THE ROLE OF PERCEIVED RISK IN

NEW PRODUCT ALLIANCES

By

“RUBY” PUI-WAN LEE

A dissertation submitted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY College of Business and Economics

MAY 2003

© Copyright by “RUBY” PUI-WAN LEE, 2003 All Rights Reserved

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of “RUBY” PUI-

WAN LEE find it satisfactory and recommend that it be accepted.

____________________________ Co-chair

____________________________ Co-chair

____________________________

ii

ACKNOWLEDGEMENT

I would like to acknowledge several important people for their contribution to my

academic development. First of all, I would like to thank Dr. Jean Johnson for her consistent

faith in my ability. I am grateful that she gave me an opportunity to pursue my doctorate and

walked me through the entire program. She never gave up hope on me even when there were

several moments that I had doubts on myself.

I am deeply indebted to Dr. Rajdeep Grewal who has inspired me to develop my research

interests and skills earlier in my career life. I have learned much through his wealth of ideas and

many words of encouragement.

I would also like to thank Dr. John Cullen. Dr. Cullen is Professor at the Department of

Management. His depth and width of knowledge in organizational research stimulated me to

think outside a narrow paradigm.

I am fortunate to meet many other faculty and friends within and outside the Department

of Marketing during my doctoral life. They have been very supportive. My dissertation cannot

be completed without the financial support from the Department of Marketing, Dr. Jean Johnson,

Dr. Donald Stem, and Amit Saini, and research assistance from Brittney, Jodie, and Dr. Kathleen

Warren of the College of Engineering and Architecture. I have very much appreciated their help

with my dissertation.

I am deeply indebted to my family. My two-year-old boy, Leon, has a terrific sense of

humor despite his age. He and baby Hayden have been a tremendous source of great joy. My

husband, Kenny, has been very accommodating and has made personal sacrifice.

iii

THE ROLE OF PERCEIVED RISK IN

NEW PRODUCT ALLIANCES

Abstract

By “Ruby” Pui-Wan Lee, Ph.D. Washington State University

May 2003

Co-chairs: Jean L. Johnson and Rajdeep Grewal This study investigated the relationship between perceived risk types and governance

attributes in new product alliances. The typology of perceived risk including default

(performance and implementation) risk, knowledge leaking risk, relational risk, and reputation

risk was developed. It was suggested that a firm’s different types of risk perception had effects

on the use of governance attributes (contract explicitness and relational components) and that

such governance attributes would influence new product success. Organizational distance, the

dissimilarity between two firms, was believed to moderate the impact of perceived risk types on

governance attributes. Hypotheses were developed on the basis of past studies and field

interviews. Data collected through mail survey of top management executives in high-tech firms

were used to test the hypotheses.

Results from 107 firms suggested that the hypotheses were partially supported. The tests

of hypotheses indicated that firms in new product alliance activities faced various types of risk.

In particular, default risk, relational risk, and reputation risk were found to have direct effects on

the levels of contract explicitness. The use of relational attributes (flexibility and mutuality) was

found to be associated with default risk only. Organizational distance was found to have no

iv

moderating effect on the relationship between risk types and governance attributes. Further,

results suggested that high levels of contract explicitness and relational attributes had positive

effects on new product success.

v

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENT ………………………………………………………………… iii

