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This is to certify that the
dissertation entitled

THE DEVELOPMENT of MARKET EFFICIENT
TECHNOLOGICAL INNOVATION:
A ‘HOLISTIC’ STUDY FROM MULTIPLE
PERSPECTIVES

presented by

Ferdane Nukhet Harmancioglu

has been accepted towards fulfillment
of the requirements for the

Doctor of Philosophy Marketing
degree in

 

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' 06

06/0 [20

 

Date

MSU is an Afiirmative Action/Equal Opportunity Institution

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
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6/01 c:/ClRC/DateDue.p65—p.15

 

 

THE DEVELOPMENT of MARKET EFFICIENT
TECHNOLOGICAL INNOVATION:
A ‘HOLISTIC’ STUDY FROM MULTIPLE
PERSPECTIVES

By

F erdane Nukhet Harmancioglu

A DISSERTATION
Submitted to
Michigan State University
In partial fulfillment of the

requirements for the degree of

DOCTOR OF PHILOSOPHY

Department of Marketing and Supply Chain Management

2006

 

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ABSTRACT

THE DEVELOPMENT of MARKET EFFICIENT TECHNOLOGICAL
INNOVATION: A ‘HOLISTIC’ STUDY FROM MULTIPLE PERSPECTIVES

By
F erdane N ukhet Harmancioglu

The primary objectives of this dissertation correspond to the three lenses
through which technological innovation development is examined: (1) the scholarly
stream of research on innovation, (2) internal and (3) external product development.
The focus of this dissertation is on the contribution of both internal and external new
product innovation development to firm performance and market value. The three
studies in this research provide innovation researchers with a theory-driven meta-
analysis of the innovation literature; offer to practitioners an exhaustive list of factors
important both for the internal and external development of technological innovation;
and finally, advance the methodological stature with which determinants of
innovation success are examined.
STUDY #1. To serve as a theoretical foundation for the subsequent studies in the
thesis, the first lens scrutinizes ‘new product innovation’ by conducting a meta-
analysis on marketing, management and engineering studies that have examined
empirically the relationships of innovation with its antecedents and outcomes. This
first study provides researchers with objective empirical generalizations, as well as
investigates sources of inconsistencies in the literature. Substantive or
methodological notions that vary across studies are tested to determine whether they
moderate the relationships identified (i.e., moderate the effect sizes).
STUDY #2. In the new product literature, resources, capabilities and strategic
orientations have been directly linked to positional advantages and performance

outcomes. However, the implementation of these orientations and process activities

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has generally been neglected in new product development models. The key to
innovation success is acknowledged to be the match between what is needed and what
can be developed internally. In light of the findings from the meta-analysis, the
second lens focuses on internal development of technological innovation at the project
level and provides an operationalization of this match. Due to the complexity of the
proposed model, hypotheses are explored through partial least squares analysis (PLS).
This analysis also provides practitioners with a comprehensive list of internal,
environmental and supply-chain related factors important for successful development.
STUDY #3. The third lens concerns external innovation: through technology
outsourcing, firms develop capabilities and flexibilities they lack or have lost due to
technological discontinuities and globalization. Despite its importance, little attention
has been paid to empirically validating risks and benefits of strategic outsourcing
relationships in the context of modular systems and global technology intensive
markets. Event study analysis of abnormal stock returns was used to examine the
value of external innovation development through technology outsourcing.

CONTRIBUTIONS. This dissertation constitutes a comprehensive study on the
outcomes of technological innovation, providing implications from three lenses: the
extant research, internal and external development. The three studies employ three
types of data (i.e., the literature, primary and secondary data) and use multiple
methodologies (i.e., meta-analysis, PLS and event study). Thus, this research offers
cumulative insights from the innovation literature, empirically investigate models of
the ‘values’ of both internal and external innovation, and uses diverse analysis
techniques in a comprehensive investigation of the determinants of new product

innovation success.

Copyright By
F ERDANE NUKHET HARMANCIOGLU

2006

DEDICATION
This dissertation is dedicated to my parents, Nilgun and Omer Harmancioglu
and my grandparents, Neriman Bayraktar, Bedia and Mustafa Harmancioglu with
gratefulness for the endless love I have received, the wonderful life I have lived and
for the person I have become. It is also dedicated to M. Berke Gur with thankfulness

for all the meaning he has added to my past and my future.

 

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ACKNOWLEDGEMENTS

Having completed my studies at Michigan State University, I would like to
express my appreciation to those who supported me during my doctoral studies. First,
I would like to acknowledge my committee: Dr. Cornelia Droge, my advisor, for her
constant support and encouragement. She challenged me to pursue better ideas, better
analyses, and better writing from the very beginning. She never hesitated to provide
me with her guidance about analysis and writing as well as professionalism and
academia. As she said, she has become ‘a second mother’ to me. I extend my
gratitude to my committee co-chair: Dr. Roger J. Calantone for providing me many
professional opportunities, and most of all, making me feel cared and supported
whenever I needed it the most. I am also very grateful to Dr. Tamer Cavusgil for
believing in me from the start. Without his appreciation, I would not have
accomplished any of what I achieved. I would also deeply thank Dr. David A.
Griffith for his support, suggestions and constant offers for help.

Several other people supported me throughout my studies. I present my
sincere appreciation to my advisor during my Masters studies at Bosphorus
University, Dr. Muzaffer Bodur. She encouraged me to take this joumey of
continuing my studies in the US. I would also like to thank Dr. Seyda Deligonul. He
gave me immeasurable morale support during my first year. Dr. Regina McNally and
Dr. Chris White provided valuable advice on my thesis ideas as well as my career
path. My friends at MSC (Zeynep, Carol, Berk, Mike, Steven, Joey, Billur and
Wessley) made this process both enjoyable and bearable. I will miss our
conversations about simply everything. Moreover, the MSC office staff has also been
very helpful throughout my Ph.D. studies. Financial support was received from MSC,

PDMA and AMS.

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My family -- Nilgun and Omer Harmancioglu, Neriman Bayraktar, Bedia and
Mustafa Harmancioglu -- continuously provided me with unconditional love and
always expresses their pride in whatever I chose to do. I love you more than anything
in my life and am very grateful for all the sacrifices that you have made and will make
for my education, happiness and future. I have been very blessed with amazing
friends. Words cannot express my love and gratitude to M. Berke Gur -- he brought
light to my days; Zerrin and Kcnan Cosguner and Cigdem Haser -- they made East
Lansing a home. My friends in Turkey: Didem Aksoy, Muzeyyen Isler, and Gamze
and Onur Kutlukaya always called, sending their good thoughts and energies
whenever I needed them. Special thanks goes to my fiiends at the Academy of Dance
Arts — for always cheering me on.

Finally, as a woman, a teacher and a researcher, I am indebted to Mustafa
Kemal Ataturk, the founder of Turkish Republic, for his encouragement for Turkish

women and his belief in education, science and teachers.

vii

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TABLE OF CONTENTS

LIST OF TABLES ..................................................................................................... xi
LIST OF FIGURES ................................................................................................. xiii
CHAPTER 1 DISSERTATION INTRODUCTION .................................... 1
CHAPTER 2 INCONCLUSIVE INNOVATION “RETURNS”: A
META-ANALYSIS OF RESEARCH ON INNOVATION IN NEW PRODUCT
DEVELOPMENT ............................................................................... 7
2.1 INTRODUCTION ........................................................................ 7
2.2 SOURCES OF INCONSISTENCIES: POTENTIAL MODERATORS ......... 10
2.2.1 Level of Analysis: Potential Substantive Moderator ..................... 10
2.2.2 Perspective: Potential Substantive Moderator ............................. 11
2.2.3 Operationalization of Innovation: Potential Methodological
Moderator ...................................................................... 12
2.3 HYPOTHESES ........................................................................... 13
2.3.1 Antecedent to Innovation: Environmental Turbulence (Market and
Technological) ................................................................ 1 5
2.3.2 Antecedent to Innovation: Market Orientation (Customer and
Competitor) ................................................................... 1 7
2.3.3 Antecedent to Innovation: Organizational Structure ..................... 20
2.3.4 Outcome of New Product Innovation: New Product Performance. . . .23
2.4 METHOD ................................................................................. 24
2.4.1 Sample of Articles ............................................................ 25
2.4.2 Variable Coding and Data Analysis ........................................ 26
2.5 RESULTS ................................................................................. 29
2.5.1 Baseline Model: Context for Hypothesis Testing ........................ 29
2.5.2 Hypothesis Testing ............................................................ 31
2.5.2.1 Environmental Turbulence and New Product Innovation ...... 31
2.5.2.2 Customer Orientation and New Product Innovation ............ 33
2.5.2.3 Competitor Orientation and New Product Innovation .......... 33
2.5.2.4 Organizational Structure and New Product Innovation. . . . . . ...34
2.5.2.5 New Product Innovation and New Product Performance ...... 34
2.6 DISCUSSION AND CONCLUSION ................................................ 38
2.6.1 Baseline Model: The Interrelationship Among Constructs .............. 38
2.6.2 Moderator: Program Versus Project Level of Analysis .................. 40
2.6.3 Moderator: Firm versus Customer Perspective ........................... 43
2.6.4 Moderator: Operationalization of the New Product Innovation
Construct ....................................................................... 44

viii

 

CHAPTER3 MARKET EFFICIENT TECHNOLOGICAL INNOVATION: A

DETAILED MODEL WITH MARKETING IIVIPLICATIONS ..................... 46
3.1 INTRODUCTION ..................................................................... 46
3.2 BACKGROUND AND MODEL OVERVIEW ..................................... 48
3.2.1 Theoretical Background ...................................................... 48
3.2.2 Model Overview and Model Constructs ................................... 50
3.3 RESEARCH HYPOTHESES ......................................................... 52
3.3.1 Sources of Advantage ......................................................... 52
3.3.1.1 The Effects of Strategic Fit (Function-Specific Sources of
Advantage) (H1- H4) ................................................ 54
3.3.1.2 The Effects of Project-Specific Sources of Advantage (H5,
H6) ...................................................................... 59
3.3.2 NPD Process Implementation Proficiency ................................. 63
3.3.2.1 The Effects of NPD Activities (H7, H8, H9) ..................... 63
3.3.2.2 The Effects of Development Speed and Development Cost
(H10, H11) ............................................................ 65
3.3.3 Positional Advantages ........................................................ 66
3.3.3.1 The Effect of Degree of Customer Need Met (H12) ............ 66
3.3.3.2 The Effect of Product Advantage (H13) .......................... 67
3.3.4 Market Environment (H14-H15) ........................................... 67
3.4 METHODOLOGY ....................................................................... 69
3.4.1 Sampling Frame .............................................................. 69
3.4.2 Analysis ........................................................................ 70
3.5 RESULT: MEASUREMENT VALIDATION ...................................... 71
3.6 RESULTS: STRUCTURAL MODEL ................................................ 75
3.6.1 Results for H1- H4. a-e (Please refer to Table2-3) ........................ 76
3.6.2 Results for Tests of Mediation (H1-H4.f-g) (Please refer to Table 2-
4) ................................................................................. 78
3.6.3 Results for H5-H6. a-e (Please refer to Table 2-5) ........................ 79
3.6.4 Results for H7-H1 l. a-e (Please refer to Table 2-6) ...................... 80
3.6.5 Results for H12-H13 and Tests of Moderation (H14-H15) (Please refer
to Table 2-7 and 2-8) ......................................................... 81
3.7 DISCUSSION ........................................................................... 83
3.7.1 Strategic Fit: Direct Effects .................................................. 84
3.7.2 Strategic Fit: Indirect Effects ................................................ 86
3.7.3 Project-Specific Sources of Advantage .................................... 88
3.7.4 Proficiency of NPD Process Implementation .............................. 89
3.7.5 Positional Advantage and Environmental Factors ........................ 89
3.7.6 Limitations and Future Research ............................................ 90

CHAPTER 4 GLOBAL STRATEGIC OUTSOURCING FOR RESOURCE
DEFICIENT, MARKET EFFICIENT TECHNOLOGICAL INNOVATION. . .92

4.1
4.2
4.3

INTRODUCTION ...................................................................... 92
TECHNOLOGY OUTSOURCING LITERATURE ............................... 95
THEORETICAL FOUNDATIONS AND MODEL OVERVIEW ............... 98

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4.3.1 Agency Theory ............................................................... 100

4.3.2 Resource Dependence Theory ............................................. 101
4.3.3 Transaction Cost Theory .................................................... 101
4.3.4 Overview of The Model ..................................................... 103
4.3.4.1 The Effects of Task Characteristics ................................. 104
4.3.4.1.1 Degree of Modularity ............................................ 105
4.3.4.1.2 Strategic Value of the Project .................................... 106
4.3.4.1.3 Buyer Experience ................................................ 107
4.3.4.1.4 Overall Production Cost of the Buyer ......................... 108
4.3.4.1.5 Geographical Dispersion ........................................ 108
4.3.4.1.6 Cultural Distance ................................................. 109
4.3.4.2 The Effects of Switching Costs ..................................... 110
4.3.4.2.] Component Purchase Concentration ........................... 111
4.3.4.2.2 Degree of Supplier Involvement ............................... 111
4.3.4.2.3 Size of the Contract ............................................... 112
4.3.4.2.4 Duration of the Contract ......................................... 113
4.3.4.2.5 Supplier Capabilities ............................................. 113
4.3.4.2.6 Asset Specificity of the Technology ........................... 114
4.3.4.3 The Effects of Environmental Uncertainty ........................ 115
4.3.4.3.1 Technological Heterogeneity ................................... 115
4.3.4.3.2 Technological Discontinuity .................................... 116
4.3.4.4 Moderating Effects of Modularity, Strategic Value, Buyer
Experience and Costs ................................................ 117
4.3.4.4.] Degree of Modularity ............................................ 117
4.3.4.4.2 Strategic Value of the Project ................................... 118
4.3.4.4.3 Buyer Experience ................................................ 118
4.3.4.4.4 Overall Production Cost of the Buyer ......................... 120
4.4 DATA AND METHODS ............................................................. 120
4.4.1 Analysis and Operationalization of Dependent Variable ............... 122
4.4.2 Operationalization of Independent Variables ............................ 124
4.5 RESULTS ............................................................................... 129
4.5.1 Event Window Determination: Market Efficiency and Unanticipated
Events Assumptions ......................................................... 129
4.5.2 Explanation of Abnormal Returns ........................................ 131
4.6 DISCUSSION ........................................................................... 137
CHAPTER 5 CONTRIBUTIONS AND FUTURE RESEARCH ................... 144
5. I INTRODUCTION .................................................................... 144
5.2 STUDY RESEARCH QUESTIONS AND CONTRIBUTIONS ............... 145
5.3 FURTHER RESEARCH RECOMMENDATIONS ............................. 161
APPENDICES ................................................................................ 163
A. META ANALYSIS (CHAPTER 2) SUPPORTING DOCUMENTS ........... 164
B. INTERNAL INNOVATION DEVELOPMENT (CHAPTER 3)
SUPPORTING DOCUMENTS ...................................................................... 166
B] SELECTED QUESTIONNAIRE ITEMS .......................................... 167

 

 

 

C. EXTERNAL INNOVATION DEVELOPMENT (CHAPTER 4)
SUPPORTING DOCUMENTS ................................................... 178

BIBLIOGRAPHY ............................................................................... 1 80

 

 

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Table 1-1. The Intended Contributions of The Studies ...................................... 5
Table 2-1. Key Constructs and Hypotheses ................................................................. 16
Table 2-2. Weighted Average Correlations: The Overall Sample ............................... 28
Table 2-3. Fit Statistics for Structural Equation Models ............................................. 29
Table 2—4. Overview of the Results of Hypothesis Testing ......................................... 30
Table 3-1. Construct Definitions .................................................................................. 51
Table 3-2. Measures and Loadings ............................................................................... 73
Table 3-3. Results (H1- H4. a-e) .................................................................................. 77
Table 3-4. Results for Tests Of Mediation (H1-H4.f—g) ............................................... 79
Table 3-5. Results (HS-H6. a-e) ................................................................................... 80
Table 3-6. Results (H7-H11. a-e) ................................................................................. 81
Table 3-7. Results (H12-H13) ...................................................................................... 82
Table 3-8. Results for Tests Of Moderation (H14-H7.a-b) .......................................... 82
Table 3-9. Variance Explained Results ...................................................... 83
Table 4-1. Average Abnormal Returns ...................................................................... 130
Table 4-2. Cumulative Abnormal Returns around Technology Outsourcing
Announcements .............................................................................................. 130
Table 4-3. Descriptive Statistics ................................................................................. 132
Table 4-4. Correlation Matrix .................................................................................... 133
Table 4-5. Model Results (Estimates and Significance Levels) ................................. 135
Table 4-5. Model Results (Estimates and Significance Levels) (cont’d.) .................. 136
Table A-1. Statistics Related to Selected Articles ...................................................... 165
Table 8-1. The Literature and The Hypotheses Developed ....................................... 168
Table 8-1. The Literature and The Hypotheses Developed (cont’d) ......................... 169

xii

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Table 8-1. The Literature and The Hypotheses Developed (cont’d) ......................... 170
Table B-2. Descriptive Statistics of Firms in the Sample .......................................... 171
Table B-3. Correlation Matrix .................................................................................... 172

Table B-4. Measures and Loadings: Common Method Variance (CMV)

Controlled ................................................................................................ 173
Table B-5. Results (H1- H4. a-e): CMV Controlled ................................................. 174
Table B-6. Results for Tests Of Mediation (H1-H4.f-g): CMV Controlled .............. 175
Table B-7. Results (HS-H6. a-e): CMV Controlled ................................................... 175
Table B-8. Results (H7-H11. a-e): CMV Controlled ................................................. 176
Table 89 Results (H12-H13): CMV Controlled ...................................................... 177
Table B-10. Results for Tests Of Moderation (H14-H7.a-b): CMV Controlled ........ 177
Table C-l. Summary of Theories and Proposed Model ............................................. 179

xiii

 

 

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Figure 2-1. Structural Equation Mode] on the Overall Sample .................................... 32
Figure 2-2. Multi-group Model Based on Level of Analysis ....................................... 35
Figure 2-3. Multi-group Model Based on Perspective ................................................ 36
Figure 2-4. Multi-group Model Based on Operationalization ...................................... 37

Figure 3-1. Hypothesized Model Showing Mediation by NPD Process Implementation
and Moderation by Market Factors ............................................................ 53
Figure 4-1. Hypothesized Model on Determinants of Global Technology Outsourcing

Returns ...................................................................................................... 1 04

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CHAPTER 1
DISSERTATION INTRODUCTION

New product innovations have the capacity to alter market dynamics and
create or increase environmental turbulence (Chandy and Tellis 2000; Gatignon and
Xuereb 1997; Han et al. 1998). However, they are also seen as the source of
competitive advantage and a response to economic recession, and particularly, a
survival mechanism against fierce competition from lower-cost producers in Asia
(Garcia and Calantone 2002; Hurley and Hult 1998). The findings of a PDMA study
of best practice firms show that nearly half of their sales are derived from new
product innovations (Di Benedetto 1999). Companies operating in a variety of
industries including Microsoft, Canon, Toyota, Samsung, Apple and Coca-cola are
heavily focused on new product innovations and spend immensely on research and
development rather than on low cost initiatives. Others such as Procter & Gamble and
General Electric have chosen to collaborate with academic scholars to develop their
own approaches to new product development (NPD).

Meanwhile, the failure rate is as high as nearly 50% of the products introduced
in the market. As an illustration, the pharmaceutical business requires massive
investments -- it costs more than $800 million to develop and market the average new
drug -- but the marginal cost of producing an extra pill approaches zero (21 July 2003;
The Wall Street Journal). Competitive pressures in most industries have created a
tradeoff between time to market and product quality and performance. Many firms
have responded to this pressure by sacrificing quality of execution for the sake of
speed and skipping NPD process activities, or resorting to external sources for
components and product technologies through outsourcing. While efforts in

shortening the NPD processes and focusing on rapidly executable (incrementally

 

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in the rush to accelerate schedules and the missing of opportunities for more
profitable ‘breakthrough’ innovations, outsourcing has created investors’ concerns
regarding the intellectual property rights. Despite this, the technology outsourcing
market is continuously expanding: HP, Cisco, Lucent Technologies, Ericsson and
Nokia have preferred outsourcing for flexibility, less cost and timing advantages.

Therefore, in the contemporary business environment characterized by
escalating environmental uncertainty, a firm’s survival depends on understanding how
to be innovative and commercialize innovative products (Kotabe and Swan 1995;
Rowley, Behrens et al. 2000). Due to the current environment of escalating
uncertainty, the importance given to innovation and innovative capabilities has
dramatically risen. This interest is further reflected in the academic literature: studies
on customer-relevant innovation and NPD processes are among the Marketing
Science Institute’s (MSI) most recent research priorities (2004-2006). Accordingly,
this dissertation centers on the development of ‘market efficient’ technological
innovation, that is, the allocation of resources and investing in newness in the right
places and at the right time for maximum value. Referring to Garcia and Calantone
(2002), technological innovation is defined as an iterative process concerning the
technological development of an invention -- initiated by the perception of a new
market and/or service opportunity for the invention (p. 112). This innovation process
comprises all activities from the market introduction of the invention though diffusion
and adoption, including all development, production, and marketing tasks necessary
for its commercial success (Garcia and Calantone 2002, p 112).

The primary objectives of this dissertation correspond to the three lenses

through which technological innovation development are examined: (1) the scholarly

 

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stream of research on innovation, (2) internal and (3) external product development.
The three studies in this research provide innovation researchers with a theory-driven
meta-analysis of the innovation literature; offer to practitioners an exhaustive list of
factors important both for the internal and external development of technological
innovation; and finally, advance the methodological stature with which determinants
of new product innovation success are examined by using partial least squares (PLS)
and event study analysis techniques.

The first lens scrutinizes ‘new product innovation’ by conducting a meta-
analysis on marketing, management and engineering studies that have examined
empirically the relationships of new product innovation with its antecedents and
outcomes. This study extends previous meta-analytic works by drawing upon 46
independent samples from 43 studies (published from 1970-2004), with a total sample
size of 4801. The goals are to derive generalizations from the marketing,
management and new product literatures, as well as to investigate sources of
inconsistencies in the findings. Selected substantive or methodological artifacts that
vary across studies are tested to determine whether they moderate model relationships
(i.e., the effect sizes of relationships of interest). The overall objective is to propose a
synthesized model that permits evaluation of key mediators and moderators. Using
structural equation modeling technique, a ‘theory-driven’ model that includes
customer orientation, competitor orientation, organizational structure, technological
turbulence, market turbulence, innovation, and new product performance (as well as
the moderating artifacts) is examined. Through this, the first study provides
researchers with objective empirical generalization from a theory-driven meta-

analysis, as well as investigates sources of inconsistencies in literature.

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and aims to elucidate the notion of ‘fit’ from firm and customer perspectives. Until
recently, the latter has been mostly neglected in the organizational innovation
literature. For innovation development success, synergistic fit with the firm's existing
marketing and technical skills and resources, as well as its fit with customers’ needs
and skills are mandatory. Moreover, this study develops measures to assess ‘fit’ by
indicating the degree of new resources and capabilities required for the product and
the extent of synergy the project entails with the existing structure (as opposed to
measures such as “having more than adequate resources” in prior literature) (Song and
Parry 1997). Referring to Day and Wensley’s (1988) source-position-performance
framework, the overall objective is to develop and test an exhaustive model on
determinants and outcomes of market efficient innovation development. In parallel to
the four major categories of new product success determinants identified by
Calantone and Montoya-Weiss (1994) in their meta-analysis, this research focuses on
how internal factors (i.e., strategic fit of the project, project related sources of
advantages and proficiency in NPD process) influence positional advantage and
performance of the project, determined by external environmental factors. Thus, this
analysis will also provide practitioners with a comprehensive list of internal,
environmental and supply-chain related factors important for successful development.

The third lens concerns external innovation: through outsourcing, firms
develop capabilities and flexibilities they lack or have lost due to technological
discontinuities and globalization. Despite its importance, little attention has been paid
to empirically validating risks and benefits of strategic outsourcing relationships in
the context of modular systems and global technology intensive markets. Buyers

generally strive to minimize the likelihood of opportunistic expropriation of tacit

 

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technological knowledge, eliminate the difficulties of monitoring partners due to
geographical or cultural distance, and avoid switching costs. Thus, grounded in the
marketing, management, and international business literatures, the primary objective
of this third study is to develop and test a model on the determinants of the market
value of external innovation development. These antecedents are classified according
to the task characteristics (i.e., degree of modularity, strategic importance of the
development project, size and duration of the contract, geographic dispersion, cultural
proximity and role of a legal advisory), switching costs (i.e., component purchase
concentration, degree of supplier involvement, asset specificity, project related know-
how of the buyer, and overall product cost of the buyer) and the degree of
environmental uncertainty (i.e., technological heterogeneity and discontinuity). This
research also provides implications for managers on the returns and risks of global
strategic outsourcing. The focus is on the unique consequences of loosely coupled
systems, i.e., modular systems employed in external innovation development. Event
study analysis of abnormal stock returns is used to examine the value of external
innovation development through technology outsourcing.

TABLE l-l. THE INTENDED CONTRIBUTIONS OF THE STUDIES

 

 

 

 

 

[For academics: For practitioners
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Overall, this dissertation constitutes a ‘holistic’ study on the development of
technological innovation providing implications from three lenses, i.e., the extant
research, internal development and external development. The three studies span
different models, three types of data (i.e., the literature, primary and secondary data)
and use multiple methodologies (i.e., meta-analysis, PLS and event study). Thus, this
research offers cumulative insights from the innovation literature, empirically
investigates models of the ‘values’ of both internal and external innovation
development, and uses diverse analysis techniques in a comprehensive investigation

of the determinants of innovation success.

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CHAPTER 2

INCONCLUSIVE INNOVATION “RETURNS”: A META-ANALYSIS OF
RESEARCH ON INNOVATION IN NEW PRODUCT DEVELOPIVIENT

2.1 INTRODUCTION

Innovation in new product development (NPD) is addressed in large amounts
of research in the marketing, management and new product literatures. Deriving
substantive conclusions from this body of research acquires urgency in the face of a
business environment characterized by escalating environmental uncertainty and the
increased rates of product innovation required to survive (Kotabe and Swan 1995;
Rowley et al. 2000). However researchers obtain different results due to the difficulty
of controlling research environments, the lack of acknowledged common definitions,
and the variety of methods and settings employed (Hedges and Olkin 1982).
Literature reviews attempt to achieve consensus in disparate findings, but ordinary
reviews can be dependent on subjective selections and evaluations of the researchers,
resulting in inconsistent interpretations across different reviewers (Wolf 1986).
Reviews also preclude the empirical analysis of the impact of study characteristics.
Meta-analysis however allows more objective and rigorous evaluation of a body of
literature by standardizing results across studies and controlling for various
substantive and methodological characteristics.

There are two key problems that meta-analysis can address. The first is that
disparate results may be due to methodological or substantive moderators (or
“artifacts” as defined by Hunter and Schmidt 1990). Meta-analysis allows
examination of the underlying grounds for inconsistencies and their impact on the
strengths of the relationships (i.e., effect sizes; Wolf 1986; Rosenthal 1991; Hunter
and Schmidt 1990). In particular, the level of analysis, the perspective, and the

measurement of the new product innovation construct are examined; the first two are

 

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substantive moderators while the latter is a methodological moderator. Regarding the
level of analysis, researchers have studied the phenomenon either at the
project/product level or the firm/ SBU/ program level (Johne and Snelson 1988), the
difference being that the latter’s domain includes multiple projects/products.
Regarding perspective, the firm (i.e., internal) versus the customer (i.e., external)
perspective can be contrasted, while some researchers propose that innovation be
investigated from a combined perspective. In addition, the operationalization of
innovation is often unidimensional and categorical, particularly in the earlier literature
(Danneels and Kleinschmidt 2001; Garcia and Calantone 2002), but Green, Gavin and
Aiman-Smith (1995) and others argue that innovation or innovativeness should be
seen as a continuum with multiple dimensions. Classifications of projects, such as
radical versus incremental or technological versus administrative, may be
oversimplifying the construct.

The second key problem that meta-analysis can address is amalgamating
causal relationships (Garcia and Calantone 2002; Kotabe and Swan 1995). The
innovation construct has been specified as an independent variable, a dependent
variable or a moderator (Kleinschmidt and Cooper 1991; Danneels and Kleinschmidt
2001). In the new product development (NPD) literature, the diverse labels,
categorizations and the differences in causal role have resulted in significant
inconsistency in actual empirical results (Garcia and Calantone 2002; Kotabe and
Swan 1995; Danneels and Kleinschmidt 2001). For instance, the effects of innovation
on success or performance outcomes as well as its relationship with organizational
structure are rigorously debated in the literature. Meta-analysis permits synthesis and
re-testing of these relationships with cumulative data to unveil overall tendencies

(V ismesvaran and Ones 1995). This study aims to investigate relationships that have

not necessarily been empirically examined in the same model or manuscript. Using
structural equation modeling, an overall model that includes customer orientation,
competitor orientation, organizational structure, technological turbulence, market
turbulence, new product innovation, and new product performance, as well as the
moderating variables described previously, are explored.

Other meta-analyses, primarily of new product success factors, have been
performed. The first meta-analysis was by Montoya-Weiss and Calantone (1994),
who examined 18 determinants of new product success using average effect sizes
(i.e., correlations), and summary counts. Later, Henard and Szymanski (2001)
collected 41 studies published through January 1999. While they examined
measurement of new product success and sample characteristics as moderators, they
did not investigate possible moderating effects of substantive issues. They also did not
explore any mediating relationships within an overall model of the antecedents of new
product success. This study extends these works by providing a meta—analysis of
marketing, management and new product studies (1989-2004) that have examined the
relationships of innovation with either antecedents (e.g., customer orientation) and/or
outcomes (new product performance). The overall goal is to propose a synthesized
model that permits evaluation of key mediators and moderators.

Brief descriptions of the potential moderators are first presented, followed by
the key hypotheses that provide the scaffold for the overall model to be explored.
Next the methodology, included the literature base examined and analyses performed
are described. Finally, the results and their implications for future research are

discussed.

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2.2 SOURCES OF INCONSISTENCIES: POTENTIAL MODERATORS

Three potential moderators are proposed to affect the degree to which
“innovation” is related to its antecedents and outcomes in this study: level of analysis
(projch product level versus firm/ SBU/ program level); firm versus customer
perspective; and operationalization. These moderators were identified from an initial
review of 113 articles published in 12 peer reviewed marketing, new product and
management journals from 1970 to 2004 (a full description of the journals and the
articles selected is in ‘Methodology’ below).
2.2.1 Level of Analysis: Potential Substantive Moderator

Studies at the product/project level conceptualized new product innovation as
an iterative process initiated by the perception of a new market and/or service
opportunity, which leads to development, production, and marketing tasks striving for
the commercial success (Garcia and Calantone 2002). Researchers employing this
level of analysis examine activities needed to design, produce and deliver a new
product as well as all product/project characteristics that determine success. The
independent variables typically include: product characteristics, that is, product
superiority, complexity, advantage, newness, degree of customization; and project
characteristics, such as the way firms organize NPD projects, the formality of the
NPD process, the extent of actual use of the innovation, NPD management, and
application of NPD tools (Spivey et al. 1997; Srinivasan et al. 2002; Song and
Montoya-Weiss 1998; Sethi et al. 2001; Veryzer, Jr. 1998; Kessler and Chakrabarti

1999; Bonner et al. 2002).

On the other hand, at the firm/ SBU or program level, researchers have
analyzed innovation on a broader scale as ‘a means of changing an organization,

whether as a response to changes in its internal or external environment or as a pre-

10

 

 

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emptive action taken to influence an environment’ (Damanpour 1991, p. 556). This
encompasses both actions taken by the firm, such as the number of and changes in
new products or services introduced and also the attitude of the firm as indicated by
the emphasis on R&D, technological leadership and new product innovation
(Calantone et al. 1994; Hultink et al. 1997; Ozsomer et a1. 1997; Brown and
Eisenhardt 1995). On the whole, the studies at the firm/SBU/program level have
investigated the effects of firm strategies, orientations, resources, capabilities, size and
innovation environment on new product innovation or on firm performance, often
moderated by type of innovation and/or environment.
2.2.2 Perspective: Potential Substantive Moderator

The perspective (firm versus customer) is the other dimension that has led to
disparate definitions, operationalizations and empirical results. First, the customer
perspective was often conducted by studying usage patterns (Gatignon and Xuereb
1997; Kotabe and Swan 1995; Danneels and Kleinschmidt 2001; Cooper 2000). It
sought to differentiate the types of innovation by how drastically the product was
changed: either evolutionary versus revolutionary innovation (Lynn and Akgun 2001)
or radical versus incremental (Etllie et al. 1984). Overall, a customer perspective
focuses on the degree to which new products are perceived as totally different and
requiring major changes in customers’ thinking and behavior; or on dramatic leaps
from current customer consumption requirements and experiences, and thus the
degree of leaming effort required by customers (Atuahene-Gima 1996; Sengupta
1998; Michael et al. 2003).

