/CHANDY/PRABHU (2003) PAUWELS/SILVA-RISSO/ SRINIVASAN/HANSSENS (2004), and LEIFER/KASTHURIRANGAN/ROBESON (2006).

558

See HERTENSTEIN/PLATT/VERYZER (2005), p.11-12.

559

The variable RI (return index) from Datastream was used which not only includes price changes in the stock, but all other relevant return information, e.g. dividends.

134

Empirical Study

on their availability. This contributed to minimize the effects of geographical differences, in addition to limiting industry effects by choosing an industry-specific approach.560 Concluding, Tobin’s q and TRS vs. industry were used to measure firm performance. 3.4

Operationalization of Contingency Variables

In the following chapter, the operationalization of the contingency variables business strategy and environmental uncertainty is specified.561 Whereas business strategy is a potential influencing factor at the firm level, environmental uncertainty measures a possible impact at the industry level. 3.4.1 Measure for Business Strategy Business strategy characterizes how a firm decides to compete with other companies.562 PORTER’s typology was chosen to describe different types of business strategy.563 Each of PORTER’s three strategy types was operationalized as a first order factor. The items were predominantly adopted from ZAHRA and COVIN who built on the seminal work of DESS and DAVIS.564 Differentiation strategy is characterized by three indicators. The three items deal with differentiation through marketing or advertising means. Further alternatives for differentiation exist,565 however, DESS and DAVIS considered the selected three most important.566 The cost leadership construct consists of four indicators. Three items refer to cost consciousness on the input side (operating efficiency, cost of production, and cost of raw material). The fourth indicator (competing on price) views cost leadership from an output and market perspective. Last, focus strategy, is described by

560

See PLATT/PLATT (1991), p.1183ff and VENKATRAMAN/RAMANUJAM (1986), p.808.

561

For details on the breadth of these two contingency variables, see chapter IV-3.

562

See WALKER/RUEKERT (1987), p.16.

563

See PORTER (1985), p.11ff.

564

See ZAHRA/COVIN (1993), p.461 and DESS/DAVIS (1984), p.467ff.

565

See e.g. quotes for differentiation strategy from document analysis in Table IV-11.

566

See DESS/DAVIS (1984), p.476.

Operationalization of Constructs

135

four indicators, of which one is phrased in reversed manner. The items address ways of focusing a firm’s business: offering specialty or few products, or serving few customer segments. The entire operationlization of the contingency variable business strategy is shown in Table IV-7. Table IV-7: Operationalization of Business Strategy Factor Differentiation

Cost Leadership

Focus

R

Indicator

Author

z

The intensity of our advertising is very high, compared to the competition.

z

Based on ZAHRA/COVIN (1993)

z

We are very innovative regarding our marketing techniques and methods.

z

Based on DESS/DAVIS (1984)

z

We emphasize the building of a strong brand identification.

z

Based on ZAHRA/COVIN (1993)

z

We enjoy a very high level of operating efficiency.

z

Based on ZAHRA/COVIN (1993)

z

We are very efficient in securing raw materials.

z

We predominantly compete on price.

z

We put emphasis on finding ways to reduce cost of production.

z

We target one or very few clearly identified customer segments.

z

Based on ZAHRA/COVIN (1993)

z

We offer products suitable for high price segments.

z

We offer specialty products.

z

We offer a broad line of products.

R

= reversed item

3.4.2 Measure for Environmental Uncertainty Environmental uncertainty refers to the degree and unpredictability of change in an organization’s environment.567 It is operationalized along two dimensions: market and technological uncertainty.568 Market uncertainty encompasses three aspects of change in the market: customer preferences, customer groups and competitors. The six items indicating market uncertainty cover these aspects and different facets of each. For changes in customers’ preferences and customer groups items from JAWORSKI/KOHLI were applied.569 As to changes in competitors and their activities items

567

See DANNEELS/SETHI (2003), p.A3.

568

See e.g. JAWORSKI/KOHLI (1993), p.53ff.

569

See JAWORSKI/KOHLI (1993), p.68.

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Empirical Study

from DANNEELS/SETHI and LI/CALANTONE were adopted.570 Technological uncertainty corresponds to the rate of technological advances within an industry.571 It is operationalized using four indicators from JAWORSKI and KOHLI with the fourth indicator being formulated in reversed manner.572 The entire operationalization of market and technological uncertainty with its respective sources is outlined in Tables IV-8 and IV-9. Table IV-8: Operationalization of Market Uncertainty Factor

Indicator

Author

z

Customers in our industry tend to look for new products all the time.

z

JAWORSKI/KOHLI (1993)

z

Customers’ product preferences change quite a bit over time in our industry.

z

Based on JAWORSKI/KOHLI (1993)

Customers

z

In our industry, we are witnessing demand for products and services from customers who never bought them before.

z

Based on JAWORSKI/KOHLI (1993)

Competitors

z

In our industry, it seems that we get new competitors all the time.

z

Based on DANNEELS/SETHI (2005)

z

Competitors change their strategy constantly in our industry.

z

Based on DANNEELS/SETHI (2005)

z

In our industry, market shares are very unstable among the key competitors.

z

Based on LI/CALANTONE (1998)

Customer Preferences

Table IV-9: Operationalization of Technological Uncertainty Factor Technological Uncertainty

Indicator z

The technology in our industry is changing rapidly.

z

Technological changes provide big opportunities in our industry.

z

It is very difficult to forecast where the technology in our industry will be in the next 2 to 3 years.

z

Technological developments in our industry are rather minor.

Author z

JAWORSKI/KOHLI (1993)

R R

= reversed item

The impact of environmental uncertainty on the innovation strategy – performance relationship was on the one hand measured as a second order factor by combining market and technological uncertainty (= environmental uncertainty), and on the other hand by analyzing the individual

570

See DANNEELS/SETHI (2005), p.51 and LI/CALANTONE (1998), p.13ff.

571

See e.g. ZHOU/YIM/TSE (2005), p.47.

572

See JAWORSKI/KOHLI (1993), p.68-69.

Operationalization of Constructs

137

impacts of the two first order factors, market and technological uncertainty, separately. 3.5

Operationalization of Control Variables

Apart from the above described contingency variables, additional aspects of a firm were controlled which might have an impact on the innovation strategy - performance relation. Specifically, firm size, industry sector, geography, R&D rate and ROA were controlled for. Firm size was measured as number of employees, the most common measure of size in the innovation literature.573 As with previous authors, alternative definitions for firm size (e.g. sales volume) correlated strongly with number of employees and were therefore not considered.574 For industry the same sector classification from STOXX was applied as for the firm performance indicator, TRS vs. industry.575 The chosen aggregation level is comparable to a 2-digit SIC code level, commonly used in innovation and strategy research.576 Geography was operationalized at the continent level, given the country level resulted in very small and not meaningful sub-samples. R&D rate refers to a firm’s spending on research and development relative to total sales. The indicator was directly obtained from Datastream. ROA, return on assets, is an important measure of companies’ profitability and is defined as the ratio of net income (after tax) divided by total assets.577 It is frequently used to measure short-term firm performance.578 The ROA data were also directly retrieved from Datastream. The five control variables were incorporated into the analysis to account for their potential influence on the innovation strategy – performance relationship.

573

See e.g. AGARWAL (1979), p.404ff, ETTLIE/RUBENSTEIN (1987), p.95, CHANDY/TELLIS (2000), p.6, DANNEELS/SETHI (2005), p.24.

574

For similar observations see e.g. CHANDY/TELLIS (2000), p.6 and TSAI (2001), p.1000.

575

See chapter IV-2.4 and Table IV-1.

576

See e.g. CHAUVIN/HIRSHEY (1993), p.130 and LANGERAK/HULTINK/ROBBEN (2004), p.79ff.

577

See e.g. BREALEY/MYERS (2003), p.828f.

578

See e.g. SHORT/KETCHEN/PALMER/HULT (2007), p.155.

138

4

Empirical Study

Results

4.1

Descriptive Statistics

The central methodology applied to test the research hypotheses is multiple regression analysis, a multivariate approach. Results from these more complex multivariate models are preceded by descriptive statistics from the mail survey and illustrative output from the document analysis.579 For the descriptive statistics, uni- and bivariate approaches were used which consider one or two variables at a time. These techniques serve to describe the structure of the sample (e.g. means, frequencies).580 4.1.1 Demographic Characteristics 4.1.1.1

Firm Size

Size is a central characteristic of a firm, which is, for example, reflected in personnel and financial resources (e.g. number of employees, total sales, market capitalization). SCHUMPETER claimed that large firms have an advantage over smaller competitors with regard to technical innovations,581 because they have more resources to pursue radical and riskier innovations582 and can realize economies of scale and scope in the conduct of research.583 However, the empirical evidence on the subject of firm size and innovation productivity is far from clear.584 Size was measured in number of employees and total sales. Tables IV-10 and IV-11 give an overview of these two size measures, for the overall sample, different industry sectors and continents.

579

Output from the document analysis is only provided for the two constructs studied: innovation strategy (via the four dimensions) and business strategy (See IV-2.3).

580

See e.g. BORTZ/DOERING (2003), p.673ff.

581

See SCHUMPETER (1972), p.143ff.

582

See ETTLIE/RUBENSTEIN (1987), p.90 and HENDERSON/CLARK (1996), p.33.

583

See HENDERSON/CLARK (1996), p.32 and LIN/CHEN (2005), p.157.

584

See e.g. ETTLIE/RUBENSTEIN (1987), p.89 and DAY (1994), p.155. Also see introduction in chapter I.

Results

139

Table IV-10: Employees in 2005 - by Sector and Continent Sector / Continent Overall Sample

N

xmean

xmin

xmax

SD

122

28,572

30

250,000

41,460

Auto & Parts

8

24,036

665

70,400

25,480

Basic Resources

6

27,585

2,211

65,000

23,813

Chemicals

6

35,735

3,000

80,945

27,117

Construction Materials

4

6,899

543

13,327

5,401

12

51,727

30

250,000

84,565

Food & Beverage Health Care

25

22,238

101

90,924

26,691

Industrial Goods

24

17,350

126

104,000

26,551

3

42,430

6,090

109,000

57,732

Personal & Household Goods

12

47,517

385

159,226

50,906

Technology

22

27,063

110

150,000

39,007

Europe

97

25,067

30

250,000

42,044

North America

22

46,255

5,050

150,000

37,006

3

57,141

297

150,000

65,613

Oil & Gas

Other

Participating firms had on average 28,572 full time employees in 2005. The standard deviation at 41,460 is relatively high, reflecting the fact that the spread in terms of employees is large. The smallest firm had 30 employees and the largest 250,000. The largest firms came from the food & beverage and personal & household goods sectors and are global players. The smallest participants were from food & beverage, health care and technology sectors. Especially from the health care (incl. biotechnology) and technology sectors several smaller start-up like firms participated, often included in the NASDAQ or TecDax stock indices. Average total sales (Table IV-11) of participating firms mounted to 9.8 billion Euros in 2005. Again, the standard deviation of 25.1 billion Euros indicates a significant spread, ranging from 7.6 million to 248 billion Euros. Manufacturing companies from the oil and gas sector had by far the highest sales, demonstrating the special position of this industry. Apart from the oil and gas sector, the top sectors in terms of employees (food & beverage, personal & household goods, chemicals) also had the highest average sales in 2005. Similarly, firms from North America were on average

140

Empirical Study

larger in terms of employees and in terms of total sales.585 The observation is in line with the finding that number of employees and total sales strongly correlate (r=0.92). Given previous studies made similar observations586 and to ensure a parsimonious model, only number of employees was included as a measure of firm size in further analyses. Table IV-11: Total Sales 2005 (thousand €) - by Sector and Continent Sector / Continent Overall Sample

N

xmean

xmin

xmax

SD

122

9,824,332

7,582

247,761,952

25,116,722 3,585,452

Auto & Parts

8

2,921,145

82,401

9,933,000

Basic Resources

6

8,047,409

419,835

17,877,298

6,873,201

Chemicals

6

12,868,561

1,437,516

42,744,896

15,060,513

4

1,067,649

61,900

1,954,571

777,965

Food & Beverage

Construction Materials

12

13,104,796

73,437

58,565,360

18,237,751

Health Care

25

6,068,092

9,341

25,896,778

8,019,867

Industrial Goods

24

3,505,202

16,457

18,042,199

5,238,541

3

88,491,986

2,134,311

247,761,952

138,095,569

Personal & Household Goods

12

11,747,089

77,600

46,924,406

13,919,687

Technology

22

11,177,598

7,582

73,489,870

19,410,167

Europe

97

8,324,290

7,582

247,761,952

26,566,200

North America

22

17,505,990

736,051

73,489,870

18,085,285

3

1,993,531

26,518

4,516,560

2,296,080

Oil & Gas

Other

4.1.1.2

Industry Sector

An intersectoral research approach was chosen in order to derive comprehensive and context independent conclusions concerning the innovation strategy – performance relationship.587 122 firms from 10 manufacturing super-sectors588 participated in this study. To illustrate the 10 supersec-

585

This does not mean that companies in North America are overall larger than Europeans, but is specific to this sample, which includes over three times more European firms with a much broader spread in terms of firm size, e.g. with the smallest as well as the largest firm both from Europe.

586

See chapter IV-3.5.

587

See chapter GATIGNON/XUEREB (1997), p.82 and AVLONITIS/GOUNARIS (1999), p.1012 for similar argumentations and chapter IV-2.1 for more details on the sample frame.

588

The supersector aggregation level of the STOXX/Dow Jones ICB industry classification was chosen. Sectors are one aggregation level below supersectors.

Results

141

tors sectors belonging to each supersector and example firms are given in Table IV-12. Overall, healthcare, industrial goods, and technology are the three super-sectors most strongly represented in this sample. Table IV-12: Sectors Represented in Sample Supersector

N

%

Health Care

25

20%

Industrial Goods

24

20%

Technology

22

18%

Food & Beverage

12

10%

Personal & Household Goods

12

10%

Auto & Parts

8

7%

Basic Resources

6

5%

Chemicals

6

5%

Construction Materials

4

3%

Oil & Gas

3

2%

4.1.1.3

R&D Expenditure

Sectors Pharmaceuticals and biotechnology z Healthcare equipment (incl. medical technology) z

Industrial engineering Electronic and electrical equipment z Aerospace and defense z General industrials

Illustrative Examples z z

Merck (US) Novartis (CH)

z z

z z z z

z z

Technology hardware and equipment Software and computer services

z

Food producers Beverages

z

Personal goods Household goods z Leisure goods z Tobacco

z

z

Alstom (FR) Schindler (CH) Cisco (US) Hewlett Packard (US) Diageo (UK) Unilever (NL)

z z

z

Automotive suppliers

z z

Industrial metals Mining

z

Commodity and specialty chemicals

z z

z z z z z z z z

z z

Oil and gas producers Oil equipment, services and distribution

z z

Beiersdorf (DE) Nike (US) Federal-Mogul (US) Valeo (FR) Alcan (CA) Corus (UK) BASF (DE) Syngenta (CH) Wienerberger (AT) Geberit (CH) Shell (NL) Cooper Cameron (US)

R&D expenditure relates to three different kinds of innovation activity: basic research, applied research, and experimental development. The three categories are distinguished in terms of their distance from application.589 R&D expenditure is typically measured and compared via a firm’s R&D rate and is defined as the ratio of R&D expenditure to total sales.590 The R&D rate is one of the major indicators used for innovation analysis. However, as mentioned at the beginning, there is no clear evidence that R&D intensity, e.g. reflected in the R&D rate, is a performance indicator.

589

See SMITH (2005), p. 153.

590

See e.g. SMITH (2005), p.155.

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Empirical Study

Figure IV-7 gives an overview of the sample’s R&D rates by industry sector. Figure IV-7: R&D Rates per Sector

15

R&D as % of Total Sales (2005)

10.1

10.0

10

Ø 5.6 5

4.2 3.5

3.3 2.6 1.1

1.0

0.9

0.5

0

Technology

Health Care

Auto Parts

Chemicals Industrial Personal & ConFood & Basic Oil & Gas Goods Household struction Beverages Resources Goods Materials

Even though several sector-specific sub-samples are limited in size,591 the results are in line with larger studies.592 The OECD four tier model terms industries with an R&D rate over 5% high-tech industries.593 The OECD states an average R&D rate of 11.3% for the pharmaceuticals sector and 8.2-10.5% for computing, radio, television, and communications equipment, very similar to the rates for this sample.594 Thereby, pharmaceuticals correspond to health care in this study and communications and other equipment to technologies. Industries with a R&D rate of 3-5% are medium-high-tech industries. Auto parts, chemicals and industrial goods fall in this category, again in line with the OECD results. Industries with a 13% or below 1% R&D rate are termed medium-low-tech and low-tech in-

591

See Table IV-12.

592

See e.g. SMITH (2005), based on OECD studies, and LEIFER/KASTHURIRANGAN/ROBESON (2006).

593

See SMITH (2005), p.155ff.

594

For these and other R&D rate results mentioned, see SMITH (2005), p.157.

Results

143

dustries, respectively. Construction materials, food & beverages, basic resources and oil & gas fall into these categories.595 The average R&D rates shown in Figure IV–7 have large standard deviations associated to them. For the technology sector R&D rates range from 0.8% to 34.8% and for health care from 0.1% to 20.7%.596 The large spreads in R&D rate confirm one of SMITH'S key findings that innovation inputs (and outputs) are distributed highly asymmetrically across all sectors.597 4.1.2 Descriptive Results for Innovation Strategy Variables 4.1.2.1

Descriptive Results for Innovativeness

Innovativeness was measured along two first order factors, market and technology innovativeness. The overall dimension, the second order factor, innovativeness, is based on the mean of the two first order factors.598 Table IV-13 shows the descriptive statistics for innovativeness based on the results from the mail survey.599 Table IV-13: Innovativeness – Descriptive Statistics Variable

N

xmean

SD

xmin

xmax

122

3.54

1.08

1.55

6.25

Market Innovativeness (1st ord.)

122

3.70

1.12

1.20

6.40

Technology Innovativeness (1st ord.)

122

3.38

1.26

1.00

6.50

Innovativeness (2nd order factor)

With a mean of 3.5, when the maximum possible value is 7, the firms in the sample show on average incrementally to moderately innovative new product portfolios. Market innovativeness, which encompasses aspects like

595

For personal and household goods there is no comparable sector in SMITH’S study.