ABSTRACT ………………………………………………………………………………. iv

LIST OF TABLES ………………………………………………………………………… ix

LIST OF FIGURES ……………………………………………………………………….. xi

CHAPTER

1. INTRODUCTION ………………………………………………………………... 1

The Role Perceived Risk Types ………………………………………………. 1

Purpose of Study and Research Goals ………………………………………… 4

Organization of The Dissertation ……………………………………………… 6

2. LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK ……………….. 7

Conceptual Foundations of Perceived Risk Types ……………………………. 7

Literature Review on Governance Mechanisms and Contracts ……………….. 21

Conceptual Model and Hypotheses ……………………………………………. 30

Effects of Perceived Risk Types on Governance Attributes ...………………… 32

The Moderating Role of Organizational Distance in Risk-Governance

Attributes Relationships ..…………………………………………………. 38

Effects of Governance Attributes on New Product Success …………..………. 42

Summary ………………………………………………………………………. 44

vi

3. RESEARCH METHOD ……………………………………………………………. 46

Research Context ……………………………………………………………… 46

Preliminary Fieldwork ………………………………………………………… 47

Instrument Development Procedures and Measures ………………………….. 49

Sample Frame and Procedures ………………………………………………… 57

Pilot Study …………………………………………………………………….. 60

Main Study …………………………………………………………………….. 66

Response Bias and Validity Checks …………………………………………… 69

Summary ………………………………………………………………………. 72

4. DATA ANALYSIS AND RESULTS …………………………………..………….. 73

Respondent Characteristics and Descriptive Statistics ………………………... 73

Measure Validation ……………………………………………………………. 76

Hypothesis Testing and Results ……………………………………………….. 85

5. DISCUSSION AND CONCLUSION ……………………………………………… 93

Implications ..…………………………………………………………………... 93

Contributions …………………………………………………………………... 95

Limitations and Future Research Directions .………………………………….. 99

BIBLIOGRAPHY …………………………………………………………………………. 102

vii

APPENDIX

A. Prenotification Letters and Cover Letter for Pilot Study …………………………. 116

B. Summary of Indepth Interviews …………………………………………………… 121

C. Original Scale Items ……………………………………………………………….. 124

D. Cover Letters and Questionnaire for the Main Study ...…………………………… 129

viii

LIST OF TABLES

Page

1. Classification Schemas and Definitions of Governance Structure Used in Marketing .. 24

2. Summary of Hypothesized Relationships …………………………………………...… 45

3. Constructs and Definitions …………………………………………………………….. 51

4. Item Factor Loadings and Reliability Analyses ……………………………………….. 64

5. Correlation Matrix of Variables after Purification …………………………………….. 66

6. Sampling Frames and Response Rates ………………………………………………… 68

7. Comparison of Early Respondents to Late Respondents ……………………………… 69

8. Comparison of Respondents to Non-Respondents ……………………………………. 70

9. Comparison of Pilot to Main Responses ……………………………………………… 71

10. Characteristics of Firms and Key Informants …………………………………………. 74

11. Composition of Partner Firms …………………………………………………………. 75

12. Characteristics of Partner Firms ……………………………………………………….. 76

13. First-order and Second-order Loadings of Performance Risk and Implementation Risk 78

14. First-order and Second-order Loadings of Flexibility and Mutuality ………………… 80

15. Results for Confirmatory Factor Analysis Models ……………………………………. 81

16a. Confidence Intervals, Average Variance Extracted, and Construct Reliability – Risk

Types ………………………………………………………………………………… 83

16b. Confidence Intervals, Average Variance Extracted, and Construct Reliability –

Governance Attributes ..……………………………………………………………… 83

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16c. Confidence Intervals, Average Variance Extracted, and Construct Reliability – New

Product Success, Organizational Distance, and Alliance Experience ...……………… 84

17. Correlation Matrix and Summary Statistics ……. …………………………….………. 85

18. Hierarchical Regression Models ………………………………………………………. 89

19. Regression Model Results ……………………………………………………………... 91

20. Regression Models for Testing Mediating Effects ..…………………………………... 92

x

LIST OF FIGURES

Page

1. Full Conceptual Model ………………………………………………………………… 31

2. Effects of Risk Perceptions on Governance Attributes ……………………………...… 32

3. The Moderating Role of Organizational Culture …..………………………………….. 39

4. Effects of Governance Attributes on New Product Success .………………………….. 43

5. Respondent Identification Procedures and Results ...………………………………….. 58

6. Revised Model (Main Effects and Moderating Effects) ………………………………. 86

7. Revised Model (Effects of Governance Attributes on New Product Success) ..………. 90

xi

Dedication

This dissertation is dedicated to my parents, my brothers and sisters, and my parents-in-law

who provided both emotional and financial support

xii

CHAPTER ONE

INTRODUCTION

If I ask why you believe any particular matter of fact…, you must tell me some reason; and this reason will be some other fact, connected with it. But as you cannot proceed after this manner, in infinitum, you must at last terminate in some fact, which is present to your memory or senses; or must allow that your belief is entirely without foundation.

Hume 1748, p. 46

THE ROLE OF PERCEIVED RISK TYPES

Risk is a central notion in strategy literature (Aaker and Jacobson 1990) and the role of

risk has been implicitly explicated in interfirm research. For example, Mishra, Heide, and Cort

(1998, p.277) suggest that “customers are faced with both adverse selection and moral hazard

problems that involve, respectively, uncertainty about supplier characteristics and the risk of

quality cheating”. Similarly, Houston and Johnson (2000, p. 4) observe that Hercules

Corporation “feared customers would steal its know-how, while important customers grew

increasingly concerned over their dependence on Hercules” and they further propose that

asymmetric information cause “buyers to fear that a supplier’s product performance claims may

be exaggerated”. Although these studies express how the role of risk plays out in interfirm

research, do not explicitly include it in their conceptual frameworks.