Within the firm perspective, major new product innovations are seen as
requiring a great variety of resources and a departure from existing technology and

practices; and hence are inherently more uncertain than incremental advances

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(McDermott and Colarelli O’Connor 2002). Studies that have employed this
perspective have delineated the extent to which the product technology involved is
different from prior technologies, and/or the skills and processes necessary for
development and commercialization are different (Chandy and Tellis 2000;
McDermott and Colarelli O'Connor 2002; Veryzer, Jr. 1998; Micheal et al. 2003;
Ottum and Moore 1997; Sethi 2000; Kessler and Chakrabarti 1999). Based on this
logic, most of the studies within this perspective have scrutinized the impact of the
NPD processes, strategic orientations and organizational capabilities on product
innovation (Kessler and Chakrabarti 1999; Sethi et al. 2001; Gatignon and Xuereb
1997; Lukas and Ferrell 2000; Atuahene-Gima and Ko 2001) and/or on success
(either firm or project) (Song and Montoya-Weiss 1998; Olson et al. 1995; Bonner et
al. 2002; De Brentani 2001; Atuahene-Gima 1995).
2.2.3 Operationalization of Innovation: Potential Methodological Moderator
No consensus exists regarding the operationalization of the innovation
construct. An area of disagreement is the use of categorical versus continuous
variables (Garcia and Calantone 2002). In the sample of articles in this analysis,
studies using categorical measures generally split samples into the resultant categories
(such as radical vs. incremental, discontinuous vs. continuous, architectural vs.
modular, administrative vs. technical) to scrutinize the moderating effect of
innovation (e.g., Song and Montoya-Weiss 1998). Garcia and Calantone (2002)
highlight that this method limits external validity, fiirther comparison to another
study, and integration for a combined analysis. Meanwhile, continuous measures
generally used 1-5 or 1-7 Likert scale items, operationalizing new product innovation
as a perceptual variable (Olshavsky and Spreng 1996; Sahay and Riley 2003; Waarts

et al. 2002). Among the studies that have employed continuous measures, some used

12

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single items such as frequency count measures of new product introductions (e.g.,
Markham and Griffin 1998), while others utilized multidimensional scales assessing
the degree of creativity, newness (to the firm and/or customer), fit/synergy (to the
firm and/or customer), uncertainty and advantage the new product entails (e. g.
Moorman, Miner 1997; Atuahene-Gima and Ko 2001).

Green, Gavin and Aiman-Smith (1995) argue that new product innovation
should be seen as a continuum with multiple dimensions and that classification as
radical or incremental (as opposite ends of a continuum) is an oversimplification.
Montoya-Weiss and Calantone (1994) assert that little attention has been paid to
construct validity and that there is no consistency in operationalization in terms of
categorical versus continuous measurement (Garcia and Calantone 2002). This lack of
consistency may have led to contradictory results and confusing implications.
Accordingly, this study examines whether the linkages of innovation to antecedent
and outcome variables vary based on how innovation was operationalized.

2.3 HYPOTHESES

Having defined the potential moderators, selected antecedents and outcomes
that are associated with “new product innovation” in the literature are discussed in
this section. Based on the conducted review, new product innovation is defined in this
study as a process comprising the technological development and market
commercialization of an invention -- initiated by the perception of a new market
and/or service opportunity. This process covers all development, production,
marketing tasks and the adjustments made in the organization for the market
introduction of the invention to the end users through to its diffusion and adoption
(Garcia and Calantone 2002). Accordingly, studies that have conceptualized

innovation as familiarity (i.e., the product’s familiarity/ synergy with the firni's prior

13

 

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customers and technologies) and as synergy (fit with the firrn’s marketing and
technical resources and skills) are included in this analysis (Danneels and
Kleinschmidt 2001).

A model that is rooted in the resource-based theory (RBV) and contingency
framework of industrial organization (10) paradigm is built and empirically tested.
These theories, which take into account the influential factors on the conduct of firms’
competitive actions and performance, are generally viewed as complementary
(Mahoney and Pandian 1993; Barlett and Goshal 1991; Barney 2001; Zen and
Cavusgil 2002). While 10 focuses on industrial and product market factors as the
determinants of the firm's strategy, which in turn impact firms’ economic
performance, RBV views the distinctive ways by which firms manage their resources
and capabilities as the enduring sources of competitive advantage (Porter 1981;
Peteraf 1993; Day 1994; Mahoney and Pandian 1993; Madhok 2002).

The basic tenet of RBV is that firms with superior - namely, rare, non-
imitable and non-substitutable- resources gain sustainable competitive advantages in
the marketplace (Peteraf 1993, p 180). In other words, internal organizational
resources and capabilities are strong determinants of the firms’ strategy and
performance. Correspondingly, the innovation researchers that draw upon RBV have
advocated the notion that a company creating a superior, unique and novel product
should enjoy competitive advantage in the market and, hence commercial success
(Friar 1995; Gatignon and Xuereb 1997). The contingency (i.e., strategy-structure-
performance) framework of 10 comprises that external factors determine the firms’
competitive actions as well as the strategies potentially available to the firm (Porter
1981; Miller 1987; Miles et al. 1978; McKee et al. 1989). Strategy is the firms’

response to the environmental dynamics and focuses on adapting to their industry.

14

Thus, the fit of the firrn’s strategy and structure to the external environment engenders
superior performance (Covin and Slevin 1989; Miller and Friesen 1982). NPD studies
that refer to the contingency framework focus on the influence of strategies,
organizational structures and processes on the development and marketing of products
(Li and Atuahene-Gima 2001; Gatignon and Xuereb 1997; Atuahene-Gima and Ko
2001). Accordingly, in this study, the relationships of new product innovation with
market and technological turbulence, with customer and competitor orientation, with
organizational structure, and with new product performance are examined. The
baseline hypotheses, as well as hypotheses concerning whether a potential moderator
actually moderators a particular baseline hypothesis, are summarized in Table 2-1.
The baseline hypotheses are labeled H1 through H6; the hypotheses concerning
moderation are labeled Hla, b, c through H6a, b, 0.

2.3.1 Antecedent to Innovation: Environmental Turbulence (Market and
Technological)

Turbulent environments imply dynamic and volatile conditions as a result of
uncertain and unpredictable changes in demand and growth rates, continuously
emerging or eroding competitive advantages, and low barriers to entry/exit, all of
which continuously change the competitive structure of the industry (Miller and
Friesen 1978; Bourgeois and Eisenhardt 1988; Covin and Slevin 1989; Atuahene-
Gima 1995; Ozsomer et al. 1997). Such conditions may further lead to difficulties for
the firm in obtaining accurate and timely information, render obsolete a firm’s formal
assessment system and/or signal the opening of product opportunities (Calantone et al.
1997). Two main sources for turbulence are identified in the literature: first,
technological innovations which accelerate the rate of change in the marketplace and
cause product obsolescence to occur more quickly; and second, continuous changes in

customers' preferences/demands, in price/cost structures, and in the dynamics of

15

 

 

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competition (Cooper and Kleinschmidt 1987; Calantone et al. 1994; Li and Calantone

1998; Moorman and Miner 1997; Han et al. 1998; Mullins and Sutherland 1998;

Souder et al. 1998).

TABLE 2-1. KEY CONSTRUCTS AND HYPOTHESES

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

PREDICTOR CRITERION PROPOSED HYPOTHESES
MODERATORS
Market New Product H1 Positive
Turbulence Innovation Level of Analysis Hla No
Perspective Hlb Yes
Operationalization ch Yes
Technological New Product H2 Positive
Turbulence Innovation Level of Analysis H2a No
Perspective H2b Yes
Operationalization H2c Yes
Customer New Product H3 Positive
Orientation Innovation Level of Analysis H3a No
Perspective H3b Yes
Operationalization H3c Yes
Competitor New Product H4 Positive
Orientation Innovation Level of Analysis H4a No
Perspective H4b Yes
Operationalization H4c Yes
manhational New Product H3 Negative
Structure Innovation Level of Analysis H3a Yes
(mechanical) Perspective H3b Yes
Operationalization H3c Yes
New Product New Product H6 Positive
Innovation Performance Level of Analysis H6a Yes
Perspective H6b Yes
Operationalization H6c Yes

 

As per RBV and contingency theories suggest, firms attempt to pursue

emerging opportunities and thus place greater emphasis on new product innovation in

order to establish competitive advantage in rapidly changing environments (Calantone

et al. 2003; Atuahene-Gima and K0 2001). Such environmental settings also bring

about new venture opportunities as a result of emerging new, unserved customer

needs and benefits. However, in an environment with sudden and dramatic changes, a

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delay in action may inhibit success (Ozsomer et al. 1997; Bourgeois and Eisenhardt
1988; Calantone et al. 2003). Thus environmental turbulence will lead to both
initiation of innovative projects and an innovative posture across the firm.

In this analysis, it is posited that level of analysis will not moderate the
relationship between environmental turbulence dimensions and new product
innovation. However, these linkages may vary due to perspective. In studies that use a
firm perspective, interest often lies in technology push and thus the effect of
technological turbulence on new product innovation may be stronger; whereas in
studies that employed a customer perspective, the focus is often on market turbulence
(i.e. fluctuations in customer demand and unpredictable competitor actions) and thus
the path from market turbulence may be greater. Operationalization of innovation may
also moderate these relationships. Thus:

H1: The relationship between market turbulence and new product innovation will be
positive. This relationship (a) should hold irrespective of (i. e. not moderated by) the
level of analysis, however, may be moderated by (b) the perspective and (c) the
operationalization of innovation.

H2: The relationship between technological turbulence and new product innovation
will be positive. This relationship (a) should hold irrespective of (i.e. not moderated
by) the level of analysis, however, may be moderated by (b) the perspective and (c)
the operationalization of innovation.

2.3.2 Antecedent to Innovation: Market Orientation (Customer and
Competitor)

Radical product innovations incorporate substantially different core
technologies and provide substantially higher customer benefits relative to previous or
competing products. The creation of new product innovations that can easily be

differentiated by customers encourages firms to be market oriented. Thus the

17

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examination of the strategic orientations of the firm (both customer and competitor)
plays a crucial role to enhanced understanding of the product innovation process and
the factors leading to higher performance (Atuahene-Gima and K0 2001; Gatignon
and Xuereb 1997).

Researchers pursuing a cultural view of “orientation” have defined the
construct as the pattern of shared values and beliefs that help individuals understand
organizational fianctioning and provide norms for behavior (Deshpande and Webster
1989; Desphande et al. 1993). “Orientation” has been viewed as a capability enabling
a business to anticipate changing conditions and to respond to market requirements
(Lukas and Ferrell 2000) and facilitating innovation via understanding the articulated
needs of customers and the actions of competitors (Han et al. 1998). According to
Narver and Slater (1990), market oriented firms most effectively and efficiently create
superior value for customers and achieve competitive advantage; the necessary
behaviors associate with customer orientation (i.e., the understanding of the current
and latent needs of target customers to be able to create superior value for them) and
competitor orientation (i.e., a constant monitoring of the short-term strengths and
weaknesses and long—term capabilities and strategies of both current and potential
competitors).

Customer and competitor orientations have been linked to new product
innovation in several studies. Lukas and Ferrell’s (2000) findings indicated that
customer and competitor orientations jointly increase the introduction of new-to-the-
world products, while competitor orientation alone increases the introduction of me—
too products. Gatignon and Xuereb (1997) found that higher customer and competitor
orientations allowed firms to develop more radical, less costly and thus higher

performing innovations. However, Han, Kim and Srivastava’s (1998) results show a

18

 

 

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positive relationship between customer orientation and innovation, but not between
competitive orientation and innovation. This may be because the former two studies
incorporated the perspective of the customer, whereas the latter adopts a firm (that is,
an internal) perspective. These studies also diverge in their operationalizations of the
innovation construct.

On the other hand, customer and competitor orientations should each have a
positive impact on innovation both at the project and program level. To be ahead of
the competition and meet customers’ expectations, firms will aspire to develop unique
and highly differentiated products and also achieve lower cost and more effective
information sharing through process innovations. While moderation is not expected
by level of analysis, perspective may moderate the relationship between these
orientation constructs and innovation: this positive relationship may be weaker in
studies with a firm perspective compared to those with a customer perspective. A
technology push or an internal need may also trigger the development of an
innovative product or the implementation of novel processes. Consequently, customer
and competitor orientation may explain less of the variation in the innovation
construct conceptualized with an internal firm perspective. Finally, different
operationalizations of innovation may also lead to discrepancies in the results.
Specifically, categorical and/or unidimensional measurements may result in the loss
of variance in the innovation construct. Thus:

H3: Customer orientation will have a positive influence on new product innovation.
This relationship (a) should hold irrespective of (i. e. not moderated by) the level of
analysis, however, may be moderated by (b) the perspective and (c) the

operationalization of the innovation construct.

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This relationship (a) should hold irrespective of (i. e. not moderated by) the level of
analysis, however, may be moderated by (b) the perspective and (c) the
operationalization of the innovation construct.
2.3.3 Antecedent to Innovation: Organizational Structure

Miller (1987) defined organizational structure as the ‘enduring allocation of
work roles and administrative mechanisms that allow organizations to conduct,
coordinate, and control their work activities and resource flows’ (p 8). A critical
problem for organizations is to create and work within structures that effectively
coordinate the NPD process, facilitate the sharing of information and other resources
across functional areas, and provide mechanisms for decision-making and conflict
resolution (Crawford 1984; Achrol 1991; Ottum and Moore 1997; Song et al. 1997;
Jassawalla and Sashittal 1998; Song and Montoya-Weiss 1998; Song et al. 1998;
Souder et al. 1998; Malt: and Kohli 1996; Adams et al. 1998; Troy et al. 2001). RBV
and industrial organization perspectives corroborate that strategy generally determines
structure (Miles, Snow et al. 1978; Miller and Friesen 1982). Structure is considered
the result of a firm’s strategic focus on the external environment and market dynamics
(Calantone et al. 1994; Day 1994; Matsuno et al. 2002; Calantone et al. 2003).

Structure has been classified as organic versus mechanistic: tasks with high
uncertainty supposedly require organic structures, whereas tasks with low uncertainty
require mechanistic approaches in order to be successful. Based on this notion,
uncertain tasks such as complex innovation projects cannot be successfully pursued in
highly centralized, formal, and bureaucratic structures (i.e., mechanistic). Such
structures overburden CEOs with time pressure or lack of assistance to initiate

complex innovation, and inhibit creativity and input from diverse sources (Miller et

20

 

al. I988“. I

structures _

 

innovation ‘
and Hause:

Surl
regarding '1
innovation;
contended

reducing Ct

 

technology
individual:
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technologi
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argued. S
For lnSla
formalize
reSPOItsit
formalit)

Inn()Valit‘

“New“
product
SOIUIlQn

formal I

al. 1988; Covin and Slevin 1989; Hage and Dewar 1973). But flexible organic
structures enhance receptivity to new technology and facilitate new product
innovation (Olson et al. 1995; Utterback and Abernathy 1975; Knight 1987; Griffin
and Hauser 1996; Matsuno et al. 2002; Sethi et al. 2001).

Surprisingly, in the innovation literature, findings from empirical studies
regarding the impact of different dimensions of organizational structure on successful
innovations have been mixed. Miller and Friesen (1982) and Meyers et al. (1999)
contended that centralization may facilitate (rather than hinder) innovation by
reducing conflict and ambiguity, leading to a more uniform response to the incoming
technology. In contrast, Dewar and Dutton (1986) claim that decentralization provides
individuals greater autonomy to decide and act, leading to more exchange of ideas,
and thus familiarizing employees and decreasing the uncertainty associated with
technological change. Interestingly, all results of these studies show that centralization
was not a significant predictor of new product innovation, regardless what the authors
argued. Similarly, several studies on the impact of formalization hold opposing views.
For instance, Bonner et a1. (2002) and Ayers et al. (1997) have asserted that
formalized procedures can regulate the tasks people perform and the role
responsibilities in the NPD process. Tatikonda’s (1999) results indicated that
formality of the project execution is positively (not negatively) related to new product
innovation.

In addition to these contradicting results, different types of organizational
structures expand the debate on structural solutions to effectively coordinate new
product development. It may be possible that there is no one specific structural
solution: mechanistic versus organic (e.g., centralization versus decentralization,

formal versus informal) may not constitute substitutes, but rather complements. This

21

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notion is consistent with the organizational control literature, which supports the use
of a portfolio of controls (Eisenhardt 1985; Jaworski 1988; Jaworski et al. 1993).
Functional organizations were the acknowledged organizational structures in the
earlier literature (Olson et al. 1995; Achrol 1991; Ayers et a1. 1997; Griffin and
Hauser 1996). However, they were later thought to discourage cooperation across
functions, rather than to resolve conflicts and create harmony. Other organizational
structures, such as matrix organizations and project teams, have been advocated to
increase integration across functions and overcome the weaknesses of traditional
structures (Achrol 1991; Workman Jr. 1993). These new structures are designed to:
reduce differences between functional responsibilities, allowing processes to be
executed and resolving conflicts; improve decision-making; encourage the cross-
functional development of innovative products; and enhance the likelihood of product
development success. In the NPD literature, other alternatives have been proposed for
the management of product development; for example, a formal management process
(e.g., stage-gate processes) in which the required tasks, their sequence and the
employees responsible for their completion are specified explicitly over a time
sequence (Griffin and Hauser 1996).

These conflicting results in the new product innovation literature may be
primarily due to the level of analysis employed. Even though, on the whole
mechanistic approaches may hinder new product innovation, they may be beneficial at
the program level in coordinating multiple product development initiatives, making
priorities and goals explicit to project teams. By building consensus, such approaches
may allow more effective innovative strategies overall and foster an innovative
posture (i.e., a culture that nurtures integrated innovation). However, formalized and

centralized structures may freeze the status quo and inhibit the diffusion and

22

 

communic.

oreanic st.

 

networks I
(Montoya-'
and empox'
the project

Mo

employed

 

 

focus may
other studi
structure It
construct '
PYOduct in
H5-' Med
innat'utz'u
PWSpectz
2.3.4 (j
T
the liter;
differen
I Gang“
Ali et a
“meme
Huh It

achley

communication of ideas among the project team members. Thus at the project level,
organic structures characterized by cross-functional teams and informal social
networks may generate creativity by facilitating information and resource flows
(Montoya-Weiss and Calantone 1994; Olson et al. 1995). Decentralization, autonomy
and empowerment may lead to conflict resolution and effective decision-making at
the project level.

Moreover, divergent rationales and results may be due to the perspective
employed in conceptualizing innovation. Studies employing a purely internal, firm
focus may have endorsed centralization as beneficial for conflict resolution, whereas
other studies with an external, customer perspective may have defended an organic
structure for more creative customer solutions. Finally, the operationalization of the
construct may have an impact on the strength of the organizational structure- new
product innovation linkage. Thus:

H5: Mechanical organizational structure will be negatively related to new product
innovation. This relationship will be moderated by: (a) the level of analysis, (b) the
perspective and (c) the operationalization of the innovation construct.

2.3.4 Outcome of New Product Innovation: New Product Performance

The effect of new product innovation on performance outcomes is debated in
the literature. For some researchers, innovative products create more opportunities for
differentiation, hence have relative advantage over existing products or competitors
(Gatignon and Xuereb 1997; Kleinschmidt and Cooper 1991; Song and Parry 1996;
Ali et al. 1995). However, new product innovation also constitutes a response to the
uncertainties in a firm’s entrepreneurial environment (Han et al. 1998; Hurley and
Hult 1998). Therefore, a focus on innovation might provide a firm with the means for

achieving higher returns (Firth and Narayanan 1996; Griffin and Page 1996).

23

Dr

unique kt

    
  

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etal. 1991‘
ditferentia
and Rube.
Sengupta
familiar.
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IT
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Pers‘p

2.4

of]

Drawing upon RBV, some studies have maintained that innovating firms with
unique knowledge and resultant capabilities, as well as superior and novel products,
enjoy high performance (Calantone and Di Benedetto 1988; Ozsomer et al. 1997; Han
et al. 1998; Friar 1995). More innovative products provide value to customers and are
differentiated from competitors; consequently, competitive advantage is greater (Ettlie
and Rubenstein 1987; Kleinschmidt and Cooper 1991; Gatignon and Xuereb 1997;
Sengupta 1998). Other studies have concluded that less innovative products are more
familiar, less uncertain, may have higher synergies and hence greater success.
However, Tatikonda (1999) and Calantone et al. (1994) demonstrate no significant
relationship between the degree of innovation and performance.

These discrepancies may be due to the level of analysis employed. Extraneous
factors not considered at the project level (such as product characteristics) may exert
an effect on the performance of a particular project. On the other hand, in studies at
the program level, an innovative posture or strategy on a broader scale may entail
competitive advantage and success. Moreover, the relationship between new product
innovation and performance may also vary due to the perspective (firm versus
customer). Innovation constructs developed with an internal focus may not explain
much of the variation in the performance outcomes. Finally, operationalization of the
construct may affect the strength of this linkage. Hence:
H6: The relationship between new product innovation and new product performance
will be positive. This relationship will be moderated by (a) level of analysis, (b)
perspective and (c) operationalization of innovation the construct.
2.4 METHOD

Meta-analysis is an objective and efficient way to summarize and make sense

of large literatures (Wolf 1986; Rosenthal 1991). It integrates results, revealing

24

cumulative knowledge and general principles (Hunter and Schmidt 1990). It can also
highlights gaps, provide new research directions by examining possible moderating
and/or mediating variables, and help pinpoint the underlying reasons for contradictory
conclusions. The key concerns are relationship robustness and the specification of
conditions that limit generalizability: both relationship trends and the potential
substantive or methodological moderators of effect sizes1 (such as disparate
definitions, unit of analysis, methodological designs or samples) can be recognized.
2.4.1 Sample of Articles

New product innovation has been described as radical, incremental, really-
new, imitative, discontinuous, architectural, modular, evolutionary, administrative and
technical, innovativeness, advantage and newness (see e. g., Garcia and Calantone
2002), although the distinctions among some of these terms are sometimes vague.
Using J STOR and ProQuest (ABI Inform) databases, articles containing these
keywords and published in scholarly and peer reviewed marketing, new product and
management journals from 1989 to 2004 were searched. The journals were: Journal
of Marketing, Journal of Marketing Research, Journal of Product Innovation
Management, Organization Science, Management Science, Academy of Management
Journal, Strategic Management Journal, Administrative Science Quarterly, Journal of
Business Research, European Journal of Marketing, Industrial Marketing
Management, IEEE Transactions on Engineering Management and Journal of
Business and Industrial Marketing. Literature reviews were also gathered and
reviewed for additional references. Thus, some articles, a few prior to the 15-year

range (back until 19705), were found because they were referenced in another article.

 

I Effect size is defined as the degree to which the phenomenon is present in the population or the
degree to which the null hypothesis is false. It indicates the magnitude of the relationship or treatment,
in the form of either the degree of associations between two variables as assessed by Pearson Product
Moment Correlation coefficient, or, the difference of means between two groups as measured by
Student’s t-test (Wolf 1986; Rosenthal 1991).

25

To
lnnOVaHVC
gathered.
whether ll:
Some off.
or examin-
correlatiorf
Weiss 19w
means as
correlation

samples in

 

 

1983‘. Tali
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are Stanec
Similar to
2.4.2 V2

1},
rather lbs
“990). T
data Sets

independ(

To create the data set of articles, articles that examined the adoption of
innovative products by consumers were first excluded. As a result, of 143 articles
gathered, 113 were eligible for inclusion. Second, the 113 were evaluated as to
whether they operationalized innovation and empirically tested relevant relationships.
Some oft-cited articles were excluded either because they did not report correlations
or examine relevant hypotheses: 89 contained empirical analyses, 48 of these reported
correlations and 43 had hypotheses of interest. Two articles (Song and Montoya-
Weiss 1998; Yoon and Lilien 1985) conducted t-tests and reported the difference of
means as effect sizes; these studies were added by converting effect sizes into
correlations (as in Cooper 1998; Hunter and Schmidt 1990). Several studies split
samples into groups and reported correlations for each group (e.g., Miller and F riesen
1982; Tatikonda 1999), while one employed two different sampling frames to test the
same model (i.e., Yoon and Lilien 1985). These studies were treated as independent.
This procedure resulted in 46 data entries with total sample size of 4801; these articles
are starred in the Reference list. The characteristics of this subset of articles were
similar to those of the entire sample (see Table A-l).

2.4.2 Variable Coding and Data Analysis

This present meta-analysis was conducted using correlations at the test level
rather than study level following the procedure proposed by Hunter and Schmidt
(1990). The technique and statistic2 for detecting outlier coefficients in meta-analytic
data sets proposed by Huffcut and Arthur (1995)3 was employed. For the 46

independent samples, reported correlations were corrected for attenuation using

 

2 Sample adjusted meta-analytic deviancy (SAMD) statistic, which is derived by dividing the
difference between the value of each individual correlation coefficient and the sample weighted mean
coefficient computed without the study correlation by the standard error of the difference.

3 This procedure led to eliminate only one correlation for the relationship between organizational
structure and new product innovation.

26

Hunter an;
of reliabil:

following

Tr.
average c

relationshi

7' 2 SW r

To.

methOdOlU

 

Hunter and Schmidt’s (1990) artifactual distribution approach“, in which mean values
of reliabilities are employed. Each reported correlation (er) was corrected using the

following formula:

where: rc is the corrected correlation;
my and fry are the reported reliabilities
for each scale
Then, each correlation was then corrected for sampling error and weighted
average correlations were calculated based on sample sizes for each of the

relationships using (Table 2-2):

I“ = 2W3“, / 2W,- = 2[Nil}]/ ZN,- where: r is weighted average correlation;

N is the sample size reported in the
corresponding study

To study the moderating impact of the three proposed substantive and
methodological artifacts, the remaining variance after correction for sampling error

was first gauged by subtracting variance due to sampling error

(S 2e = (1— FY / W) from total variance in individual correlations

— 2
(Sr2 = Z[Ni (r; —r) ]/ 2N1) Hunter and Schmidt (1990) suggest the analysis of

the effect of research artifacts if this remaining variance is nontrivial (p. 110). The
remaining variance in the sample correlations less the variance due to sampling error
was large for all hypothesized relationships (at least 60%; except for competitor
orientation-new product innovation). Thus, the analysis of moderating effect of the
proposed substantive (i.e., level of analysis and perspective) and methodological (i.e.,

measurement of new product innovation) artifacts was supported. The data was then

 

4 The underlying reason is that Cronbach’s alpha values were not available in every study and were
reported sporadically.

27

 

panitionet

of anal) s.

, l
perSpectn

innovation
multidime-

subgroup.

 

 

 

 

 

 

partitioned into different groups based on the three moderators proposed: (1) the level
of analysis, i.e., organization/ SBU level/ program versus project level; (2)
perspective, comprising firm versus customer; and (3) operationalization of
categorical-unidimensional versus continuous-

innovation, comprising

multidimensional. Sample size weighted average correlations were calculated for each

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

subgroup.
TABLE 2-2. WEIGHTED AVERAGE CORRELATION S:
THE OVERALL SAMPLE
1 2 3 4 5 6 7
1 NP 1.00
Performance
2 Innovation 0.39 1.00
(24)
3 Market 0.01 0.08 1.00
Turbulence (10) (14)
Technological -0.01 0.14 0.30 1.00
Turbulence (6) (6) (5)
Organizational -0.36 -0.20 -0.11 -0.01 1.00
Structure (3) (l 2) (2) (1)
Customer 0.30 0.26 0.03 0.10 -0.42 1.00
Orientation ( 14) (1 8) (7) (5) (7)
Competitor 0.30 0.24 0.17 0.10 -0.79 0.74 1.00
Orientation (4) (6) (3) (2) 7(3) (4)
STANDARD 1.84 1.53 1.05 1.69 1.21 1.48 1.94
DEV

 

 

Note: The numbers in parentheses indicate the number of correlations obtained for
calculation.

To test the hypotheses, the overall sample (N = 4801) was first analyzed in a
structural equation model using the mean sample size (n = 200) on which the
individual correlations were based (Henard and Szymanski 2001). Subsequently, this
analysis was repeated for each moderator group by splitting the overall mean sample
size in proportion to each group sample size, i.e., ‘level of analysis’ (n= 200 *

3361/4801= 140 for program level and n = 200 * 1440/4801 = 60 for project),

‘perspective’ (n= 200 * 3843/4801 = 160 for firm and n = 200 * 958/4801 = 40 for

28

  
 

customer).

employed
groups (Be
are in Tab
each of t‘.
h}pothesi

TAB]

‘

'3'

 

 

customer), and ‘operationalization’ (n= 200 * 488/4801 = 20 for categorical-
unidimensional and n=200*4313/4801= 180 for continuous-mu]tidimensional).
2.5 RESULTS

Using the correlations as inputs, structural equation modeling technique was
employed for the overall sample and then a 2-group path analysis for each of the split
groups (Bollen 1989; Hoyle 1995; Duncan et al. 1999). The fit statistics for all models
are in Table 2-3; the results for the overall sample are in Figure 2-1 and the results for
each of three split groups are in Figures 2-2, 2-3, 2-4. The conclusions from the
hypothesis tests are summarized in Table 2-4.

TABLE 2-3. FIT STATISTICS FOR STRUCTURAL EQUATION MODELS

 

 

 

 

 

 

STRUCTURAL MULTI-GROUP MULTI-GROUP MU LTI-GROUP
MODEL ON MODEL BASED MODEL BASED MODEL BASED
OVERALL ON LEVEL OF ON ON OPERATIO-
SAMPLE ANALYSIS PERSPECTIVE NALIZATION
(n=200) [n(program) = 140; [n(firm) = 160; [n(categorical-
n(project) = 60] N(customer) = 40) singular) = 20;
n(continuous-
multiple) = 180)
2 19.955 (df= 3; 43.701 (df= 17; 164.306 (df= 12; 68.704 (df= 16;
Z p=.0002) p=.0004) p=.000) p=.000
NFI .958 .911 .820 .886
CF] .963 .940 .825 .906
RMSEA .169 .126 .181 .182

 

 

 

 

 

2.5.1 Baseline Model: Context for Hypothesis Testing

 

All hypotheses were tested within the context of the baseline model shown in
Figure 2-1. This model specified the effects of environmental turbulence on
orientations, organizational structure and new product innovation, all of which in turn
influenced new product performance. Thus: (1) the environmental turbulence

constructs were modeled as completely exogenous (environment comes first in the

traditional hierarchy); (2) the strategic orientation constructs are antecedents to

29

organizational structure, new product innovation and performance (strategy precedes
structure) and hence strategic orientation was modeled as a partial mediator of the
relationships of environmental turbulence with organizational structure and new
product innovation; and (3) organizational structure and new product innovation were
modeled as partial mediators of the relationships between customer and competitor
orientations and new product performance. Finally, the effects of environmental
turbulence variables on new product performance were specified as completely
mediated by strategy, structure and innovation.