596

In fact, the maximum R&D rate for health care in the sample amounts to 596%, and not 20.7%. Given the 596% rate is from a small biotechnology company and constitutes an obvious outlier, it was left out from the analysis in order not to distort the findings.

597

See SMITH (2005), p.168.

598

In line, all other second order factors of this study were derived by calculating the mean of the corresponding first order factors.

599

For xmin and xmax non-integer results are possible given some firm results are calculated averages of two respondents. For details see chapter IV-2.2.

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Empirical Study

new customer benefits, required change in attitude and behavior, etc. is somewhat more pronounced (3.7) than technology innovativeness (3.4). The maximum values for market and technology innovativeness are amongst the lowest across all first order innovation strategy dimensions. These results refer to firms’ entire new product portfolio and do - by definition - represent averages across the portfolio. This methodological challenge600 might influence the relatively conservative results for innovativeness, shown above. However, the results exhibit enough variance to facilitate further analysis. Apart from the mail survey, the innovativeness of firms’ new product portfolio was also investigated via document analysis.601 The purpose was to validate the responses from the survey. Table IV-14 shows examples of quotes extracted from annual reports and web pages illustrating how firms indicate the innovativeness of their new product portfolio in publicly available information. The quotes are ordered from lower levels of innovativeness to higher ones. The key words referring to the level of innovativeness are marked in italics. For confidentiality reasons firm, division and product names are disguised. Statements are left in their original language. Quotes in Table IV-14 demonstrate different levels of innovativeness. As to market innovativeness, product variations – mentioned in the first quote - stand for incremental modifications of existing products.602 Similarly, new forms of packaging and functional improvements (e.g. 20% less heat in shoe) represent incremental innovations. In contrast, drugs hitting indications which no existing product - including competitors’ - ever addressed indicate a more radical new product portfolio. For technology innovativeness several quotes relate directly to indicators used in the mail survey.603 Further development of the existing technology corresponds to the item ‘minor improvements of previous technology’ from the questionnaire. Replacing an existing mechanical process is another formulation for

600

See chapter IV-3.2.1.

601

See chapter IV-2.3.

602

For an extensive review and description of different levels of innovativeness, i.e. incremental, moderate, radical/breakthrough, see GARCIA/CALANTONE (2002), p. 117ff.

603

See chapter IV-3.2.1 and Table IV-2.

Results

145

the item ‘making an old technology obsolete’. If a firm gives priority to true scientific innovations, it is a clear indicator for a higher level of technology innovativeness. As to overall innovativeness, improving existing products and broadening their applications refers to an incrementally new product portfolio. Intending to produce (“we will”) more significant innovations in the future means that the current new product portfolio is incrementally new. Emphasizing big new consumer innovations indicates a higher level of innovativeness. Table IV-14: Innovativeness – Quotes from Document Analysis Factor Market Innovativeness

Technology Innovativeness

Overall Innovativeness

Quotes 1.

„… umfassende Marketingaktivitäten sowie die konsequent verfolgte Premiumstrategie, die sich besonders in den Produktvariationen widerspiegelt“, Swiss food firm

2.

„Dabei konzentrieren wir uns immer stärker auf die Entwicklung innovativer Dosierungs- und Darreichungsformen…“, German healthcare firm

3.

„… dadurch 20% weniger Wärmeentwicklung im Schuh… Kosteneinsparungen durch ca. 20% geringeren Lackverbrauch…“, German chemicals firm

4.

„Innovative Anwendungsforschung sowie Vorentwicklung von Textilien mit vollständig oder teilweise neuen Funktionalitäten“, Austrian auto parts firm

5.

„A very high percentage of the molecules in our pipeline are innovative, meaning that they work by a mechanism or hit a target that no existing drug addresses“, US healthcare firm

1.

„Neben der Technologie-Weiterentwicklung bei unseren bestehenden Produkten als Service für alle Geschäftsfelder…“, German chemicals firm

2.

„The [name] smart value actuation system replaces the conventional mechanical operation of engine valves“, French auto parts firm

3.

„… [name] has changed the practice of medicine“, US healthcare firm

4.

„[Name] Labs‘ function is to deliver breakthrough technologies and to create business opportunities that go beyond [name] current strategies“, US technology firm

5.

„Priority is given to programs that represent true scientific innovations“, US healthcare firm

1.

„Die Erweiterung des Anwendungsspektrums von bereits vermarkteten Produkten soll durch die Identifikation von neuen Indikationen sowie durch die Entwicklung von verbesserten Formulierungen erfolgen“, German healthcare firm

2.

„Entwicklung neuer Lebensmittel (Innovation) und die ständige Verbesserung bestehender Produkte (Renovation) …zu den Grundpfeilern der Strategie von [Name]“, Swiss food firm

3.

„We will [!] focus our innovation program on fewer but more significant innovations…“, German personal goods firm

4.

„We place great value on big, new consumer innovations“, US personal goods firm

In addition to relevant text material some firms’ web pages include visual overviews of their new products.604 These served as another source of information on the innovativeness of firms’ new product portfolios.

604

See, for example, Beiersdorf’s new products of the Nivea product line: http://www.nivea.de/innovationen, or Manner’s new product overview, under http://www.manner.at/produkte.html.

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Empirical Study

For each firm as much relevant information as possible was extracted concerning the innovativeness of a firm’s new product portfolio. Subsequently, innovativeness was rated on a scale from 1-3, which was later transformed into a 1-7 scale to make the results from the document analysis comparable to the survey results. Results from the two approaches were compared and measures taken if they did not coincide.605 4.1.2.2

Descriptive Results for Distance to Core Business

Distance to core business corresponds to how far away a firm’s innovations are from its core business. Descriptive statistics - based on the results from the mail survey - are given in Table IV-15. Table IV-15: Distance to Core Business – Descriptive Statistics Variable

N

xmean

SD

xmin

xmax

122

3.65

1.02

1.25

5.81

Market Familiarity

122

3.45

1.29

1.00

6.75

Technological Familiarity

122

3.86

1.85

1.00

6.50

Distance to Core Business

Means are relatively low indicating that firms’ innovation portfolios are close to their core business. This means that firms produce innovations in areas relatively familiar to them. It holds true for both first order factors, market and technological familiarity.606 Distance to core business was also analyzed as part of the document analysis. Table IV-16 demonstrates illustrative examples of how companies communicate their innovations’ distance from their core business. In addition to market and technological familiarity a third overall category was added. The statements referring to market familiarity deal with how close firms’ new products are to existing ones. The first two quotes indicate a high proximity of innovations to the core business. Quotes three and four correspond to new products being distinct from existing ones, however, with a strong relation. The last quote (under market familiarity) states that this firm creates completely new product categories – a sign

605

The procedure was the same for each innovation and business strategy dimension analyzed via document analysis, and is therefore only mentioned once. For details on the comparison and measures taken if results did not coincide see chapter IV-2.3.2.

606

Items related to internal configuration were elminiated. For details see IV-4.2.2.2.

Results

147

for innovations being further away from the core business. The statement corresponding to technological familiarity refers to the survey item ‘different technologies are combined in a new way’.607 The firm claims to have a strong capability in combining and leveraging technologies across different businesses. The quotes under overall distance to core business refer to the overall business and its core competencies. The first two statements indicate that innovations are either based on core competencies or that they ought to strengthen the core business. Both stand for innovations being close to the current business. The third quote demonstrates a common pattern. Many firms innovate close to the core business and further away. The last statement describes a central R&D function with the mandate to come up with innovations beyond the core business. Most probably, however, this central R&D unit is an addition to other R&D units located in business units which innovate more closely to the core business. Table IV-16: Distance to Core Business – Document Analysis Factor Market Familiarity

Quotes 1. „At [name] we focus on one goal: to meet consumer‘s skin and beauty care needs [=current core business]“, German personal goods firm 2. „The majority of [name] research is conducted in our business groups, which develop the products and services we offer to customers“, US technology firm 3. „[Name] verfolgt die Strategie, sein künftiges Wachstum… durch die Entwicklung von neuen Produkten für bestehende Märkte auf eigenes Risiko voranzutreiben“, Austrian auto parts firm 4. „[Name] hat mit … zwei weitere Produktlinien dem Produktportfolio hinzugefügt… neue Produktbereiche, die in einem sinnvollen Zusammenhang mit den bestehenden Arbeitsgebieten stehen“, German auto parts firm 5. „This ability to transfer knowledge from one business to another has been an extraordinary factor in the company‘s growth… Creating entirely new [product] categories and benefits…“, US personal goods firm

Technological Familiarity

1. „The breadth of our business creates opportunities to connect technology across categories in unexpected ways…e.g. [name] used one of its laundry competencies - controlling calcium in hard water to achieve superior cleaning - to create calcium delivery technologies that are being used in food and beverage products and in leading-edge drugs like [name]”, US personal goods firm

Overall – Distance to Core Business

1. „… kommt der Entwicklung von neuen, das Kerngeschäft stärkenden Produktentwicklungen eine besondere Bedeutung zu“, German healthcare firm 2. „Wir konzentrieren uns auf unsere Kernkompetenzen… Wir stützen uns auf die eigene Wertschöpfungskette von der Carbonfaser bis zum Composite… Portfolioausrichtung auf innovative Materialien und Technologien, die auf unseren Kernkompetenzen aufbauen“, German chemicals firm 3. „We invest a substantial portion [about 70%] of our resources in R&D activities within our principal business groups…[At the same time]…[Name] has renewed its core businesses and created innovative businesses in entirely new areas…“, Finnish technology firm 4. „… zentrale Forschung und Entwicklung geschaffen… Aufgabe ist es vor allem, losgelöst vom Tagesgeschäft längerfristige Forschungsprojekte anzugehen, die über die klassischen Kernkompetenzen von [Name]… hinausgehen“, German auto parts firm

607

See Table IV-4, third item under technological familiarity.

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Empirical Study

The text material relevant for distance to core business was more limited than for innovativeness. Hence, additional observations became important. First, new product overviews proved helpful. Comparing the pictures and names of new products with established products gave a clear idea about how close or distant the new product portfolio of a firm is from its core business. Second, some firms’ web sites include patent overviews occasionally even categorized by product lines or businesses. Similarly, comparing the patents with existing product categories often answered the question of distance to core business. Combining these observations with relevant quotes (like in Table IV-18) provided sufficient material for the document analysis of the distance to core business dimension. 4.1.2.3

Descriptive Results for Driver of Innovation

For driver of innovation three first order factors were investigated: market orientation, technology orientation, and competitive response. The second order factor, driver of innovation, consists of all three forces. It describes how pronounced, on average, a company's choices are about what drives their innovations. The descriptive results from the mail survey are given in Table IV-17. Table IV-17: Driver of Innovation – Descriptive Statistics Variable

N

xmean

SD

xmin

xmax

122

4.82

0.89

3.00

6.72

Market Orientation

122

5.22

1.06

2.50

7.00

Technology Orientation

122

4.76

1.19

2.33

7.00

Competitive Response

122

4.48

1.10

2.00

7.00

Driver of Innovation

The overall high mean of 4.8 is driven by all three forces, with market orientation showing the highest average (5.2). This means that firms in the sample are relatively active in discovering new customer needs, working with lead users, etc. The mean for technology orientation is somewhat lower (4.8). This might originate from technology being of varying importance across different industries. Distinct from findings in the document analysis (see below), firms admitted in the survey that their innovation activities are also inspired by observing competitors. Competitive response exhibits an average mean of 4.5.

Results

149

In line with the innovativeness dimension, driver of innovation was also investigated as part of the document analysis. Table IV-18 contains illustrative examples of how firms indicate the forces driving their innovations in publicly available secondary data. Concerning market orientation numerous firms mention and describe how they collaborate with (lead) customers – confirming the high mean from the mail survey. Whereas some firms already systematically tap into lead users as a source for innovation, others are on the verge of institutionalizing it. Moreover, quotes three to five for market orientation demonstrate how firms use various communication channels to talk, understand and collaborate with their customers. Having a CEO listening to customers in a chat room is certainly still an unusual phenomena, but a clear sign of a strong market orientation. As to technology orientation the two first statements relate to ‘scanning for new technologies’, an aspect also tested in the questionnaire.608 Finders, mentioned in the second quote, are commonly termed idea or trend scouts.609 Their objective is to look for new ideas and trends outside the firm. If a company sets industry wide technology standards, as mentioned in the fifth quote, it has to be at the forefront of technology and thus have a strong technology orientation. Few quotes could be found relating to competitive response as a driver of innovation. This is not necessarily a reflection of firms’ practices, but rather motivated by political considerations. Mentioning in a public document that one’s innovations are mainly driven by observing competitors’ innovation activities might have a negative connotation and harm firm image. In contrast, in the survey, firms gave relatively high scores to items indicating competitive response as a driver of innovation – as shown below in Table IV-18.

608

See Table IV-3.

609

See e.g. VAHS/BURMESTER (2002), p.176.

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Empirical Study

Table IV-18: Driver of Innovation – Quotes from Document Analysis Factor Market Orientation

Quotes 1. “Die Entwicklung innovativer Produkte bei einer möglichst antizipierenden Ausrichtung auf die Kundenbedürfnisse…”, Austrian auto parts firm 2. ”Instead of developing products exclusively „for“ customers, we are conducting these activities more and more „with“ customers on a collaborative basis“, German household goods firm 3. ”We also utilize research committees and advisory boards made up of athletes, coaches, orthopedists… It [evolution] comes from talking with consumers and serving athletes“, US personal goods firm 4. ”We get out into people‘s homes, into where they buy the products, and get to consumers at the front of a product‘s string to understand consumer‘s wants and needs. [Name] has transformed itself into a consumer driven enterprise”, US household goods firm 5. “We gather requirements directly through tens of thousands of customer interactions daily, organized events, and customer panels… I [CEO] hang out in chat rooms where actual users chat about [name] and our competitors. I listen to their conversations as they discuss their purchases and their likes and dislikes… This approach is direct, customer-driven innovation”, US technology firm

Technology Orientation

1. “This collaboration makes it possible for us to recognize technological trends early and assess their business potential.“, German household goods firm 2. “ Finder sind wissenschaftliche Experten, die gleichzeitig über einen guten Geschäftssinn verfügen. Ihre Aufgabe ist es, externe Chancen für die Entwicklung neuer Therapien zu identifizieren…”, Swiss healthcare firm 3. “[Name] maintains strong global contacts to monitor and influence technological developments… Technology is an enabler, very often a key enabler, of a new business”, Finnish technology firm 4. “For more than two decades, [name] has played a leadership role in the translation of innovative science and technology into breakthrough human therapeutics“, US healthcare firm 5. “Dabei setzt [Name] ständig technologische Standards für die gesamte Branche”, Swiss healthcare firm

Competitive Response

4.1.2.4

1. “Wir werden unsere Kernkompetenzen weiterhin stärken und gleichzeitig Partnerschaften schließen, um Zugang zu Innovationen, Technologien, Kompetenzen und Fachwissen zu verschaffen…”, US healthcare firm

Descriptive Results for Innovation Field Orientation

With the highest mean (4.9) of all second order innovation strategy factors, many firms in the sample claim to have set up their innovation activities along innovation fields. Also, all first order factors610 exhibit relatively high means, with definition criteria and synergies standing out. The descriptive statistics for innovation field orientation are presented in Table IV-19. The first order factor, definition criteria, investigates along which criteria (technologies, customer needs, customer groups, or core competencies) innovation fields are defined. A very high mean of 5.2 might indicate that

610

Multitude of projects (see IV-3.2.4) serves only as a descriptive variable for innovation field orientation (see IV-4.4) and is therefore not included in the innovation field orientation construct. Time horizon (see IV-3.2.4) was eliminated as first order factor. For details see chapter IV-4.2.2.4.

Results

151

the means of all four criteria were high and that firms define their innovation fields based on a combination of all four criteria. This question was further analyzed in chapter IV-4.4. Table IV-19: Innovation Field Orientation – Descriptive Statistics Variable

N

xmean

SD

xmin

xmax

122

4.89

0.75

3.13

6.81

Definition Criteria

122

5.15

1.01

2.67

7.00

Mightiness of Projects

122

4.43

1.56

1.00

7.00

Organizational Formality

122

4.70

1.19

1.75

7.00

Synergies

122

5.28

0.94

2.75

7.00

Innovation Field Orientation

Mightiness of projects tries to understand of which size - in terms of financial and human resources - firms’ innovation projects are. The highest standard deviation of 1.6 across all first order innovation strategy factors indicates that mightiness varies much between firms. Organizational formality of innovation fields also differs significantly between firms, indicated by a standard deviation of 1.2. Synergies received the highest mean (5.3) across all first order factors, meaning that firms intend to exploit synergies across different innovation projects. In line with the other three innovation strategy dimensions, innovation field orientation was also investigated as part of the document analysis in order to complement and validate the mail survey results. Illustrative quotes from annual reports, web pages and other publicly available secondary information are shown in Table IV-20. Sufficient text material could be identified for the factors definition criteria, organizational formality and synergies. Moreover, an overall category on innovation field orientation was added. Within overall innovation field orientation several statements indicate that companies focus their innovation activities within certain areas. Terms ranging from cluster to domain, area or theme are used for what this study terms innovation field. For healthcare companies innovation fields tend to be defined along therapeutic classes,611 as shown in statement four. Concerning the four definition criteria (technologies,

611

Also see HENDERSON/COCKBURN (1996), p.34.

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Empirical Study

customer groups, customer needs, core competences) examples could be found for all criteria except core competencies. This does not mean that firms do not consider their core competencies when defining innovation fields, but only that they do not publicly mention it. Innovation fields are often defined along a combination of these four criteria.612 The example mentioned under ‘customer needs’ illustrates this multi-criteria approach. At first sight, the German personal good firm’s innovation fields skin ageing, skin sensitization and chrono- and photo-biological skin processes seem to be purely defined along customer needs. However, knowing and investigating the specific firm one finds out that this company has extensive expertise in skin care and thus the three innovation fields were probably defined taking into account customer needs and core competencies. As to organizational formality, the three first quotes refer to the survey indicator ‘organizational unit(s) particularly created for our focus areas in innovation’.613 Both chemicals firms have created special units within which they accommodate their innovation fields. The French automotive supplier has also created a separate organization for its three innovation domains. A Projekthaus, as mentioned in the third quote, is an organization and location specifically created for a certain innovation field. The relevant firm has formalized innovation field orientation to the extent of locating each innovation field in a separate building, called Projekthaus. Considerably less text material was available with regard to the first order factor synergies. Nevertheless the four selected quotes illustrate the different aspects tested in the mail survey.614 The first statement demonstrates that synergies are an important aspect in new product development. The second quote falls under the survey indicator ‘innovation projects use common resources’ [here: medical competence center] thus realizing economies of scope in research.615 The third quote corresponds to the survey indicator ‘collaboration […] permits the access to a wider number of new knowledge sources’. Through cross-functional collaboration across different locations scientists from this firm can tap into a broader

612

This was confirmed in the mail survey. See chapter IV-4.4 for details.