It is believed that more attention should be given to the role of risk in interfirm

relationships. To illustrate, consider the following examples. In some cases, the focal firm may

have more information than its partner on the products it sells. Asymmetric information between

the focal firm and the partner firm makes the former think of being opportunistically exploited

by the latter (Mishra et al. 1998). In other situations, strategic partners are not willing to share

1

information or knowledge because the social ties embedded in the relationship are weak

(Granovetter 1973; Rindfleisch and Moorman 2001). In either of the above situations, the

perception of risk plays a critical role. Revisit the first scenario. The focal firm may not really

exploit its partner even though it has the information to do so. However, the partner firm might

feel as though exploitation is a very real possibility. Thus, it is what the partner firm perceives

about the risk of being exploited opportunistically that matters. That is what drives its actions

and decision-making. In the second example, the weak social ties may create risk in executing a

plan perceived by partners. These examples suggest that risk, apart from other widely researched

areas such as opportunism, is a distinctive concept that influences ways a firm makes its

decisions. Hence, knowing what risk is and how it relates to decisions made within interfirm

relationships is crucial.

This dissertation examines the role of perceived risk in a setting of new product alliances

(NPA). In high-tech industries and other sectors, a growing number of firms enter into different

forms of interfirm relationships, such as alliances, partnerships, or joint ventures, to develop new

products (Rindfleisch and Moorman 2001; Robertson and Gatignon 1998). Among these types

of interfirm relationships, the use of alliances is increasingly common (Reuer et al. 2002).

An alliance is an “ongoing, formal, business relationship between two or more

independent organizations to achieve common goals” (Sheth and Parvatiyar 1992, p. 72).

Forming alliances to create new products, in fact, is very important and is said to help access to

resources, reduce risks, and shorten innovation time, among others in today’s competitive

environments (Varadarajan and Cunningham 1995).

New product alliances are different from any other forms of alliance activities (e.g., joint

ventures) in that NPAs involve the acquisition and utilization of information and resources

2

between alliance partners that is related to research and development and product innovations

(Rindfleisch and Moorman 2001). Since NPAs involve close interactions and information

sharing between alliance partners, firms are exposed to different types of risk and a lack of

understanding of these risks results in alliance failures or premature termination of alliance

relationships (Das and Teng 1996). As a result, it is important to uncover the relationship

between risk perceptions and what kind of mechanisms can be used to handle the risk

perceptions. The use of a contract is one way to manage risk. A large body of research (e.g.,

Lusch and Brown 1996) suggests that various contractual components are critical in governing

interfirm relationships. Specifically, explicitness, flexibility, and mutuality are three key

attributes examined in interorganizational research (e.g., Lusch and Brown 1996). These

governance attributes are suggested to have effects on performance outcomes (Cannon et al.

2000). Following extant research on contracting, this study also examines the effect of various

governance attributes on new product success which aims at providing insights for practitioners

in the use of various components in their contracts.

In addition, organizational distance, defined as the dissimilarity between firms (Simonin

1999), was included in this study. Specifically, organizational distance relates to the degree of

dissimilarity between alliance partners in terms of their management styles, philosophies, and

business practices (Simonin 1999; Tyebjee 1988). When the organizational distance between

two alliance partners is close, it implies that their business practices, for example, are likely to be

similar resulting in reduced conflicts and misunderstanding (Mosakowski 1997; Pangarkar and

Klein 2001). Thus, knowing the impact of organizational distance is critical in strategic alliances

(Simonin 1999).

3

PURPOSE OF STUDY AND RESEARCH GOALS

The purpose of this dissertation is two-fold. First is to relax the assumption of risk

neutrality proposed in transaction cost economics (Williamson 1975). Specifically,

Williamson’s (1975) transaction cost analysis rests on three behavioral assumptions,

opportunism, bounded rationality, and risk neutrality, to predict the choice of relationship

governance relationships. Throughout the last two and a half decades, most researchers focus on

the assumptions of opportunism and bounded rationality to predict the alternative governance

structures, while the assumption of risk neutrality remains silence (c.f. Rindfleisch and Heide

1997). As Chiles and McMackin (1996, p. 75) observe, “the assumption of risk neutrality has

gone virtually unnoticed.” Since risk is such an important factor in making decisions (Kohli

1989), it is critical to understand the role of risk and how risk is going to affect the choice of

attributes in a contract between firms.

The second purpose is to uncover the different dimensions of risk in interfirm

relationships. Risk has been acknowledged as a multi-dimensional construct elsewhere in the

marketing literature (e.g., Bauer 1967; Dowling 1986). However, its multiple facets have not

been examined in interfirm research. It is particularly important to understand the various types

of risk in interfirm settings since past research indicates that the concept of risk is comprised of

various aspects. Ignoring anyone of them will lead to incorrect conclusions (Kohli 1989). Thus,

there is a need to understand the nature of risk and its role in interfirm alliances.