TABLE 24. OVERVIEW OF THE RESULTS OF HYPOTHESIS TESTING

RELATIONSHIP PROPOSED HYPOTHESES and
MODERATORS FINDINGS

Market Turbulence
to New Product Innovation

T Turbulence
to New Product Innovation

Customer Orientation
to New Product Innovation

Orientation
to New Product Innovation

Structure
to New Product Innovation

New Product Innovation
to New Product
Performance

Level of

Level of

Level of

Level of

Level of

Level of

30

H1: + not
Hla: no not
Hlb:

ch: not
H2: +

H2a: no not
H2b:

H2c: not
H3: + not
H3a: no not
H3b: not
H3c:

H4: + not
H4a: no not
H4b:

H4c: S
H5: -

H5a:

H5b:

H50:

H6: +

H6a:

H6b:

H6c:

 

’1‘}.
fit index i
0.169. re~

(13005 t

 

 

 

either CU>
positivel}

turbulent;

(n= -.054

 

Was not si
other han
meaning
Competju
mechanig
related u
Or to ne-
POSitiVe

and On

i””0Vati.
2.5.2 I
2.5.2.] I

Figure :

DOSiti\.e]

The fit was good when tested with the overall sample: Bentler-Bonett normed
fit index (NFI), comparative fit index (CFI) and RMSEA value were 0.958, 0.963 and
0.169, respectively (see Table 2-3). Nine out of 17 specified paths were significant at

a S 0.05 or better (Figure 2-1). Market turbulence did not exert a significant impact on

either customer orientation or organizational structure (71 = .001; y5= .025), but was
positively related to competitor orientation (y3=.100). In contrast, technological
turbulence was not significantly related to customer or competitor orientation (n=
.153; 74: .054), but negatively affected organizational structure (i.e., mechanistic)
(Y6: -.084). The relationship between market turbulence and new product innovation
was not significant (77: .021; failing to support H1). Technological turbulence, on the

other hand, had a positive impact on new product innovation (73: .127, as in H2),
meaning that technological turbulence encouraged innovation. Both customer and

competitor orientation were related positively to organizational structure (i.e.,

mechanistic) (Bl= .265; [32: .596; respectively), however, neither were significantly
related to new product innovation ([33: .114; B4: -.063; failing to support H3 and H4)

or to new product performance ([36= .058; B7= .004). Mechanical structures had a
positive impact on new product innovation (B5 = .257, contrary to expectations in H3)

and on new product performance ([33 = .227). As expected in H6, new product

innovation was positively linked to new product performance ([39 = .308).

2.5.2 Hypothesis Testing

2.5.2.1 Environmental Turbulence and New Product Innovation. As shown in
Figure 2-1, technological turbulence, not market turbulence was significantly and

positively related to new product innovation, supporting H2 and not H] (see Figure 2-

31

 

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not supported (see Figure 2-4).
2.5.2.2 Customer Orientation and New Product Innovation. Customer
orientation did not exert a significant impact on new product innovation in the overall
sample (see Figure 2-1 and Table 2-4), failing to support H3. This relationship was
moderated by ‘level of analysis’ and ‘operationalization’ supporting H3c, but not H3a
or H3b (see Table 2-4; H3a proposed no moderation when in fact there is, and thus
H3a is not supported). The relationship between customer orientation and new
product innovation was positive at the program level versus negative at the project
level (Figure 2-2) and positive in categorical-unidimensional as opposed to n.s. in
continuous-multidimensional (Figure 2-4). The strength of this relationship did not
vary across ‘perspective’ groups (see Figure 2-3).
2.5.2.3 Competitor Orientation and New Product Innovation. Similarly, H4 was
not supported with a nonsignificant effect of competitor orientation on new product
innovation in the overall sample (Figure 2-1). This relationship was moderated by all
proposed moderators, supporting H4b and H4c, but not H4a (see Table 2-4; H4a

proposed no moderation when in fact there is, and thus H4a must be rejected). The

33

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n.s.: see F

2.5.2.4 O

 

strength of this relationship varied across program vs. project, i.e., it was positive at
project level as opposed to n.s. at program level (Figure 2-2). The relationship
between competitor orientation and new product innovation was stronger in the ‘firm’
perspective (vs. customer, where it was negative; see Figure 2-3) and categorical-
unidimensional operationalization (vs. ‘continuous-multidimensional’, where it was
n.s.; see Figure 2-4).

2.5.2.4 Organizational Structure and New Product Innovation. The impact of
organizational structure on new product innovation was positive overall (Figure 2-1),
contradicting H5 (which proposed a negative relationship). This relationship was
moderated by ‘level of analysis,’ ‘perspective’ and ‘operationalization’, supporting
H3a, H3b, and H3c. Mechanical organizational structure had a positive influence on
new product innovation in the ‘project level’ versus a nonsignificant impact in the
program level group (Figure 2-2). ‘Perspective’ moderated both the strength and the
directionality of the effect — negative for the ‘firm perspective’, whereas positive for
‘customer perspective’ (Figure 2-3). This path was positive in the ‘categorical-
unidimensional’ group, but n.s. in the ‘continuous-multidimensional measurement’
group (Figure 2-4).

2.5.2.5 New Product Innovation and New Product Performance. In the overall
sample, the relationship between new product innovation and new product
performance was significant and positive, supporting H6. The strength of this
relationship was significantly moderated by only ‘perspective’: greater when a
customer perspective was employed (Figure 2-3). It did not vary across ‘level of
analysis’ or ‘operationalization’ (see Figure 2-2 and 1-4). Therefore, H6b, not H3a

and H3c was accepted (Table 2-4).

34

    

 

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2.6 DISCUSSION AND CONCLUSION

The innovation literature comprises ambiguous findings regarding not only the
influence new product innovation exerts on new product outcomes, but also the
degree to which certain intraorganizational factors, such as orientations and structure,
lead to new product innovation. Consequently, a meta-analysis was conducted and
structural equation modeling analyses were performed to derive empirical
generalizations, to investigate mediating relationships, and to scrutinize possible
moderators that may have lead to the discrepancies across studies.

2.6.1 Baseline Model: The Interrelationship Among Constructs

Using the entire sample, the hypotheses were first tested in the context of the
baseline model in Figure 2-1. Market turbulence was positively related to competitor
orientation, however, technological turbulence did not exert a significant impact.
Thus, rising hostility in a firm’s market environment, but not technological
advancements in its industry, increases the importance given to monitoring of
competitor actions and strategies. This finding is supported in the industrial
organization (10) paradigm (Porter 1981; Grant 1991), which views strategy as a
firm’s response to the competitive industry environment On the other hand, neither
market nor technological uncertainty affected customer orientation. This may signal
that firms focus on and are responsive to their customers irrespective of the level of
turbulence in their environment.

Technological turbulence was negatively related to mechanical structures and
had a positive impact on new product innovation. Hence, rapid technological
advancements led firms to become more organic and to innovate. Market turbulence
did not exert any significant influence on either organizational structure or new

product innovation. On the other hand, both market and technological turbulence had

38

indirect
construct

orientatic

 

SU'UCIUI’C s

I
l
l
that firm‘

I
environn‘,
suggest t'
which a}
environn:
thus reqc

Processes

 

 

conflict

EllVlIOnnj

perfonm
PTOduct

Orlerrtati
Support:
mediate
basic It
then in
M ahon
Compe

Simila]

indirect effects on new product innovation through either strategic orientation
constructs and/or organizational structure. Customer orientation and competitor
orientation also preceded new product innovation indirectly through mechanical
structures. These findings are contrary to the contingency framework, which proposes
that firms adopt less centralized, more organic structures in dynamic, uncertain
environments (Covin and Slevin 1988; Bourgeois and Eisenhardt 1988). The results
suggest that firms in turbulent environments create internal administrative structures,
which allow them to efficiently manage any necessary adjustments. In uncertain
environments that demand fast reactions, building consensus may take too much time,
thus requiring a continual crisis orientation, a centralized approach and formalized
processes. Centralization and formalization may facilitate implementation by reducing
conflict and ambiguity, leading to a more uniform response to the changes in the
environment (Meyers et al. 1999).

Moreover, the effects of customer and competitor orientation on new product
performance were strictly indirect through both organizational structure and new
product innovation. The direct paths from customer orientation and competitor
orientation to new product performance were nonsignificant. This result generally
supports the three-tier contingency model with structure and innovation as complete
mediators of the strategy-performance relationship. This finding also confirmed the
basic tenet of RBV, that is, firms may achieve superior performance if they can utilize
their internal resources and capabilities in distinctive ways (Penrose 1959; Day 1994;
Mahoney and Pandian 1993; Grant 1991). This study focused on customer and
competitor orientations but an entrepreneurial or technological orientation may play a

similar role as complete mediator.

39

Mechanical structures had a positive impact on new product innovation
(contrary to what was expected), and also on new product performance. This may
indicate that innovation requires more mechanistic approaches, through which (1)
possible conflicts in NPD processes can be prevented, and (2) integrated and
harmonious operations can be implemented across functions. Finally, as expected,
innovation was positively linked to new product performance.

2.6.2 Moderator: Program Versus Project Level of Analysis

Level of analysis was the first substantive moderator. The findings indicated
that it moderated five of the six hypothesized paths, as well as many of the other
interrelationships among constructs in the baseline model (such as competitor
orientation to organizational structure, for example; see Figure 2-2). New product
innovation was positively linked with new product performance regardless of level
the analysis. Comparing these patterns, the results exhibit that with the exception of
the relationship from mechanical structures and competitor orientation to new product
innovation, the strengths of the paths from a program level analysis are greater or
equal in rank order to the strengths of the paths from a project level analysis. This
conclusion holds for the hypothesized paths as well as for the other interrelationships
among constructs in the baseline model.

Splitting the entire sample into program vs. project level groups provided
interesting results and signified the influential factors on new product innovation at
these two distinct levels. At the program level, both market and technological
turbulence are significantly and positively related to new product innovation (at the
project level, both were nonsignificant). Therefore, environmental turbulence directly
impacts program level new product innovation (which subsequently increases new

product performance), supporting the literature that states that rising environmental

4o

uncertainty increases the rate and level of innovation required to survive (e.g., Kotabe
and Swan 1995). Market turbulence also had an indirect influence on new product
performance through competitor orientation. On the whole, it can be concluded that
the way for the program to succeed in turbulent markets is monitoring of competitor
actions and implementing innovative strategies. Customer orientation also engenders
new product innovation at the program level: focus on customers’ existing
preferences may lead to bold actions and organizational focus on new product
innovation. Moreover, customer orientation has a strictly indirect effect on new
product performance through new product innovation, as proposed in the principles of
RBV theory and the three-tier contingency model of the strategy-performance
relationship (as in the baseline model findings). This suggests that a market focus may
only engender superior performance for programs through the development or
improvement of new product innovation processes and new methods for doing
business.

At the project level, customer and competitor orientations as well as
organizational structure were influential on new product innovation. Moreover, the
impacts of customer and competitor orientations on innovation were partially
mediated by organizational structure (i.e., there were direct effects on innovation, as
well as indirect effects on innovation through structure). Stating it differently,
strategic orientation precedes innovation, both directly and indirectly through
structure. Therefore, focus on either customer needs or competitors lead firms to
centralize and formalize their processes and activities at the project level.
Subsequently, this match between strategic orientations and internal structure

engenders new product innovation.

41

The negative direct linkage between customer orientation and innovation at
the project level may be explained if the focus on customers’ existing preferences
leads to reactive strategies and incrementally innovative products, rather than bold
and radical ones. For projects to be perceived as new, exploration and proactiveness
may be required; thus innovation may be negatively related to orientations that
emphasize the contrary. Finally, market turbulence has a significant influence on new
product innovation (and new product performance) only through competitor
orientation. Thus, lower level decision processes in new product projects regarding
new product innovation are affected by market turbulence only through competitor
oriented strategies.

The finding that mechanical structures-new product innovation link is n.s. at
the program level but positive at the project level suggests that lower level decision
processes and product development activities require formalization and/or
centralization. The involvement of top management (i.e., centralization) may be
beneficial in reacting in a timely and integrated manner, since conflicts and
ambiguities are reduced through a higher locus of control (Miller and Friesen 1982;
Meyers et al. 1999). This may lead to faster decision-making, the building of
consensus, and enhanced sharing and interaction across different departments. The
greater efficiency of formal and/or centralized mechanisms with regards to time and
functional disagreement resolution may result in more proficient processes (Olson et
al. 1995). A protocol or formal agreement on product performance specification (i.e.,
a product charter or business plan) may minimize mismatches, conflicts and
misunderstandings among team members (Crawford 1984). Therefore, all these

factors would further facilitate innovation at the project level.

42

 

does ind

l
l
I
thellotl

A
|
for the l'
(the link

innovatid

testing 51

 

With me“

|
customer
DEW PTO
shifts in
the C1151.
CUSIQmC
Compm:
‘Cllslon
may le
and fa.
“Egan.
ianV;

fonTlE

2.6.3 Moderator: Firm versus Customer Perspective

Perspective was the second substantive moderator, and the results show that it
does indeed moderate five of the six hypotheses (the exception is the positive path
from customer orientation to new product innovation). In addition, it moderates six of
the 11 other paths comprising the baseline model, for a total of 11 of 17 paths.

As for the directionalities proposed in the six original hypotheses, the results
for the firm perspective show that four were supported and positive as hypothesized
(the links from customer orientation and technological turbulence to new product
innovation were n.s.). Examining the customer perspective, the results of hypothesis
testing show three unexpected results. First, market turbulence was positively linked
with new product innovation when the firm perspective is taken, but negative when
customer perspective is adopted. This suggests that market turbulence may engender
new product innovation in the presence of high competitive intensity and frequent
shifts in competitor actions and strategies. However, it may decrease innovation when
the customer perspective is taken due to rapid product obsolescence and changes in
customers' perceptions of innovativeness of product and companies. Similarly,
competitor orientation was negatively linked with new product innovation in the
‘customer perspective’ subgroup, suggesting that monitoring of competitor actions
may lead to reactive strategies and incrementally innovative products rather than bold
and radical ones. Interestingly, from a firm perspective, mechanical structures were
negatively linked with new product innovation; however, it positively affects
innovation when a customer perspective is taken. This suggests that more
formalization and centralization, which entail efficiency in decision making and in
responding to their environment, may enable innovations that are viewed by

CUSIOI‘IICI’S as new in the market.

43

lr
onentatii
organiza
structure
structure
completc
new pro-c
tested fri

2.6.4 .\

Operatior
h§pothe >
item" sul
results 0
CUSlOmcr
innovatit
PTOduct
calegcm' g
technolo‘
product
OperallOr
l'
Construc.

both “,1

Organiu.

 

In particular, from the customer perspective, the direct path from customer
orientation to new product innovation was not significant while its relationship with
organizational structure was significant. This supports the traditional strategy-
structure link in the contingency framework of IO paradigm. Since organizational
structure then influenced new product innovation, it would mean that structure is a
complete mediator of the strategy-new product innovation relationship. Lastly, the
new product innovation- new product performance has more explanatory power when
tested from the customer perspective.

2.6.4 Moderator: Operationalization of the New Product Innovation Construct

One methodological moderator was investigated in this analysis -- namely,
operationalization of the innovation construct - and found that it moderate four of the
hypothesized paths (the relationships being significant only in the ‘categorical single
item’ subgroup). This demonstrates that measurement can have a major impact on the
results of a structural model including one of new product innovation. The paths from
customer and competitor orientations and organizational structure to new product
innovation were nonsignificant in the case of continuous/multiple measures of new
product innovation. Thus, operationalization of new product innovation using a
categorical single item appears to exaggerate the strength of these relationships. The
technological turbulence - new product innovation and new product innovation-new
product performance links were positive and significant regardless of type of
operationalization of innovation.

Finally, for three of the baseline model paths of the interrelationships among
constructs, the strengths of the paths were different but the signs were the same and
both were significantly different from zero (except for market turbulence-

organizational structure link, which was n.s. in ‘categorical unidimensional’ group).

44

and the
|
innovatit
|
new pro-
|
overall r‘
I
level an...
overall l
particula
nonsigni
product
research
antecede
Oriented
OVerall, 1

“CW’ pro“

holds fo‘l

 

On the whole, the evaluation of the findings of the two substantive moderators
and the six core hypotheses (Table 2—4; five concerned antecedents to new product
innovation and one dealt with the relationship between new product innovation and
new product performance) provides interesting insights. First, market turbulence is
overall not a direct antecedent to new product innovation, but for program or firm
level analysis, this relationship may be positive. Second, technological turbulence is
overall positively related to new product innovation. But researchers examining
particular projects or taking a firm perspective may find this relationship
nonsignificant. Third, customer orientation is not overall a direct antecedent of new
product innovation, but program research may find this path positive while project
research may find it negative. Fourth, competitor orientation is not overall a direct
antecedent to new product innovation, however it may be positive for project or firm
oriented research. Fifth, mechanical structures encourage new product innovation
overall, but not in program level research (n.s.), nor in firm-level analysis. Finally,
new product innovation is a direct antecedent to new product performance, and this
holds for program or project level research and for firm or customer oriented research.
Thus, a synthesis of the innovation research corroborated the premises of the two
major theoretical foundations (i.e., RBV and contingency frameworks) regarding the

determinants and outcomes of new product innovation.

45

 

 

 

 

rapid an

and inte
im'estrr
2001 ).

Rindfl:
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only I

Custoi

CHAPTER 3

MARKET EFFICIENT TECHNOLOGICAL INNOVATION: A
DETAILED MODEL WITH MARKETING IMPLICATIONS

3.1 INTRODUCTION

The key to maintaining a competitive position in the marketplace is the ability
to repeatedly commercialize successful new products (Griffin and Page 1996).
However, due primarily to the escalation of research and development (R&D) costs,
rapid and radical technological developments, the shortening of product life cycles
and intensified international competition, returns on new product development (NPD)
investments are often ephemeral, imitable or not satisfactory (Li 1999; De Brentani
2001). The failure rate of NPD activities is as high as 50% (Wind and Mahajan 1997;
Rindfleisch and Moorman 2001), and consequently, internal innovation development
is perceived as high risk and difficult (Calantone and Di Benedetto 1988; Song and
Montoya-Weiss 1998). The most prevalent reasons for product failures include not
only the lack of certain technical product features, but also the inability to meet the
customer needs and the poor administration of marketing activities (Cooper and
Kleinschmidt 1987; Calantone and Di Benedetto 1988; Atuahene-Gima and Ko
2001). Meanwhile, the existence of environmental threats increases the rate of
innovation required to survive (Kotabe and Swan 1995; Rowley et al. 2000). This
situation has led firms to invest in internal resources, competencies and activities for
developing unique and qualified products, to speed product development, and to
improve process efficiency and effectiveness (Olson et al. 1995; Li and Calantone
1998).

Research on NPD projects has focused on the internal factors, i.e., factors
under managerial control (Di Benedetto 1999; Montoya-Weiss and Calantone 1994).

Resources, capabilities and strategic orientations have been directly linked to

46

 

I
position-
howevel
claims
brainsto

. 5
ideas .

in Day
should 1
innovati
factors (
implemT
performl
Specific;
Specific
Perform.
impleme

HOMO};

CUSIOmC

 

CaPabili:

positional advantages and performance outcomes. Implementation (or execution)
however has generally been neglected in NPD models, but a recent press release
claims that the chances of success are enhanced not through investing in
brainstorming and experimentation, but through implementation of the generated
ideass.

The overall objective of this research is to develop and test a model grounded
in Day and Wensley’s (1988) source-position-performance framework. The model
should be useful for both the prediction and the understanding of market efficient
innovation development at the project level. This study focuses on how internal
factors (i.e., the strategic fit of the project, project related sources of advantages, and
implementation proficiency in NPD process) influence positional advantage and
performance of the project, and include the role of environmental factors. More
specifically, this study investigates whether the strategic fit of the project and project
specific advantages have direct impacts on positional advantages and project
performance, or an indirect effect mediated by the proficiency in their
implementation. The major categories of NPD success determinants identified by
Montoya-Weiss and Calantone (1994) in their meta—analysis are included.

Successful innovation may depend on familiarity (i.e., close to the firm’s prior
customers and technology) and on synergy (fit with the firm’s resources and
capabilities) (Danneels and Kleinschmidt 2001). In other words, the key is a match
between what is needed (i.e., market pull) and what can be developed internally (i.e.,
technology push) (Day 1994; Li and Calantone 1998). Yet, the operationalization of
this match is a frontier issue in innovation research. Therefore, this essay aims to

elucidate the notion of ‘fit’ both from the firm and the customer perspectives. The

 

5 The Key to Innovation: Overcoming resistance, C10 magazine, October 15, 2005

47

 

 

 

 

 

 

TCSOUIC c’

COllStl'UC

paths an

 

of mana
3.2 B

3.2.]

theories
literati“
depend
Climate
have re
firm's i
Promo:
lie, R.
1987; S
imPact
{calms-Q
Kleinse

Song er

latter has generally been neglected in the prior innovation literature. Moreover, this
study develops measures to assess ‘fit’ through the degree of new resources and
capabilities required for the product and the extent of synergy the project entails with
the existing structure (as opposed to measures such as “having more than adequate
resources” in prior literature) (Song and Parry 1997).

The essay will begin with an overview of the model and by defining model
constructs. Hypotheses are then developed and are tested (for direct versus indirect
paths and for moderation). The presentation of the results is followed by a discussion
of managerial and theoretical implications.

3.2 BACKGROUND AND MODEL OVERVIEW
3.2.1 Theoretical Background

There is a rich stream of literature, employing diversity of perspectives and
theories that focuses on the determinants of new product success. Some NPD
literature has supported the notion that ongoing success in innovation development
depends on implementing the right mix of new product strategy, competencies and
climate for innovation (Gatignon and Xuereb 1997; Barczak 1995). Many studies
have reported significant positive relationships between new product success and the
firm's ( 1) marketing resources and skills (i.e., marketing research, advertising and
promotion, and sales force and distribution), and (2) technical resources and skills
(i.e., R&D, engineering, and production (Cooper 1979; Cooper and Kleinschmidt
1987; Song and Parry 1996; Montoya-Weiss and Calantone 1994). Others analyzed
impact of structural elements, such as senior management commitment, effective
teamwork and cross-functional integration, on NPD outcomes (Cooper and
Kleinschmidt 1995; Ottum and Moore 1997; Ayers et al. 1997; Atuahane-Gima 1996;

Song et al. 1997). Furthermore, positional advantages, which include measures of

48

 

 

 

 

 

product
product
ldenililc

Cooper

between
executit
These r
perfomi

tactics 2

 

 

based 0!
Strategic
Ketcher
not aut.
Perform
and tim
of a ne

em'lTOn
and Sle'
SOuder

consequ
for firm

CTEate a

product differentiation (i.e., product uniqueness, superiority and quality) and the
product’s capability to reduce consumer costs or solve a customer problem, were also
identified as important determinants of success (Cooper 1979; Kleinschmidt and
Cooper 1991; Li and Calantone 1998).

Aside from direct linkages, few studies also analyzed mediating relationships
between skills/resources and product advantage (i.e., the impact of quality of
execution of NPD process activities; Calantone et a1. 1996; Song and Parry 1997).
These notions were corroborated in Day and Wensley’s (1988) source-position-
perforrnance (SPP) framework, which proposes a mediating impact of the quality of
tactics and implementation on the conversion of superior skills and resources into
based on cost and/or product differentiation. Positional advantages in turn impact
strategic and financial performance outcomes (Song and Parry 1997; Hult and
Ketchen 2001). More specifically, they contend that superior skills and resources are
not automatically converted into positional advantages or bring about a certain
performance payoff, but are mediated jointly by the quality of tactics, implementation,
and timing. Thus, in the NPD context, SPP framework suggest that the performance
of a new product innovation in the market is influenced by firrn’s marketing and
technical resources and skills, but only through the quality of NPD process execution.

Furthermore, contingency theory focuses on the moderating effect of
environmental variables (such as, market potential and competitive intensity) (Covin
and Slevin 1989; Atuahene-Gima 1995; Calantone et a1. 1997; Song and Parry 1997;
Souder and Song 1998; Calantone et al. 2003). Uncertainties in the environment are
consequences of market forces and technological advancements, and create impetus
for firms to implement changes and invest in internal resources. The challenge is to

create a linkage between internal organization and the external environment and to

49

 

SITUC

and

l99‘

reqi

and

ski?

th

Cl

structure accordingly: managers must allocate their marketing and technical resources
and skills efficiently in order to develop what is desired by the market (Di Benedetto
1999). In this study, skills and needs alignment6 is defined as the fit between project
requirements and functional skills (Song et al. 1997; Atuahene-Gima 1996; Cooper
and De Brentani 1991); ‘fit’ implies neither too little nor too much of the appropriate
skills (Souder et al. 1997).
3.2.2 Model Overview and Model Constructs

Song and Parry (1997) contend that the SPP framework incorporates much of
the recent literature on NPD success determinants, these being: the firm’s internal
environment (i.e., marketing and technical resources), NPD process factors, product
competitive advantage and the competitive environment (Cooper 1979). Moreover,
applications of contingency theory notions to NPD have been widely used to analyze
the impact of the dynamics of a firm’s target market on the effectiveness of its NPD
decisions and skills. Accordingly, referring to the SPP and contingency frameworks,
constructs scrutinized in this study are: (1) Strategic fit: manufacturing, marketing,
supply chain, and research and development fit; (2) project-specrfic sources of
advantage, such as project climate and project formality (project definition/ protocol);
(3) proficiency in NPD process: idea development and opportunity analysis, technical
assessment and testing, product development activities, speed to market and
development costs; (4) positional advantage of product difl’erentiation, i.e., degree of
customer need met, uniqueness, technology advancement degree, degree of
customization and sustainable advantage to the firm; and (5) market environment,
specifically, market potential and market competitiveness. The performance outcomes

variables focus on whether the firrn’s profitability, sales and technical objectives were

 

6 Also identified as ‘innovation-firm synergy’ in the literature (Cooper and Kleinschmidt, 1987;
Atuahene-Gima, 1996; Song and Parry, 1996; Danneels and Kleinschmidt 2001)

50

 

VIVI-

. . ‘ .0; ii} In: -llhulfflihhfi'lltlgfli":A I1.

met (HL
1996'. S

definitit

____
7 Constru
Strategr'

 

 

 

 

 

 

met (Hultink and Robben 1995; Cooper and Kleinschmidt 1987; Griffin and Page

1996; Song and Parry 1996; Atuahene-Gima 1995). Table 3-1 presents conceptual

definitions of all constructs.

TABLE 3-1. CONSTRUCT DEFINITIONS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Constructs Definitions

Strategic Fit indicates the degree to which the internally available marketing,
manufacturing, supply-chain and R&D capabilities match the
requirements for the new product project.

Supply-chain Fit indicates fit of the project with existing
distribution channels, target market and market
demand.

Marketing Fit signifies the fit in terms of advertising, market
research and promotional requirements.

R&D Fit incorporates the fit of the project requirements
with the existing R&D expertise, the current
level of R&D expenditures and R&D personnel
training.

Manufacturing Fit entails the fit with regards to development
requirements, existing plants and technologies.

Project-specific Project F ormality refers to the formal design of roles and
Sources of mechanisms to control and integrate work
Advantage activities and resource flows.

Project Climate Defined as the degree of commitment and
collaboration among the members having an
active role in the project.

NPD Process the quality of implementation of marketing activities (i.e.,
Implementation exploration, concept development, market research and analysis);
Proficiency technical activities (i.e., developmental stages of prototype
development, prototype testing, manufacturing start-up, pilot and full
production); and finally, timeliness and development cost.
Positional Degree of indicates whether the product offers potential
Advantages/ Customer Need for reducing consumer costs and expanding
Product Met consumer capabilities.
differentiation Product Advantage denotes whether the product offers improved
quality, superior technical performance, and a
higher benefit-to-cost ratio.
Project the overall performance of the project relative to technical, financial
Performance and marketing objectives.
Market Market Size indicates the attractiveness of a target market in
Environment terms of number of potential customers.

Market Growth incorporates the demand in the market and the
importance to customers of products
addressing their needs.

Market refers to the concentration and intensity of

Competitiveness rivalry within the firm's target market.

 

 

51

 

The l
and project-
proficiency
then influer
between str
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The model is depicted in Figure 3-1. The strategic fit of the project (HI-H4)
and project-related sources of advantage (HS-H6) are hypothesized to enhance the
proficiency in implementation of NPD processes. NPD implementation proficiency
then influences product positional advantages (H7-Hl 1). Note that no direct effect
between strategic fit and NPD implementation proficiency is hypothesized (dotted
line in Figure 3-1). In other words, NPD implementation proficiency is modeled as a
complete mediator of model relationships. Proposing and then testing this complete
mediation (versus partial mediation) is one important contribution of this research.

Positional advantages are hypothesized to impact project performance (H12-
H13), but these relationships are moderated in part by factors not under the control of
decision-makers. Environmental factors (i.e., market potential and market
competitiveness) will determine the strength of these paths. In general, this
moderation effect is supported by NPD contingency theory; that is, the relationships
from internal factors to new product success are contingent upon environmental
turbulence (Atuahene-Gima 1995; Calantone et al. 1997; Souder and Song 1998;
Calantone et a1. 2003). Testing moderation is a second important contribution of this
research. The next sections develop hypotheses of how these constructs relate to one
another (Figure 3-1).

3.3 RESEARCH HYPOTHESES

3.3.1 Sources of Advantage

Successful new products result from processes and activities that allow
effective allocation and use of firm's existing resources and capabilities (Gupta et al.
1986; Wheelwright and Clark 1992; Moorman 1995). As per Day and Wensley
(1988) suggest, firms may achieve performance superiority through their distinctive

competencies in deploying their resources and skills (also see Peteraf 1993; Prahalad

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and Hamel 1990; Day 1994). Firms utilize their capabilities to cultivate rent-
generating resources and match them to external conditions to gain sustainable
competitive advantages (Friar 1995; Li and Calantone 1988; Atuahene and Ko 2001).
These suggest that firm-specific advantages enable effective NPD processes (Li 1999;
Song and Parry 1997). Following Song and Parry (1997), this research distinguishes
between function-specific sources of advantage (that is, strategic fit of the project
across all functions) and project-specific sources of advantages.

3.3.1.1 The Effects of Strategic Fit (Function-Specific Sources of Advantage)
(Hl- H4)

In the NPD literature, resources are classified as: (1) market resources and
activities, which include preliminary market research, sales projections, market plan
development, sales-force, promotion/advertising efforts; and (2) technical resources
and activities, consisting of production resources, engineering and R&D efforts
(Calantone and Di Benedetto 1988). Strong marketing and technical resources
enable better performance of marketing and technical activities in particular those
related to innovative products, beginning with idea generation and ending with a
product launch (Barzcak 1995; Atuahane-Gima 1996; Veryzer 1998; Song and
Montoya-Weiss 1998; Calantone et a1. 1997).

Accordingly, the literature differentiates between technological fit, that is,
between the product’s technology and the technological skills of the firm and
marketing fit, that is, between marketing, salesforce and distribution requirements and
the available skills (Cooper and Kleinschmidt 1987; Song and Parry 1996, 1997).
‘Strategic fit’ in this research includes all functions (i.e., marketing, manufacturing,

supply-chain and R&D) that play a role in innovation development. The definition is

54

”’7, in which “fit

based on Venkatraman’s (1989) conceptualization of “fit as matching
is a theoretically defined match between two related variables” (p 430).

In other words, ‘strategic fit’ is defined as the degree to which the internally
available marketing, manufacturing, supply-chain and R&D capabilities match the
requirements of the new product project (also refer to Table 3-1). Accordingly, fit is
evaluated based on the extent to which the capabilities available for developing a
product technology match its requirements or important characteristics. More
specifically, supply-chain fit taps the suitability of the project to the distribution
channels, target markets and demand forecasts that the firm employed in previous
development projects. Marketing fit signifies the match of the current project to
existing advertising, market research and promotional activities and skills. R&D fit
incorporates the match in terms of R&D expertise, the current level of R&D
expenditures and R&D personnel training. Finally, manufacturing fit entails fit with
regards to development requirements, existing plants and technologies. Overall, prior
research has shown that projects considered successful entailed higher synergy
(Cooper 1979; Zirger and Maidique 1990). Therefore, if a firm can draw on its
existing marketing, manufacturing, supply-chain and R&D competences to execute
the NPD project, it is more likely to be successful.

Technological innovations require new skills, processing abilities and systems
and entail less synergy with prior technologies and practices (Ottum, and Moore
1997; Sethi 2000; Veryzer, Jr. 1998; Song and Montoya-Weiss 1998; McDermott and
O'Connor 2002). Highly innovative products represent significant threats and

uncertainties since their degree of fit with the existing knowledge structures and with

the design, manufacturing and marketing practices of the firms is likely to be very

 

7 ‘Fit as matching’ is derived based on underlying theory without reference to a criterion/ performance
variable, although subsequently, its effect on a set of criterion variables could be examined.
(Venkatraman 1989, p. 430).

55

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low. Lack of fit requires shifts in market understanding and changes in new
processing abilities and systems (McDermott and O’Connor 2002). On the other hand,
the adoption and development of products that build on the firm's existing firm
capabilities engender higher success (Day and Wensley 1988; Song and Parry 1996,
1997). A close fit enhances the project team's ability to gather and interpret market
and competitive information (Song et al. 1997). This generates greater insight for idea
generation and screening and into business and market opportunity analysis
(Calantone et al. 1996; Cooper 1979). Accumulated expertise leads to efficient
technical assessment and development and effective product commercialization,
permitting the efficient use of technical and marketing resources.