613

See Table IV-5, first indicator under organizational formality.

614

See Table IV-5, the indicators under the factor synergies.

615

See HENDERSON/COCKBURN (1996) and the beginning of this chapter IV-3.2.4

Results

153

base of knowledge and ideas. The last and fourth quote is an example for ‘internal knowledge spillovers’,616 also tested in the survey. Transferring knowledge from one business unit to others is an important source of innovation for this US personal and household goods firm. Table IV-20: Innovation Field Orientation – Document Analysis Factor Overall – Innovation Field Orientation

Quotes 1. „Wichtig für die Umsetzung der Strategie, die rasche Einführung von Innovationen und andere Initiativen, sind die neu geschaffenen , die den Strategischen Geschäftseinheiten unterstehen. Unter versteht man speziell für bestimmte Kategorien, Marken oder Produkte zusammengestellte Marktübergreifende Teams, die sich über ähnliche Herausforderungen, Chancen und Probleme austauchen.“, Swiss food firm 2. „Wichtige technologiegetriebene Zukunftsthemen haben wir in fünf sogenannten ‚Wachstumsclustern‘ gebündelt: Energiemanagement,…“, German chemicals firm 3. „Innovationen. Die [3] Domains: Fahrerassistenz, Effizienzsteigerung im Antriebsstrang, Komfortsteigerung…“, French auto parts firm 4. „Neue, zukunftsweisende Forschungsthemen bearbeitet [Name] in Projekthäusern, die das Wissen mehrerer Geschäftsbereiche bündeln…", German chemicals firm 5. „Bei [Name] befinden sich derzeit 111 Projekte aus sieben therapeutischen Gebieten in der vorklinischen Forschung sowie 78 Projekte aus neun Gebieten in der klinischen Entwicklung“, Swiss healthcare firm

Definition Criteria

1. Technologies: „3 Projekthäuser: ProFerm, Functional Polymers, Process Intensification & 2 Science to Business Centers: Nanotronics, Weiße Biotechnologie“, German chemicals firm 2. Customer groups: „Runners in the US often run on hard surfaces while runners in Europe prefer trails. That leads to different cushioning demands and different potential injuries… Children learning to walk – and the lab has studied even them – have different stability needs…“, US personal goods firm 3. Customer needs: „To develop innovative… products, we focus on skin ageing and sensitization and on chrono- and photo-biological skin processes“, German personal goods firm

Organizational Formality

1. „Creating new business and developing forward-looking technology platforms are the tasks of [name] Technologies & Innovation, the [name] R&D unit that attends to cross-company and cross-portfolio research activities.”, German chemicals firm 2. „An innovative organization by Domains …the Domain approach allows [name] to offer automakers innovative solutions with reduced development time…“, French auto parts firm 3. „Projekthäuser: Innovation im Container! Die Projekthäuser von [Name] bestehen aus einem interdisziplinären Team von jeweils 20 bis 30 Wissenschaftlern… die buchstäblich in einem umgebauten Transportcontainer zusammenarbeiten…“, German chemicals firm

Synergies

1. „Multimedia product roadmaps have been streamlined and cross-functional synergies have been identified…“, Finnish technology firm 2. „Das medizinische Kompetenzzentrum am Standort [Name] bündelt die daraus gewonnenen Erkenntnisse und berät die Projektteams bei der Entwicklung neuer Produktlösungen“, German healthcare firm 3. „Researchers at one site typically work in cross-functional teams with scientists at one or more of the other locations; the idea is that innovation springs from broader thinking“, US healthcare firm 4. „This ability to transfer knowledge from one business to another has been an extraordinary factor in the Company‘s growth… The Company‘s entry into Food, for example, evolved directly from its soap and candle business…“, US personal and household goods firm

The discussed text material was complemented with further observations. Most importantly names of innovation fields were compared to existing

616

See HENDERSON/COCKBURN (1996), p.35f.

154

Empirical Study

business units. Many firms claim to have some sort of innovation field orientation; however, investigating the names of innovation fields reveals that many are identical to the names of business units and not specific to innovation. Combining these observations with the text material (as indicated in Table IV-20) offered a substantive basis to judge firms’ level of innovation field orientation. 4.1.3 Descriptive Results for Performance Variables Performance variables are important when deriving recommendations, for research and practice. The descriptive statistics for the three different performance variables are shown in Table IV-21. Innovation performance,617 based on subjective measures from the mail survey, displays a mean of 4.9 and a standard deviation of 0.9. The results indicate a relatively high level of innovation performance across participating firms. The results for innovation performance might suffer from common source bias618 given the data were gathered from the same person as the independent variable, innovation strategy. However, given the informants for this survey were carefully pre-identified and are executives who have a good overview of their firms’ innovation success, their assessments should be realistic.619 Therefore, the use of the subjective innovation performance measure - in addition to the following objective measures - should be uncritical. Table IV-21: Performance Variables – Descriptive Statistics N

xmean

SD

xmin

xmax

122

4.94

0.88

2.50

7.00

Financial

122

4.74

1.31

1.50

7.00

Market

122

5.10

0.97

2.00

7.00

Technical

122

4.97

1.03

2.00

7.00

Tobin‘s q

121

2.15

1.66

0.65

10.12

TRS vs. Industry

122

0.99

0.27

0.44

2.29

Variable Innovation Performance

Firm Performance

617

The aspect ‘internal/process’ (see IV-3.3.1 and Table IV-10) was eliminated from the innovation performance construct. For details see chapter IV-4.2.3.

618

For details and bibliographical reference see chapter IV-1.

619

For a similar argumentation see e.g. KRIEGER (2005).

Results

155

Tobin’s q and TRS vs. industry are both objective firm performance measures - predominantely capital-market based.620 A mean of 2.2 for Tobin’s q means that participating firms expect healthy profitability in the future, well above the current asset base.621 The high standard deviation of 1.7 and the spread of values from 0.7 to 10.1 indicate, however, that the performance situation - measured by Tobin’s q - varies significantly across the sample. A mean of 0.99 for TRS vs. industry could be interpreted as the sample being representative for the overall market. A value of 1 means, for one firm, that its performance equals the average performance of its industry. A value of 1 for the entire sample could therefore suggest that the performance of the sample is – on average - in line with the industry or the market. However, given the cross-sectional and cross-national approach taken for this study, the results can not be interpreted this way. The other descriptive statistics, e.g. SD, xmin, xmax, suggest a good and wide spread of TRS vs. industry in the sample. 4.1.4 Innovation Strategy – Performance Correlations Table IV-22 shows the correlations between the four second order innovation strategy dimensions and the three performance variables. Table IV-22: Correlations: Innovation Strategy, Performance Variables Variable

6.

Mean

SD

1.

1. Innovativeness

3.54

1.08

1

2. Distance to Core Business

3.65

1.02

.506**

1

3. Driver of Innovation

4.82

0.89

.369**

.223*

1

4. Innovation Field Orientation

4.89

0.75

.328**

.217**

.515**

1

5. Innovation Performance

4.94

0.88

.494**

.274**

.596**

.458**

1

6. Tobin‘s q

2.15

1.66

.337**

.172

.148

.131

.456**

1

7. TRS vs. Industry

0.99

0.27

.166

.020

.251**

.113

.251**

.046

2.

3.

4.

5.

7.

1

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

620

Tobin’s q is capital market data based, but also contains some accounting data.

621

A value of 1 for Tobin’s q means that the expected profitability equals the book value of assets (or in the original formula, the replacement costs of assets).

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Empirical Study

Some interesting observations can be made: ƒAll four innovation strategy dimensions correlate positively and significantly with each other. This means that the more pronounced a firm pursues one aspect of innovation strategy (one dimension), the more positive it also responded for the other three dimensions. However, the intensity of correlation varies, e.g. with distance to core business being least correlated with the other three dimensions. ƒThe two strongest correlations are between innovativeness and distance to core business and between driver of innovation and innovation field orientation. The first one indicates that more innovative products in the market are at the same time further away from a firm’s core business. The second one suggests that firms which more proactively use different drivers of innovation also demonstrate a higher innovation field orientation. ƒConcerning the performance variables, innovation performance is positively and significantly correlated with Tobin's q and TRS vs. industry. For Tobin's q the correlation is much stronger. Tobin’s q and TRS vs. industry are, however, not correlated. This suggests a clear difference between the accounting cum capital market based measure (Tobin’s q) and the purely capital market based measure, TRS vs. industry. ƒAll four innovation strategy dimensions are positively and significantly correlated with innovation performance; however distance to core business at a considerably lower level. Innovativeness is the only innovation strategy dimension significantly correlated with Tobin’s q and driver of innovation the only one significantly correlated with TRS vs. industry. 4.1.5 Descriptive Results for Contingency Variables Environmental uncertainty and business strategy are applied as contingency variables. The objective is to understand whether these industry and firm level characteristics have an influence on the innovation strategy

Results

157

- performance relationship. Descriptive statistics based on results from the mail survey are provided in Table IV-23. A mean of 3.9 for environmental uncertainty indicates that participating firms operate on average under moderately uncertain circumstances. The first order factor, technological uncertainty, received a significantly higher mean (4.2) than market uncertainty (3.6). Moreover, technological uncertainty shows a relatively high standard deviation (1.3) meaning that the level of technical uncertainty across firms varies significantly. Table IV-23: Contingency Variables – Descriptive Statistics Variable

N

xmean

SD

xmin

xmax

122

3.91

1.06

1.25

6.42

Market Uncertainty

122

3.60

1.10

1.17

6.00

Technological Uncertainty

122

4.22

1.33

1.00

7.00

Environmental Uncertainty

Differentiation

122

4.15

1.31

1.00

7.00

Cost Leadership

122

4.65

1.00

1.75

7.00

Focus

122

4.44

1.02

1.50

6.50

With regard to business strategy all three strategy types show similar means of above 4. This denotes that representatives for all three business strategies can be found in the sample. Moreover, the three strategies are, on average, executed at a similar level. The highest standard deviation for differentiation (1.31) reflects a much broader spread of results. Specifically, some firms identified strongly with the items measuring differentiation strategy, whereas others not at all. Like previously done for the four innovation strategy dimensions, firms’ business strategies were also analyzed in the document analysis. Table IV-24 shows illustrative statements from publicly available secondary information referring to firms’ business strategies. Substantive text material could be identified for PORTER’s two strategies, differentiation and focus. Phrases on cost leadership were rare.622 The plentiful statements on differentiation illustrate various facets of this strategy type. The quotes cover the three indicators from the mail survey and go beyond. The first quote

622

This is probably less driven by companies not pursuing cost leadership strategies, than by the more negative connotation of cost leadership.

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Empirical Study

originates from a firm which spends significant amounts on various types of advertising. Quote two corresponds to the second item in the mail survey. This firm adopts innovative marketing methods and channels (e.g. new media) to reach their customers. Statements three and four come from firms which attribute high priority to strong brands. The fifth quote introduces design as an alternative way of differentiation. Last, statements six through eight demonstrate firms which distinguish themselves through innovative products and/or superior technology. Table IV-24: Business Strategy – Quotes from Document Analysis Factor

Quotes

Differentiation

1. “Advertising and promotion costs were $ 1,600 million for 2005… To help market our products we contract with prominent and influential athletes, coaches, etc… we actively sponsor sporting events and clinics… and through aggressive marketing”, US personal goods firm 2. “New media will enable us to find more relevant ways to connect with consumers… It’s clear we need new channels to reach consumers. Brands that rely too heavily on mainstream media… will lose touch”, US personal and household goods firm 3. “Many of our brands are synonymous today of whole product categories”, German personal goods firm 4. “Aufbau von starken Marken… nach wie vor Priorität…”, Swiss food firm 5. “[Name] has always recognized the importance of design in its product development, both in building the brand and creating a full end-user experience”, Finnish technology firm 6. “In every case, [name] extraordinary R&D organization has played a key role in building some of the world’s most successful brands”, US personal and household goods firm 7. “…the key to long-term success in the high-technology industry is ongoing strategic investment and innovation… To position ourselves… we have continued to rely on innovation and on taking good business risks”, US technology firm 8. “This is all part of our continuous drive to differentiate ourselves from the competition through our technology…”, French auto parts firm

Cost Leadership

1. “Für die [Name] hat daher weiterhin das Identifizieren von Rationalisierungspotenzialen entlang der gesamten Wertschöpfungskette hohe Priorität”, German healthcare firm 2. “Wir beabsichtigen, unsere Wettbewerbsfähigkeit durch Kostensenkungen und effizienteren Einsatz unserer Ressourcen zu stärken”, German healthcare firm 3. “Unsere Kostenführerschaft erreichen wir durch integrierte Produktionsanlagen, innovative Verfahren und Grossanlagen, German chemicals firm

Focus

1. “[Name] entwickelt, erzeugt, wartet und vertreibt Motor- und Antriebssysteme für Nischenmärkte der Motorsport- und Luftfahrtindustrie”, Austrian auto parts firm 2. “… focusing strongly on professional market segments in which productivity, print quality and user friendliness are of primary importance”, Dutch technology firm 3. “[Name] ist das innovativste Unternehmen der Dentalbranche… [Name] ist weltweiter Marktführer bei innovativen, ästhetischen Dentallösungen”, Swedish healthcare firm 4. “[Name] ist ein multinationales Unternehmen mit konsequenter Ausrichtung auf die renditestarke Spezialchemie…”, German chemicals firm 5. “Mehr als ein viertel Jahrhundert Erfahrung in der Dialyse, zukunftsweisende Forschung, Weltmarktführer bei Dialysetherapien und Dialyseprodukten”, German healthcare firm

As to cost leadership the first two quotes correspond to the survey indicator on operational efficiency. The third statement refers to optimizing production costs, also tested in the mail survey. Finally, the five extracts un-

Results

159

der focus strategy illustrate the different ways of pursuing a niche strategy. Statements one and two stand for focus strategies defined along one or few customer segments. The Swedish healthcare firm (quote 3) focuses on the high price segment of aesthetic dental solutions. Last, the German chemicals and healthcare firms (quote four and five) concentrate on specialty products in their respective fields. Overall, the document analysis proved a valid approach to classify firms’ business strategies - in addition to the mail survey. 4.2

Validation of Measures

4.2.1 Methodical Considerations The most important criteria to validate formative constructs is indicator collinearity.623 Collinearity denotes the degree of linear dependency between more than two indicators.624 Several problems are associated with collinearity. First, with increasing collinearity the contributions of the individual independent variables to the explanation of the variance of the dependent variable become difficult to identify.625 Second, collinearity can impact the estimation of regression coefficients and their statistical significance.626 At the extreme, with perfect collinearity, the estimation of coefficients via least squares method becomes impossible.627 Third, formative constructs are composed of different aspects as regards content. In the form of indicators, these aspects contribute individually and independently to the construct. If two or more indicators are linearly dependent the underlying aspect receives more weighting than others. This can change the content specifications of the construct.628 Summarizing, high collinearity can result in imprecise, unstable and difficult coefficient estimations. If

623

See SCHULZ (2006), p.180ff.

624

See BELSLEY (1991), p.7.

625

See DIAMANTOPOULOS/WINKLHOFER (2001), p.202.

626

See HAIR ET AL. (1998), p.169.

627

See BELSLEY (1991), p.22.

628

See SCHULZ (2006), p.183.

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Empirical Study

collinearity exists, the affected indicators need to be removed from the measurement model.629 Three procedures are commonly applied to identify indicator collinearity: ƒCorrelation matrix: This is the easiest method to reveal collinearity. The correlation matrix is calculated for all items of a certain construct. Correlations of over 0.9 are clear indications for collinearity.630 For correlations beyond 0.7 the affected items should be verified in respect of content. The correlation matrix approach can only identify linear dependencies between two variables but not between three or more. Hence, additional methods are required. ƒTolerance/Variance Inflation Factor (VIF): To derive the tolerance and VIF measures multiple regressions are calculated where each indicator is explained through all other items. The tolerance measure is defined as the portion of the variance which cannot be explained by the other indicators. It is the difference between one and the coefficient of determination (R). VIF is defined as the reciprocal value of tolerance.631 For perfect linear independency, tolerance reaches its maximal value of one and VIF its minimal value of one. VIF should not exceed a threshold value of 10, which corresponds to a multicollinearity of 0.95.632 However, for each construct the acceptable level of collinearity should be separately defined driven by content considerations. As a trigger for further verifications a VIF value of 2 can be applied which corresponds to a collinearity of 0.7.633 The tolerance/VIF approach does, however, not provide any information about the number of linear dependencies and the involved variables.634

629

See SCHULZ (2006), p.183 and DIAMANTOPOULOS/WINKLHOFER (2001), p.272.

630

See BACKHAUS ET AL. (2000), p.42.

631

See HAIR ET AL. (1998), p.191.

632

See HAIR ET AL. (1998), p.193.

633

See SCHULZ (2006), p.184 and OPP/SCHMIDT (1976).

634

See BELSLEY (1991), p.28.

Results

161

ƒCondition index: To address the weakness of the tolerance/VIF approach BELSLEY developed the condition index.635 The condition index is a measure of near-dependencies. A high condition index indicates the existence of near-dependencies. For the condition index BELSLEY suggests a threshold value 30. The three approaches - correlation matrix, tolerance/VIF and condition index - were applied to validate the formative constructs of the survey. 4.2.2 Validation of Innovation Strategy Variables 4.2.2.1

Validation of Innovativeness

Table IV-25 shows the indicator collinearity statistics for the innovativeness construct – divided into the two first-order factors market and technology innovativeness. For reasons of scope and not to loose perspective only critical correlations are mentioned. Table IV-25: Innovativeness – Collinearity Statistics Factor / Indicator

VIF

Condition Index

Correlations

Market Innovativeness 1.

The majority of our innovations address completely new customer benefits.

1.878

2.

The majority of our innovations offer customers unique advantages over competitors‘ products.

1.810

3.

The majority of our innovations require changes in established attitude and behavioral patterns from customers.

1.856

4.

The majority of our innovations require major learning efforts by mainstream customers.

2.140

5.

The majority of our innovations involve high switching costs for mainstream customers.

1.621

6.