Toward this end, this dissertation follows the framework from consumer behavior

research (e.g., Bauer 1967) and builds on theories from multiple disciplines to delineate a set of

perceived risk types (performance, implementation, knowledge leaking, relational, and

4

reputation risk) that is pertinent to alliances. I suspect that certain types of perceived risk are

more salient than others in various specific circumstances. Thus, to test the effects of perceived

risk types, this study focuses the influence of risk types on the use of governance attributes in

new product development alliances.

Different types of perceived risk play a critical role in a situation where the focal firm has

to decide how explicit and how normative (levels of flexibility and mutuality) its contract is

going to be in order to maintain and develop successful NPAs. An explicit contract is a written

agreement detailing the extent to which a firm is going to manage various situations if they

happen. In contrast, a flexible contract describes how much adaptation and changes a firm will

make for its partner whereas mutuality of a contract depicts the extent to which a firm shares

support and stands for its partners to handle different future contingencies. The primary

difference between an explicit contract and a normative contract is that the former one

emphasizes its formal presentation and legal bindings, whereas the latter one addresses the

relational components between parties (Macneil 1980). For example, when the focal firm

perceives a high risk in damaging its performance, it may contract with its partner in the most

explicit ways so that all contingencies are accounted for so as to avoid losses in the future. In

addition, extant research suggests that the design of contracts plays an important role in firm

performance (e.g., Lusch and Brown 1996). The following research, thus, attempts to examine

the relationship between governance attributes and firm performance (in terms of new product

success).

Accordingly, the first objective of this research is to identify various types of perceived

risk which are pertinent to NPAs. The second objective is to empirically examine the

consequences of perceived risk types on the design of contracts and the moderating role of

5

organizational distance on the relationship between perceived risk types and governance

attributes. The third purpose is to study the outcome of different governance attributes on new

product success. Taken together, this dissertation attempts to address the following questions:

1. What types of perceived risk can be found in NPAs and what are their theoretical

underpinnings?

2. Which types of perceived risk result in the use of which attributes in a contract to maintain or

govern an alliance for new product development?

3. What is the moderating effect of organizational distance on the relationship between risk

perceptions and governance attributes?

4. What are the performance implications (i.e., new product success) of various governance

attributes?

ORGANIZATION OF THE DISSERTATION

This dissertation is structured as follows:

Chapter 1 provides an overview of the study. Chapter 2 reviews the literature pertaining

to the major constructs in this dissertation and details the conceptual model and the development

of hypotheses. Chapter 3 outlines the research method including the operationalization of

concepts, the sample design, the development of survey instrument, and the data collection

procedures. Chapter 4 presents data analysis procedures and the findings of this study. Chapter

5 provides discussion, conclusion, and recommendations.

6

CHAPTER TWO

LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

The view that the precision of science and of scientific language depends upon the precision of its terms is certainly very plausible, but it is none the less, I believe, a mere prejudice.

Popper 1945

The role of risk is critical yet has not been understood in interfirm research. It is believed

that more attention should be given into this area. In this chapter, I draw on various disciplines

to review the concept of risk. My discussion throughout the dissertation focuses primarily on the

perception of risk rather than the risk itself. Since perceived risk has been acknowledged as a

multidimensional construct, a set of perceived risk types is identified on the basis of different

theoretical underpinnings. It has also been suggested that perceived risk plays a critical role in

the design of contracts. Organizational distance is proposed to moderate such relationship. In

the second section of the chapter, literature reviews on the two major sets of constructs,

governance structures and contracts, will be provided. The final section outlines the conceptual

model and details the development of hypotheses including the discussion of organizational

distance for this research.

CONCEPTUAL FOUNDATIONS OF PERCEIVED RISK TYPES

The Meaning of Risk

In the past decades, risk has been defined in many different ways which affect the

theoretical development of risk both in marketing and management strategy. According to

Random House Webster’s College Dictionary (2000), risk means the chance of loss. Within

financial literature, risk is associated with default, bankruptcy, or ruin (Horrigan 1966) and is

7

described largely by variability of returns (Libby and Fishburn 1977) or capital asset pricing

model (Sharpe 1964). Risk has also been studied in the management literature (March and

Shapria 1987) where risk is interpreted as the probability of uncertainty associated with the

outcome of a decision. Similar to financial studies, return variance and capital asset pricing

model are the most popular measures to capture the meaning of risk (e.g., Robins and Schachter

1994). Within marketing, particularly in the consumer behavior literature, risk is defined as a

subjective assessment of negative outcomes of a decision (Bauer 1967).