Cooper and Kleinschmidt (1987) compared success rates among new product
types based on project fit and familiarity and found only modest differences; they
concluded that synergy may not be particularly significant in success/failure
prediction. Unfortunately, they did not distinguish between different dimensions of
fit. Song and Parry (1996; 1997) incorporated both technological and marketing
dimensions of synergy, both of which exhibited significant positive effects on
performance through their influence on the proficiency of development activities.
Increases in a project's fit led to improvements in the quality of implementation
during the NPD process (Song and Parry 1997).

Moreover, Ali et al., (1995) and Kessler and Chakrabarti (1999) found that
firms pursuing to develop a unique and differentiated product took longer time and
that the amount of change attempted in a project is negatively related to speed. Since
radical changes and projects entail greater complexity, risk and uncertainty, the
information needs, workloads, and the number of people involved in projects are

greater, and thus the cost of development is higher (Kessler and Chakrabarti 1999).

56

 

Men the t

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Song and h
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while execu
execution or
and product:

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lead to mo:

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When the firm relied on technology never previously used, development activities
such as idea development and screening, technical development, and strategic
planning need to be executed more effectively and timely (Lee and O’Connor 2003;
Song and Montoya-Weiss 1998). In light of these findings, higher fit of a
development project with the firm’s existing resources and skills is expected to
decrease the amount of reinterpretations and reconfigurations of prior experiences
while executing the NPD process. This signifies proficient, timely and less costly
execution of NPD process activities such as idea development, technical assessment
and production.

Thus, fit vis-a—vis supply chain, marketing, R&D and manufacturing should
lead to more proficient activities, increased speed and decreased cost. However,
supply-chain and market fit should not influence technical assessment proficiency
since the resources associated with these functions are not employed in this stage.
Therefore:

H1. Supply chain fit is positively related with proficiency in (a) idea development and

opportunity analysis (b) product development, and (c) development speed, but (d) is
negatively related with development cost.

H2. Marketing fit is positively related with proficiency in (a) idea development and
opportunity analysis, (b) product development, and (c) development speed, but (d) is
negatively related with development cost.

H3. R&D fit is positively related with proficiency in (a) idea development and
opportunity analysis, (b) technical assessment, (c) product development and ((1)
development speed, but (e) is negatively related with development cost.

H4. Manufacturing fit is positively related with proficiency in (a) idea development
and opportunity analysis, (b) technical assessment, (c) product development and (d)
development speed, but (e) is negatively related with development cost.

Day and Wensley (1988) argue that information resources and skills are only
‘means to an end’ and that competitive advantages and performance outcomes are

what managers are mostly concerned about. Thus, these sources of advantages apply

to activities, and these activities should be implemented effective and efficiently.

57

   
  

lhey also a
advantages i
and \‘s’ensle

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and technicz
and technic
technical a
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They also advocate that the role of implementation in models of the conversion of
advantages into payoffs has generally been neglected in the marketing literature (Day
and Wensley 1988, p. 5). In innovation research, few studies test the impact of
implementation proficiency or quality of NPD process activities on the extent to
which sources of advantages lead to performance advantages. As an illustration,
Calantone, Schmidt, and Song’s (1996) results indicate that proficiency in marketing
and technical activities mediate relationships between marketing, R&D, engineering
and technical resources and skills and new product success. The proficiency of
technical and market-oriented activities allow firms to ensure that they employ their
functional expertise most effectively; for example, firms do not introduce
technologically advanced products for technology's sake (Barzcak 1995).

Relevant information resulting from marketing activities about the market,
consumer preferences, competitive products and strategies, can be utilized in more
adept decision-making (such as how much promotional or distribution support to
render, what product concepts to bring to prototype, or what features to build into the
final product) (Calantone and DiBenedetto,1998). According to the NPD literature,
gathering information on the quality of current competitive products brings about
opportunities to improve firms’ own product offerings (Song and Parry 1996).
Technical development proficiency is also strategically important since R&D of new
products can shorten cycle and lead times of entry, bringing about positional
advantages. Consequently, as Day and Wensley (1988) suggest, marketing and
technical development proficiencies, speed and cost can be interpreted as ‘mediating
events’ between the firm’s internal resources (advantages) and its (external)
competitive advantages (p. 6). Thus, sources of advantage are expected to influence

positional advantages only through the proficiency of NPD process activities.

58

   
   
 
   
   
  
 

HI. NPD
beni'een 51
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advantage

H3 . NPD
between R

H4. NPD
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advantage.

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consist of
adapted to
1994; Olso
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In
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Coordina1e

HI. NPD process implementation proficiency completely mediates the relationship
between supply chain fit and (f) the degree of customer need met and (g) product
advantage.

H2. NPD process implementation proficiency completely mediates the relationship
between marketing fit and (f) the degree of customer need met and (g) product
advantage.

H3. NPD process implementation proficiency completely mediates the relationship
between R&D fit and (f) the degree of customer need met and (g) product advantage.

H4. NPD process implementation proficiency completely mediates the relationship
between manufacturing fit and (f) the degree of customer need met and (g) product
advantage.

3.3.1.2 The Effects of Project-Specific Sources of Advantage (H5, H6)

Teamwork, top management support and commitment, formal process use and
; cross-functional integration are cited among important determinants of NPD success
(Song et al. 1997; Cooper and Kleinschmidt 1987; Veryzer 1998). Workman,
Homburg, Gruner (1998) classifies organizational dimensions in two categories, that
is, structural and nonstructural dimensions. Structural dimensions comprise aspects
of organizational structuring, such as reporting relationships, and bureaucratic
dimensions including formalization, and standardization. Nonstructural dimensions
consist of the use of cross-functional teams and organizational forms that are more
adapted to rapidly changing environments (Achrol 1991; Day 1994; Germain et al.
1994; Olson et a]. 1995; Hurley and Hult 1998; Kahn 1996). Accordingly, project-
specific sources of advantage are categorized into two groups, project formality
(structural dimensions) and project climate (nonstructural dimensions).

In the studies linking organizational aspects to new product success
(Jassawalla and Sashittal 1998; Song et al. 1998; Song et al. 1997; Ottum and Moore
1997; Olson et al. 1995; Li 1999; Ayers et al. 1997; Maltz and Kohli 1996; Adams et
al. 1998; Souder et al. 1998), it is generally agreed that in order to effectively

coordinate the NPD process, structures must encourage the sharing of information and

other resources across functional areas, and provide mechanisms for decision-making

59

and conflic
project fOV
mechanisn
Particularl'
enduring c
flow of ph

\e
consistent
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Structures
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Others poi
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and conflict resolution. Referring to Olson et al.’s (1995) definition of structure,
project formality is conceptualized in this study as the formal design of roles and
mechanisms to control and integrate work activities and resource flows (p.49).
Particularly, the stage-gate (phase review) process of Cooper, a well-known and
enduring of mechanisms used to attain these goals, provides a formal and sequential
flow of phases.

Veryzer (1988) found that half of sample firms followed a formal and
consistent process for managing discontinuous NPD efforts. However, in general,
more innovative firms are believed to be loosely structured, implementing flexible
coordination mechanisms (Calantone et al. 1995). It has generally been advocated that
in order to be efficiently and effectively executed, highly uncertain tasks required
structures characterized by fluidity and flexibility in the task execution, decentralized
decision-making and few formal procedures. Moreover, as Mintzberg (1979) and
others point out, mechanistic structures restrict the ability of functional specialists to
communicate directly and share resources with one another, inhibit communication,
creativity and input from diverse sources (Covin and Slevin 1989; Miller et al. 1988).
The uncertain tasks in idea generation and opportunity analysis phases may not be
effectively pursued in mechanistic and bureaucratic structures. Informal and flexible
structures, on the other hand, provide individuals greater autonomy to decide and act,
and lead to more exchange of disperse ideas (Dewar and Dutton 1986; Hage and
Dewar 1973; Tatikonda 1999). Consequently, flexibility may decrease perceived
uncertainty related to technological change, enhance the receptivity to new
technology, and facilitate creativity and idea generation (Utterback and Abernathy

1975; Griffin and Hauser 1996; Knight 1987; Matsuno et al. 2002; Sethi et al. 2001).

60

 

 

 

 

 

 

In c
and functio
(Olson et 3'
regulate the
input and
following al
conceptleat
the productl
Chakrabarttl
fonnalizingl
from keepirl
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By I
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(including n.

 

 

other Produ,

 

In contrast, the greater efficiency of formal mechanisms with regards to time

and functional disagreement resolution may result in more proficient process activities
(Olson et a1. 1995). Ayers et a1. (1997) assert that such formalized procedures can
regulate the tasks people perform and assign role responsibilities, and thus facilitate
input and involvement from other departments whenever necessary. Moreover,
following a formal process coupled with clear time based goals and a clear product
concept leads to decreased cycle time and development costs and increased success of
the products (Cooper and Kleinschmidt 1987, 1995; Griffin 1997; Kessler and
Chakrabarti 1999). Therefore, cost and time efficiencies may be achieved by
formalizing development activities, whereas information sharing and creativity result
from keeping the process open and flexible. As Johne and Snelson (1988) assert, idea
generation requires freedom of thought and autonomy for action, whereas the
implementation of NPD process requires unified purpose. To achieve balance
between formality versus flexibility, Johne (1984) suggests use of informal and
unstandardized procedures in the initiation phase but more formal and rigid controls
as the development reaches its commercialization. Thus:
H5. Project formality is related with proficiency in (a) idea development and
opportunity analysis negatively; (b) technical assessment positively; (c) product
development positively; (d) development speed positively and (e) development cost
negatively.

By project climate, this research refers to the degree of commitment and
collaboration among the members playing an active role in the project. Song and
Parry (1997) define internal commitment as the existence of a group of individuals
(including members of top management, project leaders, project team members, and

other product champions) who push a development project forward toward successful

61

 

 

|
nammna

Griffin and
functional

manufactur
members e
procedures
1994; Le ar
and adequa
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and market

These advar

 

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al. 1998; 1
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1999). Otf
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execution and commercialization (Johne and Snelson 1988; Clark and Fujirnoto 1990;
Griffin and Hauser 1996). Teams can be composed of individuals from a variety of
functional areas, such as marketing, research and development (R&D),
manufacturing, and purchasing (Sethi 2000). Visionary leaders and committed team
members ensure that the project is viewed as high priority and that operating
procedures are executed without any impediments (Souder et al. 1997; Lee and Na
1994; Le and O’Connor 2003). Top management and project leaders ensure the flow
and adequacy of resources, motivate team members, reduce delays and costs by
providing timely decisions, and facilitate the assimilation and application of technical
and market-oriented knowledge (Ali et al. 1995; Kessler and Chakrabarti 1999).
These advantages raise the level of marketing and technical development proficiency,
increase speed, and improve cost structure.

Cross-functional integration or collaboration is defined as the
interdependency, information sharing and cooperation among departments that is
required to achieve unity of effort (Song and Parry 1997; Ayers et al. 1997; Souder et
al. 1998; Li 1999; Olson et al. 2001). It is generally operationalized in terms of
communication, transfer of information, and/or the degree of cohesion across
functional departments (Ottum and Moore 1997; Song et a1. 1997). Collaboration
integrates knowledge of what is needed in the market with knowledge of how to
create a product to meet that need (Griffin and Hauser 1996; Song et al. 1997; Li
1999). Other benefits of cross-functional integration include information gathering
and dissemination skills affecting product design and launch (Song and Parry 1997),
the generation of new product ideas, the development of effective sales forecasts,
product testing and modifications. A multifunctional team promotes cross-fertilization

of ideas and iterative learning, which contributes to the establishment of common

62

goals. As Calantone, Vickery and Droge (1995) assert, a time-based NPD advantage
generally involves the combination of inputs from various functional areas. Thus,
collaboration during the NPD process plays an important role in improving the quality
of product development execution as well as enhancing the firm’s market position
through decreasing development time and costs (Norton, Parry, and Song 1994).
Consequently, it is hypothesize that:

H60. Project climate is positively related with proficiency in (a) idea development
and opportunity analysis; (b) technical assessment; (c) product development; and (d)
development speed; but (e) negatively related with development cost.

3.3.2 NPD Process Implementation Proficiency

3.3.2.1 The Effects of NPD Activities (H7, H8, H9)

NPD activities are defined as ‘value-generating subprocesses’ that aim to
accomplish a functional commercial benefit to the customers (Cooper and De
Brentani 1991; Kleinschmidt and Cooper 1991; Song et al. 1998). Development
projects progress though a series of stages, such as opportunity identification, concept
development, product design, process design and commercialization (Atuahene-Gima
1996; Barczak 1995; Calantone et al. 1997). ‘Stage gate’ systems, originally
developed by Dr. Robert G. Cooper, divide the development process into a set of
stages, each composed of a group of activities (Griffin 1997). The early stages consist
of idea development and screening (that is, generation and evaluation of potential
solutions to the identified strategic opportunities), business and opportunity analysis
(activities that involve converting new product ideas into well-defined product
attributes that meet market demand) and technical assessment (designing,
engineering, testing and building desired physical product entity) (V eryzer 1998;
Song and Montoya-Weiss 1998). Thus, early in the process, the concept is evaluated

with respect to the market opportunity and customer needs. The concept is then

63

refined, its technical feasibility is examined, and the design phase begins (Griffin and
Hauser 1996; Meyers et a1. 1999).

Many studies identify different NPD activities as important determinants of
innovation success (Cooper and De Brentani 1991; Cooper and Kleinschmidt 1987;
Dwyer and Mellor 1991; Song and Parry 1996, 1997; Di Benedetto 1999; Veryzer
1998; Souder and Jenssen 1999). Studies report positive and significant correlations
between new product success and development proficiency measures, which include
idea generation, market research, predevelopment planning, concept definition and
evaluation, technical assessments, product development and test-marketing (Cooper
1979; Song and Parry 1996). Cooper’s (1979) results point to proficiencies in
conducting marketing and development activities as important discriminators of new
product success and failure.

In the early stages of the process, product ideas are generated based on internal
sources and market needs. After screening of the potential projects, a priority list is
generally formed and concept development is initiated. The concepts are evaluated
with respect to market opportunities and customer needs, followed by an assessment
of their technical feasibility. Technical assessment generally focuses on the technical
attributes of the product. After the concepts are refined based on these evaluations,
production start-up instigated (Griffin and Hauser 1996; Griffin 1997). Product
development is believed to be less costly and the firms are more likely to exploit
market opportunities if trivial or risky projects are eliminated in advance through tests
of their technical performance and marketing potential. Calantone and Di Benedetto
(1998) found that superior performance with respect to marketing and market
intelligence activities allow the firm to perform technical activities better and

influence the ultimate positional advantage. Consequently, proficient activities (i.e.,

64

idea generation, technical assessment and product development) should enhance an
innovation’s technical performance while idea generation and product development
activities should improve the degree to which it is superior in meeting market

demand. Thus:

H7. Proficiency in idea generation and opportunity analysis is positively related to
(a) the degree of customer need met and (b) product advantage.

H8. Proficiency in technical assessment is positively related to product advantage.

H9. Proficiency in product development is positively related to (a) the degree of
customer need met and (b) product advantage.

3.3.2.2 The Effects of Development Speed and Development Cost (H10, H11)

NPD speed (or cycle time) and costs have consistently been considered critical
competitive variables. Ali et al. (1995) and Kessler and Chakrabarti (1999) define
speed to market (or cycle time) as the time elapsed between initial stages (that is, the
beginning of idea generation) to the ultimate commercialization. Stating it differently,
achieving speed refers to accelerating activities that occur throughout the
development process. Product obsolescence necessitates timely introduction and low
cost production of new products. Most buttress that speed in innovation development
engender success since firms can reap pioneering advantages by being prompt and
timely in exploiting opportunities and/or responding to their environments (Kessler
and Chakrabarti 1999). Consequently, one would expect that the faster and the less
costly a firm can develop a new product, the greater the likelihood it can reap
pioneering advantages and higher returns. This brings us to:

H10. Development speed is positively related to (a) the degree of customer need met
and (b) product advantage.

H11. Development cost is negatively related to (a) the degree of customer need met
and (b) product advantage.

65

3.3.3 Positional Advantages

Positional advantages can be obtained through lowering of costs and creating
value to customers (Day and Wensley 1988). According to research on the adoption
and diffusion of innovations, typically analyzed according to Rogers’ (1976) scheme,
the relative advantage, compatibility with potential adopters, the trialibility and
observability of new products all exert positive effects on successful adoption
(Gatignon and Robertson 1989). Meanwhile, firms creating superior, unique and
novel products should enjoy competitive advantage in the market and hence
commercial success (Friar 1995; Gatignon and Xuereb 1997; Atuahene and Ko 2001).
They point to the importance of products that, in the eyes of the customer, provide
high performance and economic advantages (benefit to cost ratio) (Cooper and
Kleinschmidt 1987; Montoya-Weiss and Calantone 1994; Song and Parry 1996;
Souder et al. 1997). Hence, the market leverage gained via these products should be
assessed based on dimensions addressing both technologies (i.e., product advantage)
and markets (i.e., the degree of customer need met; Firth and Narayanan 1996).
3.3.3.1 The Effect of Degree of Customer Need Met (H12)

Products that better match consumer needs are more likely to be successful in
the market (Cooper and Kleinschmidt 1987; Maidique and Zirger 1984). Some also
emphasize a product’s potential for reducing consumer costs and expanding consumer
capabilities (Bhoovaraghavan et a1. 1996; Sengupta 1998; Michael et a1. 2003;
Hultink and Robben 1999). Day and Wensley (1988) assert that such products offer
greater potential for customer satisfaction and loyalty, and thus should entail higher
performance. Consequently,

H12. Degree of customer need met is positively related with project performance.

66

3.3.3.2 The Effect of Product Advantage (1113)

Product advantage refers to the degree to which the new product provides
customers with improved quality, superior technical performance and higher benefit—
to-cost ratio (Calantone and Di Benedetto 1988; Song and Parry 1997). Montoya-
Weiss and Calantone ( 1994) and Langerak et al.’s (2004) results exhibit a positive
relationship between product advantage and new product performance, while Henard
and Szymanski’s (2001) meta-analysis identified product advantage as the most
important factor to explaining the success of the new products. Moreover, Cooper and
Kleinschmidt (1987) and others have found a positive relationship between new
product success and measures of product advantage, such as the presence of unique
features and product quality (Song and Parry 1996; Souder et al. 1997; Sethi 2000).
Thus, it is posited that:

H13. Product advantage is positively related with project performance.
3.3.4 Market Environment (Hl4-H15)

Cooper and Kleinschmidt (1987) describe an attractive market for a new
product as one with a high grth potential, large size, weak competition and lacking
intense competitive activity. The success of a newly introduced product cannot be
adequately explained without considering the market dynamics (Montoya-Weiss and
Calantone 1994; Calantone et al. 1997). As mentioned, contingency theory takes into
account the influential internal and external factors on the conduct of firms’ business
and competitive actions. This framework posits that firms gain competitive advantage
if their internal environment (i.e., strategies, resources and internal capabilities) are
matched appropriately to environmental opportunities (Porter 1981; Mahoney and
Pandian 1993). The contingency approach focuses on how firms structure and

construct their culture and strategies due to the environmental turbulence they face;

67

this determines the extent to which these intra-organizational factors translate to
performance outcomes (Prescott 1986; Atuahene-Gima 1995; Calantone et al. 1997;
Souder and Song 1998; Calantone et al. 2003; Han et a1. 1998). Day and Wensley
(198 8) also argue that the conversion of a positional advantage into performance
outcomes is contingent upon other factors. Consequently, in this study, it is propose
that the project's environment (that is, market size, potential and competitiveness)
moderates the relationship between positional advantages (i.e., either the degree of
customer need met or product advantage) and project performance.

Market potential signifies the attractiveness of a target market, that is, its size
and growth (Song and Parry 1997). A large market with a high growth potential
should amplify the rewards incurred from the product's quality, performance, or
benefits perceived by the market (Cooper 1979; Cooper and Kleinschmidt 1987; Song
and Parry 1994). Song and Parry’s (1997) results indicate that the relationship
between product differentiation and relative product performance was moderated by
increases in market potential. Thus, referring to contingency theory, the linkages from
the degree of customer need met and product advantage to project performance is
predicted to be strengthened in markets that are of larger size and higher growth.

Market competitiveness, on the other hand, refers to the intensity and
concentration of rivalry within the firm's target market. When market competitiveness
is high, a new product with perceived benefits and advantages should elicit aggressive
responses from competitors, which could adversely affect project performance
(Cooper 1979; Debruyne et a1. 2002). Despite the significant and negative correlation
obtained between market competitiveness and new product success in Cooper (1979)
and Song and Parry’s (1994) studies, other studies have failed to find a significant

relationship between market competitiveness and new product success (e. g., Cooper

68

and Kleinschmidt 1987). Debruyne et al. (2002), on the other hand, argued that
competitors fail to respond to radical innovations and to new products that employ a
niche strategy, but react if a new product can be assessed within an existing product
category. Calantone, Schmidt, Di Benedetto’s (1997) results also show that a hostile
competitive environment increased the impact of NPD proficiency. The degree to
which meeting customer needs lead to success may be diminished by the rivalry in the
market, but product advantage should bring about higher returns as competition
intensifies. Thus:

H14. The positive relationship between the degree of customer need met .and project

performance should be significantly stronger as (a) market size increases, (b) market
growth increases, but weaker (c) market competitiveness increases.

H15. The positive relationship between product advantage and project performance
should be significantly stronger as (a) market size increases, (b) market growth
increases and (c) market competitiveness increases.
3.4 METHODOLOGY
3.4.1 Sampling Frame
The sampling frame, obtained from a commercially supplied list, comprised
600 North American firms operating in chemical, biochemical and pharmaceutical
industries. Respondents to the mail survey included product line managers, NPD
managers and product managers. Each firm was contacted by phone to encourage the
participation by the correct key informant prior to sending the survey. Respondents
provided information for their latest new product project (on the market five years or
less) that involved the development of any product that has not been previously
produced or sold by their company or division. The respondents were also asked to
identify these projects as successes versus failures (Cooper and Kleinschmidt 1987).
The mailingof the surveys generated 306 usable questionnaires yielding a

response rate of 51%. The descriptive statistics describing the sample of participating

firms are provided in Table B-2. A comparison of project success (success vs. failure)

69

and indicated firm characteristics (i.e., firm size, annual sales, export sales, percentage
sales and profits generated by new products) were performed to identify any
significant difference that may exist between successful and failed projects. The t-
tests revealed no significant differences at .05 level (except for R&D expenditure)
indicating lack of serious problems resulting from firm demographics. Majority of the
respondents were employed in the marketing function (N=130), followed by R&D
department (N=110) and general management (N =1 8).
3.4.2 Analysis

The proposed model was examined using partial least squares analysis (PLS).
PLS was selected to test the hypotheses since it is intended for causal-predictive
analysis in explaining complex relationships (i.e., when the number of indicators are
beyond 40 or 50), factors that are collinear, and/or interaction effects (F omell and
Bookstein 1982; Hulland 1999). The objective of PLS, first proposed by Wold (1963),
is the explanation of variance by ordinary least squares (OLS) (Barclay 1991). OLS
estimates each of the latent variables as an exact linear combination of its indicators,
with the goal of maximizing the explained variance for the indicators and latent
variables. Following a series of OLS analyses, PLS optimally weights the indicators
such that a resulting latent variable estimate can be obtained. Accordingly, it avoids
the indeterminacy problem and provides an exact definition of component scores.
Because the iterative algorithm generally consists of a series of OLS analyses,
identification is not a problem for recursive models nor does it presume any
distributional form for measured variables. PLS is considered superior to other
techniques (such as factor analysis, multiple regression and path analysis) because it
tests the measurement model within the context of a structural path model (Keil et a1.

2000). Chin (1998) contended that PLS, compared to other path-analytic techniques,

70

required minimal demands on measurement scales, sample size, and residual
distributions.
3.5 RESULT: MEASUREMENT VALIDATION

The PLS model was analyzed in two stages: (1) the assessment of the
unidimensionality, reliabilities and validity of the measurement model, followed by
(2) the evaluation of the structural path model. Using Hulland’s (1999) guidelines, the
adequacy of the measurement model was tested by examining: (1) unidimensionality
of the constructs, (2) scale reliabilities, and (3) convergent and discriminant validity.
Principle component analysis with varimax rotation was first performed to assess the
unidimensionality of each construct. Only the first eigenvalue was greater than one;
this supported their unidimensionality (Gerbing and Anderson 1988).

The scale reliability of the measures was evaluated by examining the loadings
of the items on their corresponding factors (F omell and Larcker 1981; Hulland 1999).
PLS revealed high loadings (> .63) for all scales in the measurement model, which
provided support for their reliability (see Table 3-2) (Churchill 1979; Peter 1979;
Bagozzi and Yi 1988; F omell and Bookstein 1982). Convergent validities were then
assessed by calculating their internal composite reliabilities (ICR)8 and average
variance extracted (AVE)9 (see also Table B-3) (Fomell and Larcker 1981; Chin
1998). These reliability coefficients ranged from 0.74 to 0.91, providing strong
support for each latent variable (Nunnally 1978; Gerbing and Anderson 1988;

Bagozzi and Yi 1988). The reported AVE’s in PLS were acceptable (i.e., at least .58),

 

8 Internal Composite Reliability (ICR) is similar to Cronbach’s alpha, however, does not assume tau
equivalency among the measures and assumes all indicators are equally weighted. The ICR statistic
represents a ratio consisting of the squared total of the variance explained for each manifest variable
divided by the sum of the squared total of the variance explained plus the total of the unexplained
variance. An ICR greater than .7 is considered adequate to achieve sufficient reliability.

9 Average Variance Extracted (A VE) attempts to measure the amount of variance that a latent variable
captures from its indicators relative to the amount due to measurement error. The ratio is the total of
variance explained divided by the sum of variance explained and variance unexplained. An AVE
greater than .5 is considered adequate in the sense the manifest variables measure what is intended.

71

showing strong support for substantial explained variance in each dependent variable
(Fomell and Larcker 1981; Chin 1998). Finally, discriminant validity was evaluated
by testing whether the AVE of each construct (the average variance shared between a
construct and its measures) was greater than the shared variance between the construct
and other constructs in the model (square of correlation between the two constructs)
(Fomell and Larcker 1981; Hulland 1999; Agarwal and Karahanna 2000). The
AVE’s of the constructs were all higher than their shared variances, and thus, all
constructs in the model exhibited discriminant validity.

All scale items are shown in Table 3-2'0. All items were specified as reflective
indicators comprising 1 1-point semantic differential scales loading on their respective
constructs. Fit measures were gauged using multiple item scales except for R&D fit,
which was measured using a single item scale (see Table 3-2 and Appendix B).
Supply chain fit incorporates four items, i.e., the degree to which the project
requirements match with existing distribution capacity, target market, customer needs
and existing channels (ICR=.87). The marketing fit construct consists of three items
encompassing fit with advertising, promotion, and market research activities
(ICR=.85). Manufacturing fit was measured using four items that gauge fit regarding
existing technologies, plants, manufacturing and development skills (ICR=.91). The
three items for project climate assessed cross-functional integration, the extent to
which a multidisciplinary team approach was employed, and team commitment
(ICR=.86). Similarly, the five items for project formality include the formality of
NPD process and the degree to which target market, product concept, benefits,

positioning and features are defined (ICR=.91).

 

'0 The PLS construct level statistics (AVE and ICR) indicate a fit for the manifest variables to the latent
variables’, however, they do not give an indication of overall model fit or how the latent variables co-
vary with one another.

72

TABLE 3-2. MEASURES AND LOADINGS

  

  

SUPPLY CHAIN FIT

ICR=.87; AVE= .62; sqrt AVE=.79

 

 

   
  

 

  

 

 

 
 
 
 

 

 

 

 

 

 

 

 

 

 

  

 

  
   
  
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Distribution capacity . _ 0.87
Varget market ‘1 7 0.67 ’
Customer needsimilarity _ $0.73 ..
Existin channels 0.86
MARKETING FIT ICR=.85; AVE= .66; sqrt AVE=.81
enising _ ’05'3
Market Research 0.82 M .
Promotional activities _ 0.78 if
R&D FIT Sin le item NA
MANUFACTURING FIT ICR=.91; AVE= .72; sqrt AVE=.85
lManufacturing ‘ q [782 77777
Plant .085
Development ..... . 0.93. _._
Technolo 0.79 ‘
PROJECT CLIMATE ICR=.86; AVE= .68; sqrt AVE=.82
(Cross-functional integration (T84
Team for entire project ’7 0.89 llllll
Team commitment 0.73
PROJECT FORMALII’Y ICR=.91; AVE= .64; sqrt AVE=.EJ
Target market defined E781
Prodirct concept definedfii: H i N 0.80 4
Benefits defined . . 0.82
Positioning defined, , p p 085
Features defined ‘ 0.84 .
rocess 0.63
IDEA DEVELOPMENT ICR=.85; AVE= .60; sqrt AVE=.77
Initial screening 0.80 ,
Preliminary market assessment ,,,,, 0.82 .
Detailed market study . 0.72
Business anal sis 0.74
CHNICAL ASSESSMENT ICR=.85; AVE= .65; sort AVE=.81
Preliminary technical assessment .. (ll—IE; . ..
Customer tests 0.70 _, ,
ln-house iroduct testin 0%
PRODUCT DEVELOPMENT |CR=.82; AVE= .69; sort AVE=.83
Pilot production 0.78
Production start-up 0.88
DEVELOPMENT SPEED ICR=.88; AVE= .79; 8qu AVE=.89
ima efficiency , g 0737
. .-herence to time schedule _ 0.91
DEVELOPMENT COST Single item NA
CUSTOMER NEED MET ICR=.HJ; AVE= .69; sort AVE=.83
Benefit importance to customer ‘ 0.86
Benefits easytp communicate .,.”0-?5. A . ‘
Perceived as useful ________ . (0.89
Visible benefits ‘ I 0.82
PRODUCT ADVANTAGE ICR=.88; AVE= .65; sort AVE=.81
Superiority . ., . _ 0.8-9
Relative product quality 0.94 ..
Unique attributes 7 gggggg 0.72 ggggg
lmact on customer 0.64
IMPACT ON PERFORMANCE ICR=.88; AVE=-.71; sqrt AVE=.84
Mechnical success rating ‘ .. (0.83, g y
Profitability rating ., ...... , ., 9:90
Sales/crofit im-act on company _ 0.84
MARKET SIZE Single item NA
MARKET GROWTH Single item NA
MARKET COMPETITIVENESS fitment NA

 

 

 

73

NPD process proficiency measures were gauged using multiple item scales
except for development cost, which was measured using a single item scale. Idea
development proficiency incorporated competence in initial screening, preliminary
market assessment, detailed market study, and business analysis (ICR=.85). Technical
assessment proficiency includes skills in preliminary technical assessment, customer
tests, and in-house product testing (ICR=.85). Product development proficiency items
assess pilot production and production start-up skills (ICR=.82). Development speed
is evaluated by time efficiency of the project execution and the team’s adherence to
the time schedule (ICR=.88).

The three ‘customer need met’ items were developed to assess the degree to
which the product provided benefits important to the customer, easy to communicate,
visible and perceived as useful (ICR=.90). Product advantage was measured by the
degree to which the product was superior relative to other competitor products, had
unique attributes, high relative quality and great potential to bring about impact on
customers (ICR=.88). To measure project performance, respondents rated the
project’s profitability, technical success and sales/ profit impact on their company on
three items (ICR=.88).

The moderating market environment factors were each measured using single
measures: market size was assessed by asking the respondents the number of potential
customers in their main target market, whereas market competitiveness was measured
by the number of competitors. Market growth was measured using a 11 point
semantic differential item indicating the pace of growth in the market (i.e., negative
growth vs. fast growth; e. g., 15% per year). Firm size (by incorporating the natural
logarithms of annual revenue) and number of employees were employed as control

variables (due to the large variance of the reported answers).

74

3.6 RESULTS: STRUCTURAL MODEL

The PLS construct level statistics (AVE and ICR, explained above) indicate a
fit for the manifest variables to the latent variables; however, they do not give an
indication of overall model fit or how the latent variables co-vary with one another.
Since PLS is designed to maximize prediction, the emphasis is put on explanatory
power to maximize variance in the dependent variables based on the independent
variables in the model. Consequently, the degree to which PLS models accomplish
this objective is evaluated based on prediction oriented measures (R2) (instead of
covariance fit as is attempted in SEM) (F omell and Cha 1994; Hulland 1999). Tables
3-3, 3-4, 3-5, 3-6, 3-7 and 3-8 show the results for the hypothesized model: the path
estimates along with an indication of the significance of the hypotheses. Finally,
Table 3-9 presents the variance explained for each dependent construct.