The majority of our innovations are similar to our main competitors’ products. R

1.640

12.991 .617** with Item 3, .579** with Item 5

Technology Innovativeness 1.

The majority of our innovations are based on substantially different core technology never used in our industry before.

1.602

2.

The majority of our innovations involve technology that makes old technologies obsolete.

1.698

3.

The majority of our innovations use new technology that permits quantum leaps in performance.

1.782

4.

The majority of our innovations use technologies that have an impact on or cause significant changes in the whole industry.

1.494

5. R

The majority of our innovations use technologies which represent minor improvements over previous technologies.

= reversed item

635

See BELSLEY (1991), p.40ff.

R

1.613

8.850

.435**-.526** with all other items

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Empirical Study

Applying the thresholds (see IV-4.2.1) no serious collinearity problems can be identified with regard to market and technology innovativeness. However, a closer investigation of the 4th item for market innovativeness shows not only a VIF value over 2,636 but also high and significant correlations with its two adjacent items. Especially between item 3 and 4 certain connections exist, which might have lead the respondents to believe that the two items are similar. ‘Changes in established attitude and behaviour’, item 3, can implicate ‘major learning efforts’, item 4. Hence, the 4th item was eliminated. Consequently, the condition index for market innovativness changes to 11.7 and the highest VIF value is then 1.841 (item 1). For technology innovativeness the last item shows relatively high and significant correlations with all other items. Given this item investigates technology innovativeness overall, the correlations are comprehensible. GATIGNON and XUEREB, where the item originates from,637 use it reflectively638 in contrast to the formative constructs in this study. After eliminating the 5th item, the condition index for technology innovativeness is 7.843 and the highest VIF value 1.672 (item 3). 4.2.2.2

Validation of Distance to Core Business

Table IV-26 lists the collinearity statistics for the distance to core business construct – divided into the three first order constructs market familiarity, technology familiarity and internal configuration. For the market familiarity construct the third item is a borderline case in terms of collinearity. Its VIF value is almost 2 and it shows strong correlations with two other items. Especially the correlation with item 2 is comprehensible. Even though asking for two different aspects of customers customer needs and customer groups – items 2 and 3 can appear similar to respondents. Because of this medium linear dependency and to prevent collinearity issues at first and second order construct levels,639 the item

636

OPP/SCHMIDT (1976) suggest further collinearity investigations for VIF values above 2.

637

See GATIGNON/XUEREB (1997), p.89.

638

See chapter IV-3.1 for the differences of formative and reflective measurement models. For reflective constructs indicator correlations are not a problem, in contrast to formative constructs, which were used in this study.

639

See chapter IV-4.2.5 and 4.2.6.

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163

was eliminated. After elimination the highest VIF value was 1.661 (item 1) and the condition index resulted in 8.386. For the technological familiarity no collinearity issues could be identified. The newly developed, third item proved to be a valid indicator. The third construct, internal configuration, encompasses remaining aspects of distance to core business. For reasons of scope, DANNEELS and KLEINSCHMIDT’S multi-item constructs market and technology fit,640 were tested via two summarized items (2 and 3). In addition, two items from SALOMO were measured, which generally form part of a multi-item construct.641 This approach of pooling items did not work. Table IV-26: Distance to Core Business – Collinearity Statistics Factor / Indicator

VIF

Condition Index

Correlations

Market Familiarity 1. The majority of our innovations represent new product categories – types of products that our firm had not made and/or sold before.

1.704

2. The majority of our innovations serve new customer needs – customer needs we had not served before.

1.715

3. The majority of our innovations generate new customers for our firm – customer we had not sold to before.

1.904

The majority of our innovations take us up against new competitors – competitor firms we have never faced before.

1.903

4.

5. The majority of our innovations are improvements of our existing products. R

9.889

.575** with item 2, .563** with item 4

1.391

Technological Familiarity 1. For the majority of our innovations, the core technologies (e.g. engine for cars) involved in our innovations are new for our firm.

1.323

2. For the majority of our innovations, the peripheral technologies (e.g. lacquering for cars) involved in our innovations are new for our firm.

1.260

3. For the majority of our innovations, different technologies are combined in a new way for our firm.

1.194

10.019

Internal Configuration 1. Overall, our internal configuration (e.g. organization, processes) usually had to be changed significantly in order to develop and introduce our innovations.

1.759

2. Our firm’s commercial people/skills/resources (e.g. marketing, sales, distribution, customer service) had to be changed for our innovations.

2.019

3. Our firm’s technical people/skills/resources (e.g. in R&D, engineering, production) had to be changed for our innovations.

1.976

4. Our firm’s informal organization (e.g. culture) had to be changed for our innovations.

1.685

R

8.701

All four items with strong positive and significant correlations among each other – from .501** - .671**

= reversed item

640

See DANNEELS/KLEINSCHMIDT (2001), p.366.

641

See SALOMO (2003), p.14. The multi-item construct is called ‘internal resource fit’.

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Empirical Study

All four items of internal configuration show relatively strong, positive correlations among each other. Moreover, the VIF values lie above or close to 2. Eliminating one item did not improve the collinearity statistics. Moreover, from a content perspective the elimination of one item vs. another item could not be convincingly argued. Therefore, the entire first order construct, internal configuration, was removed focussing the distance to core business construct on the familiarity aspect.642 4.2.2.3

Validation of Driver of Innovation

Table IV-27 lists the collinearity statistics for the driver of innovation construct – divided into the three first-order factors market and technology orientation and competitive response. Table IV-27: Driver of Innovation – Collinearity Statistics Factor / Indicator

VIF

Condition Index

Correlations

Market Orientation 1.

We continuously try to discover additional needs of our customers of which they are unaware.

1.361

2.

We reflect on how customers use our products to discover new customer needs and applications.

1.659

3.

We work closely with demanding users in order to recognize customer needs months or even years before the majority of the market recognizes them.

2.103

We have specifically assigned personnel to extrapolate key trends to gain insight into what users in a current market will need in the future.

1.264

Individuals from our manufacturing and R&D departments interact directly with customers to learn how to serve them better.

1.550

4.

5.

15.725

Technology Orientation 1.

We systematically scan for new technologies inside and outside the industry.

1.122

2.

Our incentive system strongly encourages our R&D personnel to invent.

1.469

3.

Technological developments are of highest priority for our top executives.

1.671

4.

Our new products are always at the leading edge of technology.

1.518

12.878

Competitive Response 1.

In order to gain insight into the innovation activities of our competitors, we are involved in industry associations.

1.288

2.

In order to gain insight into the innovation activities of our competitors, we engage in joint research programs with them.

1.200

3.

We have assigned staff to monitor competitors’ innovation activities.

1.172

4.

Our sales people share information about competitors (new) products with our innovation staff.

1.043

642

12.274

See DANNEELS/KLEINSCHMIDT (2001), p.360f and 366.

.545** with item 2, .577** wiht item 5

Results

165

Concerning the market orientation construct, the third item not only has a VIF value beyond two, but also high and significant correlations with items 2 and 5. In fact, items 2, 3 and 5 test different aspects related to customers and their use of a firm’s product. To respondents who read once and relatively quickly over the questionnaire, these items may have appeared similar. Therefore, item 3 was eliminated. After elimination the condition index for market orientation is 13.358 and the highest VIF value now 1.492 (item 2). For technology orientation, no collinearity issues exist. Also for competitive response the four indicators neither show strong correlations nor high VIF values and the condition index value is well below 30. The two newly developed items for competitive response - item 1 and 2 – proved to be valid measures. 4.2.2.4

Validation of Innovation Field Orientation

Table IV-28 lists the collinearity statistics for the innovation field orientation construct – divided into five first order constructs.643 Concerning the first construct, definition criteria, no collinearity issues are evident. Nevertheless, given items 2 and 3 are fairly correlated they were combined into one item.644 Subsequently, the condition index decreased to 12.691 and all VIF values are smaller than 1.1. The two items for mightiness of projects prove linearly independent with correlation, VIF and condition index values significantly below any threshold. The two items for time horizon are more problematic. They not only show a significant, positive correlation among each other. In contrast to the vast majority of items for innovation field orientation, these two items are negatively correlated with all performance indicators. Like the mightiness construct, time horizon measures whether innovation projects are larger in size than the industry average. Given the results, respondents might have misunderstood the time horizon items in a negative sense, e.g. that a firm takes too long to develop innovations and is not enough results oriented. Hence the time hori-

643

The aspect ‘multitude of projects’ served only descriptive purposes and was neither included in the innovation field orientation construct nor in the regression analysis.

644

As an alternative to removing correlated items, ALBERS/HILDEBRANDT (2005), p.14, suggest building indices, e.g. based on means.

166

Empirical Study

zon construct was eliminated. Mightiness measures another facet of the same aspect. Table IV-28: Innovation Field Orientation – Collinearity Statistics Factor / Indicator

VIF

Condition Index

Correlations

Definition Criteria: For our innovation activities, we have defined areas of focus along the following criteria: 1.

Certain technologies

1.053

2.

Certain customer groups (e.g. pharmacies, doctors, pregnant women)

1.282

3.

Certain customer needs (e.g. hydration, infection prevention, age defiance)

1.309

4.

Certain core competencies (e.g. Sony‘s competence to miniaturize, 3M‘s with sticky tape, IKEA‘s in modular furniture design

1.091

Correlate at .445** 13.768

Mightiness of Projects 1.

2.

Relative to our industry, our innovation projects are to a large degree characterized as heavy-weight projects in terms of expenses?

1.187

The vast majority of our innovation projects have 3 or more full time project members (or equivalent)?

1.187

6.265

Time Horizon of Projects 1.

2.

Within our innovation project portfolio, a significant proportion of our projects (>30%) require a longer development time than average in our industry.

1.260

Within our innovation project portfolio, a significant proportion of our projects (>30%) have a longer payback time than average in our industry.

1.260

6.280

Correlate at .454**

Organizational Formality 1.

We have organizational units that were created particularly for our focus areas in innovation.

1.348

Positive, significant at ** with all items

2.

A defined set of rules and policies exist governing the collaboration among innovation projects.

1.653

.532** with item 3, .451** with item 4

3.

On the status of their collaboration with other innovation projects, project leaders must submit regular and formal progress reports.

1.589

4.

We have appointed specific liaison personnel to support collaboration between innovation projects.

1.456

5.

Informal networks of personnel across innovation projects exist.

1.207

6.

Part of the compensation of individual leaders of innovation projects is based on the success of all innovation projects within a given focus area.

1.314

21.319

Synergies Our innovation projects use common resources (e.g. people, equipment) with related innovation projects.

1.369

2.

In our firm, collaboration among different innovation projects leads to dilution of focus on individual project objectives. R

1.196

3.

In our firm, collaboration among innovation projects leads to more efficient utilization of resources required for innovation.

2.143

4.

In our firm, collaboration among our innovation projects leads to improvements in the capabilities of our innovation staff.

2.188

In our firm, collaboration among innovation projects permits the access to a wider number of new knowledge sources (e.g. scientists, related institutes).

1.498

Our innovation projects benefit from internal spillovers of knowledge when related innovation projects (e.g. results of text interesting for several projects).

1.835

1.

5.

6.

R

= reversed item

22.245

.664** with each other

.562** with item 6

Results

167

Regarding collinearity for the organizational formality construct, the high condition index is most critical. For organizational formality on its own it is acceptable, because below 30. However, given organizational formality later enters an equation with ten other first order constructs,645 a VIF value above 21 is not acceptable. Investigating correlations of the indicators, the first two items exhibit several relatively high correlations. Eliminating these two items still leaves the construct with four items and a broad selection of formal and informal organizational aspects. The condition index improves significantly, to 17.984. For the synergies construct two items have VIF values over two and the condition index is similarly high as for organizational formality. Even though addressing two different aspects of synergies, items 3 and 4 are phrased in the same way and positive sense. This could explain the relatively high VIF values and correlation. Item 3 was removed. To reduce the overall high condition index, item 5 was also eliminated. Items 5 and 6 both address knowledge benefits thanks to the realization of synergies across innovation projects. The two items can appear similar, hence one (item 5) was eliminated. The condition index decreased significantly, to 18.453. After the elimination of items 3 and 5, the synergies construct remains a multi-item construct (4 items) and covers a broad spectrum of facettes charaterizing synergies. Concluding it is worth highlighting that innovation field orientation is a new concept - never operationalized as a multi-item construct before. Adaptations are common when developing new constructs.646 4.2.3 Validation of Innovation Performance Table IV-29 shows the collinearity statistics for innovation performance divided into four first order factors:647 financial, market, technical and process.

645

All first order contructs defining innovation strategy sum up to 11, which are then aggregated to four second order constructs, the four innovation strategy dimensions.

646

See e.g. SALOMO (2003), p.12-14; KRIEGER (2005), p.217; SCHULZ (2006), p.256.

647

The ‚overall’ item (see chapter IV-3.3.1, Table IV-6) served only process purposes.

168

Empirical Study

Table IV-29: Innovation Performance – Collinearity Statistics Factor / Indicator

VIF

1.

Our sales from new products as percentage of total sales have been high – compared to our key competitors.

2.289

2.

Our profits from new products as percentage of total profits have been high – compared to our key competitors.

2.352

3.

Our market share has been high – relative to the competition.

2.279

4.

Our sales growth has been high – relative to the competition.

5.099

5.

Our growth in profit has been high – relative to the competition.

3.255

6.

Our market share growth has been high – relative to the competition.

4.461

Condition Index

Correlations

Financial

23.121

.723** with item 3, .801** with item 5, .859** with item 6

.708** with item 3, .859** with item 4, .788** with item 5

Market 1.

Compared to competitors’ innovations, customers are more satisfied with our product innovations.

2.072

2.

Compared to the competition, our innovations enhance our competitive advantage more.

2.403

.692** with item 1, .647** with item 3 18.586

3.

Relative to competitors, our innovations contribute more strongly to a positive image of our company.

1.978

4.

Overall, our firm’s innovation portfolio is successful in opening new markets for our firm.

1.344

Technical 1.

2.

3.

In terms of quality, our new products are better than new products offered by competitors (e.g. function better, last longer).

1.659

In terms of technical performance, our new products are superior to competitors’ new products (e.g. significantly quicker, stronger and/or with additional features).

1.777

Overall, our firm’s innovation portfolio is successful in enabling us to get into new technologies for our business.

1.179

Correlate at .630** 13.283

Process 1.

Compared to our competitors, our new product development cycle time has been relatively short.

1.474

2.

Compared to competitors, our costs for new product development are low.

1.493

3.

Compared to competitors, we exploit more input and output synergies across our innovation activities.

1.385

12.692

.453**-.513** among each other

Several issues of collinearity can be observed. Multiple indicators show correlations beyond 0.7 and VIF values of over two. This is probably driven by the fact that all indicators were previously applied for reflective constructs (where correlations are desired),648 and not formatively as in this study. For the financial aspects of innovation performance items 4 and 6 were eliminated. Both aspects, sales and market share, remain repre-

648

See chapter IV-3.1 and Figure IV-6 for more details.

Results

169

sented in items 1 and 3. The resultant maximum VIF value is 2.33. At the same time, the condition index significantly decreases from above 23 to 13.275. As to the market aspect of innovation performance, item 2 exhibits most linear dependency. Especially items 2 and 3 cover similar underlying topics. Eliminating item 2 results in a reduced condition index of 14.968 and all VIF values remain below two. For technical performance items 1 and 2 correlate strongly. Apparently respondents could not recognize the difference between quality and technical performance. Hence item 2 was dropped. All process indicators correlate relatively strongly with each other. Moreover, item 1 and 2 show negative relations with other performance measures. Given all other innovation performance indicators show positive relations with Tobin’s q and TRS, respondents might have misunderstood these two items. They might have associated low investments in innovation and limited innovation activity with short development time (item 1) and low development costs (item 2). Given a multiitem approach was followed for all constructs all three items of process were removed. Excluding the process aspect leaves the innovation performance construct with a purely external perspective. This means that firms’ innovation performance is exclusively compared to its main competitors - most relevant to investors.649 4.2.4 Validation of Contingency Variables 4.2.4.1

Validation of Business Strategy

Table IV-30 shows the collinearity statistics for the three business strategy variables: differentiation, cost leadership, and focus strategy. No significant linear dependencies can be identified. All correlations lie below 0.7. No VIF value is above 1.9 and the three condition indices are all smaller than 15. In contrast to other constructs so far, almost all items of business strategy were tested in similar combinations before. This contributes to explain the good collinearity statistics.

649

As mentioned before, more investor-related performance measurement is evoked in marketing and innovation research, see e.g. LEHMANN (2004), p.73ff.

170

Empirical Study

Table IV-30: Business Strategy – Collinearity Statistics Factor / Indicator

VIF

Condition Index

Correlations

Differentiation 1.

The intensity of our advertising is very high, compared to the competition.

1.476

2.

We are very innovative regarding our marketing techniques and methods.

1.380

3.

We emphasize the building of a strong brand identification.

1.268

10.203

Cost Leadership 1.

We enjoy a very high level of operating efficiency.

1.477

2.

We are very efficient in securing raw materials.

1.548

3.

We predominantly compete on price.

1.106

4.

We put emphasis on finding ways to reduce cost of production.

1.178

14.035

Focus 1.

We target one or very few clearly identified customer segment(s).

1.216

2.

We offer products suitable for high price segments.

1.823

3.

We offer specialty products.

1.683

4.

We offer a broad line of products.

13.897

R

R

1.173

= reversed item

4.2.4.2

Validation of Environmental Uncertainty

Table IV-31 shows the collinearity statistics for the second contingency variable, environmental uncertainty. It is split into the two first order factors, market and technological uncertainty. For market uncertainty, there are no collinearity issues. All correlations are below 0.6, VIF values smaller than 1.9 and the condition index well below 30. For technological uncertainty, the first item shows linear dependency – primarily with item 4, but also with items 2 and 5. Items 1 and 4 address similar aspects; however, item 4 in a reversed manner. Hence, item 1 was removed, still leaving technological uncertainty as a multi-item construct with three clearly distinct indicators. After removal of item 1 no VIF value for technological uncertainty is larger than 1.6 and the condition index decreased to 10.231.

Results

171

Table IV-31: Environmental Uncertainty – Collinearity Statistics Factor / Indicator

VIF

Condition Index

Correlations

Market Uncertainty 1.

Customers in our industry tend to look for new products all the time.

1.616

2.

Customers’ product preferences change quite a bit over time in our industry.

1.864

3.

In our industry, we are witnessing demand for products and services from customers who never bought them before.