The wide range of views and definitions of risk corresponds to two somewhat

contradictory perspectives. From a decision theoretical perspective, an individual is assumed to

know all possible alternatives and risk is subsumed in his/her choice among the alternatives.

Since the set of alternatives is known, the individual can assign probability to each alternative

and rank them according to their levels of risk (Pollatsek and Tversky 1970). In addition, risk is

concerned with potential outcomes that can be negative, positive, or both in association with

related situations. From a behavioral perspective, in contrast, scholars (e.g., Bauer 1967) argue

that there is no way an individual can know all the possible choice situations, assigning

probability to each situation is therefore impossible. Further, different from the decision

theoretical perspective, risk is viewed primarily as the perception or subjective assessment of a

negative consequence of a decision made (Das and Teng 1996).

My dissertation adopts the latter view of risk. I argue that often times, managers in a firm

do not know all possible consequences and therefore they are not able to assign probabilities to

the negative outcomes accordingly. My assertion stems from what has been acknowledged in

transaction cost economics as the behavioral assumption of bounded rationality, which refers to

human behavior that is “intendedly rational, but only limitedly so” (Simon 1961, p. xxiv). In

8

other words, managers in a firm are “limited in knowledge, foresight, skill, and time” rather than

being lightning calculators (Simon 1957, p. 199). If the managers were the calculators, they

would have identified exactly what they needed and would have assessed all possible situations

they would have to face. Incomplete information and knowledge are the source of uncertainty

causing the managers to perceive risks associated with alternative situations and the decisions

made in those situations (Richardson 1960).

Although some researchers argue that risk is the other side of trust (e.g, Boon and

Holmes 1991), other scholars maintain that risk is different from trust (e.g., Das and Teng 1996).

Das and Teng (2001) contend that trust becomes irrelevant when risk does not exist, however,

risk remains even though trust appears. The authors further suggest that trust such as goodwill

trust and competence trust is a mechanism to reduce risk (Das and Teng 2001). I concur with the

notion that trust is different from risk in which trust involves positive expectations and is

interrelated to commitment (Morgan and Hunt 1994). When trust exists, performance risk can be

eliminated to a large extent. However, trust is not a panacea of risk. Other types of risk such as

reputation risk may still appear even if trust exists (Das and Teng 1996).

It is also important to demonstrate clearly the difference between risk and perceived risk.

Risk is the probability of loss while perceived risk is a manager’s subjective assessment

regarding the probability of loss due to a decision or an action. Thus, risk may not arise when a

firm does not perceive so. For instance, the focal firm does not feel the risk even though its

partner is, in fact, exploiting it. In other words, it is the risk perceived by the focal firm, rather

than the risk itself, that influences its assessment of alternatives and its decision making in

managing interfirm relationships (Sitkin and Weingart 1995). Throughout the dissertation, I

focus on perceived risk as opposed to risk. The actual probabilities of loss are irrelevant to a

9

firm’s reaction to risk because that is not what drives it to make decisions (Das and Teng 2001).

Instead, my view is that the firm only reacts to the risk it perceives, i.e., subjective interpretations

of that risk (Cunningham 1967; Stone and Gronhaug 1993).

So, what is really meant by perceived risk? A review of literature reveals that defining

perceived risk is fraught with controversy and confusion even within the same discipline such as

in marketing (Stone and Gronhaug 1993). Some researchers (e.g., Bauer 1967; Cunningham

1967) define it in terms of uncertainty and consequences. Other researchers (e.g., Bettman 1973;

Dowling 1986) refer to it as levels of importance of a decision and the chance of negative

consequences associated with the decision. For the purpose of this dissertation, the latter view

provides a more appropriate reflection of perceived risk for two reasons. First, chance already

underlines the notion of uncertainty. Because of cognitive limitations and lacking full

information, a firm is not certain about a situation such that it can only estimate the chance or

probability of experiencing a negative outcome. Second, importance is another critical

component of perceived risk. A decision that a firm has to make must have a significant impact

on its well-being (Dutton and Duncan 1987). Only when the decision is crucial to the firm, the

possible negative outcomes associated with it become influential (Lounamaa and March 1987).

In light of this literature, the definition of perceived risk adopted here is the focal firm’s

subjective assessment of levels of uncertainty and magnitude of negative consequences

associated with its partner firm.

The Multiple Types (Dimensions) of Perceived Risk

Extant research in marketing has indicated that perceived risk is one of the important

elements in consumer buying decisions (e.g., Bauer 1967; Dowling 1986; Stem et al. 1977).