Since the data comprised responses from single informants, common method
variance was statistically controlled for using two techniques (Podsakoff et al. 2003):
first, Harman’s one-factor test, i.e., a test on whether a factor analysis yields a single
factor when all constructs are analyzed, was conducted. The results of principal
components analysis without rotation exhibited 12 factors with eigenvalues greater
than 1.0 accounted for approximately 75% of the total variance. Thus, the results of
the Harman’s one factor test indicated that the variables did not form only a single
higher-order factor. Second, following Podsakoff et al.’s (2003) and Netemeyer et al.
(1997)’s guidelines, a ‘same-source’ factor (i.e., single-common-method-factor) was
incorporated to the indicators of all constructs in the model. This model in which the
same-source factor loadings were estimated freely was compared to a constrained
model in which the loadings of all indicators to the same source were set to zero. The

difference between these models represents a significance test of whether there exists

75

an effect of a common-method factor. A confirmatory factor analysis yielded a Z

difference of 674.2 (df = 45, p<.01). Hence, this significant difference between the
two models suggested that a same source factor was evident. Following this, a path
analysis through PLS was conducted for the unconstrained model to investigate the
effects of common-method variance on the indicator loadings and model paths.
Despite this, the indicator loadings to their theoretical factorsll as well as the paths
among the model constructs12 all remained significant with trivial attenuation (or
inflation) (see Appendix B Table B4- Table B-10). On the whole, considering the
weak points of all possible techniques to assess method biases (Podsakoff et al. 2003),
it was concluded that there was some effect of common methods variance, but the
results remained consisted when this effect was controlled for.

3.6.1 Results for H1— H4. a-e (Please refer to Table 3-3)

Supply chain fit was significantly positively related to idea generation

proficiency (71 a =.087, p<.10), product development proficiency (71b =.364, p<.01),
development speed (ch = .121, p<.10), providing support for Hla, ch and Hld.

Contrary to what was expected, it also increased development cost (71d = .160, p<.05),
thus Hle was rejected. The results also showed that marketing fit exerted a negative
impact on idea development (72a = -.164, p<.05) and development cost (72d = -.154,
p<.05). Therefore, H2a was rejected whereas H2d was supported. Marketing fit,

however, did not have any significant effect on product development (3% = .055, n.s.)

or development speed (‘y2c = .05 5, n.s.; not providing support for H2b or H2c). R&D

 

” Except for the loadings of product advantage, which were slightly inflated.

'2 Except for marketing fit-product development, marketing fir-product advantage, product
development-project formality, idea development-customer need met, idea development-product
advantage, development cost-customer need met and moderating effect of market size on product
advantage - project performance.

76

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77

fit did not exert any significant influence on idea generation (“Y3a = -.014, n.s.) or
product development (’y3c = .042, n.s.) or cost (Y3e = -.030, n.s.; not providing support
for H3a, H3c or H3c), but improved technical assessment (73b = .161, p<.01) and
development speed (73d = .091, p<.10; confirming H3b and H3d). Manufacturing fit

improved idea generation (74a = .101, p<.05; as predicted in H4a), but did not

significantly influence on any of the remaining proficiency measures.
3.6.2 Results for Tests of Mediation (Hl-H4.f-g) (Please refer to Table 3-4)

Contrary to predictions, supply chain fit had negative direct impacts on the
degree of customer need met (71f: -.298, p<.01) and product advantage (71 g = -.248,

p<.01), contradicting Hlf and ng. The relationship from supply-chain fit to product
advantage was mediated by product development proficiency and development cost.
Additionally, supply—chain fit indirectly affected on the degree of customer need met

through development proficiency and development speed. Marketing fit, on the other

hand, directly and positively influenced the degree of customer need met (72f: .454,

p<.01) and product advantage ('yzg = .361, p<.01), as opposed to what was predicted in

H2f and H2g. It also indirectly affected product advantage through impairing
proficiency in idea development and opportunity analysis and decreasing
development cost. Its indirect impact on degree of customer need met was mediated
by reducing idea development proficiency.

R&D fit influenced the degree of customer need met directly and negatively

(7n = -.113, p<.05; as opposed to H31), as well as, indirectly through increasing
development speed. It did not have a significant direct impact on product advantage

(')(3,3 = .029, n.s.; supporting H3g), however, an indirect effect through enhancing

78

technical assessment. Manufacturing fit also did not exert any direct influence neither

of the degree of customer need met (74; = .055, n.s.) and product advantage (‘yzg =

.016, n.s.; in accordance to H4f and H4g). However, it impacted product advantage

and degree of customer need met indirectly by enhancing idea generation proficiency.

TABLE 3-4. RESULTS FOR TESTS OF NIEDIATION (fil-H4J-g)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Positional Advantages] (f) (g)
Customer Need Met Product Advantage
Strategic Fit

H1 Supply Chain Fit H1(f): no direct rel. H1(g): no direct rel.
Y1f= -0.298 (t=- 4.4350) v19 = -0.248 (t=-4.1047)
Conclusion: Reject Conclusion: Reject

H2 Marketing Fit H2(t): no direct rel. H2(g): no direct rel.
Y2f = 0.454 (t=10.8392) ng = 0.361 (tr-5.3736)
Conclusion: Reject Conclusion: Reject

H3 R&D Flt Hero: no direct rel. H3(g): no direct rel.
Y3f = -0.113 (t= -2.1762) V39 = 0.029 (n.s.)
Conclusion: Reject Conclusion: Support

H4 Manufacturing Fit H40): no direct rel. H419): no direct rel.
V4, = 0.055 (n.s.) Y4g = 0.016 (n.s.)
Conclusion: Support Conclusion: Support

 

 

 

 

 

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, ** and *** indicate 0.1, 0.05 and 0.01 significance levels,

respectively.

3.6.3 Results for H5-H6. a-e (Please refer to Table 3-5)

Project formality and project climate fostered proficiencies in idea

 

development (y5a = .510, p<.01; 763 = .310, p<.01), technical assessment (75b = .419,

p<.01; 76b = .208, p<.01), and product development (75c = .236, p<.01; 76;: = .190,
p<.01), supporting H5a, H5b, H5c, H6a, H6b and H6c. Project formality decreased
speed (75d = -.363, p<.01), at the same time reducing cost (‘Ysc = -.167, p<.05; as
opposed to H5d, but parallel to H5e). On the contrary, project climate increased speed
('Yod = .664, p<.01) while increasing development cost (76d = .386, p<.01; providing

support for H6d, but leading to the rejection of H6e).

79

TABLE 3-5. RESULTS (ES-H6. a-e)
H5 H6

Project Formality Project Climate

Process Proficiency

Idea Development : + :
Opportunity Analysis 1 (t=7 4) (t=5.

echnical Assessment ; + ; +
0.419 (t=6.7800) " .208 (t=3.8019)

Product Development ; c ; +
(t=2.8331) 5c = 190 (t=2.7031)

Development Speed ; ; +
.363 (t= 4.3861) 664 (t=9.6924)

Development Cost . e .
68 = (i=4

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, ** and *** indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

3.6.4 Results for H7-Hll. a-e (Please refer to Table 3—6)

The NPD activities proficiencies significantly enhanced degree of customer
need met and product advantage were idea development ([31a = .203, p<.01; B", =
.187, p<.01; as predicted in H7a and H7b) and product development ([33, = .399,

p<.Ol; [331, = .450, p<.01; as in H9a and H9b). Contrary to H8, proficiency in technical
assessment decreased product advantage (sz = -.171; p<.01). Development speed
significantly reduced customer need met ([34a = -.121, p<.01; contrary to HIOa), did

not affect product advantage ([341, = .006, n.s.; not providing support for H10b).
Development cost did not exert any significant impact on degree of customer need

met ([353 = -.024, n.s.; not providing support for Hlla), but decreased product

advantage (05b = -.O78, n.s.; parallel to H1 lb).

80

TABLE 3—6. RESULTS jI_I7-Hll. a-e)

 

Advantagesl (a) I (b)

 

      

Customer Need Met I Product Advantage

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NPD Process Proficiency
H7 Idea Development an H7(a): + [H7(b)2 +
09!”an “"1"" [[513 = 0.203 (t=3.7033) Iii", = 0.187 (t=2.6808)
IConclusion: Support IConclusion: Support
IHB Technical Assessment H8: +
0a. [32b = -0.172 (t= -3.7707)
Conclusion: Reject
H9 Product Development IH9(a): + H9(b): +
"33,, = 0.399 (t=8.5931) 63., = 0.450 (t=7.9771)
IConclusion: Support iConclusion: Support
H10 Development Speed IH10(a): + IH10(b): +
[[34a = -0.121 (t= -2.3573) “34., = 0.006 (n.s.)
IConclusion: Reject [Conclusionz Not Support
H11 Development Cost IHI 1 (a): .. IH11(b)1 -
[658 = -0.022 (n.s.) [65,, = -0.078 (t= -1.5005)
IConclusion: Not Support IConclusion: Support

 

 

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, ** and *** indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

3.6.5 Results for H12-H13 and Tests of Moderation (HM-HIS) (Please refer to
Table 3-7 and 2-8)

The degree of customer need met and product advantage both enhanced the
project’s impact on performance ([16 = .299, p<.01 and [37 = .421, p<.01; providing
support for H12 and H13). The linkage from degree of customer need met to project
performance was affected by neither market size (1311:: = -.168, n.s.), market growth
(13)“, = .097, n.s.), nor market competitiveness (011C = -.089, n.s.), providing lack of
support for H14a, H14b and H140. The relationship between product advantage and

project performance was negatively moderated by market size (1312,, = -.405, p<.10)
and market growth (1312b = -.525, p<.05), but positively by market competitiveness

([312c = .389, p<.05), contrary to what was expected (H15a, H15b and H150). Market

size and market growth had positive direct impacts on project performance ([33 = .654,

81

p<.01 and Bo = .297, p<.10, respectively), whereas market competitive a significant

negative direct influence (I310 = -.109, p<.01). Finally, annual sales exert a negative

effect on performance ([313 = -.058, p<.05), but number of employees was not

significantly related ([314 = -.029, n.s.).

TABLE 3-7. RESULTS (212-1113)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Outcomesfl
Project Performance
Posltlonal Advantages
H12 Customer Need Met H12: +
[36 = 0.299 (t=3.0141)
Conclusion: Support
H13 Product Advantage H13: 4-
Pr = 0.421 (t=4.1370)
Conclusion: Support
Market Size _
.. 8 [33 - 0.654 (t=3.7253)
o _
g .5 Marketemwth 69 = 0.297 (t=1.5216)
E 9 Competition Concentration
I310 = -0.109 (t= 0.3530)
g g Annua' 33'“ [313 = ~0.058 (t=-1.6578)
5 '5 Number of Employees __
0 > 514 ‘ -0.029(n.s.)
TABLE 3-8. RESULTS FOR TESTS OF MODERATION l4-H7.a-b
Pathsl H14 l H15 1
Customer Need Met - Projecthroduct Advantage - Project]
Moderators Performance Perionnance
(a) Market Size H14(a): + H15(a): +
[5113 = -0.168(n.s.) B12, = -0.405 (t= -1 .3741)
Conclusion: Not Support Conclusion: Reject
(b) Market Growth H14(b): + H15(b): +
1311., = 0.097(n.s.) 1312., = 0525 (t=-1.998)
Conclusion: Not Support Conclusion: Reject
(c) Market Competitiveness H14(c): - H15(c): -
1311:: = -0.089(n.s.) 612,, = 0.389 (t=2.0164)
Conclusion: Not Support Conclusion: Reject

 

 

 

 

 

 

 

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, ** and *** indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

82

TABLE 3-9. VARIANCE EXPLAINED RESULTS

 

 

 

 

 

 

 

 

 

 

Dependent Variables Multiple R2 I
Idea Development 0.50]
Technical Assessment 0.41]
Product Development 0.3g
Development Speed 0.27]
Development Cost 0.13]
Product Advantage 0.41]
Customer Need Met 0.36]
Project Performance Impact 0.54]

 

 

3.7 DISCUSSION

Internal innovation development is perceived as high risk and difficult
venture. Despite the most prevalent product failure reasons that are primarily
associated with the market side of innovation, research on NPD projects has focused
on internal factors from the firm’s perspective. However, in the extant literature, it is
widely accepted that a match between what is needed (i.e., market pull) and what can
be developed internally (i.e., technology push) is necessary. The operationalization of
this match is still not provided in the literature. Thus, this study elucidates the notion
of ‘strategic fit’ as the degree of new resources and capabilities required for the
product (based on Venktraman’s conceptualization of fit as matching). Measures to
assess ‘strategic fit’ that incorporates all functions (that is, supply-chain, marketing,
R&D and manufacturing) relevant in NPD process are provided.

In the innovation literature, resources, capabilities and strategic orientations
have been directly linked to positional advantages and performance outcomes.
Despite its importance being stressed in popular press, the implementation of these
orientations and process activities has generally been neglected in new product
models. Thus, drawing upon Day and Wensley’s (1988) framework, whether a firm’s
internal environment (i.e., the strategic fit of the project, project specific sources of

advantages and proficiency in NPD process implementation) influences positional

83

advantage and performance outcomes is scrutinized in this study. The moderation
effects of external environmental factors on these relationships are also tested within
the context of contingency theory (which states that the relationships from internal
factors to innovation success are not under the total control of the managers, but are a
least partly determined by the market forces). Thus, two contributions of this research
lie in providing an operationalization of ‘strategic fit’ of the project and incorporating
the implementation aspects in technological innovation models. The comprehensive
model serves to aid in both the prediction and the understanding of internal innovation
development at the project level. As Day and Wensley suggest (1988), achievement of
performance superiority depends on identification of skills that “exert most leverage
on positional advantages” (p. 5). Accordingly, this analysis may provide managers an
understanding of the proj ect-level factors that engender positional advantages and
performance returns and whether they are mitigated by market dynamics. Thus, this
study may aid their decision-making and identification of skills and process activities
to allocate their resources and investments to. Due to the complexity of the proposed
model, hypotheses were explored through a component-based partial least squares
analysis (PLS).
3.7.1 Strategic Fit: Direct Effects

The findings demonstrated that supply chain fit (which reflects the degree to
which the new projects exploits existing distribution channels and past projects’ target
market and market demand), enhanced idea development, product development
proficiency and development speed, but also elevated costs. The synergy with the
firm’s supply chain skills resulted in more effective new product idea generation and
more proficient and timely development activities; however, supply chain fit

deteriorated cost structure, which further impacted positional advantage. One of the

84

reasons may be the shifting of bargaining power to suppliers due to prior transactions
and commitments, thus increasing dependence and transaction costs.

On the other hand, the project’s fit in terms of advertising, market research and
promotional requirements inhibits idea generation skills, which may be because of a
focus on ideas that are guaranteed to sell. Such synergy did not significantly enhance
development capabilities or timeliness. Moreover, similar to supply-chain fit,
marketing fit lead to higher costs, which may result from inefficiencies due to
maturations and decreasing demand or neglected opportunities. On the whole, these
first two aspects of fit incorporating the customer-side both increased development
costs; but supply chain fit enhanced, whereas marketing fit diminished idea
development proficiency. Therefore, the findings indicate that less departure from the
existing value-chain, but more departure from the marketing gestalt may be beneficial
for the generation of new ideas and the identification of opportunities.

The results indicated that the project possible requirement’s match between
firm’s R&D expertise, level of R&D expenditures and R&D personnel’s training does
not determine how effective novel product ideas are generated, how well
opportunities are identified or how proficient development activities are executed. In
other words, the degree to which firms are inventive and entrepreneurial in the earlier
stages of their processes or skillful in product development may not be determined by
the extent to which the project entails radical changes in research and development.
However, R&D fit enhances technical assessment proficiency and development
speed; that is it allows the deficient-free and timely response to customer
expectations. Additionally, the findings related to manufacturing fit show the

importance of internal sources for idea generation and gaining competitive

85

advantages. The project requirements need to be met with the existing development
capabilities and manufacturing resources.

Overall, it can concluded that for better idea development and opportunity
analysis, higher supply chain and manufacturing fit, but less marketing fit is required.
For more proficient technical assessment and product development, higher R&D and
supply-chain fit, respectively, may be desirable.

3.7.2 Strategic Fit: Indirect Effects

As mentioned, this study examined whether strategic fit of the project had
indirect effects on positional advantages and project performance mediated by the
quality of tactics and implementation. The results indicated that only manufacturing
fit has strictly indirect (positive) effect on returns through idea generation proficiency,
whereas supply-chain, marketing and R&D fit influence positional outcomes both
directly and indirectly through NPD activities. Thus, fit of the project with regards to
the market side of the NPD project are automatically converted into positional
advantages to a certain degree.

These conversions are also mediated by proficiencies in idea generation
(marketing fit-degree of customer need met and product advantage; supply chain fit-
degree of customer need met and product advantage), product development (supply
chain fit—product advantage and degree of customer need met), development speed
(supply chain fit—degree of customer need met and product advantage; R&D fit-degree
of customer need met) and development cost (marketing fit-product advantage; supply
chain fit—product advantage). Interestingly, supply-chain fit decreases positional
advantages both directly and through enhancing idea generation and product
development. This indicates that NPD process proficiency failed to compensate for

supply chain fit’s negative impact on positional advantages. This may be due to

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repeated focus on the target market and demand forecasts of previous projects, which
may preclude the firm from providing novel and superior benefits over current
products and thus lose positional advantages. Thus, an incremental approach in
decision making with regards to supply-chain resources and capabilities seems to
eventually inhibit both the degree to which customer needs are met and the profile of
products as unique, new and having high quality.

The new project’s synergy with the firm’s existing marketing (i.e., advertising,
market research and promotional capabilities) enhances its positional advantages.
Marketing fit also indirectly affects both product advantage and the degree to which
customer needs are met through decreasing idea development proficiency. This
suggests that superior market positions can be achieved in two ways: (1) the
advantages accrued as a result of exploiting existing advertising, promotion and
research capabilities in the products offered and (2) more effective idea generation,
which actually requires departure from existing marketing resources and skills.

The impact of R&D fit on product advantage is strictly indirect through
technical assessment proficiency. The benefit from the match of the project
requirements with the available R&D expertise, i.e., enhanced technical assessment,
ultimately decreases product uniqueness. Thus, the existing R&D expertise, level of
R&D expenditures and R&D personnel’s training enables more effective technical
assessment during the NPD process, which further diminishes product advantage.
This suggests that increasing R&D expertise and technical assessment capabilities
limits the level of uniqueness of the developed product.

Both supply chain and R&D fit increase development speed, which further
deteriorates the degree to which customer needs are met. This may be explained by

the notion that firms opted to ‘focus on trivial, rapidly executable development

87

projects for the sake of increasing speed to market’ (Calantone et al. 1997, p. 179) and
fail to meet what is needed in the market. What is more, supply chain fit indirectly
reduces product advantage through increasing costs. This may suggest that focus only
on projects that require existing supply chain capabilities may entail costs related to
losing new relationship opportunities (i.e., opportunity costs) and prior transaction
commitments that creates a lock-in situation (i.e., sunk costs). On the other hand,
marketing fit increases product advantage through reducing development cost. Thus,
use of existing advertising, promotion and research capabilities enhances product
advantage both directly and indirectly by decreasing associated costs.
3.7.3 Project-Specific Sources of Advantage

In this analysis, the impact of project-specific sources of advantage on the
proficiency of NPD process implementation is investigated. It was advocated that the
effects of project formality and project climate on the effectiveness of idea generation
and opportunity analysis vary: project formality diminished, whereas project climate
enhanced this proficiency. In the literature, it is generally accepted that formal
systems tend to repress creativity, brainstorming and experimentation. However,
contrary to what was expected, the findings indicated that formal management process
and cross-functional integration actually reduce uncertainties and enhance idea
generation, possibly through specifying the required tasks and their sequence and
encouraging open channels of communication and information sharing. Project
formality and climate also improved technical assessment and product development:
efficiencies attained through formal mechanisms and cross-functional integration did
lead to better execution.

Moreover, these project-specific advantages displayed contrary effects on

development speed and cost: Project formality reduced, while project climate

88

increased speed and cost. F ormalized procedures can regulate the tasks people
perform and assign role responsibilities, and thus facilitate input and involvement
from other departments whenever necessary. This may lead to cost efficiencies,
however bureaucratic requirements associated with formality may be time-
consuming. Committed team members under the supervision of their project leaders,
more direct communication and cooperation seem to bring about contrary
consequences, that is, making the development project more timely but more costly.
3.7.4 Proficiency of NPD Process Implementation

It was predicted that the execution of NPD process activities are key to
achieving positional advantage in the market. The results indicated that proficient idea
generation and product development activities led to positional advantage, in the form
of product advantage and responsiveness to customer needs. However, the findings
showed that proficiency in technical assessment diminished product advantage. This
may result from a focus on technical attributes and performance of the product that
may lead to failure in considering product quality and benefits in the eyes of the
customers. Development speed, contrary to the general assumption in the literature,
hindered the degree to which customer needs were met. This may be due to neglect of
consumer readiness for the sake of development speed. Interestingly, high
development cost weakened product advantage, but did not influence firms’
effectiveness in meeting customer demand. Increasing cost diminished the quality and
performance level of the new product, however did not affect how the customer
perceived its benefits.
3.7.5 Positional Advantage and Environmental Factors

Positional advantage should pay off, that is, lead to superior project

performance. The findings indicated that both meeting customer needs and product

89

advantage significantly enhanced the project’s impact on performance. This signifies
the importance of exceeding customers’ needs over what is offered in the market and
investing to gain relative product advantages. However, as the contingency
framework suggests, the extent to which such positional advantages lead to
performance returns should be moderated (i.e., strengthened or weakened) by the
market environment. Market environment brings about opportunities for the firm, but
also a certain level of unpredictability regarding external changes which involve
threats to the survival and growth of the firm. Subsequently, market size and growth
should enhance the level of returns finns incur from their positional advantage.
However, the uncertainty and hostility as a result of competitor actions should inhibit
the degree to which meeting customer needs and product advantage lead to higher
project performance. The results provided mixed support for the hypotheses.

Product advantage (that is, products with higher quality, technical
performance and benefit-to-cost ratio) engender superior performance consequences
as the size and the growth of the market decreases. On the other hand, the relationship
between product advantage and project performance is positively moderated by
market competitiveness. Thus, the relationship between product advantage and project
performance becomes stronger as the market demand shrinks and the competition
becomes more intense. In contrast, meeting customer provides higher returns
irrespective of the market conditions. Thus, investing in meeting customer needs
always proves rewarding regardless of whether rivalry is severe or customer demand
is changing.

3.7.6 Limitations and Future Research
The measurements were primarily perceptual and collected from single

respondents, all of whom were employed at firms operating in chemical, biochemical

9O

and pharmaceutical industries. Gonul et al. (2001, p.79) mention that marketing
strategies employed in these industries may be different from others due to difficulties
in identifying the decision maker and the actual buyer of the product. The particulars
of these industries may have limited the generalizability of the model, thus, firrther
research is recommended to investigate other industries. Single respondents leave the
effects linking the constructs subject to common method variance. Thus, a multiple
informant design is recommended for further studies. In this study, the impact of
market factors on the strengths of key model paths was scrutinized. Future research
may investigate the moderating impact of technological factors, such as the rate of

technological change or the presence of dominant standards.

91

CHAPTER4

GLOBAL STRATEGIC OUTSOURCING FOR RESOURCE DEFICIENT,
MARKET EFFICIENT TECHNOLOGICAL INNOVATION

4.1 INTRODUCTION

Technological advances and increasing globalization characterize the current
business milieu and have radically transformed the competitive landscape.
Consequently, the study of technology-intensive (TI) markets has attracted significant
research attention in the marketing, management and engineering disciplines (Buzzell
1999; John et al. 1999; Teece 1988; Dutta and Weiss 1997; Wuyts et al. 2004). These
markets are characterized by considerable uncertainty due to heterogeneous and
rapidly changing technologies, and by the fact that buyers frequently lack relevant
prior experience (Bourgeois and Eisenhardt 1988; Glazer 1991; von Hippel 1986). To
survive in such environments, firms increasingly strive to develop capabilities and
achieve strategic flexibility by building closer outsourcing relationships and adopting
modular systems (Harrigan 1985; Weiss and Heide 1993; Sanchez 1995). They
choose to outsource either a portion or the entire product development process to
survive and be more responsive to the market dynamics (Garud and Kumaraswamy
1995; Schilling 2000).

According to the Quarterly Index (January 13, 2005 in The Wall Street
Journal; January 14, 2005 in Silicon.com) from outsourcing advisory firm TPI, the
value of major outsourcing contracts awarded in 2004 totaled over $76 billion
worldwide. The phenomenon of downstream manufacturers cooperating with
upstream suppliers to introduce new products and/ or improve the quality of existing
product lines is prevalent across a spectrum of industries including consumer-

electronics, computer software, textiles, automobiles, steel and pharmaceuticals

92

(Clark and F ujimoto 1991; Bettis et al. 1992). Although these relationships constitute
avenues for inter-firm learning and increase firms’ adaptability, they also create a
certain degree of supplier-buyer dependence and bring about additional threats such
as the potential leakage of tacit know-how and the gradual loss of knowledge-based
capabilities (Heide and Weiss 1995; Swan and Allred 2003). Thus, buyer firm’s
dependence on external suppliers for design not only puts its intellectual property in
jeopardy, but also casts doubts on how much intellectual property it really owns.
Referring to these vulnerabilities, a recent Businessweek article (Special Report by
Engardio and Einhom, March 21, 2005, p. 84) mentions that “companies worry about
the message they send to their investors” as they outsource their design activities.

Another important feature of technology-intensive (TI) markets is the
increasing utilization of modular product architectures as the basis for new product
designs and development (Sanchez 1995, 1999; Katz and Shapiro, 1994; Stremersch
et al. 2003; Staundenmayer et al. 2005). Modularity is created by standardizing the
interfaces between functional components and specifying greater reusability and
commonality among product families (Sanchez and Mahoney, 1996; Wilson et al.,
1990; John et al. 1990; Garud and Kumaraswamy 1995). Moreover, these structures
enable embedded coordination through buyers’ and suppliers’ adherence to shared
objectives and common standards, linking geographically dispersed component
developers (Kogut and Kulatilaka 1994) and making global technology outsourcing
possible (Mikkola 2003).

Research on buyer behavior in TI markets remains focused on discrete buyers’
choices in single transactions with a cost-based approach, as opposed to considering
performance outcomes of portfolios of transactions (Walker and Weber 1984; Swan

and Allred 2003; Stremersch et al. 2003). Similarly, the literature on modularity,

93

standardization and network extemalities in T1 markets focuses on the buying of
modular systems using neoclassical rationales (i.e., production-cost perspective) and
institutional economics (i.e., a transaction-cost perspective) (Wilson and et al. 1990;
Schilling 2000; Swan and Allred 2003; Stremersch et al. 2003; Schilling and
Steensma 2001). As Weiss and Heide (1993) suggest, previous research on buyer
behavior in T1 markets has focused on specific strategic decisions as opposed to
buyers' underlying processes.

Putting it all together, the study of buyer-supplier relationships in technology—
intensive modular system markets requires consideration of interdependencies across
transactions, operation costs and locations (Aulakh and Kotabe 1997; Kim and Hwang
1992). The literature may benefit from analyses of interaction effects of performance
determinants and more dynamic (continuous) systemic techniques as opposed to
earlier studies, which are rather static and conceptualize buyer-supplier exchanges as
a sequence of independent one-time events. Thus, it is critical to gain an
understanding of the characteristics of buyers’ technology outsourcing relationships in
global markets and their effects on the market value of their firms.

In summary, grounded in the marketing, management, and international
business literatures, the primary objective of this third essay is to develop and test a
model of the determinants of the market value of firms’ global technology
outsourcing relationships, classified according to the task characteristics, switching
costs and the degree of environmental uncertainty (Heide and Weiss 1995; Eisenhardt
1985; Weiss and Heide 1993; Pisano 1990). The methodology involves an event study
analysis and other secondary data sources (Fama 1991; McWilliams and Siegel 1997;
Srinivasan and Bharadwaj 2004). This research also provides implications for

managers on the returns and risks of global strategic outsourcing. The focus will be on

94

the unique consequences of loosely coupled systems, i.e., modular systems employed
in external innovation development (Schilling 2000; Sanchez 1999; Mikkola 2003).
The organization of this essay is as follows. First, an overview of the model and
definitions of constructs are provided. Descriptions of the sample and the analysis
techniques will then be presented, followed by discussion of the findings.
4.2 TECHNOLOGY OUTSOURCING LITERATURE

The bulk of the buyer-supplier literature dates back to the late 1970’s, much
originating from Coase (l937)’s transaction ‘cost’ economics (TCE). TCE focuses on
how to match transaction characteristics with governance mechanisms, and the
conditions under which either market or firm minimizes transaction costs.
Commencing with Buckley and Casson (1976), scholars utilized a cost-based
approach to explain firms’ strategic choices (i.e., choosing internalization versus
extemalization). Drawing upon both transaction cost and internalization theories
(which scrutinize the most efficient governance structures with regards to the
associated costs), these authors maintained that the ‘discrete choice’ of strategy had
the lowest cost (i.e., was most efficient; Williamson 1981; Rugman 1981; Walker and
Weber 1984). The dependent variable was usually discrete categories of entry mode,
and thus the methodologies employed were generally discriminant analysis and/or
logistic regression (Agarwal and Rawi 1992; Kim and Hwang 1992). Since the
internalization school focused its attention on firm level phenomena, the scholars
predominantly collected primary data (Erramilli and Rao 1993; Aulakh et al. 1998).

By focusing on cost minimization, these studies ignored other important
criteria, such as value enhancement and strategic proactiveness. Thus, several scholars
advocated that the reason for internalization may not be market failure but rather

value maximization and firm success. Theories such as the resource-based view,

95

resource dependency and relationship marketing were developed to explain interfinn
relationships by relation-specific assets as distinctive resources and to investigate the
performance outcomes of interdependencies among buyers and suppliers (Heide and
John 1988, 1992; Noordewier et al. 1990; Ganesan 1994). Going beyond the focus on
single transactions, efficient contracting and transaction costs, these scholars also
addressed production costs, firms’ unique bundles of both internal and external
resources and capabilities, and the interrelatedness of several exchanges (Aulakh and
Kotabe 1997; Granovetter 1973; Pisano 1990). Several studies employed this
perspective at the nation and/or industry level (Wilson et al. 1990; Weiss and Heide
1993). With the emergence of these diverse theoretical underpinnings either drawn
upon TCE solely or being integrated with TCE, alternative metric dependent variables
such as performance and data analysis methods such as moderated regression and
structural equation modeling started to proliferate (Wasti and Liker 1997; Jap and
Ganesan 2000). Moreover, the use of secondary data sources has increasingly become
prevalent in recent empirical studies, which may be due to low response rates in
surveys and challenges inherent in cross-cultural designs (Wan and Hoskisson 2003;
Shaver 1998; Makino et al. 2004; Schilling and Steensma 2001).

Previous research on buyer behavior in high technology markets has focused
on discrete buyers’ choices in single transactions as Opposed to the performance
outcomes of portfolios of transactions. Walker and Weber (1984) examined the effect
of transaction costs on “make or buy” decisions, mediated by the impacts of supplier
market competition and two types of uncertainty (i.e., volume and technological
uncertainty). Swan and Allred (2003) analyzed the factors that influence product and
process technology decisions across the continuum of options from internal

development to outsourcing. Stremersch et al. (2003) identified two focal buyer

96

decision dimensions, namely the decision of whether to outsource system integration
and the decision of how much to concentrate purchases with one or more suppliers. At
the project level, Clark (1989) examined the effect on product development of project
scope (that is, the extent to which a new product is based on unique parts developed
internally) and the content of the product (i.e., features, performance, degree of
innovation).