1.329

4.

In our industry, it seems that we get new competitors all the time.

1.612

5.

Competitors change their strategy constantly in our industry.

1.866

6.

In our industry, market shares are very unstable among the key competitors.

1.272

11.543

Technological Uncertainty 1.

The technology in our industry is changing rapidly.

2.430

2.

Technological changes provide big opportunities in our industry.

1.556

3.

It is very difficult to forecast where the technology in our industry will be in the next 2 to 3 years.

1.543

4.

Technological developments in our industry are rather minor.

R

.705** with item 4; >.52** with items 2,3

12.251

R

2.102

= reversed item

4.2.5 Validation of First-Order Constructs The individual items were subsequently aggregated to first- and second order constructs. This and the next chapter discuss the validity of these higher order constructs. 4.2.5.1

Validation of Innovation Strategy (1st Order)

For the four innovation strategy dimensions, all items were aggregated into first order constructs, resulting in 11 first-order variables.650 Table IV32 shows the collinearity statistics. No serious collinearity issues can be observed. Market and technology innovativeness correlate to some extent, however, the linear dependency is not too strong with both VIF values only slightly above 2 and the correlation smaller than 0.7. The condition

650

Two first order factors were previously eliminated for collinearity issues: internal configuration, a factor of distance to core business, and time horizon, an aspect of innovation field orientation.

172

Empirical Study

index lies around the threshold level of 30. Considering that this construct is based on 11 first order factors the collinearity statistics are acceptable. Table IV-32: Innovation Strategy, 1st Order Constructs – Collinearity First Order Factor

VIF

Condition Index

Correlations

Innovativeness z

Market Innovativeness

2.119

z

Technology Innovativeness

2.082

.653** with each other Distance to Core Business z

Market Familiarity

1.457

z

Technological Familiarity

1.373

Driver of Innovation z

Market Orientation

1.576

z

Technology Orientation

1.803

z

Competitive Response

1.456

30.376

Innovation Field Orientation z

Definition Criteria

1.309

z

Mightiness of Projects

1.214

z

Organizational Formality

1.460

z

Synergies

1.469

4.2.5.2

Validation of Innovation Performance (1st Order)

With regard to innovation performance, the items were subsumed into three first order constructs.651 The collinearity statistics for these three first order constructs are shown in Table IV-33. No collinearity issues can be observed. Financial performance is fairly correlated with market performance and market performance with technological performance. These correlations are comprehensible. Without being successful in the market, a firm would most probably not achieve financial success. Similarly, technical superiority can lead to more customer satisfaction, better firm image, etc. – aspects tested under market performance.

651

Note that the fourth first order factor, process performance, was earlier eliminated due to collinearity issues. See IV-4.2.3.

Results

173

Table IV-33: Innovation Performance, 1st Order – Collinearity First Order Factor

VIF

Condition Index

Correlations

Innovation Performance z

Financial

1.457

z

Market

1.926

z

Technological

1.432

4.2.5.3

.558** with market 17.106

.547** with technological

Validation of Contingency Factors (1st Order)

The eleven items characterizing the three different business strategies were aggregated into three first order constructs. Their collinearity statistics are listed in Table IV-34. Table IV-34: Business Strategy, 1st Order – Collinearity First Order Factor

VIF

Condition Index

Correlations

Business Strategy z

Differentiation

1.017

z

Cost Leadership

1.018

z

Focus

1.003

16.392

The VIF values are close to the ideal of one, indicating that these three constructs measure very distinct aspects. No noticeable correlations could be observed and the condition index is also well below 30. The indicators characterizing environmental uncertainty were aggregated into two first order factors: market and technological uncertainty. Their collinearity statistics - provided in Table IV-35 – do not signal any significant linear dependencies either. Table IV-35: Environmental Uncertainty, 1st Order – Collinearity First Order Factor

VIF

Condition Index

8.320

Environmental Uncertainty z

Market Uncertainty

1.363

z

Technological Uncertainty

1.363

Correlations

174

Empirical Study

4.2.6 Validation of Second-Order Constructs 4.2.6.1

Validation Innovation Strategy (2nd Order)

The eleven first order constructs characterizing innovation strategy were further aggregated into four innovation strategy dimensions.652 For each second order construct the mean of the corresponding first order factors was calculated. Table IV-36 shows the collinearity statistics for the four second order constructs. No VIF value lies above two and correlation values do not exceed .52. Innovativeness correlates significantly with distance to core business. This indicates that more innovative products are at the same time further away from a firm’s current business. Also, driver of innovation is correlated with innovation field orientation. It means that firms’ which are more active looking for new products – be it driven by a strong market or technology orientation or by following competitors – are more likely to have set up their innovation activities along innovation fields. Both aspects seem to characterize firms more advanced in innovation management. Last, the overall condition index at 21.119 is well below the threshold value of 30. Table IV-36: Innovation Strategy, 2nd Order Constructs – Collinearity Second Order Factor

VIF

Innovativeness

1.508

Distance to Core Business

1.350

Driver of Innovation

1.449

Innovation Field Orientation

1.405

Condition Index

Correlations .506** with Distance to Core Business

21.129

4.3

.515** with Innovation Field Orientation

Testing of Hypotheses

The analysis of the innovation strategy – firm performance relationship via multiple and moderated regression analysis stands at the core of this research. The study follows ALBERS/HILDEBRANDT call to return to regression

652

Also see Table IV-32.

Results

175

analysis for success factor research.653 The regression results are presented in this chapter. Before it is briefly explained how the regression models were validated. 4.3.1 Methodical Considerations The validation of a regression function assesses how good the model explains reality. The assessment is split into two steps: verification of the regression function and verification of the regression coefficients.654 Verification of regression function This first test deals with the entire regression function. It investigates how good the dependent variable is explained by the independent variable(s). R2 and F-test are typically applied: ƒR2 – Coefficient of Determination: R2 measures the goodness of fit between the overall regression function and the empirical data. R2 is defined as the ratio of the explained variance divided by the total variance. Hence, a value of 0.4 means that 40% of the variance is based on factors included in the regression function and 60% on factors not accounted for in the regression model. In addition to the standard coefficient of determination, adjusted R2 is also reported. In addition it also accounts for the number of explanatory variables (= regressors). Therefore the adjusted R2 is always smaller than the standard one. ƒF-test: The F-test addresses the question whether the model is valid for the entire population and not only for the sample. Beside the variance it also considers sample size. At the core of the F-test stands a test where an empirically derived value is compared to a theoretical threshold value. To determine the theorectical value confidence levels need to be defined – for this study set at 99% and 95% (two-tailed). If the empirical F value is larger than the theoretical one, then the null hypothesis is rejected and the relationship is significant. Otherwise no relationship exists.

653

See ALBERS/HILDEBRANDT (2005), p.1-33.

654

For details on regression methodology, see e.g. BACKHAUS ET AL. (2000), p.20ff.

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Empirical Study

Verification of regression coefficients After assessing the overall regression function, the individual regression coefficients need to be validated. The t-test is applied.655 Confidence levels are set at the 99%, 95% and 90% level. Similar to the F-test an empirical value is calculated and compared to a theoretical one. If the empirical value is larger than the theoretical one, the finding is significant. R2, F-test and t-test were applied to validate the regression models. 4.3.2 Innovation Strategy – Performance Relationship 4.3.2.1

Second Order Innovation Strategy Dimensions

The relationship between innovation strategy and firm-level performance was analyzed via multiple regression analysis. Table IV-37 shows the results based on second-order innovation strategy factors. Table IV-37: Innovation Strategy (2nd) and Performance – Regression Model 1 ‡ Innovation Performance

Model 2 ‡ – Firm Performance (Tobin’s q)

Model 3 ‡ – Firm Performance (TRS vs. Industry)

.315***

.308***

.097

.020

-.005

-.121

.344***

-.013

.310***

.186**

.021

-.067

Size

-.058

-.068

.086

Industry Sector

.037

.108

.037

Geography (Continent)

.097

.103

-.073

R&D Rate

-.068

.005

.083

R2

.477

.130

.107

.435

.059

.034

11.283***

1.845*

1.475

Innovation Strategy - Second Order Factors

Innovativeness Distance to Core Business Driver of Innovation Innovation Field Orientation

Adjusted F

R2

‡ Standardized regression coefficients Two tailed t-tests. *** p < .01; ** p < .05; * p < .1;

655

For details see e.g. BACKHAUS ET AL. (2000), p.29ff.

Results

177

In addition, four potentially relevant contingencies – firm size, industry sector, geography, and R&D rate - were controlled for. The regression results were calculated with three firm-level performance indicators: innovation performance, Tobin’s q, and TRS vs. industry. Whereas the first indicator is specific to innovation, the other two are overall firm performance metrices. Applying the three performance measures, results in three different regression models, Model 1, 2 and 3. The results indicate that innovation strategy accounts for a significant portion of the variance in innovation performance (R2=0.477) and firm stock market performance (R2=.130 and R2=.107). Whereas the validity of Models 1 and 2 is (very) good, Model 3 is a borderline case. Its confidence level is somewhat below 90%. The results of Model 1 indicate a positive relationship between innovation strategy and innovation performance. Three of the four innovation strategy dimensions exhibit significant and positive regression coefficients. For distance to core business, no performance impact could be observed. This means that for performance considerations it is irrelevant whether a firm’s new product portfolio is closer to the core business or not. For Model 2 innovativeness has a strong, significant and positive impact on firm performance, measured by Tobin's q. For Model 3 driver of innovation exhibits a significant positive relation with firm success, measured by TRS. The results of Models 1, 2 and 3 hold true after controlling for firm size, industry, geography, and R&D rate. Including the control variables slightly increases the variance explained, however, does not impact the observed significances. Additional analysis shows that R&D rate not only has no influence on the tested models, but also - on its own - has no influence on either innovation performance or firm performance. ROA was also controlled for. It does not change the significant relationships discussed. In order to present parsimonious models ROA was not included in further analyses. Based on the three models shown above, the first four research hypotheses were verified. Hypothesis 1 suggests that an increase in innovativeness of a firm’s new product portfolio positively influences firm-level performance. That is, companies which develop products newer to the market

178

Empirical Study

should be more successful. Hypothesis 1 is supported by the empirical data. Innovativeness is significantly and positively related to innovation and firm performance (Tobin’s q). Hypothesis 2 claims that firm’s with a new product portfolio further away from the core business score worse in terms of firm-level performance than companies with innovations close-to-home. Hypothesis 2 needs to be rejected. Contrary to expectations, no significant relation with innovation or firm performance could be observed. This means that distance to core business of new products and firm-level success are independent of each other. Hypothesis 3 speaks to the forces driving innovation in a firm. It suggests that a combination of market and technology orientation positively influences firm-level performance. Hypothesis 3 could be confirmed. Driver of innovation656 is significantly and positively related to innovation performance and firm performance (TRS vs. industry). Further analysis at first order level657 reveals that market and technology orientation are both significantly and positively related to innovation performance, however, competitive response shows no relation. This means that the positive performance effect of the second order factor is predominantly driven by market and technology orientation. This finding confirms the hypothesis that a combined market and technology orientation promises most success. Hypothesis 4 claims that innovation field orientation has a positive effect on firm-level performance. Firms which have set up their innovation activities along innovation fields should be more successful than those innovating along existing product lines or in form of individual, unrelated projects. Hypothesis 4 is partially confirmed. Innovation field orientation is significantly and positively related to innovation performance. However, the regressions on firm performance were not significant.

656

The second order factor, driver of innovation, was formed based on the means of all three drivers (market and technology orientation, competitive response). Hence, for the second order factor the hypothesis implies that the more actively a firm drives innovation overall, the more successful it is.

657

See chapter IV-4.3.2.2.

Results

179

The empirical results show that in order to be successful it is less important how much a firm spends on innovation (R&D rate), but for what kind of innovations it uses the resources (innovativeness), through which forces and how actively it drives innovation (driver of innovation) and how innovation activities are set up (innovation field orientation). The results imply that the right innovation strategy can positively influence firm-level performance. 4.3.2.2

First Order Innovation Strategy Dimension

The second order factors discussed in the previous chapter were formed by combining first order factors. In this chapter the results for these first order innovation strategy factors are reported. Looking at first order factors helps to understand the outcome for second order factors. Again three models are presented for the three different performance variables. The results are shown in Table IV-38. Model 4’s validity is very good. Moreover, the first order factors explain a large portion of the variance in innovation performance (R2=.506). Model 5 also explains a sufficient portion of firm performance variance, measured by Tobin’s q (R2=.149). Model 5 is significant at the 90% level. In line with Model 3, the validity of Model 6 is a borderline case with the confidence level somewhat below 90%. The first order results provide interesting insights into the four innovation strategy dimensions. For innovativeness neither of the two first order dimensions, market innovativeness or technology innovativeness, is dominant. Technology innovativeness is positively and significantly related to Tobin’s q, but only at the 90% level. This could mean that successful companies’ innovations are newer with regard to market and technological aspects. In other words, it is not enough to have technologically superior and newer products. They need to be similarly innovative in terms of new customer benefits, unique advantages over competitor products, etc.

180

Empirical Study

Table IV-38: Innovation Strategy (1st) and Performance – Regression Model 4 ‡ Innovation Performance

Model 5 ‡ – Firm Performance (Tobin’s q)

Model 6 ‡ – Firm Performance (TRS vs. Industry)

Market Innovativeness

.107

.054

-.046

Technology Innovativeness

.124

.231*

.126

Market Familiarity

.090

.062

-.161

Technological Familiarity

-.099

-.073

.057

Market Orientation

.252***

.107

-.004

Technology Orientation

.341***

.059

.178

Competitive Response

-.096

-.112

.154

Focus Area Definition Criteria

-.002

-.036

-.057

Mightiness of Projects

.110

.106

.019

Organizational Formality

.109

.057

.076

Synergies

.074

-.080

-.127

R2

.506

.149

.127

.457

.063

.039

10.247***

1.730*

1.449

Innovation Strategy – First Order Factors

Adjusted

R2

F

‡ Standardized regression coefficients Two tailed t-tests. *** p < .01; ** p < .05; * p < .1

First order factors for distance to core business do not shed new light on the second order results. Neither market nor technological familiarity is close to any performance effect. The first order results for driver of innovation provide details to confirm Hypothesis 2.658 Both, market and technology orientation are positively and significantly related to innovation performance.659 Competitive response has no significant impact. This means that successful firms are not only at the forefront of technology and have excellent R&D capabilities (technology orientation), but are at the same time closer to their customers who also inspire them for new products (market orientation). For innovation field orientation, none of the

658

See previous chapter, IV-4.3.2.1.

659

Non-linearity was tested via moderated regression, but no evidence was found.

Results

181

four first order factors shows any direct relation with success. The two factors, ‘mightiness of projects’ and ‘organizational formality’, seem slightly more important, but given they also have no significant relation with success the difference is neglectable. In other words, several aspects must be in place together such that innovation field orientation has an impact on performance. 4.3.3 Contingencies Business strategy and environmental uncertainty were investigated as potential contingencies using moderated regression analyses. Results are reported in this chapter. Moreover, the empirical results are compared with the relevant hypotheses. 4.3.3.1

Moderated Regression Results for Business Strategy

The underlying assumption was that innovation strategy is more effective when in line with a firm's overall business strategy. Business strategy was investigated using PORTER’S three strategies: differentiation, cost leadership and focus. For each of these three business strategies potential moderation effects on the innovation strategy – performance relation were analyzed. Innovation strategy was represented via the four second order factors. The regressions were calculated for both innovation and firm performance (Tobin’s q) as dependent variable.660 The results are shown in Table IV-39. Table IV-39: Business Strategy – Moderated Regression Innovation Performance Innovation Strategy Dimension Innovativeness

Differentiation

Cost Leadership

Firm Performance (Tobin’s q)

Focus

Differentiation

Cost Leadership

Focus

n.s.

n.s.

n.s.

.280***

n.s.

n.s.

.142*

n.s.

n.s.

n.s.

n.s.

n.s.

Driver of Innovation

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Innovation Field Orientation

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Distance to Core Business

Two tailed t-tests. *** p < .01; ** p < .05; * p < .1

660

TRS vs. industry was not used as firm performance measure given Models 3 and 6 already exhibit poor validity without moderation effects. See chapters IV-4.3.2.1/2.

182

Empirical Study

Overall, few moderation effects could be identified. First and as expected, the innovativeness – firm performance relation is positively moderated by differentiation. For companies pursuing a differentiation strategy product innovation is more important than for e.g. cost leaders. In fact, innovative new products can be the aspect of differentiation. Second, the relation of distance to core business with innovation performance is also positively moderated by differentiation. For differentiators it pays off to innovate further away from the core business. Taking the two moderation effects together differentiators can take advantage of more innovative products which are further away from their core business. The two aspects can come together, but must not. A firm can also produce highly innovative new products which are close to its core business. All other tested moderations show no significant effects. Therefore, split group analysis was undertaken in addition to moderated regression.661 For each business strategy, four groups with different levels of parameter value were created. Each group contained a quarter of the overall sample. Then correlations of the four innovation strategy dimensions with innovation and firm performance (Tobin’s q) were calculated.662 The results were compared with the overall sample. Two interesting observations could be made. First, for strong cost leaders product innovativeness is not related to performance. The finding is in line with the argumentation for Hypothesis 5a.663 Cost leaders were expected to have no impact on the innovativeness – performance relationship. Second, split group comparison showed that strong cost leaders have their innovation activities more organized along innovation fields than the overall sample. The empirical results support Hypotheses 5a to 5d to a limited extent:

661

For a similar procedure, when pure modification effects are not significant, see e.g. SLATER/NARVER (1994), p.52. They call it subgroup analysis. JAWORSKI/KOHLI (1993), p.62, test for moderating effects by performing - only - split group analysis, without prior testing of moderated regression analysis with interaction terms.

662

Correlations were calculated per group instead of regressions given the sub-samples (per type of business strategy) were too small to produce valid regression models.

663

See chapter III-2.2.1.1.