10

Nevertheless, even though many studies (e.g., Kohli 1989; Sitkin and Weingart 1995) have

considered the effect of perceived risk elsewhere, it has been treated as a single dimensional

construct rather than a multidimensional construct suggested in the consumer behavior literature

(e.g., Kaplan et al. 1974; Kim and Lennon 2000).

Campbell and Goodstein (2001, p. 440) give an example of wine purchase to demonstrate

the multiple dimensions of perceived risk, “if a consumer is considering buying an unfamiliar

bottle of wine for a dinner party, the perceived risk associated with the purchase could arise

because she does not know how the wine will taste (performance risk) and is worried that her

guests will think poorly of her if it is not a good wine (image risk).” The example illustrates that

at least two types of perceived risk including performance risk and social (or image) risk are

embedded in a buying decision.

Ignoring the multiple dimensions of perceived risk is problematic (c.f., Dowling 1986;

Stone and Gronhaug 1993). For example, in his study, Kohli (1989) proposes that an

organizational decision will be made contingent upon several categories of factors including

buyer characteristics, individual behavior, and situational characteristics such as perceived risk

and time pressure. In this manner, perceived risk is treated as a conditional factor determining

the impact of an individual’s resources on buying decisions. Nevertheless, the author does not

find that perceived risk plays a moderating role in organizational decisions. Regarding the

surprising result, Kohli (1989, p. 60) suggests “the measurement of perceived risk may have

been too global to capture its moderating effect. In future studies, it may be useful to … ask

more directly about the specific nature of the uncertainty faced by the members.”

While critical, the role of perceived risk is still not understood in interfirm research and

the concern regarding the effect of various aspects of perceived risk remains (Kohli 1989).

11

Following the framework in consumer behavior research (Dowling 1986), I examine the role of

perceived risk and propose a set of perceived risk types that derives from and within the

exchange relationship between two firms in the subsequent sections.

Risk Typology

It has been suggested that perceived risk is more than a unidimensional construct (c.f.,

Jacoby and Kaplan 1978; Stone and Gronhaug 1993). Here specifically, it involves various

facets that affect the focal firm’s relationship with its partners. The risk perceived by the focal

firm in association with its reputation is different from a risk in relation to its performance, for

instance. As such, it is critical to separate the possible types of perceived risk and identify

components relating to them. My field interviews and exhaustive review of interorganizational

studies and other literatures including research on implementation, organizational culture,

identity, image, and reputation suggests that five types of perceived risk can be observed in

interfirm alliances. They involve performance, implementation, knowledge leaking, relational,

and reputation risk. Each of them is examined in greater detail in the following sections.

Perceived Performance Risk. Perceived performance risk has been defined in the

marketing literature as the probability that a product purchased results in failure to function as

expected (Jacoby and Kaplan 1978). Recently, a similar notion of perceived performance risk

has also been examined in the management literature. Das and Teng (1999, p. 51) define it as

“the probability that an alliance may fail even when partner firms commit themselves fully to the

alliances.” Perceived performance risk in their sense involves “the prospect of achieving the

strategic goals of the alliance, given full compliance by all partners” (Das and Teng 1996, p.

829). For the purpose of the dissertation, I follow the definition of perceived performance risk

12

that has been recognized in marketing literature (e.g., Bauer 1967; Bettman 1973) with an

emphasis on interfirm relationships. That is, perceived performance risk is defined as the focal

firm’s extent of uncertainty that the products/services or activities provided by its partner firm

fails to function as expected and the negative consequences associated with the failure.

Performance risk lies in the heart of Williamson’s (1975) transaction cost economics.

Transaction cost analysis suggests that firm managers are bounded rational meaning that they

may not have complete information to make rational decisions. In new product alliances, the

focal firm may not have complete information or knowledge about its partner, and therefore may

perceive a risk embedded in a situation where the partner may not deliver what is needed to

achieve the strategic goals predefined in the relationship. Thus, outcomes, i.e., strategic goals,

become a critical denominator of performance risk. Often time, however, it is not easy for a

firm to specify performance outcomes or when the outcomes are too ambiguous that the firm

cannot quantify and measure them (Heide and Miner 1992; Ouchi 1980) and is referred to as

“performance ambiguity” (Heide and John 1990). Under conditions of high task

interdependence between two firms, intangibility of strategic goals and invisibility of a firm’s

contributions to the final outcomes are rendered ambiguous (Jones 1987). Heide and John

(1990) also found that performance ambiguity requires a firm to pay more effort to verify the

ability of its partner.