More recently, a new literature stream on outsourcing relationships has
emerged employing content—based event studies and examining the market reaction to
outsourcing arrangements. Based on outsourcing announcements across a variety of
industries from 1990 to 1997, ‘Hunton, Reck and Hayes (1998) found a significant
positive return one day after (day +1) the announcement (p < 0.10). In addition, their
results indicated an inverse relationship between the market perception and the buyer
firm’s size. Similarly, Peak, Windsor and Conover (2002) examined how the market
reacted to IT outsourcing announcements in particular. The results of both studies
exhibited a significant positive return to the IT outsourcing announcement during the
event period (p < 0.05 and p< 0.10, respectively). These studies also examined the
influence of factors, such as primary objective, asset specificity, resource dependency
and technological discontinuity, on the investor’s reactions to IT outsourcing
announcements. However, there is clearly a paucity of studies on the market’s
evaluation of firm’s technology outsourcing activities, that is, whether and how
technology outsourcing announcements lead to significant abnormal returns in stock
prices.

On the whole, there is a need for research on the performance outcomes of
partnerships that exhibit increasing levels of suppliers’ involvement in the product

development processes, and thus entail certain risks, such as know-how leakage and

97

diffusion to competitors. Aberdeen's “Outsourced Manufacturing Strategies
Benchmark Report” (“Produce Significantly Different Results, According to New
Aberdeen Report”, Business Wire, 12 October 2004) indicates that managers’
concerns center around losing control in outsourcing ventures. Buyers generally strive
to minimize the likelihood of opportunistic expropriation of tacit technological
knowledge, eliminate the difficulties of monitoring partners due to geographical or
cultural distance, and avoid switching costs (Tidd 1995; Pisano 1990). These notions
necessitate the empirical validation of risks and benefits of strategic outsourcing
relationships in the context of modular systems and global TI markets -- a topic that
has drawn significant interest in recent years, but which has only been studied
conceptually to date. Accordingly, this study extends previous studies by employing
event analysis of abnormal stock returns to examine the market value of external
innovation development. The market’s reaction to technology outsourcing
arrangements has been a proxy for the business value of such initiatives. This study
investigates the extent to which the key factors associated with the risks of technology
outsourcing arrangements (including task characteristics, environmental uncertainty
and switching costs) influence the market’s reaction as measured by the cumulative
abnormal returns (CAR).
4.3 THEORETICAL FOUNDATIONS AND MODEL OVERVIEW

Past research has suggested that the problems that exist in high-technology
markets are of two different kinds from a buyer's perspective. First, these markets are
characterized by considerable uncertainty due to heterogeneous and rapidly changing
technologies, and to the fact that buyers frequently lack relevant prior experience
(Glazer 1991; Teece 1988; von Hippel 1986; Dosi 1988). Thus, the buyers choose to

outsource their product development activities and engage in partnerships with their

98

suppliers in order to combine suppliers’ resources and capabilities with their
knowledge base (W uyts et al. 2004; Swan and Allred 2003; Appleyard 2003; Athaide
and Stump 1999; Howells et al. 2003; Pennings and Harianto 1992). They aim to
enhance their flexibility and productivity and to lower transaction and production
costs (Lambe and Spekman 1997; Ragatz et al. 1997). Second, these outsourcing
relationships lead the buyers to face switching costs, as a result of earlier
commitments to particular product technologies or suppliers (Heide and Weiss 1995;
Heide and John 1998; Stump and Heide 1996). As a result, even though these
relationships constitute avenues for inter-firm learning and increase firms’
adaptability, they create a certain degree of supplier-buyer interdependence (Dutta
and Weiss 1997; Ireland et al. 2002; Ahuja 2000; Gulati and Garguilo 1999). Overall,
the costs and consequences of outsourcing for the buyer firms include external
dependence, functional mismatches, and coordination difficulties, along with the
gradual loss of internal design, manufacturing, and other knowledge-based
capabilities (Mikkola 2003; Schilling 2000; Wilson et al. 1990). Moreover, the most
important risk associated with these linkages (one that can lead to loss in competitive
power) is likely leakage through suppliers of both technical and marketing know-how
to competitor firms (especially at the design stage) (Dutta and Weiss 1997).

These arguments are embedded in streams of research such as agency,
resource dependence and transaction cost theories (as detailed below; see Ouchi 1979;
Jaworski 1988; Pfeffer and Salancik 1978; Eisenhardt 1985). Referring to the factors
that determine risks of outsourcing relationships identified in the previous literature,
the determinants of value of global technology outsourcing arrangements were
classified into three broad categories in this paper: i.e., task characteristics (including

knowledge and experience of the partners regarding the task and the costs of

99

obtaining information), switching costs (including sources of dependencies and the
characteristics of the parties involved) and the degree of environmental uncertainty
(Bergen et al. 1992; Heide and Weiss 1995; Eisenhardt 1985; Lawless and Price 1992;
Weiss and Heide 1993; Pisano 1990).

4.3.1 Agency Theory. The focus of agency theory is determining the most efficient
governance mechanisms for a particular relationship from the principal's point of
view, given the determinants of risks of the arrangement (i.e., the characteristics of
the parties involved and the degree of environmental uncertainty, the task complexity
and the costs of obtaining information for the monitoring of the agent; see Ouchi
1979; Eisenhardt 1985, 1989; Anderson 1995; Kraft 1999). The primary assumptions
of agency theory are information asymmetry (i.e., the principal lacks complete
information as to what the behavior of the agent will be), the uncertainty related to the
outcome of the agent’s behavior, the self-interest seeking features and divergent goals
of the parties. Bergen and et al. (1992) assert that the buyer-supplier link can also be
viewed as an agency relationship as the buyer (i.e., the principal) attempts to gain
accurate product/ component information and desired benefits from a supplier (i.e.,
the agent). Generally, agent and principal risks occur due to discrepancies between the
objectives, knowledge and capabilities of the buyer firm versus those of the supplier
firm. Due to difficulties buyers face in monitoring their behavior, agents may be
tempted to exhibit opportunistic behavior in the forms of moral hazard13 , adverse
selection” and/ or imperfect commitment (Ouchi 1979; Bergen et a1. 1992). In the

case of technological outsourcing relationship, such behaviors may be detrimental

 

1’ The moral hazard problem occurs as a result of shirking or evasion of obligations in the ongoing
relationship. These are considered forms of opportunism since one of the parties to the exchange is
purposely withholding effort or somehow refraining from performing agreed-on actions (Wathne and
Heide 2000).

’4 Adverse selection indicates a situation where one party/ supplier deliberately committing to a
contract that they know they would not be able to fulfill. This may be viewed as opportunism in the
sense that one party purposely withholds critical information (Wathne and Heide 2000).

100

particularly in instances where certain factors, such as nonmodular (i.e., tightly
integrated) systems and high supplier involvement, increase know-how leakage.

4.3.2 Resource Dependence Theory. Resource dependence theory views interfirm
governance as a strategic response to conditions of uncertainty and dependence
(Pfeffer and Salancik 1978; Heide 1994; Anderson and Narus 1990). The basic
assumptions are that the lack of sufficient resources and/or capabilities to complete a
task creates dependence on the parties from whom the resources are obtained and
introduces uncertainty into a firm’s decision making (Heide 1994; Ganesan 1994;
Heide and John 1992).This uncertainty occurs to the extent that the resource flows are
not subject to the firm’s control, and may not be predicted accurately. This notion is
applicable to technology outsourcing relationships that are usually initiated due to the
buyer’s need to control key technologies in the value chain and manage the
technological turbulence they face in their operating environment. Links with
suppliers can help reduce the cost of components through specialization and the
sharing of information on costs, but can also be a source of technology when a firm
does not have the competence to develop a critical component in-house (Tidd 1995).
However, according to the theory, principal (buyer) risk occurs due to the principal’s
lack of experience and expertise and the agent’s (supplier) capabilities with the
activity to be outsourced. Buyer firms that lack the knowledge and experience
necessary to evaluate the quality of the outsourcing service may encounter problems
since they make themselves vulnerable to the agent’s opportunistic behavior.

4.3.3 Transaction Cost Theory. Transaction cost theory focuses on how to match
transactions of different characteristics with governance mechanisms, and the
conditions under which either market or firm (vertical integration) serves to minimize

transaction costs. Emphasis is given to transaction costs, i.e, actual and opportunity

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costs of transacting under various governance structures. The theory argues that
transaction specific investments and the uncertainty of transactions give rise to
transaction costs and possibly create market failure; that is, they become inefficient
means to mediate exchange (Rindfleisch and Heide 1997; Bergen et al. 1992). TSIs
increase the investors’ asset specificity and dependence on the transaction or
relationship and give rise to safeguarding problems, while external and internal
uncertainty leads respectively to adaptation and evaluation problems (Stump and
Heide 1996). On the whole, according to the transaction cost theory, transaction risks
result from transaction-specific factors, including asset-specificity, frequency,
interdependency, and technological uncertainty.

Transaction cost theory is consistent with resource dependence theory in that
they both view non-market governance as a response to environmental uncertainty
and dependence (Heide 1994; Reuer 2001). Regarding the latter, transaction-specific
assets can be argued to constitute dependence, because their presence makes exchange
partners irreplaceable or replaceable only at a cost (Barney and Ouchi 1986). Agency
and transaction cost theory are also complementary: both theories, based on the
assumption that parties are motivated by economic self-interest and may engage in
opportunistic behavior, examine efficiency aspects of how firms organize functional
relationships (Bergen et al. 1992). While transaction cost theory deals with the
reduction of ex-post transaction costs through aligning transactions with appropriate
governance mechanisms, agency theory adopts an ex-ante view of relations between
principal and agent (i.e., minimization of ex-post costs through ex ante alignment of
incentives).

Thus, these three theories serve to explain how buyer firms would react and

behave in their technology outsourcing relationships in accordance to the extent of

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their transaction-specific investments (or the factors that contribute to the risk of their
suppliers ’ engaging in opportunistic behavior), their interdependencies and
environmental uncertainty. Therefore, factors that would contribute to the
effectiveness and efficiency of a particular technology outsourcing relationship should
comprise: the degree of modularity of development systems, the strategic importance
of the contract to the buyer firm’s performance, buyer experience and supplier
capabilities, the number of other supplier relationships of the buyer, the degree of
supplier involvement in the relationship, the size and duration of the contract,
geographic and cultural distance between the partners, and the technological
environment.
4.3.4 Overview of The Model

The model, of which the unit of analysis is the technology outsourcing
announcements, is depicted in Figure 4-1. Buyers are hypothesized to receive
abnormal returns in their outsourcing agreements, based on: (1) task characteristics
[i.e., degree of modularity (H1), strategic importance of the development project (H2),
buyer experience (H3), overall production cost of the buyer (H4), geographic
dispersion (H5) and cultural distance (H6)]; (2) switching costs [i.e., component
purchase concentration (H7), degree of supplier involvement (H8), the size (H9) and
duration of the contract (H10), supplier capabilities (H11), and the asset specificity of
the technology (H12)]; and finally, environmental uncertainty [i.e., technological
heterogeneity (H13) and discontinuity (Hl4)]. Lastly, this research explores four
moderation relationships: the impact of component purchase concentration and of the
degree of supplier involvement on firm market value from will be moderated by the
degree of modularity (H15), the strategic importance of the development project

(H16), buyer experience (H17) and buyer production costs (H18).

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FIGURE 4-1. HYPOTHESIZED MODEL ON DETERMINANTS OF GLOBAL
TECHNOLOGY OUTSOURCING RETURNS

 

 

Task Characteristics

 
  
    

HIS—H18
(a-b)

 

   

Switching Costs:

H13—H14

  

4.3.4.1 The Effects of Task Characteristics

Task characteristics studied in the agency theory literature can be broadly
included in two categories: behavior observability (i.e., ability to gather information
about agent behavior) and outcome measurability (i.e., ability to specify and track
desired outcomes) (Eisenhardt 1985; Jaworski 1988; Oliver and Anderson 1994;
Lawless and Price 1992). The extent to which the principal is knowledgeable about
the task has been cited to have an impact on the ability to monitor agents’ behavior
(Choudhury and Sabherwal 2003). Correspondingly, this study incorporates variables

that may have an impact on monitoring ability in global technology outsourcing

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relationships, such as degree of modularity, strategic value of the contract, buyer
experience, buyer overall production costs, geographical dispersion and cultural
distance of relationship partners.
4.3.4.1.] Degree of Modularity

Schilling (2000) defines modularity as “a continuum describing the degree to
which a system's components can be separated and recombined and the extent to
which the system architecture enable the mixing and matching of components” (p.
312). Systems are said to have a high degree of modularity when their components
can be disaggregated and recombined into new configurations with little loss of
functionality (Schilling 2000; Schilling and Steensma 2001; Mikkola 2003). The
components of such systems are relatively independent of one another, but require
compatibility with the overall system architecture to be easily recombined with one
another (Garud and Kumaraswamy 1995; Sanchez 1995; John et al. 1999).

Organizational systems become increasingly modular when firms begin to
substitute loosely coupled forms for traditional tightly integrated systems or
structures. Integrated component designs are tightly coupled in the sense that a change
in the design of one component within an integrated assembly of components will
require compensating changes in the designs of other components in the assembly,
making these product architectures difficult, costly, and time-consuming to modify
(Orton and Weick 1990; Sanchez and Mahoney 1996). Schilling (2000) refers to this
as ‘synergistic specificity’ and asserts that it is through the development of
standardized interfaces that synergistic specificity between components may be
reduced (p. 316). Through specifying and standardizing the nature of an activity and
the terms of exchange, a standard interface makes assets nonspecific (Garud and

Kumaraswamy 1995; Sanchez 1995; Sanchez and Mahoney 1996; Schilling 2000).

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As a result, the loose coupling of components facilitates greater specialization in
particular activities, and thus autonomous development of components and control of
the outputs of suppliers (Orton and Weick 1990; Staundenmayer et al. 2005). These
notions all indicate that the degree of modularity reduces the likelihood of functional
mismatches, the buyer firm’s switching costs, and its external dependence. Modular
systems also involve less disclosure of information about data and design plans.
Modularity, in other words, provides a structure that coordinates the loosely coupled
activities of component developers, reducing the risk of technology know-how
leakage and the need for close monitoring of agents’ behavior (Sanchez and Mahoney
1996). Thus:
H1: The market ’s reaction to strategic outsourcing arrangements will be positively
related to the degree of modularity of development systems.
4.3.4.1.2 Strategic Value of the Project

This construct represents the impact of the development or acquisition of the
component on organizational profitability and productivity, which would differ based
on the goal of the buyer in engaging in technology outsourcing relationships (that is,
cost reduction or operational efficiency versus revenue enhancement) (Weiss and
Heide 1995; Robertson and Gatignon 1986). The closer a particular activity of a firm
comes to its technological core and competitive advantage, the higher its asset
specificity, bringing about reluctance to relinguish control over the activity and/or the
necessity for safeguarding mechanisms (W asti and Liker 1997; Sanchez and Mahoney
1996). In other words, the strategic value of the component to the buyer firm is
expected to play a significant role in determining the level of dependency on suppliers
and the difficulty faced in monitoring suppliers’ performance (which are both

acknowledged to contribute to transaction risk). As Pfeffer and Salancik (1978)

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suggest “asymmetry is the true source of power, a result of unequal concentration of
resources or unequal perception of the importance of the exchange” (p. 52).
Accordingly, transaction risks should be lower for technology outsourcing contracts
in which the objective of the buyer firm is cost reduction or efficiency as opposed to
increasing revenues or enhancing the firm’s competitive position. Moreover, the
higher the importance of the component or the value of development project, the more
likely the buyers will be inclined to protect their tacit technological knowledge against
threats of opportunism (Dutta and Weiss 1997). However, despite the transaction
risks, a project involving the development of a proprietary technology should
engender competitive advantage in the market (Friar 1995; Gatignon and Xuereb
1997). Accordingly, the announcement of such projects would be an indicator of the
buyer firms’ future commercial success and generate more favorable market
reactions. Therefore:
H2: The market ’3 reaction to strategic outsourcing arrangements will be higher when
the goal is revenue enhancement (i. e., high strategic value of the contract to the buyer
firm) as opposed to cost reduction (i. e., low strategic value).
4.3.4.1.3 Buyer Experience

A buyer experienced in developing the technology may be more likely to be
confident and more inclined to specify the exact process the supplier should follow
(Eisenhardt 1985; Jaworski and MacInnis 1989). On the other hand, less experienced
would lead the buyer to rely on the supplier's abilities and knowledge and may lead to
vulnerability on the part of the buyer, which is referred to as information asymmetry
in the literature. Information asymmetry presents conditions typical of principal-agent
relationships, in which the distribution of information is likely to be skewed. In the

case when buyers have less information and experience than the supplier with which

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to evaluate the supplier’s performance, buyers incur monitoring costs and face
performance ambiguity. This will reduce the buyer’s ability to assess the supplier’s
performance and value of the technology (Ouchi 1979). Additionally, if the skills and
other characteristics of the supplier cannot be obtained through substitutes, they may
appear irreplaceable in the eyes of the buyers. Buyer’s reputation, patents, R&D and
development skills all signal its future success (Reuer 2001; McNamara 1999).
Overall:
H3 .° The market 's reaction to strategic outsourcing arrangements will be positively
related to the experience of the buyer.
4.3.4.1.4 Overall Production Cost of the Buyer

Production costs of the buyer are those related to the production function
(Stremersch et al. 2003). Swan and Allred (2003) assert that with a low cost goal,
internal development of a product technology should be less favorable for attaining
positional advantage and may even draw away from resources necessary for other
activities. In the case when the overall production cost is high, the buyer firm will
resort to outsourcing rather than internal development to gain product-based cost
advantages. Therefore, in the case where the overall production cost of the buyer is
high, the investors’ evaluation of technology outsourcing arrangements will be
positive:
H4: The market 's reaction to strategic outsourcing announcements will be positively
related to the overall production cost of the buyer.
4.3.4.1.5 Geographical Dispersion

This construct refers to the location of a firm's operations and linkages
throughout the world. Communication is hindered as spatial and cultural separation

increase between partners. A dispersed configuration of a buyer's supplier

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relationships across the world may increase the difficulty and the cost of coordinating
and integrating the development, manufacturing, and promotion of a product (Swan
and Allred 2003). Moreover, socialization, shared experiences, beliefs, and common
goals are more difficult to achieve between the members of a buyer firm and a
supplier firm, particularly if the supplier is remotely located. Hence, geographic
distance between the buyer and supplier not only escalate operational costs, but may
be a barrier to achieving project goals. Consequently:
H5: The market '3 reaction to strategic outsourcing arrangements will be inversely
related to the geographical dispersion of the relationship.
4.3.4.1.6 Cultural Distance

Cultural distance identifies the distance that exists between the national
cultures of the partners of the alliance (Shenkar 2001; Kogut and Singh 1988). A
crucial assumption of agency theory is that rational managers are expected to act in
their own self-interest The basic assumption of agency theory (i.e., self-interest in the
presence of diverging goals between the individual and the collective) may be
emphasized in individualistic countries (Hofstede 1980; Sharp and Salter 1997).
Moreover, cultural distance may impede goal congruence between partners,
strengthening the ‘divergence of preferences’ assumption of agency theory (i.e.,
people are assumed to have preferences for their own actions which do not necessarily
coalign with those of other organization members or partners). In these ways, cultural
distance increases the transaction risk and thus, the need for close monitoring (Reuer
2001; Merchant 2003). Thus:
H6: The market ’s reaction to strategic outsourcing arrangements will be inversely

related to the cultural distance of the partners.

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4.3.4.2 The Effects of Switching Costs

Buyer switching costs may arise as a result of prior commitments (1) to a
technology (transaction specific assets) and (2) to a particular supplier (relationship
specific assets). Asset specificity means the buyer firm has specialized knowledge or
tools with little or no use outside the transaction. Moreover, as a result of the prior
transactions and investments, buyers may have invested in assets that are
incompatible with new products. In addition to compatibility problems, buyers may
face switching costs because of established relationships with particular suppliers.

The general effect of both types of switching costs for a buyer is a disincentive
to explore new suppliers (Heide and Weiss 1995; Swan and Allred 2003).
Consequently, buyers will be motivated to stay in existing relationships to economize
on switching costs. Essentially, switching costs constitute a form of dependence,
which is described by the extent of replaceability of the exchange partner (Heide and
John 1988; Heide 1994). Agency theory predicts that the purchase of products or
services that cannot be closely monitored will lead to shirking by suppliers (Wasti and
Liker 1997). As a general rule, the buyer firm would try to detect opportunistic
behavior by the suppliers through heavy monitoring. Knowing that it is being
monitored would make the supplier less likely to shirk. In the outsourcing and
modular systems context, six variables (i.e., size and duration of the contract,
component purchase concentration, degree of supplier involvement, supplier
capabilities and asset specificity of the technology) are proposed to represent the
switching costs perceived by the buyer firm and the extent of irreplaceability of the

supplier firm.

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4.3.4.2.] Component Purchase Concentration

The presence of open standards for the interfaces between the various
components allows the system components to be sold by multiple suppliers. The
buyer need not buy all system components from the same supplier, regardless of
whether the buyer outsources the integration function; instead buyers mix and match
components from different manufacturers, reducing their dependence on a single
supplier. As a result, the buyer needs to decide whether to purchase all system
components from a single supplier (high concentration) or from multiple suppliers
(low concentration) (Stremersch, and et al. 2003; Tidd 1995; Wilson et al. 1990). The
buyer’s position is strengthened the greater the number of alternate sources of supply
and the less the transaction costs involved in switching to another supplier. This
would reduce the threat of opportunism and the necessity of monitoring. Such
relationship competencies would enhance the buyer’s ability to achieve the goals set
in their outsourcing project, and thus increase its market value:
H7: The market ’s reaction to strategic outsourcing arrangements will be inversely
related to higher component purchase concentration.
4.3.4.2.2 Degree of Supplier Involvement

Supplier involvement in NPD may be determined by the extent to which the
supplier influences decision-making during the early stages of product development,
the amount of control the buyer retains over the design, and the frequency of design-
related communication between the buyer and the supplier (W asti and Liker 1997).
As supplier involvement increases in earlier NPD stages (i.e., activities concerned
with product conceptualization and evaluation such as idea generation, concept
development, design and planning) as opposed to later stage activities (i.e.,

production, product testing and commercialization), the intangible nature of the tasks

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and the diffusion risk of tacit know-how and core technologies increase.
Consequently, buyers may be concerned about multi-client suppliers transmitting such
information to potential competitors. Critical information that leaks out to competitors
at the idea generation, design and planning stages through suppliers utilizing the same
or similar designs for different customers can constitute a serious detriment to the
buyer’s competitive power. Hence:
H8: The market ’s reaction to strategic outsourcing arrangements will be greater
when the supplier involvement is high (i.e., when the supplier contributes to earlier
NPD process stages) compared to when it is low (i. e., when the supplier contributes
to later stages).
4.3.4.2.3 Size of the Contract

The buyers’ goals in engaging in supplier relationships (that is, having access
to resources and capabilities that they lack internally) may be a major source for the
resource dependency between them and their suppliers (Kallunki, et al., 2001). In
addition to increasing the buyers’ dependency on their suppliers, the size of the
contract may raise the level of switching costs required to substitute another supplier.
Larger contracts may attract more attention in the market and may increase investors’
credibility to the buyer firms. However, the monetary size of the contract should also
play a significant role in determining the level of dependency - as the monetary size
of the contract relative to the size of the buyer increases, the buyers tend to relinquish
control over their internal resources and lose some bargaining power. This would
further add to the costs associated with switching between suppliers as well as
difficulties in the monitoring their behaviors. Hence, the risks may outweigh the risks

of large outsourcing contracts:

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H9: The market ’s reaction to strategic outsourcing arrangements will be inversely
related to the size of the contract.
4.3.4.2.4 Duration of the Contract

The duration of the contract plays a significant role, particularly in technology
outsourcing arrangements. Contracts of longer duration may become more
problematic in technology-intensive environments in which technologies rapidly
change and become outdated. A long-term commitment to a particular supplier is
likely to be less cost-effective and may reduce the buyer’s ability to leverage cost-
saving technologies. Consequently, long-term contracts may limit the buyer firm’s
strategic flexibility. The relationship may lead to a ‘lock-in’ situation where the buyer
cannot seek better options. This is a particular constraint to flexibility and competitive
power when the supplier is a poorly-performing firm. On the other hand, the market’s
evaluation of contracts of longer duration may signal success in inter-firm learning,
and thus enhanced development capabilities of the buyer. Hence, outsourcing
contracts of longer duration should increase firm market value:
H10: The market 's reaction to strategic outsourcing arrangements will be positively
related to the duration of the contract.
4.3.4.2.5 Supplier Capabilities

Factors such as supplier’s development cost advantages, ability and funding to
conduct R&D, skill and competitiveness in product development, number of patents
and other facilities may lead to certain asymmetries in the exchange relationship, thus
escalating dependence at the buyer’s expense (Clark and Fujimoto 1991; Wasti and
Liker 1997). On the other hand, a supplier’s successful performance history (i.e.,
reputation) gives the buyer an indication of the behavioral tendencies of the supplier,

reduces the need for behavior monitoring, and allows the buyer to utilize outcome-

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based contracts to a greater extent (Wasti and Liker 1997). Thus, investors may favor
collaborations with reputable suppliers. Therefore:
H1]: The market ’s reaction to strategic outsourcing arrangement will be positively
related to higher supplier capabilities.
4.3.4.2.6 Asset Specificity of the Technology

A resource can be described as asset specific if it cannot readily be redeployed
(Williamson 1981). Asset specificity is considered a critical dimension in technology
outsourcing relationships because many aspects of product development are
proprietary. From the perspective of transaction cost economics, buyer firms that
outsource highly asset-specific resources (e.g., proprietary technologies) are likely to
encounter high transaction risks. Correspondingly, the level of asset specificity should
be different among outsourcing arrangement that involve licensed and patented
technologies, proprietary innovations that were developed by particular suppliers, or
technological products to be codeveloped by the buyer and supplier. First, suppliers
are less worried about the client’s potential termination of the contract and may
behave more opportunistically, maximizing their own self-interests at the expense of
their buyers (Wathne and Heide 2000; Weiss and Heide 1993). Second, when
proprietary technologies are developed by the supplier, substituting another supplier
would be prohibitively costly and could likely result in a significantly delays. Buyers
are likely to encounter a technological lock-in scenario, in which they can be “held
hostage” to a particular technology provider. However, in the eyes of the investors,
development of proprietary technologies will create more opportunities for
differentiation and relative advantage over competitors, hence entailing future income
for the buyer firms (Gatignon and Xuereb 1997; Kleinschmidt and Cooper 1991; Song

and Parry 1996; Ali et al. 1995). Thus:

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H12: The market ’s reaction to strategic outsourcing announcements will be higher in
outsourcing arrangement that involve high asset-specific technologies @roprietary
innovations developed by the supplier or technological products to be codeveloped)
as opposed to low asset-specific technologies flicensed and patented technologies).
4.3.4.3 The Effects of Environmental Uncertainty

An important determinant of buyer decision making is environmental
uncertainty because particular market conditions impose demands on a buyers'
information processing capacity, are difficult to predict, and are beyond the control of
either principal or agent (Achrol and Stern 1988; Weiss and Heide 1993). In a general
sense, perceived uncertainty in the environment leads to uncertainty related to a task;
i.e., the difference between the amount of know—how required to complete a task and
the amount already possessed. In the context of TI markets, technological
heterogeneity and discontinuity create uncertainty regarding developing the
component (due to changes in component specifications), as individuals struggle to
understand new and incompletely specified processes or products (Burkhardt and
Brass 1990; Tushman and Anderson 1986).
4.3.4.3.] Technological Heterogeneity

Technological heterogeneity refers to a lack of a common technological
standard (Garud and Kumaraswamy 1995; Sanchez and Mahoney 1996). One
defining feature of high-technology markets is the presence of multiple, frequently
discrepant product standards and lack of a single dominant design (Tushman and
Anderson 1986; Teece 1988; Bourgeois and Eisenhardt 1988). Organizations may
have a higher preference for close monitoring and relationships with their suppliers

under conditions of high technological heterogeneity, because they want to minimize

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the information they need to process to cope with uncertainties associated with such
complexity.
4.3.4.3.2 Technological Discontinuity

High-technology markets also represent considerable uncertainty for buyers
due to technological discontinuity, which represent increasing speed and magnitude of
technological change. As stated by Von Hippel (1986), a buyer's prior technologies,
experiences and capabilities are often ‘rendered obsolete’ in such markets (p. 796).
According to Tushman and Anderson (1986), high-technology markets tend to be
‘competence destroying’, constituting a shift in the locus of technical expertise from
indusu'y incumbents to new entrants (Weiss and Heide 1993; Pisano 1990). The
introduction of fundamentally different technologies or competence-destroying
discontinuities can lead to major changes in the distribution of power and control.
Because of resource limitations, firms turn to and eventually become reliant on
external sources in developing new product and/or process technology (Kotabe and
Murray 1990; Swan and Allred 2003). Environmental uncertainty, on the whole,
involves not only lack of knowledge of the precise cost and outcomes of different
alternatives, but often also lack of knowledge of what alternatives are. Thus, this
would increase the irreplaceability of the supplier and the buyer’s dependence (W asti
and Liker 1997; Wilson et al. 1990). Therefore:
H13: Technological heterogeneity will inversely affect the market 's reaction to
strategic outsourcing arrangements.
H14: Technological discontinuity will inversely affect the market ’s reaction to

strategic outsourcing arrangements.

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4.3.4.4 Moderating Effects of Modularity, Strategic Value, Buyer Experience
and Costs

4.3.4.4.] Degree of Modularity

Buyer firms, in their outsourcing relationships, determine the number of
suppliers from whom to purchase their system components (that is, the concentration
of their component purchases and the extent to which these suppliers will engage in
their development activities; Tidd 1995; Wilson et al. 1990; Wasti and Liker 1990).
Modularity also allows independent development of technologies and embedded
coordination through adherence to common standards (Sanchez 1995, 1999).
Modular systems provide platforms that facilitate greater specialization ‘in
development processes and outsourcing activities (Staundenmayer et al. 2005).
Therefore, it is plausible to state that modularity may increase the effectiveness of
collaboration with and acquisition of components from multiple suppliers (i.e., low
concentration) as opposed to single supplier (high concentration) (Stremersch, and et
al. 2003). Moreover, higher supplier involvement (that is, when the suppliers play
active roles in more strategic phases of development such as design, R&D and
planning) may actually be inefficient and furthermore detrimental in the context of
modularity, since modular systems reduce the level of interdependency between the
buyer and supplier firms. Thus, the negative effects of component purchase
concentration and supplier involvement during the development of the product on the
stock market’s evaluation of outsourcing initiatives may diminish as modularity
increases. Therefore:
H15a: The higher the degree of modularity, the higher the negative impact of
component purchase concentration (i. e., multiple suppliers) on the market ’3 reaction

to strategic outsourcing arrangements.

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H15b: The higher the degree of modularity, the higher the negative impact of
component purchase concentration (i. e., multiple suppliers) on the market ’s reaction
to strategic outsourcing arrangements.
4.3.4.4.2 Strategic Value of the Project

The consequences of decisions related to purchase concentration and supplier
involvement should be crucial for the buyer firms in a given project/ purchase with
greater strategic value (i.e., a project which provides advantages over competitors or
in building competitive advantages; Robertson and Gatignon 1986; Heide and Weiss
1995). For initiatives with greater strategic value, the costs and risks to the buyer of
collaborating with a few suppliers (i.e., high concentration) or enabling supplier
involvement in the earlier stages of product development would outweigh the
benefits. High component purchase concentration and supplier involvement may
entail threats such as know-how leakage, gradual loss of capabilities and external
dependence, which may ultimately diminish buyer’s competitive advantage.
Therefore:
H16a: [f the goal is revenue enhancement (i.e., high strategic value) as opposed to
cost reduction (i. e., low strategic value), the negative eflect of component purchase
concentration on the market '8 reaction to strategic outsourcing arrangement will be
higher.
H16b.‘ If the goal is revenue enhancement (i.e., high strategic value) as opposed to
cost reduction (i. e., Iow strategic value), the negative eflect of supplier involvement on
the market 's reaction to strategic outsourcing arrangement will be higher.
4.3.4.4.3 Buyer Experience

Stremersch et al. (2003) advocate that buyers with low project related know-

how ‘lack the prime motivation to mix and match components from multiple vendors’

118

I.
d

(page 337). Moreover, the less experience they have, the less they are capable of
differentiating between different offerings (Weiss and Heide 1993; Heide and Weiss
1995). However, this study argues that buyers who lack relevant project experience
would benefit more from working with multiple suppliers (i.e., low concentration)
since maintaining an open consideration set and acquiring diverse and rich
information will outweigh the relevant information acquisition costs. In contrast, more
experienced and skilled buyers face less uncertainty and have less of an incentive to
search for multiple supplier partners. Staying with the existing few suppliers would be
more efficient in terms of time and costs, and thus they would be better off
collaborating with a few suppliers (i.e., high concentration).