Results

183

ƒHypothesis 5a suggests a positive moderation effect of differentiation on the innovativeness – firm-level performance relation. Hypothesis 5a is partially confirmed, because a significant and positive moderation effect could only be observed for firm performance as dependent variable, however not for innovation performance. As expected, cost leadership shows no moderation effect. ƒHypothesis 5b claims that differentiation moderates the distance to core business – performance relation. Hypothesis 5b is weakly confirmed. Differentiation proved to be a weak moderator at the 10% level, however, only with innovation performance as the dependent variable, and not with firm performance. Cost leadership has no moderation effect, as expected. ƒHypothesis 5c claims that the relation between a balanced market and technology orientation and performance is positively moderated for differentiators. Hypothesis 5c needs to be rejected. No moderation effect for any performance variable could be found. Similarly, cost leadership does not moderate the driver of innovation – performance relation either, as expected. ƒHypothesis 5d suggests that differentiation moderates the innovation field orientation – performance relation. Hypothesis 5d also needs to be rejected. In line with H5c, no moderation effect could be observed – neither for differentiators nor cost leaders. Summarizing, even though much less than expected the empirical data provide indications that an alignment of a firm’s innovation strategy with its business strategy is important, particularly for firms pursuing differentiation strategies. 4.3.3.2

Moderated Regression Results for Environmental Uncertainty

The second contingency investigated is environmental uncertainty, defined by a combination of market and technological uncertainty. Moderated regressions were calculated for the two first order factors separately (market and technological uncertainty) as well as their combination to a second order factor (environmental uncertainty). Table IV-40 shows the results.

184

Empirical Study

Table IV-40: Environmental Uncertainty – Moderated Regression Innovation Performance Innovation Strategy Dimension

Firm Performance (Tobin’s q)

Environm. Uncertainty

Market Uncertainty

Technol. Uncertainty

Environm. Uncertainty

Market Uncertainty

Technol. Uncertainty

Innovativeness

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Distance to Core Business

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Driver of Innovation

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Innovation Field Orientation

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

Two tailed t-tests. *** p < .01; ** p < .05; * p < .1

None of the regressions shows any significant effect. Therefore and in line with business strategy before, split group comparisons were undertaken. The sample was divided into four groups: very low, low, medium and high uncertainty. Two interesting observations could be made: ƒInnovativeness: At very low levels of uncertainty – be it market, technological or environmental – no significant correlation exists any more between innovativeness and any performance indicator. This means that in very stable industries the level of innovativeness of a firm’s new products has no impact on performance, probably because innovations are overall less important in such environments. ƒInnovation Field Orientation: At very low and very high levels of uncertainty, no significant correlation can be observed between innovation field orientation and performance. For stable industries innovation fields may be less relevant given innovations are overall less important. For very uncertain environments innovation fields may impose too much structure on innovation activities and limit necessary flexibility. Not withstanding these insights from split group analysis, Hypotheses 6a to 6d need to be rejected. The relations of innovativeness, driver of innovation and innovation field orientation with performance are so strong that they are not significantly influenced by environmental uncertainty.

Results

185

4.3.4 Summary of Hypotheses Testing All results from hypothesis testing are summarized in Table IV-41. The empirical data confirmed three out of the four success hypotheses, concerning the relationship of innovation strategy with firm-level performance. The data showed that innovation strategy has a significant impact on innovation and firm performance, explaining .477 and .130664 of the variance (R2) respectively. The portion of the variance explained for firm performance is very satisfactory given firm performance is influenced by many more and more important factors, like overall firm profitability, cash flow, growth, risk, and market share.665 Table IV-41: Summary of Results from Hypotheses Testing Hypothesis

Result

Rationale

H1

The innovativeness of a firm’s new product portfolio positively influences firm-level performance.

H2

The distance of a firm’s new product portfolio from its core business negatively influences firm-level performance (= the further away the worse).

H3

As driver of innovation, a combined market and technology orientation positively influences firm-level performance.

Confirmed

.344*** with innovation and .310*** with firm performance

H4

Innovation field orientation positively influences firm-level performance.

Partially confirmed

.186** with innovation performance, but not with firm performance

H5a

The positive relation between innovativeness and firm-level performance is stronger for differentiators.

Partially confirmed

.280*** moderator with firm performance, but not with innovation performance

H5b

The negative relation between distance to core business and firmlevel performance is weaker for differentiators.

Weakly confirmed

.142* (weak) moderator with innovation performance, but not with firm performance

Confirmed

Rejected

The positive relationship between a combined market and H5c technology orientation and firm-level performance is stronger for differentiators.

Rejected

The positive relationship between innovation field orientation and firm-level performance is stronger for differentiators.

Rejected

H5d

The higher the level of environmental uncertainty, the stronger H6a the positive effect of innovativeness on firm-level performance.

Rejected

The higher the level of environmental uncertainty, the weaker the H6b negative effect of distance to core business on firm-level performance.

Rejected

The higher the level of environmental uncertainty, the stronger H6c the positive effect of a combined market and technology orientation on firm-level performance.

Rejected

The higher the level of environmental uncertainty, the stronger H6d the positive effect of innovation field orientation on firm-level performance.

Rejected

.315*** with innovation and .308*** with firm performance No significant relation

No significant moderation effects

Two tailed t-tests. *** p < .01; ** p < .05; * p < .1

664

For Tobin’s q as firm performance measure.

665

See CHAUVIN/HIRSHEY (1993), p.134. Also see two recent papers by HAWAWINI/ SUBRAMANIAN/VERDIN (2003) and DESARBO/DI BENEDETTO/SONG/SINHA (2005).

186

Empirical Study

The empirical data only support the contingency hypotheses to a limited extent. Even though less pronounced than expected, the findings for business strategy suggest that an alignment of business and innovation strategy is especially important for firms pursuing a differentiation strategy less so for cost leaders and niche players (focus). However, the performance effects of innovation strategy are independent from a firm’s environmental uncertainty. 4.4

Innovation Field Orientation

As a multi-item construct innovation field orientation was not empirically investigated before.666 Given it exhibits a positive performance effect on innovation performance it is worth looking at it in more detail. A stars and laggards approach667 was applied based on split group analysis. The overall sample was divided into four equal groups (each with 30-31 firms) based on firms’ innovation performance. Group 1 shows the poorest innovation performance and is called laggards. Group 4 is most successful in terms of innovation performance and termed stars. Table IV-42 exhibits the four groups’ descriptive statistics for innovation field orientation.668 Table IV-42: Innovation Field Orientation – Means, Split Group Analysis Means – Innovation Field Orientation

Group 1 (Laggards)

Group 2

Group 3

Group 4 (Stars)

Overall Sample

Innovation Field Orientation

4.5

4.8

4.9

5.3

4.9

1st order factors: Focus Area Definition Criteria: Technology Customers Competences

4.9 5.5 4.7 4.9

5.1 5.6 5.0 5.1

5.0 5.0 5.4 5.0

5.6 5.7 5.7 5.6

5.2 5.5 5.2 4.8

Number of Focus Areas Number of Projects per F.A.

4.3 5.7

5.3 7.9

6.2 8.1

6.1 9.7

5.5 7.8

Mightiness of Projects

4.0

4.3

4.2

5.1

4.4

Organizational Formality

4.1

4.7

5.0

5.0

4.7

Synergies

5.0

5.1

5.5

5.6

5.3

F.A. = Focus Area

666

For details see chapter IV-3.2.4.

667

For a similar approach see e.g. HAMBRICK/JACKSON (2000), p.113ff.

668

Regression analysis results in poor models given the small sub-sample size of about 30 firms per group. Therefore descriptive statistics are shown instead.

Results

187

Several interesting insights can be observed: ƒStar performers not only have the highest mean for innovation field orientation overall (second order factor), but also for all first order factors. That is, for innovation field orientation to be effective a combination of aspects has to be in place. Stars not only take a holistic approach to define their innovation fields, but undertake mightier projects, create organizational support for innovation fields and fertilize collaboration among innovation projects to realize synergies. ƒFocus area definition criteria: The results for definition criteria are particularly interesting. Laggards’ innovation fields are predominantly defined along technologies – be it intentionally or by default. The more successful groups 2 and 3 also consider customer aspects - in addition to technologies - when defining innovation fields. Star performers take a holistic approach and consider customer aspects, technologies and own competencies. Considering and building on own competences seems to make a crucial difference for innovation fields to contribute to performance. ƒNumber of focus areas and number of projects per focus area: On average firms have five to six innovation fields. This means that neither one or two fields nor 10 or 20 fields seem most effective. Moreover, more successful companies, e.g. groups 3 and 4, have more innovation projects per field. Stars include on average 10 projects per innovation field, the overall sample about eight. Having more projects per field implies more focus, given a significant number of projects and resources work on related themes. Also more projects allow for more cross-fertilization and learning among projects. ƒMightiness of projects: More successful firms pursue, on average, mightier projects within their new product portfolio. Compared to the other three groups, stars’ mean represents a step change. Looking at mightiness in more detail (Table IV-43) reveals that stars’ significant higher mean is driven by more financial resources and more full-time project members. Innovation projects need criti-

188

Empirical Study

cal mass in terms of financial endowment, but even more so in terms of personnel in order to bear fruits. ƒOrganizational formality: Groups three and four have slightly higher means indicating that they provide more organizational support for their innovation fields. Looking at individual indicators for organizational formality (Table IV-43), item 4 stands out with a significantly higher score for star performers. Designing compensation not only based on the success of single innovation projects but also accounting for the innovation field as a whole seems to make an important difference. ƒSynergies: As to the realization of synergies the difference between stars and laggards is less obvious. Better performing groups 3 and 4 also score better in terms of synergies, but no single indicator is prominent. All aspects and benefits of collaboration tested contribute to the realization of more synergies. Table IV-43: Selected Innovation Field Orientation Items – Means Group 1 (Laggards)

Group 2

Group 3

Group 4 (Stars)

Relative to our industry, our innovation projects are to a large degree characterized as heavy-weight projects in terms of expenses?

3.7

4.1

4.0

4.6

The vast majority of our innovation projects have 3 or more full time project members (or equivalent)?

4.4

4.5

4.5

5.7

On the status of their collaboration with other innovation projects, project leaders must submit regular and formal progress reports.

4.3

4.9

5.4

5.3

z

We have appointed specific liaison personnel to support collaboration between innovation projects.

3.5

4.2

4.7

4.2

z

Informal networks of personnel across innovation projects exist.

5.6

5.8

6.2

6.1

z

Part of the compensation of individual leaders of innovation projects is based on the success of all innovation projects within a given focus area.

3.0

3.7

3.8

4.7

Indicator Mightiness of Projects z

z

Organizational Formality z

Synergies z

Our innovation projects use common resources (e.g. people, equipment) with related innovation projects.

5.4

5.3

5.8

5.9

z

In our firm, collaboration among different innovation projects leads to dilution of focus on individual project objectives.R

5.2

4.9

4.6

5.1

In our firm, collaboration among our innovation projects leads to improvements in the capabilities of our innovation staff.

5.0

5.4

5.4

5.8

Our innovation projects benefit from internal spillovers of knowledge when related innovation projects (e.g. results of text interesting for several projects).

4.7

4.9

5.6

5.3

z

z

R

= Reversed item

Results

189

The analysis shows that innovation field orientation needs to be carefully defined and implemented. Thereby the consideration of a firm’s own strengths and competencies is very important, not only technology and customer aspects. Moreover, in order to bear fruit innovation fields need to include a number of significantly large projects. Overall, successful innovation field orientation requires a certain level of investment.

Chapter V

Discussion & Implications

This last chapter starts with a discussion of the results from the empirical study against the postulated hypotheses and prior literature. Second, implications for innovation research as well as managers are set forth. Last, the limitations of this study are presented. The chapter concludes with an outlook for future research in this field.

1

Discussion of Results

The main purpose of this study was to contribute a multi-dimensional concept of innovation strategy to the scarce and heterogeneous literature in this field and to show that innovation strategy has an impact on firm-level performance. Based on an extensive literature review, a conceptual framework was developed and research hypotheses derived. The hypotheses were verified in a quantitative survey, specifically conducted for this research. The survey results were complemented by a document analysis, based on a broad array of publicly available secondary data. Moreover, data from financial databases were used. The research hypotheses were tested using multiple and moderated regression analysis. Specifically, the following three research questions were to be answered: ƒWhich dimensions characterize innovation strategy? ƒTo what extent does a relationship exist between innovation strategy and firm-level performance? ƒHow do contingencies influence the relationship between innovation strategy and firm-level performance? In the following, the results to these three research questions are discussed and answers provided. 1.1

Which dimensions charaterize innovation strategy?

To answer the first research question an extensive analysis of prior literature was conducted.669 Most studies on innovation strategy apply a com-

669

See chapter II-1.

192

Discussion & Implications

parative approach to measure innovation strategy, like this research. This means that innovation strategy is described along key dimensions.670 Overall 25 dimensions were identified671 used in prior research to characterize innovation strategy. The frequency of investigation of each dimension varies widely, with only six dimensions having been investigated in four or more studies.672 The multi-dimensional concept for innovation strategy suggested consists of four dimensions: the three well established dimensions, innovativeness, distance to core business, and driver of innovation, and one new aspect, innovation field orientation. Other established dimensions were not included, because they were already extensively researched (e.g. timing of market entry), because the research design did not promise to yield new and meaningful results (e.g. sources of innovation) or were included as control variable, e.g. investment level (R&D rate). All other dimensions characterizing innovation strategy in prior research have only been investigated once or twice and were therefore not considered widely accepted.673 Innovation field orientation was investigated as a new dimension, because it is a prevailing phenomenon in practice,674 but was neither conceptualized nor empirically researched as a multi-item construct before. The four dimensions selected for this research proved to be meaningful aspects of innovation strategy. The vast majority of respondents provided complete and meaningful answers in the survey.675 Furthermore, several respondents pointed out that the questions addressed very important aspects and made them think about their innovation strategy.

670

See VENKATRAMAN (1989), p.943-944 and chapter II-1.2.1

671

See chapter II-1.2.3, Table II-4.

672

See chapter II-1.2.3 and Table II-4.

673

See chapter II-1.2.3, Table II-4.

674

See JONASH/SOMMERLATTE (1999), p.26 and LAURIE/DOZ/SHEER (2006), p.80ff, and chapter II-1.2.3.4.

675

Note: Out of 129 answers from firms, 122 (95%) could be used after final screening for missing and meaningful answers.

Discussion of Results

193

Summarizing, one should consider the following six dimensions as key aspects when defining innovation strategy, given they have been researched most often as part of innovation strategy:676 ƒInnovativeness: degree of newness from market perspective ƒTiming of market entry: e.g. first mover, fast follower, me-too ƒDriver of innovation: forces driving a firm’s innovation, e.g. market orientation, technology orientation, competitive response ƒSources of innovation: internal or external to the firm, e.g. make, buy, cooperate677 ƒInvestment level: R&D expenses in percentage of revenues; also called R&D rate ƒDistance to core business: degree of newness from firm perspective For a parsimonious research framework, this study limited itself to describe innovation strategy along three out of these six established dimensions and added innovation field orientation as a new dimension. Innovation field orientation turned out as a meaningful aspect of innovation strategy, as explained and discussed hereafter. 1.2

To what extent does a relationship exist between innovation strategy and firm-level performance?

The second research question stands at the core of this study. Does a relationship exist between innovation strategy and firm-level performance? To address this performance effect, multiple regressions were calculated with the four innovation strategy dimensions as independent variables and different performance metrices as dependent variables. Moreover, several control variables were accounted for, e.g. size, industry, geography, R&D rate and ROA, which had no significant influence on the results.678 Three

676

These correspond to the six most researched dimensions as part of innovation strategy. All were investigated in at least 4 prior studies in addition to this one. See chapter II-1.2.3 and Table II-4.

677

For a study focussing solely on this dimension, see e.g. VEUGELERS/CASSIMAN (1999).

678

Given the control variables had no significant influence; no further details are reported.

194

Discussion & Implications

positive relationships were postulated between innovativeness (H1), driver of innovation (H3), and innovation field orientation (H4), and firm-level performance. In addition, one negative relationship was claimed, between distance to core business and performance (H2). The negative relationship means that the closer the new product portfolio to the core business of a firm the better for performance. Hypotheses H1, H3 and H4, were empirically confirmed. Hypothesis 2 (distance to core business and performance) was not supported. The dimension showed no significant performance effect. Given three out of the four postulated performance effects were supported by the quantitiative study, the answer to the second research question is positive: Yes, innovation strategy does influence firm-level performance! In the following, the individual effects for the four innovation strategy dimensions are discussed in detail. 1.2.1 Does a relationship exist between innovativeness and performance? Hypothesis 1 (H1) suggests that the more innovative a firm's new product portfolio the better for firm-level performance. H1 was empirically supported. Innovativeness has a strong, positive effect on innovation performance and on firm performance.679 A higher degree of innovativeness of a firm’s new product portfolio is rewarded – also by the stock market. The finding is in line with the observations from FIRTH/NARAYANAN, DANNEELS/SETHI and CHO/PUCIK.680 Their studies resemble this research most, because they measure innovativeness purely from a market perspective and at the aggregation level of the firm. Moreover, they investigate firm performance and not only innovation performance. Also, HAUSCHILDT and SALOMO observe and the overview in Table II-7 confirms that positive performance effects dominate in more recent studies.681 In other words, the benefits of a more innovative new product portfolio clearly

outweigh

the

drawbacks.

It

appears

that

KOTZBAUER’S

and

679

Firm performance measured via Tobin’s q.

680

See FIRTH/NARAYANAN (1996), p.334, DANNEELS/SETHI (2005), p.31 and CHO/PUCIK (2005), p.566. Also see chapter II-2.2.2 and Table II-7.

681

See HAUSCHILDT/SALOMO (2005), p.6-7 and chapter II-2.2.2

Discussion of Results

195

HAUSCHILDT/SALOMO’S argument which claims that the risks and costs associated with more radical innovations increase disproportionately682 does not hold true for the portfolio perspective. Given a portfolio includes new products with different degrees of innovativeness683 the higher risk from more radical innovations684 is mitigated across the entire portfolio. This means that increased costs associated with higher levels of risks from more radical innovations bear less weight at the portfolio level and have less of a negative effect on performance than at the individual project level. Moreover, it is interesting to observe that, at the second order level, innovativeness has a significant positive influence on firm-level performance, however neither market nor technology innovativeness, at the first order level, exhibit any significant relation. This finding may be interpreted in the sense that none of the two sub-dimensions dominates, but that a balanced combination is most promising. In other words, neither a very high level of technological newness on its own, nor a product with a very high level or market innovativeness on its own contributes as significantly to firm-level performance as a combined, high level of both aspects.685 Summarizing, the higher the level of innovativeness of a firm’s new product portfolio, the more advantageous for firm-level performance! 1.2.2 Does a relationship exist between distance to core business and performance? Hypothesis 2 (H2) suggests that the closer a firm’s new products to its core business, the better for firm-level performance. H2 needed to be rejected. No significant performance effect could be observed. This means

682

See e.g. KOTZBAUER (1992), p.123 and HAUSCHILDT/SALOMO (2005), p.7.