The theoretical roots of performance risk can also be traced back to social exchange

theories (e.g., Emerson 1976). Social exchange theorists (e.g., Emerson 1976) suggest that a

firm’s past history is a powerful determinant of its present performance. It reflects how a firm

performs and functions in the past and future. If a partner firm’s products and services function

as expected every time, it encourages the focal firm to repeat purchase and the consumption of

13

umbrella brands since using the same partner firm’s products and services reduces the focal

firm’s perceived performance risk (e.g., Erdem 1998; Montgomery and Wernerfelt 1992). Thus,

when the past record of a partner firm is reliable, positive, or favorable, it reduces the risk

perceived by the focal firm in association with its performance.

In addition, perceived performance risk is theoretically grounded in competence

literatures. Competence, in general, indicates the ability or fitness to perform. The performance

outcome of a new product, for example, can be in jeopardy if a partner firm is not as competent

as expected or unable to deliver services according to the focal firm’s predetermined schedule.

All these can make the focal firm perceive a risk associated with its partner’s performance.

Perceived Implementation Risk. Implementation risk corresponds to the level of

uncertainty and negative consequences associated with the interruption of strategic execution

processes when the focal firm’s partner does not implement as expected. The term,

implementation, relates to the coordination between firms on projects, strategies, or information

processing (Cespedes 1991; Kotler 1997; Wind and Robertson 1983). In practice, the focal firm

may require its partner to coordinate with it to establish joint promotions, work out pricing

strategies (Levy and Weitz 2001), develop new products (Bucklin and Sengupta 1993), and carry

out total quality management (Cannon and Perreault 1999). Thus, it is critical for the focal firm

to pair up with a partner firm that can implement as expected.

Process is a distinctive component in implementation. Implementation risk involves the

processes when the focal firm has to coordinate with its partner firm to achieve goals or complete

projects. Implementation risk is different from performance risk in which the latter relates to the

outcomes (i.e., strategic goals or objectives) predetermined between two parties which are

specified explicitly in their relationship. An ideal outcome can be high quality products or

14

services provided by a partner firm in a purchasing relationship. Yet, the partner firm fails to

deliver the products or services as expected by the focal firm. In this case, products or services

are outcomes that induce performance risk rather than processes a partner firm offers to the focal

firm.

Consider the case of an automobile manufacturer and an automobile dealer. The dealer

believes it should be able to make decisions regarding hours of operation, retail pricing, and

employee recruitment. The dealer does not follow the manufacturer’s decision to discount its

products when it believes that there is no other competitor in the same area. However, the

manufacturer believes that the dealer should consult it before making any decisions. As a result,

there exists implementation risk in the auto manufacturer-dealer relationship. The manufacturer

may fear that the dealer does not execute any of its plans and projects. Nevertheless,

performance risk may not arise in this situation since the dealer delivers quality services and

products to customers and keeps making profit as expected by the manufacturer. This example

underlines the importance of implementation processes.

Perceived Knowledge Leaking Risk. Knowledge leaking risk is referred to as the focal

firm’s subjective assessment of the probability that the relationship with a partner firm results in

disclosing sensitive and proprietary knowledge and the negative consequences associated with

such disclosure. Knowledge, in particular, tacit knowledge, embedded in a firm’s procedures,

norms, rules, and forms (March 1991), is a central notion in organizational learning (Grant

1991). Tacit knowledge is difficult to be expressed and codified and yet it must be experienced

and observed (Nonaka 1994). Knowledge facilitates a firm’ recognition, assimilation, and

application of new information that in turn enables it to develop capabilities, a critical resource to

achieve a competitive advantage (Day 1994).

15

Perceived knowledge leaking risk is theoretically grounded in capabilities and resources

and learning literatures. According to resource-based view of the firm, knowledge or

information-based capability represents a firm’s strategic asset if it is unique, valuable, difficult

to imitate and substitute, and is appropriable by the firm (Amit and Schoemaker 1993; Grant

1991). Firms do recognize the strategic value of knowledge and therefore fear that if their

knowledge will be disclosed to outsiders such as partners and competitors. Further, because a

firm’s unique knowledge contributes to distinguish itself from competitors, it has to protect its

knowledge from leaking to outsiders, including partners (Beamish and Banks 1987; Contractor

and Lorange 1986; Hamel 1991).

Leakage, sometimes, is unavoidable, however. Within a firm, leakage occurs when an

employee uses the knowledge acquired in its position to establish a competing firm (Beamish

and Banks 1987). Between firms, leakage appears when technical know-how or knowledge has

to transfer from one firm to another in order to develop new products, for example (Contractor

and Lorange 1986). Specifically, when two firms are engaged in an exchange relationship, how

can the focal firm be sure that sharing its information and knowledge with its partner will not

have any negative effect on its performance and even its survival? If the focal firm’s knowledge,

skills, and abilities can be appropriated by its partner easily, the focal firm is exposing itself in

knowledge leaking risk since it is difficult, if not impossible, to “own” its partner, making it

problematic to secure its proprietary knowledge. Most of the time, the partner firm can just walk

away with the knowledge obtained from the focal firm (Bettis et al. 1992). Therefore, the ability

of the focal firm to prevent knowledge leaking is critical.