On the other hand, buyers with less experience may be able to have access to
suppliers’ know-how and capabilities while working closely with their suppliers. In
this case, due to their need of relevant knowledge to develop a product, buyers would
attain better outcomes by enabling higher supplier involvement in their process. On
the contrary, for buyers with existing experience and capabilities, suppliers’
involvement would entail more risks (such as leakage of strategic secrets and
technical know-how and eventual loss of capabilities in developing the particular
technology) than benefits. Thus:

H 1 7a: The more (less) the buyer has project-related experience, the lower (higher)
the negative impact of component purchase concentration on the market ’s reaction to
strategic outsourcing arrangements.

H1 7b: The more (less) the buyer has project-related knowledge, the higher (lower)
the negative impact of supplier involvement on the market ’3 reaction to strategic

outsourcing arrangements.

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4.3.4.4.4 Overall Production Cost of the Buyer

External development of a product technology may both reduce production
costs and engender positional advantage and may even draw away from resources
necessary for other activities (Stremersch et al. 2003; Swan and Allred 2003). When
the overall production cost is high, the buyer firm will gain product-based cost and
differentiation advantages by collaborating with multiple suppliers. However,
outsourcing of activities in the earlier stages of the product development (i.e., idea
generation and strategic planning) should be detrimental to the buyer firm’s
competitive prospect. Therefore, such supplier involvement should entail negative
consequences irrespective of the level of the overall production cost of the buyer.
Hence:
H18a: The more (less) the level of overall production cost of the buyer, the lower
(higher) the negative impact of component purchase concentration on the market ’s
reaction to strategic outsourcing arrangements.
H18b: The level of overall production cost of the buyer will be negatively influence
the market ’s reaction to strategic outsourcing announcements irrespective of (i. e., not
moderated by) the level of overall production cost of the buyer.
4.4 DATA AND METHODS

The impact of an outsourcing agreement was measured using an event study
methodology, which investigates the statistically significant impact on financial
markets of a given type of event. Assuming market efficiency, perfect information,
and the rationality of investors, the predicted effects of an event on the NPV of cash
flows should be immediately reflected on stock prices (Chaney et al. 1991; Srinivasan
and Bharadwaj 2004). Event studies demand defining the event of interest and the

‘event window’ (i.e., the period over which the impact of the event on firms’ stock

120

 

prices will be examined). In this study, the event study encompasses the impact of an
outsourcing agreement on the buyer company’s stock price. It is also vital to identify
and isolate the effect of any confounding events pertaining to the firm that have
occurred within the event window, in order to evaluate the precise response of market
to the firm’s particular decision.

The data on outsourcing agreements involving supply, manufacturing and
development activities among both US and non-US companies were extracted from
the SDC Platinum Database between January 1982 and December 2004. Daily returns
for the US parent of the companies were obtained from the Center for Research in
Security Prices database. The initial search yielded 1568 agreements. Non-high tech
industries (17), reseller and distribution contracts (15]), equity relationships and joint
venture agreements (423) were eliminated from the sample. Announcements of buyer
firms without a public U.S. parent company (481) were not included in the analysis.
Finally, following McWilliams and Siegel (1997)’s guidelines for controling
confounding effects, agreements (22) of which any partner company disclosed any
other significant announcements surrounding the event window that might threat the
validity of the results were eliminated (Chaney et a1. 1991; Srinivasan and
Bharadwaraj 2004).

This procedure resulted in total sample size of 449 agreements announced
from April 1986 to July 2004. The agreements pertain to 17 different countries and 22
different industries at the 3-digit SIC level. Of the agreements, 176 (39.2%) involved
cross border participants. A majority of the buyers (i.e., 347; 77.28%) operated in
manufacturing industries, followed by information technology (83; 18.49%) and
telecommunications (19; 4.23%). 340 (75.7%) of the announcements entailed

agreements with a supplier operating outside the buyer firm’s industry and 402

121

L.

(89.5%) had undisclosed termination dates for the agreement. The t-tests revealed no
significant differences at the 0.05 level in abnormal returns based on the country and
industry of both the buyers and the announcements (i.e., contracts), indicating lack of
potential biases resulting due to these demographics. Moreover, there was no
significant difference in stock market reactions based on whether the announcement
entailed a prespecified agreement duration (or termination date).
4.4.] Analysis and Operationalization of Dependent Variable

The event study technique is based on the premise that reactions of the stock
market can be employed as benchmarks for evaluating the appropriateness of a
decision and as proxies for future market performance. Stock prices reflect the true
value of the firms because they are assumed to indicate the discounted value of all
future cash flows and incorporate all relevant information (Chaney et al. 1991;
Merchant 2003; Srinivasan and Bharadwaj 2004). Thus, cumulative abnormal returns
(CAR) signify investors’ beliefs about the firm’s value as a result of an event
announcement. Positive CARS indicate that most investors perceive that the event will
result in significant future cash flows, while negative CARS are expected when they
hold pessimistic views regarding the announced event.

This event study analysis was conducted following the procedure proposed by
McWilliams and Siegel (1997) (p. 652). To calibrate the dependent variable
(cumulative abnormal returns), the market model of Brown and Warner (1985) was

first estimated per firm:
Rit = at +fliRmt + 8a (1)
where, Rt, is the return on security i in period t, Rm, denotes the corresponding daily

returns on the value-weighted market portfolio (i.e., S&P 500), ai is the intercept, ,8,-

122

 

represents systematic risk, and a}, is the error term (i.e., N(O, 02)). Recent studies use
estimation periods intervals ranging from 45 days to 260 days (Madhavan and

Prescott 1995; Lane and Jacobson 1995). As the estimation period interval increases,
the statistical accuracy of parameters (i.e., a,- , fl- and bit) also increases (Koh and

Venkatraman 1991). Thus the trading days used were t = -250 and t= 10, where t=0

denotes the event. Then abnormal returns AR:

ARI! = Ru _ (at + biRmt) (2)
where a,- and b,- are the company specific OLS parameters and (a,- + biRmJ is the
predicted stock return based on the 241-day estimate period. R1, indicates the actual
return on stock i on day t. The abnormal returns (i.e., AR") are then standardized by

the estimated standard deviation (i.e., S“) following Hanvanich and Cavusgil (2001)’s

 

 

 

 

approach:
AR,
SARu = ”'37—! for stock i and day t (3)
it
where
r \
5.. = 53 “id“ rm —R"')2 (4)
\ 2(Rmt _Rm)z
\ i=1 /

 

SARi, denotes the standardized abnormal return for stock i in day t in Equation

3. In Equation 4, SE is the residual variance from the market model (i.e., Equation 1)

for stock i, whereas R m represents the average market return computed over the same

241-day estimation period used to estimate the parameters in Equation 1 for stock i,

123

and T is the number of days used to estimate the market model. The event windows,
i.e., the time period intervals around the announcement day, are denoted as d

(McWilliams and Siegel 1997). The cumulative standardized abnormal return for

stock i (i.e., CSARL, K) for d-day event window, where t e [7, K ]is calculated as:
1 i
’ ’ (i

In the literature, abnormal returns are accumulated for up to a 60 day period
(Lee and Wyatt 1990). However, in this study, [-4,0], [0,4], [-3,0], [0,3], [-4,3] and
[0,2] event windows are employed in order to reduce noise in the dependent variable
and to assess the market’s reaction pattern over time (Chaney et al. 1991; McWilliams
and Siegel 1997; Reuer 2001; Srinivasan and Bharadwaj 2004). Finally, consistent
with prior studies (Chaney et al. 1991; McWilliams and Siegel 1997; Im et al. 2001),
to examine the significance of the average CARS of N firms, the Z-statistics were
calculated, using the standard abnormal returns for each firm on day t and across all
firms. The significance of the abnormal returns based on the Z-statistic test indicates
whether technology outsourcing announcements have a significant impact on the

market value of the firms:

Z = x/N x ASCAR, (6)
where

ACSAR, =Zojcs41wv (7)

4.4.2 Operationalization of Independent Variables
Degree of Modularity. To operationalize modularity, industry level proxies were

employed (Schilling and Steensma 2001). Correspondingly, the overall use of

124

modular systems was estimated by employing three measures: the use of
extemalization (i.e., the ratio of 8 amount of total shipments to $ amount spent on
production workers); the degree of alliance formation (i.e., counts of alliances by
industry divided by the number of firms in the industry) and the use of computer
networks in the outsourcing of R&D by employment. These figures were obtained
from the US Census Bureau, the SDC Platinum Database and the RDS T ableBase,
respectively.

Strategic Value of the Project. As described earlier, the objective in engaging in
technology outsourcing relationships (i.e., to increase revenues or enhance the firm’s
competitive position versus to cut costs/ improve operational efficiency) determines
its strategic importance for the buyer firm. To gauge the strategic importance of the
contract, a categorical measure was employed: a binary variable was coded as 0 if the
announcement mentioned (in the F activa Press Releases and SDC Platinum
Database) a goal of cost reduction and 1 if it described goals such as revenue-
enhancement or it involved a technology transfer, R&D and exploration activities
(Koh and Venkatraman 1991).

Buyer Experience. A proxy was employed to gauge whether the buyer firm had the
know-how to execute the development project by coding whether the buyer firm’s
business description and industry code and those of the outsourcing arrangement were
identical (0 if same vs. 1 if different), obtained from the SDC Platinum Database
(Merchant and Schendel 2000).

Overall Product Cost of the Buyer. The overall production cost of the buyer firm
should influence the degree to which the buyer firm favors outsourcing (compared to
internal development) for the purpose of improving cost structures and gaining

product-based cost advantages. Accordingly, to estimate the level of overall

125

 

production costs of the buyer firms, the company costs of goods were gathered from
Compustat Company Balance Sheets.

Geographic Dispersion. The geographic distance between the partners in an
outsourcing relationship impede the buyer firm’s abilities to monitor their suppliers’
performance. Accordingly, a distance score between the countries-of-origin of the
firms is gauged to measure the geographic dispersion.

Cultural distance. To estimate the cultural distance between the buyer and the
supplier firms, Kogut and Singh (1988)’s index of cultural distance was employed
(this is the dominant method to measure culture distance between countries in the
literature of international business; Merchant and Schendel 2000; Merchant 2003;
Reuer 2001). The composite index is formed based on the deviation of each country
from the US. along each of the four Hofstede (1980) cultural dimensions (that is,
power distance, individualism/collectivism, masculinity/femininity, uncertainty
avoidance). The differences between countries are corrected in this index for the

variance of each dimension. Mathematically, the index has the following form:

4
2
Cd,- =2 {(10 ' 1m) /Vi}/4
i =1
where
Cdj = the cultural distance between the home country (US)
and the host countries
Iij = the index value for cultural dimension i of country j
Vi = the variance of the index of dimension i
U = home country (i.e., U.S.)
Component Purchase Concentration. The number of suppliers the buyer firm
collaborates with represents the degree to which the buyer diversifies risks and

reduces the vulnerabilities associated with relying on a single supplier. To measure

the concentration of the buyer’s component purchases, the number of other suppliers

126

1

from whom the buyer firm acquired components or developed technological products
was recorded (Stremersch et al. 2003; Kim and Park 2002). The higher the number of
suppliers the buyer collaborated with, the lower is the component purchase
concentration. This information was obtained from a search for other outsourcing
announcements of the buyer in the F activa Press Releases and the Compustat
Database (if not reported in the corresponding announcement texts).

Supplier Involvement. The intangible nature of the tasks and the diffusion risk of tacit
know-how and core technologies (and thus the supplier’s involvement) increases as
the supplier contributes to earlier NPD process stages. Parallel to this, based on the
announcement text obtained from SDC Platinum Database, a binary variable
indicated 0 if the supplier contributed to later stages versus 1 if to the earlier stages of
product development.

Size of the Contract. The monetary size of the contract signified the degree of
resource dependency between the buyer and supplier. Contract details along with the
size (annual revenue) of the buyer firms reported (in the SDC Platinum and
Compustat Databases, respectively) were collected to estimate the value of the
contracts to the buyer (Kallunki et al. 2001). Unfortunately, more than half of the
announcements did not provide contract details, perhaps due to confidentiality
reasons. However, 127 announcements did provide such information (32.5% of the
sample) and this information was thus included in the analysis. An analysis for
potential selection bias was conducted to determine if the sample of announcements
that contained this contract size information differed significantly from those
excluding it. The Levene’s test for homogeneity of variances indicated that the
variance for the CARS were not statistically different. Moreover, a t-test was

performed for equality of means between the announcements that reported vs. not

127

 

reported this information. The results showed that the CARS for the two groups did
not differ significantly (p > 0.05), thus the reporting of the contract Size (versus not
reporting) did not result in any bias.

Duration of the Contract. Contract duration is an important factor that influences the
ability and the flexibility of the buyer firm in leveraging any potential technological
discontinuity. The duration of the contracts included in this study ranged from 1 to 25
(expected) years, as collected from F activa Press Releases and SDC Platinum
Database.

Supplier Capabilities. As mentioned, supplier capabilities add to the asymmetries in
dependency at the expense of the buyer firm in developing technological products. To
measure their capabilities, the number of patents the firm had registered at the
American National Standards Institute was employed

Asset Specificity of the Technology. All events in the data were classified into two
groups, based on asset specificity. Combined resources were defined as asset specific
if they included (1) licensed and patented technologies; (2) proprietary innovations
that were developed by particular suppliers and thus cannot be readily re-sold or
redeployed by competitors; and (3) technological products to be codeveloped by the
buyer and supplier (as indicated in Factiva Press Releases and SDC Platinum
Database). These categories were coded with the dummy variable as 1 if the
outsourced functions were asset specific, otherwise as 0.

Technological Heterogeneity. To calibrate the technological heterogeneity in the
industry that pertains to the outsourcing arrangement, the number of standards
registered in each industry at the American National Standards Institute was recorded

for each of the corresponding announcement year.

128

 

 

Technological Discontinuity. Relying on Schilling and Steensma (2001)’s
measurement of technological change, total factor productivity (TFP) grth for each
industry (starting from 1982) was acquired from Bartelsman-Gray Database, which is
based on a five-factor production function (production work hours, capital, non-
production workers, non-energy materials, and energy). This index represents the
difference between the growth rate of output (real shipments) and the revenue-share
weighted average of the growth rate of each function (Schilling and Steensma 2001).
4.5 RESULTS

4.5.] Event Window Determination: Market Efficiency and Unanticipated
Events Assumptions

Table 4-1 presents the overall market reaction to technology outsourcing
announcements in the sample; i.e., the Table lists the average abnormal retums during
a ll-day period from five days before the announcement to five days after the
announcement. Interestingly, the market appears to react positively to the
announcements on the event date, whereas negatively after 3 days (p<0.05). Thus, on
the day of the announcements, such strategic initiatives are immediately perceived to
have the potential to increase future income of firms; but their market value is reduced
after three days, perhaps because some investors reconsider while other investors
engage in profit-taking.

Table 4-2 displays the cumulative abnormal returns for six different windows:
(1) a five-day pre-event period from day —4 to the event date; (2) a five-day post event
period from the announcement date to day 3 afterward; (3) a four-day pre-event
period from day —3 to the event date; (4) a four-day post event period from the
announcement date to day 3 afterward; (5) an eight day event period from day —4 to

day 3 afterward; and (6) a three-day post event period from the announcement date to

129

 

day 2 afterward. Significant results were observed before, after and during the event

period for the overall set of announcements.

TABLE 4-1. AVERAGE ABNORMAL RETURNS (N=449]

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Day Mean Positive: Generalized Z
Abnormal Negative
Return Returns

-10 0.28% 212:237 0.577
-9 0.22% 218:231 1.146
-8 -0.05% 198:251 -0.749
-7 -0.22% 205:244 -0.086
-6 -0.11% 191:258 -1.412'
-5 -0.06% 201 :248 -0.464
-4 0.28% 219:230 1.240
-3 0.05% 1982251 -0.749
-2 0.04% 2072242 0.104
-1 0.02% 196:253 0938
0 0.39% 227:222 1 .998”
1 0.02% 200:249 -0.559
2 -0.01% 1972252 -0.843
3 -0.35% 187:262 -1 .790"
4 -0.31% 194:255 -1.127
5 0.00% 217:232 1.051
6 -0.20% 209:240 0.293
7 -0.30% 199:250 -0.654
8 0.1 1% 221 :228 1.430'
9 0.28% 222:227 1 .525‘
10 0.28% 201 :248 -0.464

 

 

 

 

 

TABLE 4-2. CAR’S AROUND TECHNOLOGY OUTSOURCING

 

 

 

 

 

 

 

 

 

 

 

 

ANNOUNCEMENTS (N=44$
Event Mean Positive: Generalized
Window Cumulative Negatlve Z
Abnormal Returns
Return
(-4,0) 0.78% 220:229 1 .335’
(0,+4) -0.27% 203:246 -0.275
(-3,0) 0.51% 210:239 0.388
@+3) 0.04% 2292220 2.188“
-4,+3) 0.44% 223:226 1 .619‘
(0,+2) 0.40% 233:216 2566‘"

 

NOTE: The symbols *, ** and *** denote statistical significance at the 0.10,
0.05 and 0.01 levels, respectively, using a one-tailed test.

130

As mentioned, the market efficiency assumption implies that stock process
incorporate all information available to trnhe market (F ama 1991; McWilliams and
Siegel 1997). If the announcement or information disclosed to the investors is
reflected in the stock prices, then this assumption is satisfied. In this sample, the
three-day post event period from the announcement date to day -2 generated
cumulative abnormal returns with the highest significance level, and thus was chosen
as the input for the dependent variable. Thus, it can be concluded that the effects of
the technology outsourcing announcement events are quickly incorporated into the
stock prices of the buyer firms. Moreover, since this event window was after the
announcement and was the shortest duration, it allows control for any confounding
effects as a result of information leakage about the agreements prior to the formal
announcement. Thus, this present sample satisfied the ‘unanticipated events’
assumption, which states that information related to the technology outsourcing
initiatives is only available through the press announcements and not before.

Interestingly, the market’s reaction was approximately symmetrically
distributed: among the 449 announcements, during the (0, +2) event window 233
(51.89%) produced positive market reactions, while 216 (48.11%) generated negative
reactions. The conflicting reactions suggest that some investors perceived that for
some firms the returns from technology outsourcing outweighed the risks associated
with it; others held the opposite view.

4.5.2 Explanation of Abnormal Returns

To explain the cross-sectional variation in the abnormal returns and the
proposed model was examined using moderated regression using ordinary least
squares (OLS) estimation method (Neter et al. 1996; Kim and Park 2002; Kale et al.

2002; Merchant and Schendel 2002; Reuer 2001). Table 4-3 and Table 4-4 present the

131

descriptive statistics and the correlation coefficients, respectively, for all variables
included in the model (Figure 3-1). Note that abnormal returns has mean of zero in
Table 4-3 because the symmetrical bimodal distribution (i.e., bipolar investor

perceptions) resulted in a small absolute mean valuels .

TABLE 4-3. DESCRIPTIVE STATISTICS (E449!

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mean Std. Deviation
Abnormal Returns 0.00 0.06
Modularity 0.16 0.08
Buyer Experience 0.76 0.43
Strategic Value 0.82 0.22
Size of Contract 337.72 294.50
Duration of the Contract 3794.49 1465.67
Geographic Diflersion 4085.81 3442.36
Cultural Distance 1.51 5.40
Technological Heterggeneity 33346.99 20696.71
Technological Discontinuity 0.40 4.20
Asset specificity 0.46 0.50
Supplier involvement 0.55 0.50
Supply Concentration -5.24 9.68
Supplier Capabilities 993.96 1264.29
Buyer Costs 11587.54 23372.38

 

 

Three regression models were specified; they are shown in Table 4-5, which
summarizes the regression results for each model. In the first model, only the effects
of the two environmental uncertainty variables (i.e., technological heterogeneity and
discontinuity) were assessed. Neither of the two variables exhibited a significant
impact on abnormal returns (p<0.05). The overall model was not significant at 0.05
significance level based on a F-test (F =1 .74, p<0.18) with R2 and adjusted R2 of 0.01.

Model 2 specified only the main of the hypothesized determinants of
abnormal returns. This model was Significant (F = 1.72, p = 0.05) with slightly higher
explanatory power (R2 =0.05 and adjusted R2 = 0.02). Only two independent variables

had significant effects on the stock market evaluations of technology outsourcing (t-

 

15 Cross-tabs analysis and t-tests were conducted to identify any significant patterns between the
announcements that received positive versus negative market evaluations. The results showed that the
directionality of the abnormal returns significantly differed based only on the country-of-origin of the

agreement (12 = 33.731, df= 21, p<.05).

132

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values are starred in the table; p<0.01). Contrary to H5, geographic dispersion was
positively related to abnormal returns ([35 = 0.119). As predicted by H12, the results

indicated that whether the agreement involved an asset specific technology is

positively associated with the market’s reactions as measured by cumulative abnormal
returns ([312 = 0.127).

The third model included the main effects of all independent variables as well
as the moderating impacts of modularity, strategic value, buyer experience and costs
(modeled as the multiplicative interaction in the last eight terms in Table 4-5). This
model exhibited the highest level of significance (F=1.82, p <0.05) and highest
explanatory power (R2 = 0.09 and adjusted R2 = 0.04). Among the task characteristics,
cumulative abnormal returns as a result of technological outsourcing agreements was

positively associated with modularity (Bl = 0.315; p<0.05) and negatively with

strategic value of the project ([32 = -0.384; p<0.05). Hence, H1 was supported and H2

was rejected. Geographic distance positively affected abnormal returns ([35 = 0.126;

p<0.01). Since this effect was hypothesized to be negative when in fact it is positive,
H5 was rejected. Buyer experience, costs or cultural distance between the buyers and
suppliers did not exert significant impacts on the market’s reaction to technology
outsourcing arrangements. Therefore, H3, H4 and H6 were not supported.

Among switching costs constructs, only asset specificity was significantly and

positively related to cumulative abnormal returns ([312= 0.131; p<0.01); thus H 12 was

accepted. Accordingly, the effect of component purchase concentration, supplier
involvement, contract size, duration, or supplier capabilities was not significant. The

results did not support H7, H8, H9, H10 or H11. Moreover, neither technological

134

 

 

 

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136

heterogeneity nor discontinuity has a direct effect on abnormal returns, thus not
providing support for H13 and H14. Environmental turbulence, regardless of
measurement, does not Significantly impact the market’s evaluations of technology
outsourcing agreements.

On the other hand, component purchase concentration and supplier

involvement interacting with the degree of modularity exerted significant negative
effects on abnormal returns ([3153 = -0.l38, (3151,: -0.321; p<0.05). The interaction
effect of strategic value with supplier involvement (but not with component purchase
concentration) was significant and negative ([3161, = -0.445, p<0.01). Thus, H15a,

H15b and H16b were supported, but H16a was not supported. Finally, buyer
experience or buyer overall production costs did not moderate the relationships
between component purchase concentration and supplier involvement. These results
did not support H17a, Hl7b and H18a, but confirmed H18b.

These findings are discussed in greater detail in the Discussion section below.
4.6 DISCUSSION

Businesses in T1 markets increasingly engage in strategic outsourcing
relationships due to rapid technological developments and amplified global
competition. In particular, many firms engage in relationships with their suppliers in
order to gain flexibility, responsiveness, and competitive advantages against their
rivals. Despite benefits, such relationships entail certain costs and threats for the
buyer. These relationships may lead to asymmetries in dependence due to task
specific qualities, switching costs, and the perceived dynamics of the technological
environment. Thus monitoring and coordination mechanisms become necessary to
prevent opportunistic supplier behavior and the expropriation of buyers’ technological

know-how and commercial secrets. Furthermore, relying on suppliers’ know-how and

137

 

 

 

 

capabilities for the development of technologies may lead to loss of buyers’
capabilities and cast doubts on the amount of their tacit contributions. Both in the
scholarly literature and the business press, strategic outsourcing arrangements
involving product technologies have attracted significant attention. However, to the
best of the author’s knowledge, this is the first study that analyzed the market value of
firms’ global technology outsourcing initiatives as well as the factors that influence
their stock market evaluation.

The overall picture that emerges can be described as follows. First, modularity
exerted a positive influence on stock market evaluations as measured by cumulative
abnormal returns. As mentioned earlier, the loose coupling of component designs
within a modular architecture allows the mixing and matching of modular
components without having to redesign other components. This provides firms the
capability of developing a potentially large number of product variations with
distinctive functionalities, and thus the flexibility and speed in responding to their
market’s requirements. Since development work can proceed independently across
firms, modular systems enable the reduction of asymmetric dependencies and
monitoring costs in interorganizational relationships. More importantly, these
architectures involve less disclosure of strategic and technical information, such as
design plans and development techniques. Above all, this study demonstrates another
advantage of these loosely coupled systems, that is, reducing the investors’ risk
perceptions of global technology outsourcing relationships.

Strategic value of the project (i.e., cost reduction or revenue enhancement) was
also positively associated with the market’s reaction, suggesting that investors
perceive contracts with strategic goals (rather than efficiency goals) to lead to higher

returns. Such contracts with strategic aspirations are viewed by investors to have

138

 

VI.-

higher potential to increase the firm’s future success. Moreover, as predicted,
technology outsourcing agreements entailing asset specificity on the part of the buyer
(that is, involved proprietary technologies jointly developed, or developed by the
supplier) resulted in positive stock market reactions. In other words, the market reacts
favorably to buyers that employ suppliers’ patented and proprietary technologies.
Thus, it would be plausible to state that outsourcing projects increased finn’s value in
the stock market if they: (1) involved proprietary innovations developed jointly or by
the suppliers; and/or (2) entailed more strategic goals such as meeting demand,
developing new products or entering new markets.

Furthermore, geographic distance increased abnormal returns acquired from
outsourcing announcements. Thus, global technology outsourcing in distant locations
enhanced firm market value. The findings also call into question the general
conception in the popular press and concerns of executives regarding the difficulties
and risks of technology outsourcing and offshoring. However, cultural distance (as
opposed to geographic distance) did not have an impact on the market evaluation of
technology initiatives. Correspondingly, it may be concluded that relationship-related,
but not strategic and operational aspects of the arrangement influenced investors’
perceptions regarding the returns and risks of technology outsourcing. Furthermore,
buyer project-related experience or production costs did not have an immediate effect
on the stock market reactions. Experience and cost may be less visible to investors or
their impact may be blurred with the buyer firms’ other characteristics, such as brand
image. Further research may also test for the impact of other constructs such as
reputation.

Contract size and duration were not significantly associated with market

reaction. It appears that investors were not concerned about the size or duration of the

139

 

 

contract when they assessed the potential for rewards or the degree of the risk inherent
in outsourcing initiatives. F urtherrnore, component purchase concentration or supplier
involvement did not influence stock market reactions. Since this information is
internal, it may take a longer duration than the event date tested in this analysis for
these factors to impact firm market value. Supplier capabilities did not exert a
significant influence on abnormal returns. This may be because supplier capabilities
may be reflected into stock prices in the longer run. Another underlying reason may
be that investors may not be aware of the expertise and reputation of suppliers outside
the US. Finally, neither technological heterogeneity nor technological discontinuity
impact abnormal returns directly. Future research may test their moderating effects.
While the main effects of component purchase concentration and supplier
involvement on stock market reactions were nonsignificant, they exerted a negative
impact in combination with the degree of modularity. In other words, as expected,
modularity negatively moderated the effects of component purchase concentration
and supplier involvement on market value. As modularity increased, working with a
few suppliers and allowing for supplier involvement in the earlier stages of product
development diminished in importance in determining firm market value. High supply
concentration and high supplier involvement indicate that the buyer firms do not
employ the advantages accrued by using loosely coupled systems (i.e., their ability to
develop components independently and reconfigure modules without the loss of
functionality): the consequences are more costly than beneficial to the buyer firms.
Strategic value of the project did not significantly moderate the impact of component
purchase concentration, but exerted a negative impact on abnormal returns combined
with supplier involvement. This finding was consistent with literature that posits that

firms chose to internalize activities that were associated with their core competencies.

140

 

Thus, if the agreement entailed high value for organizational profitability and
productivity, supplier involvement to the earlier stages of development had a
detrimental effect on firm market value. The market did not favor high supplier input
to buyers’ projects of high strategic value and product technologies related to their
core competencies. Project related experience or production costs of the buyer did not
moderate the impacts of supply concentration and supplier involvement on stock
market evaluation. In other words, buyers’ experience and costs do not play a
significant role interacting with the number of suppliers that they work with and the
extent to which they outsource their product development. Similar to their main
effects, buyers’ expertise to execute a development project and overall production
costs are either not available or not of concern to investors in their evaluations of the
returns and risks of technology outsourcing.

This study has a number of implications for both research and practice. From
the research perspective, this study provides valuable insights into the consequences
of technology outsourcing, since there is a paucity of studies on the outcomes of
global outsourcing in technology-intensive markets. Furthermore, the symmetric stock
market results (i.e. half positive and half negative abnormal returns) indicate that the
argument on outsourcing’s negative impact (or positive impact) on market value or
performance should be revisited. About half of the announcements triggered positive
reactions; the other half produced negative responses. The findings of this study may
benefit practitioners by offering insights that may guide them to maximize the market
impact of their outsourcing initiatives. As an illustration, in the context of technology-
intensive markets and technology outsourcing initiatives, the market favors modular
systems since such architectures provide firms with both strategic and operational

flexibility. The empirical analysis indicated that strategic outsourcing for the

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L-

 

development of new products and proprietary technologies enhances firm market
value. This also suggests that technology outsourcing for efficiency purposes may
result in unfavorable market reactions. Outsourcing for cost reduction may lead to
potential leakage of tacit know-how and the gradual loss of knowledge-based
capabilities and thus increase investors’ uncertainty as to how much intellectual
property the buyer firms really own in the products. Consequently, executives should
expect a negative reaction from investors when they announce technology outsourcing
initiatives for attaining efficiency or for developing products for which they are
already prominent in commercializing. Managers should also take into consideration
the fact that the market favors global technology outsourcing with partners.
Therefore, it actually may be beneficial to disclose contract details.

This study assessed the risks and returns associated with technology
outsourcing and presented empirical evidence that tests prevailing claims in the
scholarly literature and popular press. Future research may expand upon the model to
include other factors that may influence firm market value as a result of technology
outsourcing initiatives. Market characteristics such as growth potential and market
concentration might explain the portion of the variance that was not explained in this
study. Since proxies were employed to measure all indicators, future studies may
examine the model using (or combined with) survey data. For instance, the degree of
modularity was operationalized at the industry level. Further research using secondary
data sources may develop a firm level measurement which may result in better
estimates of free parameters (thus may be possible through primary data collection).
In addition, although there was no significant difference observed with respect to the

abnormal returns between the announcements with contract size details vs. those

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without, a further analysis may be required to shed light on why only some firms do

not disclose this information.

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CHAPTER 5
CONTRIBUTIONS AND FUTURE RESEARCH
5.1 INTRODUCTION

This dissertation scrutinized technological innovation from three lenses: (1)
the scholarly literature was synthesized and scrutinized for effect size moderators
using a theory-driven meta-analysis; (2) the determinants of internal innovation
development (moderated by environment) were examined employing partial least
squares analysis; and (3) the effect on company value of external innovation
development within loosely coupled systems was analyzed using an event study
methodology. Overall, two research questions were addressed: (1) which factors
associated with both internal and external innovation development contributed to firm
performance and market value; and (2) under what conditions do internal and external
development engendered superior outcomes. Together, the three studies involved
different theoretical models of innovation, the analysis of various types of data (i.e.,
published literature, primary and secondary data) and use a variety of methodologies
(i.e., meta-analysis, PLS, and event study). Thus, this research derived knowledge and
new research directions concerning innovation from the extant body of literature and
from the analyses of the ‘values’ of both internal and external innovation
development.

The first essay benefits academics because the cumulative results of published
studies are examined theoretically and empirically to reveal general patterns as well as
to investigate substantive and methodological sources of inconsistencies.
Simplification could also benefit practitioners. Based on cross-sectional data of North
American firms, the second essay involves an empirical investigation of a

comprehensive model of the determinants of internal innovation success. This model

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is of theoretical interest to academics, but also tells practitioners what works if the
goal is internal development. Finally, the last essay involves an event study analysis
of the impact of outsourcing agreements on the market value of companies. This
model extends existing research and also tells practitioners whether and under what
conditions to develop externally. These contributions are detailed in the subsequent
sections below.