683

See e.g. CRAWFORD (1980), p.8 and COOPER (2005).

684

See e.g. LEIFER ET AL. (2002), p.22, and HAUSCHILDT/SALOMO (2005), p.6-7.

685

This finding tends to go in line with the observation of SORESCU/CHANDY/PRABHU (2003), p.97, that radical innovations are valued significantly more than either technological or market breakthroughs. They define radical innovations as “products that involve a substantially new technology and provide substantially greater customer benefits than do existing products” (p.88). Their finding is based on a study in the pharmaceutical industry and therefore not perfectly generalizable. However, studies assessing the performance effects of different types of innovations continue to be rare, see e.g. ZHOU/YIM/TSE (2005), p.54.

196

Discussion & Implications

that no relationship exists between distance to core business and performance. It is irrelevant for firm-level performance wether new products are closer to the core business of further away. In other words, it does not influence performance if new products are targeted towards unfamiliar markets or well-known terrain. Prior empirical evidence on this relation is very limited and does not clearly indicate whether close-to-home innovations promise more success than new products further away from the core business.686 FRITH and NARAYANAN’S study resembles this study most. They investigate distance to core business at the firm level and not only innovation, but also firm performance. They also found no relation between a new product portfolio’s newness to the firm and firm performance.687 For the familiarity subdimension (of distance to core business) COOPER found that bestperforming firms exhibit the highest degree of familiarity. Their products have similar end-use as existing products and belong to similar product classes.688 In contrast, DANNEELS and KLEINSCHMIDT could not find any performance effect for familiarity. They argue that competences are transferable and can equally be used for new products that are further away from the current business. They conclude that it is more critical that the competences required for developing innovations are in place.689 The non-significant effect suggests that the benefits and drawbacks of more or less familiar new products cancel each other out. New products closer to home benefit from existing skills and resources690 and hence implementation should be more proficient and the success rate higher.691 Furthermore, established relationships with customers, suppliers as well as internal ones can be leveraged.692 For new products further away from a firm’s core business, the key supporting argument is that these innovations open up new windows of opportunity and growth - beyond existing

686

See chapter II-2.2.4.

687

See FIRTH/NARAYANAN (1996), p.341.

688

See COOPER (1984), p.156f.

689

See DANNEELS/KLEINSCHMIDT (2001), p.369.

690

See e.g. PRAHALAD/HAMEL (1990), p.81 and DANNEELS/KLEINSCHMIDT (2001), p.361.

691

See e.g. SONG/PARRY (1997), p.71.

692

See e.g. TUSHMAN/ROMANELLI (1985), p.171f, and ZIRGER/MAIDIQUE (1990), p.873.

Discussion of Results

197

markets.693 Especially for the last argument, there is no prior empirical evidence and indication for its relative importance.694 Hence, the nonsignificant performance effect between distance to core business and firmlevel performance is not totally surprising. Concluding, for firm-level performance it is irrelevant if a firm’s new product portfolio is close-to-home or further away from the core business. The different results for the first two innovation strategy dimensions, innovativeness (positive effect) and distance to core business (no effect), confirm the author’s conviction that newness from the market perspective (innovativeness) and newness from the firm perspective (distance to core business) need to be investigated separately,695 because they are distinct and may yield different results.696 1.2.3 Does a relationship exist between driver of innovation and performance? The third hypothesis (H3) claims that a combination of market and technology orientation is more beneficial in terms of firm-level performance than any single driver on its own (e.g. market orientation, technology orientation or competitive response) or other combinations. H3 was empirically confirmed. The second order factor of driver of innovation has a strong positive effect on innovation and firm performance.697 Moreover, the two first order factors, market orientation and technology orientation, both relate positively and significantly with innovation performance. In contrast, competitive response, the third driver, has no significant impact on performance. This means that successful firms are not only at the forefront of technology and have excellent R&D capabilities (technology orientation), but are at the same time also closer to their customers (market orientation).

693

See e.g. MEYER/ROBERTS (1986), p.812 and KLEINSCHMIDT/DE BRENTANI/SALOMO (2005), p.25.

694

See chapter III-2.1.3.

695

For a similar opinion see DANNEELS/KLEINSCHMIDT (2001), p.361, and SCHLAAK (1999), p.107ff.

696

See SCHLAAK (1999), p.107ff.

697

Firm performance measured via TRS vs. Industry.

198

Discussion & Implications

The finding is consistent with prior empirical evidence. Out of six studies analyzing multiple drivers of innovation, four find that a balanced combination of market and technology orientation is most advantages for performance.698 GATIGNON/XUEREB and ETTLIE/SUBRAMANIAM investigate the same three drivers as this research.699 Unfortunately, GATIGNON and XUEREB do not report overall results, but only split by market environment. For one of the four market contexts (high demand uncertainty) they recommend a combination of market orientation and technology orientation.700 ETTLIE and SUBRAMANIAM suggest a balanced market and technology orientation.701 The results seem to confirm the hypothesized effect that a strong market and technology orientation complement each other and reduce the challenges of the two individual drivers.702 On the one hand innovations driven by a strong market orientation tend to be better aligned with customer preferences703 and market trends,704 however, on the other hand these innovations may be too close to what customers want705 and hence not innovative enough.706 Complementing market orientation with technology orientation can reduce this problem, because customers and their preferences would not be the only source of ideas, but also technically oriented people and departments, like R&D and engineering.707 From the point of view of technology orientation combining it with market orientation can, for example, help to assure that technologically driven innovations target

698

See chapter II-2.2.3 and Table II-8.

699

See GATIGNON/XUEREB (1997), p.78 and ETTLIE/SUBRAMANIAM (2004), p.99.

700

See GATIGNON/XUEREB (1997), p.87. Note: For the other market environments, they suggest other combinations or single drivers.

701

See ETTLIE/SUBRAMANIAM (2004), p.105.

702

See chapter III-2.1.3.

703

See e.g. KOHLI/JAWORSKI (1990), NARVER/SLATER (1990) and ATUAHENE-GIMA/SLATER/ OLSON (2005), p.464.

704

See e.g. ATUAHENE-GIMA/SLATER/OLSON (2005), p.467.

705

See e.g. HAMEL/PRAHALAD (1991), p.83, and CHRISTENSEN/BOWER (1991), p.198.

706

See e.g. DANNEELS (2003), p.559, and ZHOU/YIM/TSE (2004), p.42, and ATUAHENE-GIMA /SLATER/OLSON (2005), p.467.

707

See e.g. ZHOU/YIM/TSE (2005), p.46. Note: Technology orientation, by definition, adds a new source to market orientation, by which ideas are mainly supplied by observing and listening closely to customers.

Discussion of Results

199

clearly identified markets with a large enough potential – a challenge purely technologically driven innovations tend to face.708 Summarizing, a balanced market and technology orientation is best for firm-level performance, because it provides more benefits than the sum of its individual parts. Concerning the nonsignificant effect of competitive response (as a first order factor) it confirms the expectation and the doubt whether benefits or drawbacks are stronger for this third driver of innovation.709 The result may suggest that they are similarly strong. 1.2.4 Does a relationship exist between innovation field orientation and performance? Hypothesis 4 (H4), claims that innovation field orientation has a positive effect on firm-level performance. Firms which have set up their innovation activities along innovation fields should be more successful. H4 was partially confirmed. Innovation field orientation has a positive, significant influence on innovation performance, however, not on firm performance. Prior quantitative research for this new concept does not exist. Based on case studies, JONASH/SOMMERLATTE and LAURIE/DOZ/SHEER claim that an innovation field orientation is beneficial for firm performance.710 In addition, COOPER and KLEINSCHMIDT found that having ‘areas of strategic focus on which to concentrate NPD efforts’ is strongly correlated with NPD performance.711 However, they measured their concept only with one overall item and not as a multidimensional construct, like done in this research.712 Conceptually, the positive performance effect was expected. Most importantly, innovation field orientation drives a firm to make choices with regard to innovation projects. This enhances focus in NPD which increases

708

See BURGELMAN/SAYLES (1986), p.43.

709

For a more detailed discussion see chapter III-2.1.2

710

See JONASH/SOMMERLATTE (1999), p.25ff and LAURIE/DOZ/SHEER (2006), p.82.

711

See COOPER/EDGETT/KLEINSCHMIDT (2004), p.51. Their study is based on evidence from 105 US firms.

712

See COOPER/EDGETT/KLEINSCHMIDT (2004), p.51 (item): Strategic arenas defined?

200

Discussion & Implications

productivity and hence performance.713 Furthermore, synergies across related innovation projects within an innovation field also increase productivity.714 Beside economies of scope, internal knowledge spillovers are conceivable.715 Why does innovation field orientation influence innovation performance, but not firm performance? Several reasons are conceivable to explain the differences in results depending on the type of performance indicator. First, pursuing an innovation field orientation does entail additional costs, which are more strongly reflected in the measure of firm performance than in innovation performance. Defining, setting up and running innovation fields is more expensive than innovating along existing product lines. Innovation performance was measured as a multi-item construct encompassing several financial and non-financial indicators.716 However, profitability is only one of several financial indicators of innovation performance, whereas objective, firm performance indicators are strongly influenced by firm profitability. Second, innovation field orientation is highly specific to innovation, but less relevant for the overall firm. Hence, it can have less impact on firm than on innovation performance. During the document analysis much less information was available on innovation field orientation than, e.g. on innovativeness. This indicates that outputs (e.g. innovations and their degree of innovativeness) are more relevant to outsiders than how a firm got to these outputs. Third, the fact that innovation performance was measured from an internal perspective and firm performance from an external, capital market perspective, may further explain the different outcomes. Investors are foremost interested in a firm’s outputs, whereas internal perspectives on performance are influenced by multiple factors. Summarizing, even though with a weaker performance effect than established dimensions, innovation field orientation has proven to be a valid

713

See COOPER (1984), p.156 and HENDERSON/COCKBURN (1996), p.41.

714

See e.g. COOPER (1984), p.156f, HENDERSON/COCKBURN (1996), p.35f and BLAU/PEKNY/ VARMA/BUNCH (2004), p.21ff.

715

See HENDERSON/COCKBURN (1996), p.35 and chapter III-2.1.4.

716

See chapter IV-3.3.1.

Discussion of Results

201

concept by itself and a relevant success factor in the context of innovation strategy. Overall, this study confirmed that innovation strategy has a positive influence on firm-level performance, even though different performance indicators are impacted at different intensities. 1.3

How do contingencies influence the relationship between innovation strategy and firm-level performance?

Contingencies have been rarely studied in the context of innovation strategy with the most recent piece of research from ZAHRA and COVIN dating back to 1993.717 This study extends the research of contingencies in innovation strategy research. Two context factors were investigated. Business strategy was chosen as a contingency variable at firm level, because ZAHRA and COVIN observed that it influences the relation between technology policy and performance.718 Environmental uncertainty was selected as a contingency at market level, because it is most prevelant in business strategy and marketing research and receives increasing attention in innovation research.719 Eight hypotheses were formulated, associating each of the four innovation strategy dimensions with both contingency variables. 1.3.1 Does business strategy moderate the relationship between innovation strategy and performance? Business strategy was conceptualized using PORTER’S strategy types.720 The hypotheses focused on the two pure strategy types, cost leadership and differentiation.721 Out of the four hypotheses (H5a-H5d) two were weakly or partially confirmed.

717

See chapter II-3 and Table II-11.

718

See ZAHRA/COVIN (1993), p.470. Their technology policy would nowadays fall under innovation strategy.

719

See chapter II-3 and II-3.1.2 for more details.

720

See PORTER (1980), p.34ff.

721

See e.g. VAZQUES/SANTOS/ALVAREZ (2001), p.75.

202

Discussion & Implications

Hypotheses 5a suggests a positive moderation effect of differentiation strategy on the innovativeness - performance relation. H5a was partially confirmed, because differentiation effects the relations of innovativeness with firm performance, but not with innovation performance. New products are an important aspect for differentiators to distinguish themselves and create a competitive advantage.722 Using the MILES and SNOW strategy typology, GRIFFIN and PAGE’S finding indicates a positive relationship between innovativeness and differentiation.723 Moreover, ZAHRA and COVIN observe that marketing intensity - another important characteristic of differentiators724 - is positively correlated with NPD.725 Differentiation only has a moderating effect on firm but not on innovation performance. The different outcomes by performance metric may be explained by the fact that business strategy deals with the business and thus the firm as a whole. Hence, the overall firm performance indicator is more relevant than the innovation specific one. For cost leaders, a moderation effect was neither hypothesized nor empirically observed. Saving costs along the entire value chain is the imperative for cost leaders. Therefore, more radical innovations which cause higher costs should be of less interest to them.726 Similar to H5a, Hypothesis 5b claims a positive moderation of differentiation on the distance to core business - performance relation. H5b was weakly confirmed, through a moderation effect at the 10% significance level on innovation performance, however not on firm performance.727 Given the moderation effect on innovation performance is weak the varying outcomes for the two performance indicators (innovation and firm performance) should not be over-interpreted and are therefore not discussed. Developing new products further away from the core business can be a way to differentiate and grow - hence potentially attractive for differentiators. As expected, no moderation effect could be found for cost leaders.

722

See e.g. VAZQUES/SANTOS/ALVAREZ (2001), p.76.

723

See GRIFFIN/PAGE (1996), p.485.

724

See DESS/DAVIS (1993), p.463.

725

See ZAHRA/COVIN (1993), p.463.

726

See chapter IV-6.1.1.3 and SMITH (2005), p.157.

727

See chapter IV-4.3.1 and Table IV-38.

Discussion of Results

203

New products further away from the core business incur higher costs than close-to-home products; a fact less desireable for cost leaders. Hypothesis 5c claims that the relation between a balanced technology and market orientation and performance is positively moderated for differentiators. The empirical study did not confirm H5c, hence it was rejected. No significant moderation effect was found. This means that the driver of innovation - performance relation is independent of a firm’s business strategy. Whether a firm pursues a differentiation or cost leadership strategy does not change the fact that a balanced market and technology orientation is the best driver of innovation in terms of its performance effect. Prior research does not exist for this contingency hypothesis. The argumentation for a positive moderation was mainly based on the indirect effect that balanced technology and market orientation drive more radical innovations; the higher the level of innovativeness the better it is for performance (H1).728 The strength of this indirect effect was probably not enough for a significant moderation. Hypothesis 5d states that the innovation field orientation - performance relation is moderated for differentiators. H5d was rejected given no moderation effect could be observed. Similar to hypothesis H5c, the argumentation is mainly based on an indirect effect. An innovation field orientation is of longer-term nature and intended to facilitate streams of significant innovations.729 Hence the approach should be more attractive for differentiators than for cost leaders. The results of the empirical study indicate, however, that business strategy is irrelevant for innovation field orientation. In other words, independent from its overall business strategy a firm can benefit from innovation field orientation. Even though less than expected, the empirical data provide indications that an alignment of a firm’s innovation strategy with its business strategy is important and can influence firm-level performance – particularly for firms pursuing differentiation strategies. Aligning the type of new prod-

728

See chapter V-1.2.1.

729

See LAURIE/DOZ/SHEER (2006), p.80ff and chapter II-1.2.3.4.

204

Discussion & Implications

ucts, that is their level of innovativeness and distance to core business, with the overall business strategy proved to be most critical. 1.3.2 Does environmental uncertainty moderate the relationship between innovation strategy and performance? Environmental uncertainty refers to the degree and unpredictability of change in an organisation’s environment.730 Four moderation hypotheses were formulated for the four innovation strategy dimensions and their relation with performance. None of the four hypotheses (H6a-H6d) could be empirically confirmed. No significant moderation effects were observed. Hence, split group analysis was conducted in addition, providing some interesting results. Hypotheses 6a suggests that the more uncertain the environment the stronger the influence of innovativeness on performance. When customers, their needs and technologies change often and advance at high speed, a firm needs to modify and enhance its products more often, quicker and more significantly than in a stable environment.731 Hence an uncertain environment should foster innovations and innovativeness by pure necessity. Overall, prior research found equivocal results. However, studies most similar to the present one identified positive effects.732 For this study, moderated regression did not provide any significant result. However, split group analysis indicates that at very low levels of uncertainty no significant correlation exists any more between innovativeness and performance. This could mean that in stable industries the level of innovativeness of a firm's new products is not important for performance. This outcome from split group anaylsis tends to be in line with the original hypothesis, H6a. In a stable environment innovations should be less crucial. Quality, price, branding and operational efficiency may be more relevant to firms in environments with low uncertainty.

730

See e.g. DANNEELS/SETHI (2003), p.3.

731

See ZHOU/YIM/TSE (2005), p.47 and CALANTONE/GARCIA/DROEGE (2003), p.100.

732

See MILLER/FRIESEN (1982), p.11, CALANTONE/GARCIA/DROEGE (2003), p.99.

Discussion of Results

205

Hypothesis 6b suggests that in an uncertain environment it is beneficial to develop new products further away from the core business. Having new products further away from the core business in the new product portfolio promises a better success rate, particularly in a turbulent environment, where it is recommendable to be always ready with alternatives.733 No prior relevant research exists for this moderation effect. However, the argumentation for H6a was somewhat similar. Hence it is not surprising that H6b was not confirmed, because H6a was not supported either. Moreover, the main effect of distance to core business on performance (H2) was not significant either. Hypothesis 6c claims that a balanced market and technology orientation is most advantageous as driver of innovation in an environment of high uncertainty. Neither moderated regression nor split group analysis confirmed hypothesis 6c. For market orientation as a driver, prior empirical research is equivocal with a tendency towards a positive effect for recent studies.734 For technology orientation, the empirical evidence is scarce.735 For a balanced market and technology orientation, GATIGNON and XUEREB are the only ones having studied this specific moderation effect. They claim that a balanced orientation is particularly fruitful in an uncertain environment.736 The non-significant finding in the present study is possibly explained by the fact that a balanced market and technology orientation offers so many advantages versus other drivers of innovation that it is most promising in any environment.737 Hypothesis 6d claims that innovation field orientation is better for performance in a highly uncertain environment than in a context of stability. Again, moderated regression provided no significant results, and H6c needs to be rejected. However, split group analysis generated two interesting insights. First, at very low and at very high levels of environmental uncertainty, no significant correlation can be observed any more for the

733

See DANNEELS/SETHI (2005), p.16.

734

See chapter III-2.2.2.2.

735

See GATIGNON/XUEREB (1997), p.86, and chapter III-2.2.2.2 for more detail.

736

See GATIGNON/XUEREB (1997), p.81.

737

See chapters III-2.1.2 and V-1.2.2.

206

Discussion & Implications

main effect between innovation field orientation and performance. For a certain environment, the finding is in line with the hypothesis. Given innovations are less important in a certain environment,738 it is questionable to set up innovation fields which are intended to drive streams of significant innovations. In a stable environment, the cost of an innovation field orientation does not justify the gains. For a highly uncertain environment, the finding from split group analysis is somewhat surprising. It was expected that particularly in a dynamic and unpredictable context, where innovations and the level of innovativeness are essential, an innovation field orientation should bear fruits. The finding may be explained by the fact that in a very turbulent environment innovation fields are too rigid and unflexible. Innovation fields need to be defined and set up before they generate innovations. For a highly uncertain environment, this process may take too long and the structure of innovation fields may limit flexibility. To sum up, innovation field orientation is especially recommendable in moderately uncertain environments. Concluding, the main effects of innovativeness, driver of innovation and innovation field orientation on firm-level performance are so strong as to be independent of the level of uncertainty in a firm’s environment.

2

Implications

To account for different target groups, the implications of this research are split into two sections. First implications for innovation research are discussed, followed by practical implications for innovation managers. 2.1

Implications for Innovation Research

The implications for innovation research are divided into content related and methodological contributions. From a content perspective one key contribution of this research is a new and empirically assessed, multi-dimensional concept for innovation strategy. Based on an in-depth review of the literature and an extensive meta-

738

See chapter III-2.2.2.1 and Hypothesis 6a.

Implications

207

analysis innovation strategy was characterized and measured along four dimensions. Moreover the performance effect of innovation strategy was investigated, rarely done in prior studies.739 Given three out of four innovation strategy dimensions showed a significant and positive influence on innovation and/or firm performance, innovation strategy and its performance effect proved to merit more research. All four innovation strategy dimensions were conceptualized as comprehensive, multi-item constructs, building extensively on existing measures. Four points are worth highlighting concerning the measurement model for innovation strategy. First, innovativeness to the market and to the firm were conceptualized and measured as two distinct aspects of newness; the first was termed innovativeness and the second distance to core business. This research followed the call from renowned authors to distinguish the different perspectives of newness (new to whom?).740 The results confirm and support this approach. Whereas innovativeness proved to be a predictor of success, distance to core business showed no significant performance effect. Hence, based on these findings and in line with DANNEELS and KLEINSCHMIDT the author strongly recommends investigating the two perspectives of newness separately. Besides resulting in different outcomes,741 they do not necessarily correlate. A product radically new to a certain firm may only be incrementally new to the market due to previously launched competitor products. Future research on newness - to the market and/or the firm - can revert to the operationalizations of this study, which would also facilitate comparison of results. Second, this research was the first one to investigate proactive market orientation as a driver of innovation, beside technology orientation and competitive response. Moreover, few prior studies research several drivers at the same time. If so, market orientation is typically measured in the traditional, reactive way. This study’s results support the approach of investigating several drivers of innovation in parallel, given a combination of two drivers (market and technology orientation) proved most successful. Hence, for

739

See chapter II-2.2.1 and Table II-5.

740

See e.g. DANNEELS/KLEINSCHMIDT (2001).

741

This was previously shown by SCHLAAK (1999), p.107ff.

208

Discussion & Implications

future research on drivers of innovation, it is highly recommendable to look at least at market and technology orientation together. Analyzing only market orientation may provide misleading results as to the drivers behind innovation. Third, the new phenomenon, innovation field orientation, was for the first time investigated as part of innovation strategy. Furthermore, this research developed the first conceptualization and operationalization as a multi-item construct - for innovation field orientation. Measures were newly generated reverting to other streams of innovation and organizational literature to the maxium extent possible. Given the measurement model proved valid and innovation field orientation shows a significant performance effect, future research should consider this new phenomenon and use the measures established here. Fourth, this study contributes an extensive meta-analysis of innovation strategy dimensions studied in prior research. Based on the meta-analysis at least 25 different dimensions were already investigated as part of innovation strategy, however, only six of them in four or more instances. This study researched three of these six established dimensions and one new aspect (innovation field orientation). Future research in the field of innovation strategy should follow suit and focus primarily on established dimensions, such to make this heterogeneous stream of literature more comparable. Methodological implications for innovation research concern the application of document analysis as an alternative approach to data gathering and the use of objective firm performance data as success indicator. Qualitative document analysis was conducted to complement and validate answers from the written survey. It proved an effective method to validate large parts of the innovation and business strategy constructs. Availability of information varied between the four innovation strategy dimensions, depending on the interest of the subject to outsiders, confidentiality issues and political motivations. Nevertheless, by combining different publicly available sources sufficient data was accessible for most aspects of innovation and business strategy.742 Comparing survey results with informa-

742

Overall, sufficient information was available for the four second order constructs. However, for some first order constructs, particularly for the innovation field orientation dimension, data was more limited.

Implications

209

tion from the document analysis showed that answers from the questionnaire were largely in line with publicly available information.743 Concluding, the additional data collection effort caused by the document analysis was justified, because the survey data was validated and the challenge of common source bias limited.744 Particularly for research in the German speaking area where companies are overwhelmed by all sorts of academic and commercial questionnaires, document analysis offers an alternative approach to data collection. It may not be an option for all research questions given firms do not communicate much internal information in publicly available documents, however, much more data is offered on firms’ websites than some researchers may imagine. In addition to subjective innovation performance data gathered through a written survey, this research accessed objective firm performance data from financial databases. Given innovations should eventually have a positive, economic impact it was argued that aggregated innovation performance at the firm level should be reflected in firm performance.745 In fact, two of the four innovation strategy dimensions showed significant and positive performance effects on objective firm performance indicators.746 Obviously, firm performance is influenced by many and more important factors than innovation strategy.747 However, given the regression models with objective firm performance indicators as dependent variable explained satisfactory portions of the variance748 and given that two innovation strategy dimensions influence objective performance metrices, using objective firm performance data proved to be a valid alternative to subjective innovation performance. In addition, objective measures increase the credibility of research results among practitioners. Last and interestingly, perceptual innovation and objective firm performance data did not corre-

743

For one firm results differed significantly which was subsequently excluded from further analysis.

744

See chapter IV-1 for details von common method and common source bias.

745

For a more detailed argumentation see chapter II-2.1.2.

746

Innovativeness and driver of innovation exhibited both significant effects on firm performance indicators - Tobin’s q and TRS vs. industry, respectively.

747

Firm profitability, cash flow, growth and risk are some examples for important factors influencing firm performance. See e.g. CHAUVIN/HIRSHEY (1993), p.134.

748

Model 2 explained .13 and Model 3 .11 of the variance (R2). See chapter IV-4.3.2.1.

210

Discussion & Implications

late strongly and produced varying performance effects.749 This observation does not support the most common conviction in research that subjective and objective performance indicators are highly correlated and hence substitutable.750 It is true that innovation performance and firm performance do not measure exactly the same content, but additional analysis focusing exclusively on the financial items of innovation performance751 did not exhibit a higher correlation with firm performance either.752 Even though this comparison is not perfect it hints that a strong correlation between subjective and objective performance data may not always be true. Concluding and in line with prominent authors,753 I strongly encourage future research in innovation to access objective performance data from financial databases and/or firms’ investor relations sections when investigating performance effects – be it at the aggregate firm-level, as done in this study, or at the individual innovation level in form of an event study, as done in several recent papers.754 2.2

Managerial Implications

Within this chapter the study’s implications for innovation practitioners are presented resulting in concrete, pragmatic guidelines for successful innovation management. (1) Pursue innovation projects with a higher degree of newness to the market: It is rewarded!

749

Overall innovation performance correlated .456** with Tobin’s q and .251** with TRS vs. industry. Three innovation strategy dimensions showed positive and significant effects with innovation performance in contrast to one performance effect for Tobin’s q and TRS vs. industry each.

750

See e.g. DESS/ROBINSON (1984) as the commonly referred paper concerning this opinion. See e.g. LANGERAK/HULTINK/ROBBEN (2004) justifying the usage of subjective measures based on this and two other arguments.

751

That is to say items 1-6 (under ‘financial’) of the innovation performance construct. See chapter IV-3.3.1, Table IV-6.

752

In fact, the correlations with firm performance even decreased (to .425** and .153*) when measuring innovation performance solely based on the financial items.

753

See e.g. VENKATRAMAN/RAMANUJAM (1986), p.801ff; DAY/FAHEY (1988), SRIVASTAVA/SHERVANI/FAHEY (1999), p.177; and LEHMANN (2004), p.73ff.

754

See chapter II-2.2.1 and Table II-6 for an overview of recent event studies in innovation research with capital market based, objective success measures.

p.53ff;

Implications

211

New product portfolios include projects with varying degrees of newness to the market.755 It is crucial for innovation and firm performance that the new product portfolio also includes more radical innovations, not only incremental, short-term efforts. Firms need to dare and invest in really new products to be successful, because a higher degree of innovativeness is rewarded - also by the stock market. Focussing purely on incremental innovations can harm firm performance. (2) New products can be closer or further away from the core business: Distance to core business is irrelevant for success! From a portfolio perspective it is irrelevant if your innovations are close to the existing business or not. That is, if your innovations are in your home turf and you know the market, customers and competitors, does not impact performance. More important is that specific competences are in place required for new products. Necessary resources and skills can be transferred from existing products to new ones – independent if these new products are close-to-home or not.756 (3) Marketeers and R&D need to drive innovation together: Join forces, it pays off! Innovation does not equal R&D and innovations should not only be driven by R&D. New products should be moved forward by a combination of technical (e.g. R&D, engineering) and market oriented (e.g. marketing, business development, sales) people. A balanced market and technology orientation promises more success than the sum of the two individual drivers, because they complement each other. Scientists, for example, tend to drive more radical innovations than market oriented employees. However, R&D driven innovations run the risk of innovating past the market. Cooperating with marketing people can prevent this mistake. Some innovative firms have recognized the importance of joining forces between

755

See e.g. CRAWFORD (1908), p.8, and COOPER (2005).

756

This last aspect is not directly derived from the results of this study, but based on DANNEELS and KLEINSCHMIDT’S (2001, p.369) additional findings on the subject.

212

Discussion & Implications

R&D and marketing for innovation and have built innovation power houses by co-locating the two functions.757 (4) Concentrate your innovation activities in few innovation fields and benefit from focus and synergies! Innovation field orientation is a predictor for innovation success. That is, it pays off to carefully choose certain fields within which to focus your innovation activity. Within each innovation field several (ideally 8-10) related projects are bundled under a common theme. Related projects crossfertilize each other and benefit from synergies. Managers should not pursue whathever innovation opportunity emerges and looks promising, but carefully choose few (ideally 6 or less) innovation fields and concentrate innovation activity within these domains. Innovation field orientation facilitates a continuous stream of innovations capable to close the growth gap many companies are facing.758 (5) Differentiators, align your innovation strategy with your overall business strategy! For firms pursuing differentiation strategies, the first two recommendations are particularly important. Differentiators can strengthen the impact of innovation strategy on firm performance, if they align their innovation activities with their business strategy. Thereby, the kinds of innovations pursued are most crucial. More so than firms pursuing other business strategies, differentiators need to have a more radical new product portfolio. Innovations are a key aspect to differentiate and the newer to the market they are, the more they contribute to a competitive advantage. Moreover, developing new products further away from the core business constitute another way to differentiate and grow. Hence, for differentiators a new product portfolio significantly new to the market and to the firm is most advantageous.

757

Based on the author’s knowledge, BMW and Johnson & Johnson are two examples.

758

See LAURIE/DOZ/SHEER (2006), p.90.

Limitations & Outlook

213

(6) Size, industry, geography, R&D rate and environmental uncertainty are no excuse: Recommendations 1-4 apply to all firms!759 Firm size, industry sector, geography (continent), R&D rate760 and environmental uncertainty have no influence on the above recommendations. In other words, the implications for firms apply independent of the fact if a firm is small or large, in the health care or industrial goods sector, located in Europe or the US or whether it invests proportionately more or less in innovation. Moreover, whether a firm operates in an environment with high market and technological uncertainty or under stable conditions has no significant impact either. Concluding and to draw a connection to the introductory chapter, this study confirms that it is not important how much a firm spends on innovation, but rather what kind of innovations it pursues and how they are approached, via innovation strategy.

3

Limitations & Outlook

This dissertation project contributed new insights to innovation research as well as for practitioners in the realm of innovation. Nevertheless some limitations need to be kept in view while interpreting the findings. Based on these limitations an outlook for future research is added. First, innovation strategy was measured at the firm level, considering the overall new product portfolio of a firm. Respondents were asked to provide average assessments across the entire portfolio. This approach implies inaccuracies compared to a project based approach aggregated to the firm level.761 However, given that stock market listed firms conduct several

759

The recommendations apply to all firms in manufacturing sectors (not service sectors) which focus on one or few related businesses, that is to say are single or dominant business firms (see IV-2.1). Given the sample is dominated by European and North American firms the suggestions apply mainly to these two continents. The fact that the sample concentrates on stock market listed firms is less important, given it comprises small as well as large publicly listed firms.

760

R&D rate is defined as spending on R&D as percentage of total sales. It serves as a proxy for relative spending on innovation. For details see chapter I-1.2.

761

See e.g. FIRTH/NARAYANAN (1996), p.337, for an approach based on projects and later aggregated to the firm level.

214

Discussion & Implications

dozens of innovation projects in parallel762 a quantitative study at the firm level based on project-level data would entail an unmanageable and unrealistic research effort. Hence, to validate the results of this research at a greater level of accuracy, I suggest undertaking a case study approach like FIRTH and NARAYANAN did, limiting the number of investigated firms to a few dozens. Second, perceptual measures used in this study were gathered from the same informant. Information about innovation strategy, innovation performance and the two contingency variables were provided by one preidentified key informant - typically the head or a leading employee of innovation or R&D. Such an approach can introduce the possibility of common method bias.763 To complement and validate the survey based data of innovation and business strategy a document analysis was conducted, based on a wide array of publicly available firm information. For the innovation strategy construct, major deviations between the results from the written survey and the document analysis were rare except for one firm which was eliminated from the sample. For business strategy, several unexplainable deviations occurred for PORTER’S third strategy type, focus. As a consequence the regression analysis focused on the two main strategy types, differentiation and cost leadership.764 Moreover, secondary data for firm performance and five control variables were collected from objective financial databases and firms’ web sites. Beside a written survey and document analysis of publicly available information, retrieving data from financial databases constitutes the third data collection method of this research. Thus, common method bias is not considered a major concern for this study. Third, the challenge of a time lag between the implementation of a firm’s innovation strategy and its outcome was addressed using capital marketbased firm performance data (Tobin’s q and TRS vs. Industry), which also

762

FIRTH/NARAYANAN (1996), p.334, counted about 25.5 projects per firm. They also studied large firms, which all belonged to the Fortune 500 for several years.

763

See chapter IV-1 and e.g. ERNST (2001), p.195ff, and LEE/GREWAL (2004), p.169.

764

For a similar approach, focussing on differentiators and cost leaders ‘only’, see e.g. VAZQUES/SANTOS/ALVAREZ (2001), p.75.

Limitations & Outlook

account for future performance. In a recent study, LEIFER

215

ET AL.

show that

rewards for innovation only emerge after several years – even with regard to stock returns.765 They measured performance after 1, 3 and 5 years and found varying results. Given information on innovation strategy was collected in the same year (2006) as firm performance data, a longitudinal approach was so far not feasible.766 Concluding there are three particular opportunities for future research which can tie in with this study: ƒThe overall results could be validated via a case study approach which gathers data at the project level and then aggregates them to the firm level. This may provide more accurate data then the average new product portfolio data investigated in this study and could reinforce the findings of this research. ƒThe performance effects could be complemented with additional mid- and long-term performance indicators gathered in a few years from now. Beside capital market based metrices, applying accounting based performance measures would also be feasible then. The time lag between the implementation of an innovation strategy and its effect would be considered by waiting a few years. ƒLast, the conceptualization and measurement model of the new phenomenon, innovation field orientation, are worth investigating again. Through another quantitative study the newly developed measures could be validated and the results verified. In addition, via a case study approach and in close collaboration with a few firms767 innovation fields could be researched in depth to gain more insight on this new and unexplored concept. Moreover such an approach would enable to identify different models of implementing innovation fields – an aspect not researched at all to date.

765

See LEIFER ET AL. (2006), p. 27.

766

PROF. SALOMO, the advisor of this dissertation project, intends to analyze the performance effects again in a few years.

767

Specifically, the approach taken by the Radical Innovation Research Program, an initiative between the Rensselaer Polytechnic Institute’s Lally School of Management and the Industrial Research Institute, appears as an attractive way. They cooperate with twelve large companies over several years. See O’CONNER/AYERS (2005), p.24.

Appendix:

Cover Letter and Questionnaire

Study on Innovation Strategy and Firm Performance Objectives of Study ƒ Identify and understand different types of innovation strategies - via a new conceptual approach ƒ Empirically assess the relationship between innovation strategy and firm performance – using capital market data ƒ Understand how internal and external conditions of a firm influence the relationship between innovation strategy and firm performance ƒ Approach: The results of the enclosed questionnaire will be linked to capital market data of your company



Innovation Strategy Influencing Factors Firm Performance

Target Group ƒ Publicly listed companies ƒ Manufacturing companies in selected industry sectors ƒ Decision makers with a good overview of the firm‘s innovation activities



Benefits for Participating Companies ƒ Systematic reflection on your innovation strategy and activities ƒ Individual evaluation of your innovation strategy relative to top and low performers ƒ Final report of the study including key results about innovation strategies and their relationship with - capital market data based - firm performance ƒ Upon request: individual feedback session



Your Contribution ƒ Fill out the attached questionnaire (maximum duration 30 minutes) – either electronically in Word or printed out... ƒ … and send it back via fax or email – before the end of February! ƒ Note: All data will be treated in a confidential manner and only serve academic purposes. The analysis is done anonymously!



Karl-Franzens University Graz (Austria) Institute for Technology- and Innovation Management Prof. Dr. Soeren Salomo Contact person: Dipl. Wi.-Ing. Nanja Strecker Phone: ++49-175-2964133, Fax: ++49-89-25548101 [email protected] www.uni-graz.at/tim

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