5.2 STUDY RESEARCH QUESTIONS AND CONTRIBUTIONS

STUDY #1. Inconclusive New Product Innovation “Returns”: A Meta-Analysis
Of Research On Innovation In New Product Development

The primary goals of this meta-analysis were twofold. First, the innovation
literature is ripe for an objective and rigorous synthesis due to the amount of new
research and the fact that some of studies have contradictory results. This study
examined of the underlying grounds for inconsistencies and their impact on the
relationships of new product innovation with its determinants and outcomes. In the
NPD, marketing and management literatures, the differences in the level of analysis
and perspectives, as well as the diverse labels and categorizations of innovation have
resulted in inconsistencies in actual empirical results. Particularly, the effects of
environmental turbulence, strategic orientations, organizational structure, and most
importantly, new product performance are highly debated. For instance, some argued
that a focus on customers’ existing preferences (i.e., customer orientation) facilitated
new product innovation, while others argued that reliance on customer input led to
reactive strategies and incrementally innovative products rather than bold and radical
ones. The common tenet in the literature regarding the impact of the organizational
structure was that flexible organic structures enhanced receptivity to new technology.
However, some research found that centralization facilitated (rather than hindered)

new product innovations by reducing conflict and ambiguity, leading to a more

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uniform response to the incoming technology. Lastly, while some argued that more
innovative products provided value to customers and differentiation advantages
relative to competitor offerings, other studies have concluded that less innovative
products were less uncertain, more familiar and synergistic and brought about greater
success. Hence, this meta-analysis scrutinized whether the substantive and
methodological differences (i.e., level of analysis, perspective and measurement)
among the studies published in the literature limited generalizability of their results
and led to contradictory conclusions.

The second set of key question addressed in this study was whether
turbulence, strategic orientation and organizational structure enhanced (or hindered)
new product innovation and whether new product innovation improved new product
performance. Hence, this present study presented a theory.driven meta-analysis
providing an objective and rigorous evaluation of the overall tendency in a body of
literature by standardizing the results of large number of studies. Using structural
equation modeling, an overall model that included technological turbulence, market
turbulence customer orientation, competitor orientation, organizational structure, new
product innovation, and new product performance, as well as the moderating variables
described previously, was explored. The overall goal was to analyze a synthesized
model that permitted evaluation of key mediators and moderators on the relationships
of innovation with its antecedents and outcomes.

RQ(I) Did the level of analysis, the perspective and/or the measurement of new
product innovation impact the findings of the studies?

The evaluation of the findings of the two substantive moderators (i.e., level of
analysis and perspective) provided interesting insights that may benefit innovation

researchers. First, market turbulence did not overall directly affect new product

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innovation. But when the impact of the substantive artifacts was analyzed, this
relationship may be positive for program or firm level analysis. Second, technological
turbulence overall facilitated new product innovation. But researchers examining
particular projects or taking a firm perspective may find this relationship
nonsignificant. Therefore, environmental turbulence directly impacted program level
new product innovation, supporting the literature that states that rising environmental
uncertainty increases the rate and level of innovation required to survive (e. g., Kotabe
and Swan 1995). Future research may analyze whether environmental turbulence also
influenced the degree to which other firm resources (organizational structure,
marketing and technical resources and know-how) and activities (strategic planning
and idea generation) engender new product innovations.

Third, customer orientation was not overall a direct antecedent of new product
innovation, but program research may find this path positive while project research
may find it negative. Similarly, competitor orientation did not exert an overall direct
effect on new product innovation, however it may exert a direct positive influence in
project or firm oriented research. Fifth, mechanical structures encouraged new
product innovation overall, but not in program level research (n.s.), nor in firm-level
analysis. At the project level and when the customer perspective was taken, the
impacts of customer and competitor orientations on innovation were partially
mediated by organizational structure (i.e., there were direct effects on innovation, as
well as indirect effects on innovation through structure). The findings regarding
strategic orientations corroborated the principles of RBV theory and the three-tier
contingency model of the strategy-structure-performance relationship. This study
focused on customer and competitor orientations but an entrepreneurial or

technological orientation may play a similar role as complete mediator.

147

Moreover, examining the moderating impact of perspective, the results of
hypothesis testing exhibited three unexpected results regarding the effects of
turbulence, orientations and structure. First, market turbulence engendered new
product innovation within the firm perspective, whereas hindered when the customer
perspective was adopted. This suggested that market turbulence facilitated
development of new product innovations in the presence of high competitive intensity
and frequent shifts in competitor actions and strategies. To the contrary, it decreased
innovation due to rapid product obsolescence and changes in customers' perceptions
of innovativeness of product and companies. Similarly, competitor orientation was
negatively linked with new product innovation in the ‘customer perspective’
subgroup, suggesting that monitoring of competitor actions led to reactive strategies
and incrementally innovative products rather than bold and radical ones. Interestingly,
from a firm perspective, mechanical structures were negatively linked with new
product innovation; however, it facilitated innovation within a customer perspective.
Thus, more formalization and centralization, which entail efficiency in decision
making and in responding to their environment, enabled new product innovations
viewed as ‘new’ in the market. Furthermore, new product innovation directly and
positively influenced new product performance, regardless of the level of analysis
(i.e., program or project level research) or perspective (i.e., firm or customer oriented
research).

Finally, the methodological moderator investigated in this analysis -- namely,
operationalization of the innovation construct — moderated four of the hypothesized
paths (the relationships being significant only in the ‘categorical single item’
subgroup). This showed that measurement may have a major impact on the results of

a structural model (including one of new product innovation) and that categorical

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single item measures inflated the strength of these relationships. Thus, future
researchers should design their studies with caution.

Overall, a synthesis of the innovation research corroborated the premises of
the two major theoretical foundations (i.e., the RBV and contingency frameworks)
regarding the determinants and outcomes of new product innovation. This meta-
analysis may be further extended by testing the moderating effects of other study
artifacts (see Table A-l). The studies included in this analysis predominantly focused
on manufacturing industries, collected their survey data in North America and
conducted cross-sectional analyses of innovation. Future studies may control for the

effect of these characteristics.

RQ(2) What are the antecedents that significantly facilitate (or impede) new
product innovation? Does new product innovation improve (or deteriorate)
performance?

As mentioned, a structural model on the determinants and outcomes of new
product innovation was analyzed to derive empirical generalizations and to investigate
mediating relationships. The findings indicated that market turbulence, but not
technological advancements in its industry, increased the monitoring of competitor
actions and strategies. Technological turbulence, however, encouraged mechanical
structures and new product innovation. Hence, rapid technological advancements may
lead firms to become less organic to innovate more. On the other hand, neither
market nor technological uncertainty affected customer orientation. This may signal
the significance of being responsive to customer demands irrespective of the level of
environmental turbulence. Furthermore, both market and technological turbulence had
indirect effects on new product innovation through either strategic orientation

constructs and/or organizational structure. Customer orientation and competitor

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orientation also preceded new product innovation indirectly through mechanical
structures.

Thus, firms in turbulent environments organize in such a way that would
enable them to efficiently manage any necessary adjustments in accordance to the
changes in their industries. For uncertain environments that demand faSt reactions,
building consensus may take too much time; thus a continual crisis orientation, 3
cenualized approach and formalized processes may be more rewarding. Centralization
and formalization may facilitate implementation by reducing conflict and ambiguity,
leading to a more uniform response to the changes in the environment (Meyers et al.,
1999). Overall, these findings are contrary to the contingency framework of the
industrial organization (10) paradigm, which views strategy as a firrn’s response to
the competitive industry environment and proposes that firms should adopt less
centralized, more organic structures in dynamic, uncertain environments.

On the other hand, customer and competitor orientation indirectly affected
new product innovation and performance through mechanistic structures. This may
indicate that innovation requires more mechanistic approaches, through which (1)
possible conflicts in NPD processes can be prevented, and (2) integrated and
harmonious operations can be implemented across functions. Through senior level
involvement and centralized strategic planning and agenda setting, firms may achieve
both generating novel products and meeting market demand. Finally, new product
innovation engendered new product performance. Thus, as RBV and contingency
theories suggest, firms may achieve superior performance if they can utilize their
internal resources and capabilities in distinctive ways (Day, 1994; Mahoney and

Pandian, 1993; Peteraf, 1993).

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On the whole, the inherent risks associated with new product development, the
rising R&D costs as result of rapid technological development, increased competition,
and continuous changes in the demands of consumers intensifies the need for new
product innovations. Managers operating in highly turbulent environments are
confronted with the necessity of being innovative and highly responsive to the
dynamics of their industry. The findings of this study highlighted the importance of
new product innovations, as well as strategic orientations along with mechanistic
organizational structures, in coping with industry turbulence and attaining competitive
advantages.

STUDY #2. Market Efficient Technological Innovation: A Detailed Model with
Marketing Implications

Research on NPD projects has predominantly focused on internal factors and
adopted a firm perspective. Particularly, earlier literature ignores the market side of
NPD. The literature posits that highly innovative products (which entail less fit to the
firm) hinder adoption and have high costs and risks to the firm; however, at the same
time, these products provide firms with a differentiated position in the market Thus, a
combination of both firm and market perspectives (that is, considering both the
internal and the external side of new product innovation development) may ensure
higher explanatory power in the study of new product success. In the extant literature,
the match between what is needed (i.e., market pull) and what can be developed
internally (i.e., technology push) is viewed to drive success. However, the
operationalization of this match is still a frontier research issue.

Meanwhile, with their limited available resources, managers are faced with the
challenge to achieve a balance between speed to market and quality of execution in
search for success in the contemporary business environment. The competing goals of

minimizing risk by acquiring sufficient technological and market information, while

151

 

 

also minimizing costs and time to market, escalated the importance of NPD process
implementation. The quality in NPD execution allows firms to avoid sacrifices of
necessary information, mere focus on ‘doable’ incremental projects, as well as
prevent loss of opportunities for more profitable radical innovations. In the literature,
resources, capabilities and strategic orientations have been directly linked to
positional advantages and performance outcomes. The quality of the implementation
of these orientations and process activities has not generally been included in NPD
models.

Consequently, this second study constituted an empirical analysis of the
determinants and outcomes of market efficient innovation development, referring to
Day and Wensley’s (1988) work and incorporating the notion of fit. The specific
research questions addressed were: (1) does the project’s internal environment (i.e.,
the strategic fit of the project and project structure and climate) have a direct impact
on positional advantages and project performance, or exert an indirect effect mediated
by the NPD process implementation; and (2) under what conditions does internal
technological innovation development serve to attain superior performance?

Thus, this study delineated the notion of ‘strategic fit’ both from the firm and
customer perspectives. As opposed to measures such as “having more than adequate
resources” in prior literature, an operationalization to assess ‘fit’ was provided: the
degree of new resources and capabilities required for the product and the extent of
synergy the project enjoyed with the existing structure. The ‘internal innovation
development’ model proposed in this study is anchored in Day and Wensley’s (1988)
source-position-performance (SPP) framework, which linked firms’ sources of
advantage (skills and resources) to positional advantages based on cost and/or product

differentiation. The project’s internal environment (i.e., strategic fit of the project,

152

 

 

 

project related sources of advantages and proficiency in NPD process) was purported
to influence positional advantage and performance of the project, moderated by
external environmental factors. This moderation effect of environmental turbulence,
supported by NPD contingency theory, indicates that the extent to which internal
factors engender new product success is determined by external factors, and thus
partially outside the control of the managers. This analysis further provided
practitioners with a comprehensive list of internal, environmental and supply-chain
related factors important for successful development.

This study focused on the chemical, biochemical and pharmaceutical
industries, which may have limited the generalizability of the model. Further research
is recommended to investigate other industries. Despite that statistical controls were
implemented to control for common method variance, perceptual measurement and
single respondents leaves the model effects subject to this bias to a certain extent.
Thus, a multiple informant design is recommended for further studies
RQ(I) Does the project’s internal environment (i. e., the strategic fit of the project

and project structure and climate) have a direct impact on positional

advantages and project performance, or exert an indirect eflect mediated by
the NPD process implementation?

As mentioned, Day and Wensley (1988) contended that:

“Superior skills and resources are not automatically converted into positional

advantages, nor is there a certain performance payoff from superior cost or

difi‘érentiation positions. Both conversions are mediated jointly by strategic

choices, including objectives and entry timing and the quality of tactics and

implementation " (p. 6).

Accordingly, this study examined whether strategic fit of the project and
project-specific advantages (i.e., project formality and project climate) directly
influenced positional advantages versus whether NPD process implementation

mediated these effects. The results indicated that only manufacturing fit has strictly

indirect (positive) effect on returns through idea generation proficiency, whereas

153

 

 

 

supply-chain, marketing and R&D fit influence positional outcomes both directly and
indirectly through NPD activities. Thus, fit of the project with regards to the market
side of the NPD project was automatically converted into positional advantages to a
certain degree. At the same time, in line with the SPP framework, these conversions
were also mediated by proficiencies in idea generation, product development,
development speed and development cost.

Interestingly, supply-chain fit decreased positional advantages both directly
and through enhancing idea generation and product development. This indicates that a
repeated focus on the target market and demand forecasts of previous projects
enhanced internal implementation proficiencies, but impeded external market
effectiveness and the supply of novel and superior benefits over current products. On
the other hand, the new project’s synergy with the firm’s existing marketing enhanced
its positional advantages directly as well as indirectly through decreasing idea
development proficiency. This suggested that firms may attain superior market
positions in two distinct ways: by exploiting existing marketing capabilities in the
products previously offered, at the same time through more effective idea generation,
which actually requires departure from existing marketing skills. Further research
may examine the determinants and outcomes of idea generation in greater detail.

The findings regarding development speed were also interesting and deserve
further investigation. Both supply chain and R&D fit increased development speed,
which interestingly hindered the degree to which customer needs were met. This has
been frequently addressed and debated both in the academic literature and popular
press. For the sake of timeliness, firms opted to ‘focus on trivial, rapidly executable
development projects’ (Calantone, et al., 1997, p. 179), which occasionally resulted in

failure in meeting market demand. This may be due to the neglect of consumer

154

 

readiness for the sake of development speed. Researchers may examine the dynamics
among strategic planning, NPD process execution, speed to market and performance
returns.

The findings on the effects of project formality and project climate on the
effectiveness of idea generation and opportunity analysis was contrary to both the
expectations of this study and the literature (Veryzer 1988; McDermott and O’Connor
2002). The results indicated that formal management processes and cross-functional
integration enhanced idea generation, technical assessment and product development
possibly through reducing uncertainties and Specifying the required tasks and their
sequence. Moreover, project formality reduced, while project climate increased speed
and cost. Thus, project formality not only provided time and cost efficiencies, but
formal mechanisms and cross-functional integration led to better execution. Future
studies may also investigate how different levels of and approaches to cross-
functional integration impact performance outcomes. For instance, project formality
and project climate may exert a combinatorial effect on implementation effectiveness
and efficiency.

Finally, the execution of NPD process activities was purported to be the key to
achieving positional advantage in the market. The results provided partial support:
proficient idea generation and product development activities did bring positional
advantage, but proficiency in technical assessment diminished product advantage.
Therefore, a focus on technical attributes and performance of the product may lead to
a neglect of product quality and benefits in the eyes of the customers.

RQ(2) Under what conditions does internal technological innovation development
serve to attain superior performance?

The findings indicated that both meeting customer needs and product

advantage significantly enhanced the project’s impact on performance. This

155

 

 

i.“-

corroborated the significance of exceeding customers’ needs over what is offered in
the market and investing to gain relative product advantages. In addition to this, the
degree to which product advantage (but not meeting customer needs) provided
performance returns was moderated by market conditions. The relationship between
product advantage and project performance was found to be stronger as the market
demand shrinks and the competition becomes more intense. On the other hand,
investing in meeting customer needs always proves rewarding regardless of whether
rivahy is severe or customer demand is changing. Future research may investigate the
moderating impact of technological factors (such as the rate of technological change
or the presence of dominant standards), which were not included in this analysis.

STUDY #3. Global Strategic Outsourcing for Resource Deficient, Market
Efficient Technological Innovation

TI industries are increasingly characterized by firms outsourcing either a
portion of or the entire product development process. The goal is either survival or
being more responsive to the market dynamics. Even though outsourcing relationships
may enhance the buyer firms’ technical intelligence and increase their flexibility, they
create coordination difficulties, overreliance on suppliers’ resources and capabilities,
and thus potential leakage or gradual loss of tacit know-how. Consequently, the risks
of technology outsourcing as well as their wealth effects have been major concerns to
managers. Despite such attention in the business press, there is a paucity of studies on
technology outsourcing, most of which adopted a cost-based approach while ignoring
value enhancement and performance outcomes that result from such strategies. In this
thesis, the focus is on discrete buyers’ choices in single transactions. Consequently,
this study constituted an empirical validation of the performance outcomes of
strategic outsourcing relationships in the context of global technology intensive

markets and modular systems.

156

 

 

In summary, grounded in agency, resource dependency and transaction cost
theory, the primary objective of this third study was to develop and test a model on
the determinants of the market value of external innovation development. This
research aimed to provide implications for managers on the returns and risks of global
strategic outsourcing. The market evaluation of technology outsourcing
announcements was used as a proxy for the business value of such initiatives.
Furthermore, the extent to which key factors associated with such agreements
influence the buyers’ market (shareholder) value as a result of their technology
outsourcing announcement was investigated. On the whole, buyers were hypothesized
to receive abnormal returns in their outsourcing agreements, based on the task
characteristics, switching costs and the degree of environmental uncertainty.
Specifically, this study examined: (1) do technology outsourcing agreements increase
firm market value; and (2) how do the key factors related to these arrangements
(including certain risks and their interrelatedness to other projects) impact their
market evaluation?

Future research may expand upon the model to include other factors that may
influence firm market value as a result of technology outsourcing initiatives. Since
proxies were employed to measure all indicators, firture studies may examine the
model using (or combined with) survey data. Further research using secondary data
sources may develop a firm level measurement which may result in better estimates of
free parameters (which may be possible through primary data collection).

RQ(I) Do technology outsourcing agreements increase firm market value?

Interestingly, as represented in Table 4-1, the market reacted positively to the
announcements on the event date, whereas negatively after 3 days (p<0.05). On the

day of the announcements, technology outsourcing initiatives were immediately

157

 

 

perceived likely to increase firms’ future cash flows. However, their market value
reduced after three days, perhaps because some investors reconsider while other
investors engage in profit-taking. Moreover, as Table 4—2 indicates, significant
abnormal returns were generated in four event windows: (1) the five-day pre-event
period from day —4 to the event date; (2) the four-day post event period from the
announcement date to day 3 afterward; (3) the eight day event period from day —4 to
day 3 afterward; and (4) the three-day post event period from the announcement date
to day -2. Thus, the effect of technology outsourcing announcement events on firm
market value was significant and was quickly incorporated into the stock prices of the
buyer firms.

Interestingly, technology outsourcing generated positive and negative investor
reactions in approximately equal measure: half of the announcements received
favorable (positive) market reactions, while half generated negative reactions. The
conflicting reactions suggest that some investors perceived that for some firms the
returns from technology outsourcing outweighed the risks associated with it; others
held the opposite view. Further tests were conducted to identify any significant
patterns between the announcements that received positive versus negative market
evaluations. The results showed that the directionality of the abnormal returns
significantly differed based only on the country-of-origin of the agreement (12 =
33.731, df = 21, p<.05). This notion was addressed in earlier trade theories in the
international business literature (Vernon 1966; Horst 1976). These theories generally
posit that at the early stages of product development, US firms exploit their
technological advantage and develop their products for their home market. At a later
stage of the product cycle, they exported their products to other countries based on

their similarity to the US in demand patterns and supply capabilities. Gradually, as

158

the product becomes standardized or mature, the attractions of locating value-added
activities in a foreign country increase. Thus, a US firm wishing to maintain its initial
share of the foreign market is eventually forced to establish marketing, service and
production facilities in that foreign country. Similarly, the sample of firms in this
study received favorable versus unfavorable market reactions based on the countries
of the supplier firms with which they engaged in technology outsourcing
relationships. Further research may conduct a longitudinal study to understand how
the market reacts to technology outsourcing across a longer span of time.

RQ(2) How do task characteristics, switching costs and environmental uncertainty
impact the market 's reaction to global technology outsourcing?

Task characteristics, modularity, the strategic value of the project and
geographic dispersion significantly affected stock market evaluations as measured by
cumulative abnormal returns. This study demonstrated an additional advantage of the
modular systems, i.e., reducing the investors’ risk perceptions and increasing the
market value of global technology outsourcing relationships. Furthermore, contracts
with strategic goals of revenue enhancement (rather than efficiency goals with cost
reduction concerns) increased firm market value. Geographic distance also
engendered abnormal returns. Thus, global technology outsourcing in distant
locations enhanced firm market value. The findings, therefore, addressed the general
conception in the popular press and concerns of executives regarding the difficulties
and risks of global technology outsourcing.

Another interesting finding in this study was that technology outsourcing
agreements entailing asset specificity on the part of the buyer (that is, involving
proprietary technologies jointly developed, or developed by the supplier) resulted in
positive stock market reactions. Overall, this study showed that global outsourcing

projects increased firm’s value in the stock market if they: (1) involved proprietary

159

 

 

 

 

innovations developed jointly or by the suppliers; and/or (2) entailed more strategic
goals such as meeting demand, developing new products or entering new markets.

Future research may investigate using other methods the impact of the factors
associated with technology outsourcing that did not exert a significant impact on firm
market value (see Table 4-5). This model included factors that may be less visible to
investors or their impact may be blurred with the buyer firms’ other characteristics,
such as brand image. Since information related to these factors (e. g., buyer
experience, buyer costs, supply concentration, supplier involvement and supplier
capabilities) is internal, it may take a longer duration than the event date tested in this
analysis for these factors to impact firm market value. Another underlying reason may
be that investors may not have sufficient information on agreements involving non-
US partners. Finally, neither technological heterogeneity nor technological
discontinuity impact abnormal returns directly. Future research may test their
moderating effects.

The analysis of interaction effects of four task characteristics (i.e., modularity,
strategic value, buyer experience and costs) and two buyer decisions (i.e., supply
concentration and supplier involvement) provided valuable insights. The findings
suggest that for higher market value, buyer should employ the advantages accrued by
using loosely coupled systems (i.e., their ability to develop components independently
and reconfigure modules without the loss of functionality) and establish relationships
with multiple suppliers but allow for less supplier involvement. Furthermore, for the
agreements entailing high value for organizational profitability and productivity,
supplier involvement in the earlier stages of development had a detrimental effect on
firm market value. This finding corroborated the common tenet in the literature that

firms should internalize activities that are associated with their core competencies.

160

 

 

 

The market did not favor high supplier input to buyers’ projects of high strategic
value and product technologies related to their core competencies. Finally, the
nonsignificant moderating effects of buyers’ expertise to execute a development
project and overall production costs on the linkages from supply concentration and
supplier involvement to firm market value may be attributed to their unavailability or
unimportance to investors in their evaluations of the returns and risks of technology
outsourcing.

The findings of this study offered valuable insights for practitioners that may
guide them to maximize the market impact of their outsourcing initiatives.
Particularly, the market favored modular systems due to their strategic and operational
flexibility advantages. Global technology outsourcing arrangements for the
development of new products and proprietary technologies also enhanced firm market
value. Therefore, executives should expect a decrease in their future cash flows when
they announce technology outsourcing initiatives for attaining efficiency or for
developing products for which they are already prominent in commercializing. Such a
decrease may result due to potential leakage of tacit know-how and/ or the gradual
loss of knowledge-based capabilities.

5.3 FURTHER RESEARCH RECOMMENDATIONS

This dissertation examined internal and external product development
separately. The underlying motive was an interest in understanding the dynamics and
the advantages in each context, before a decision be made as to which context is better
for the new product innovation generation, and most importantly, firm market
performance. Thus, further research may simultaneously analyze internal and external
aspects to investigate how firms most effectively and effectively combine the two

approaches of development of new product innovation. Future studies may examine

161

 

ifi‘

 

 

whether other factors (and/ or linkages) may be influential in a portfolio approach or
whether the relationships among the factors studied in this dissertation change. As an
illustration, the relationship between NPD process implementation proficiency and

perceived outsourcing risk may be analyzed.

162

APPENDICES

APPENDIX A

APPENDIX B

APPENDD( C

 

 

 

163

APPENDIX A

META ANALYSIS (CHAPTER 2)
SUPPORTING DOCUMENTS

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165

APPENDIX B

INTERNAL INNOVATION DEVELOPMENT (CHAPTER 3)
SUPPORTING DOCUMENTS

166

 

 

B.l SELECTED UESTIONNAIRE ITEMS

Supply-Chain Fit:
“Was the new product aimed at your existing customers- customers you sold before?”

Marketing Fit:
“Was the advertising, promotion and marketing communications for this project the
kind you had used before?”

R&D Fit:
“Did the R&D skills and resources needed for this project fit closely with the existing
R&D skills of the company?”

Manufacturing Fit:
“Could this product be manufactured using existing company plant and equipment,
with no changes are required?”

167

 

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170

TABLE B-2. DESCRIPTIVE STATISTICS OF FIRMS IN THE SAMPLE

 

 

 

 

 

 

 

 

 

 

 

 

 

Annual Number ofIR&D % Sales by % Profits by
Company SalesWEmployees Percent New Products New Products
MM)
flan 380006 1468 3.92 18.93 22.18
Median 200000 400 3.10 15.00 18.28
Mode 100000 400 2.00 50.00 5.00
Standard 627089 4216 2.83 16.63 20.27
Deviation
Minimum 40 11 0.10 0.00 0.00
Maximum 3100000 30000 15.00 80.00 90.00

 

 

 

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172

TABLE B-4. MEASURES AND LOADIN GS:

COMMON METHOD VARIANCE (CMZI CONTROLLED

   

SUPPLY CHAIN FIT

ICR=.85; AVE= .82; sqrt AVE=.79

 

   
  

 

 

 

   
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

    

 

    

 

 

 
   

 

 

  

 

 

   

 

 

 

 

 

 

 

 

  

 

 

 

   

Distribution capacity _____ 0.15 p .
arget market 7 0.67M ‘
Customer need similarity ‘ 0.74 . . .. ,.
Existin channels 0.05
ICR=.05; AVE= .68; sqrt AVE=.01
vertising 09—3 .
Market Research . . 0Q 7
Promotional activities : 0.79
R&D FIT Sin le item NA
MANUFACTURING FIT ICR=.91; AVE= .72; sqrt AVE=.05
Manufacturing 05
Plant . ,,,,,,,,,, - 035,. I
Development .. 0.93
Technology 0.79
PROJECT CLIMATE ICR=.86; AVE= .60; sqrt AVE=.B2
Cross-functional integration .. . _‘ __ .. 084
Teamforentire project .- 0.09 _‘
earn commitment 0.73
PROJECT FORMAUTY ICR=.91; AVE= .54; sqrt AVE=E)
arget market defined _ 0.02
Product concept defined 0&3
Benefits defined.,_-,---,_. ..... - 033.--
Positioning defined . 1.15. _. -
Featuresdefined e 0.8+
Formal NPD recess 0..
[IDEA DEVELOPMENT ICR=.06; AVE= H]; sqrt AVE=.77
Ilnitial screening . . . 0.51 . _ -
Preliminary market assessment .. .. “”9091
Detailed market study . 0.72 __ ..
Business anal sis 075
TECHNICAL ASSESSMENT ICR=.05; AVE= .55; sqrt AVE=.01
Preliminary technical assessmentwm . ., E's .. . .. . . 7
Customertests _ 70.70 77777
ln-house roduct testin 0%
PRODUCT DEVELOPMENT ICR=.02; AVE= .69; sqrt AVE=.03
Pilot production 0.79
Production start-up 0%
DEVELOPMENT SPEED ICR=.00; AVE= .79; stLrt AVE=.89
Time efl’iciency _ .. .. 0.. . 4..-... 5
herence to time schedule 7 _ 0.09
DEVELOPMENT COST Single item NA
CUSTOMER NEED MET ICR=EJg AVE= .I; aqrt AVE=.03
Eenefit importance to customer .. 7 7 ,. _‘ 035...-
Benefits esy to communicate ‘ _ _ ,. ,,,,, Q77. ..
Perceived as useful _ “0%“
Visible benefits ‘ ” ‘ 0.02
PRODUCT ADVANTAGE ICR=.m; AVE=-.70; sqrt AVE=.01
309mm... ...PBB .
Relative product quality _________ .. .. 0.91
Unique attributes ‘ 0017
lm act on cUstomer 0.73
IMPACT ON PERFORMANCE ICR=.08; AVE= .71; sqrt AVE=.04
echnical success rating 0.5.]
‘Profltabilityrating ‘ y 0.934 ,
Sales/profit impact an company 003

 

 

173

 

 

 

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174

TABLE B-6. RESULTS FOR TESTS OF MEDIATION [El-H4131:
CMV CONTROLLED

(r) (9)

Fit Customer Need Met Product

Supply Chain Fit no direct rel. : no direct rel.
142*“ 100“

Marketing Fit no direct rel. : no direct rel.

.071 " -0.047(ns)

no direct rel. : no direct rel.
-0.156‘" -0.002(ns)

Manufacturing Fit no direct rel. . no direct rel.
4f = 0.019(1‘18) = -0.039(ns)

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, **, *** indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

TABLE B-7. RESULTS {ES-H6. a-el: CMV CONTROLLED

Advantages H5 H6

Project Formallty Project Climate

Idea Development
Opportunity Analysts

Technical Assessment

Product Development

Development Speed

Development Cost

66-

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. 1', **, *** indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

175

 

TABLE B-8. RESULTS jI_I7-H11. a-ej: CMV CONTROLLED

 

 

   

 

 

 

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Posltlonal Advantagesl (a) (b)
Customer Need Met Product Advantage
NPD Process Proficiency
H7 Idea Development an H7(a): + H7(b): +
Opportunity Analysts 51a -_-_ _0_207... Bib = -0.226""
Conclusion: Reject Conclusion: Reject
H8 Technical Assessment H8: +
n.a. 32!) = -0.300'“
_ Conclusion: Reject
H9 Product Development H9(a): + H9(b)1 +
Baa = 0.071: [33,, = 0.122m
Conclusion: Support Conclusion: Support
H10 Development Speed [410(3): + IH10(b): +
[34, = -0.161“‘ [34,, = -0.043(ns)
Conclusion: Reject Conclusion: Not Support
H11 Development Cost H11(a): - H1103): -
B5, = -o.044- [35,, = -o.103m
Conclusion: Support Conclusion: Support

 

 

 

 

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates

and conclusions. "‘, **, *** indicate 0.1, 0.05 and 0.01 significance levels,

respectively.

176

 

TABLE B-9. RESULTS (gm-11131: CMV CONTROLLED

Outcome Positionall

 

 

 

 

 

 

 

 

 

 

 

 

Advantages Project Performance
H12 Customer Need Met H1‘2: +
Be = 0.266"
Conclusion: Reject
H13 Product Advantage H13: +
[37 = 0.054(ns)
Conclusion: Support
Market Size
m Ba = -O.129(ns)
as 2
§ 3 Market Growth '39 = 0.046(ns)
2 § Competition Concentration
B10 = -0.042(ns)
g g Annual Sales I313 = 41111,...
C ._
o . Number of Employees
0 g 1314 = o.142***

 

 

 

 

 

 

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, **, *"'* indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

TABLE B-10. RESULTS FOR TESTS OF MODERATION l4-H7.a-b :
CMV CONTROLLED

   
  

  
  

  
  
      

H14

Need Met -
Performance

H1

511.?

1 .
Bub:

H15
Advantage -

        
       
  

       

     
 

       
 

  

+

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Not
4.
0.200(ns)
Not

-1 .008“

  

+

0.235(ns)

Not
4.

Market Size

     

      
      
     

512a =

 
     
 

    

  
   
  
  

    

1

Market Growth -
B12b =

       

.703“

  

 

                
    

 
   
 
  

Market Competitiveness

0.441 ***

H1

I311c =

1 .
I312c =

    

NOTE: Presented are statements of the hypotheses, followed by the path estimates
and conclusions. *, **, *** indicate 0.1, 0.05 and 0.01 significance levels,
respectively.

177

APPENDIX C

EXTERNAL INNOVATION DEVELOPMENT (CHAPTER 4)
SUPPORTING DOCUMENTS

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