To reduce a firm’s perception of risk associated with knowledge leaking in a new product

development alliance it involves an ability to prevent knowledge erosion and retain added value

16

for a firm’s own benefit (Hamel 1991; Kay 1993). To avoid knowledge erosion, Reed and

DeFillippi (1990) suggest that the focal firm can generate knowledge characterized by tacitness,

complexity, and specificity. In an exchange relationship, the focal firm may fear that its partner

will dissolve the relationship after the partner acquires its knowledge (Beamish and Banks 1987).

Hence, when the focal firm’s knowledge is too ambiguous for its partner to understand and

identify its causality, owning such knowledge gives no advantages to the partner firm since it is

not certain how these knowledge can apply to its situations (Peteraf 1993). As a consequence,

leaking a focal firm’s specific knowledge to its partner becomes unimportant.

Perceived knowledge leaking risk is also grounded in transaction cost economics

literatures (Williamson 1991). For example, if a partner firm that is believed to be opportunistic,

it will by some means cheat and confuse its counterpart. If the focal firm thinks that its partner

will steal its proprietary knowledge to develop new products or serve the existing market for the

partner firm’s own benefits, perceived knowledge leaking risk thus arises (Beamish and Banks

1987). So, it is how opportunistic the focal firm believes its partner is influences its perception

of knowledge leaking risk.

Perceived Relational Risk. Relationship implies long-term orientation (Noordewiser et

al. 1990) and mutual commitment between firms (Anderson and Weitz 1992). The notion of

perceived relational risk has been developed in management literature (Das and Teng 1996).

Das and Teng (1996, p. 831) define perceived relational risk as “the probability and consequence

that the partners in inter-firm alliances do not fully commit themselves to joint efforts.” I

suggest that the concept of perceived relational risk can go beyond the scope of alliances and can

be applied to any form of interfirm relationships. For the purpose of the dissertation, perceived

17

relational risk is defined as the focal firm’s levels of uncertainty and the negative consequences

that its partner firm fails to commit as expected in the relationship.

Perceived relational risk can appear on both sides of an exchange relationship (Das and

Teng 1996). The focal firm can behave opportunistic when it has “greater ability to hold up the

[partner] in future contracting” (Houston and Johnson 2000, p. 4). On the other hand, when the

focal firm has already made specific investments in a particular partner firm, and when these

investments are difficult to produce for other customers, the partner firm can take advantage over

its counterpart.

The theoretical roots of perceived relational risk can be traced back to transaction cost

economics (Das and Teng 1996; Morgan and Hunt 1994; Nooteboom et al. 1997; Williamson

1981). Trust is a primary factor leading to perceived relational risk (Das and Teng 1996). Trust

refers to “a willingness to rely on an exchange partner in whom one has confidence” (Moorman

et al. 1993, p. 82). Confidence allows the focal firm to believe its partner to behave truthfully

and be willing to rely on it. As suggested by Ring and Van de Ven (1992), trust reduces the

concern about opportunistic behavior, and therefore suppresses the relational risk perceived by

the focal firm. When trust disappears, the focal firm will monitor its partner in order to reduce

its perceived relational risk (Williamson 1975). In contrast, when the focal firm holds belief in

its partner, the focal firm may not acknowledge or perceive any risk in their new product alliance

activities (Chiles and McMackin 1996). Trust, therefore, influences the perception of risk in a

relationship (Ring and Ven 1992; Zand 1972).

Additionally, a partner’s opportunistic intention is another factor influencing the focal

firm’s perceived relational risk. Opportunism is defined as “self-interest seeking with guile” in

transaction cost economics (Williamson 1975, p. 6). It implies that the partner firm seeks by all

18

means to cheat, mislead, and confuse the focal firm in order to obtain advantages (Williamson

1985). In a recent study, Dahlstrom and Nygaard (1999) propose that opportunism promotes

bargaining, monitoring, and maladaption costs. Nevertheless, some of their findings do not

support their hypotheses. It may be suggested that opportunism does not induce transaction costs

directly. Rather, such hypothetical link can be established only if a firm acknowledges a risk

embedded in an exchange relationship. That is, when the focal firm believes that its partner

engages in opportunistic behavior, such belief will induce a fear that leads to a stronger

perception of relational risk. Consequently, transaction costs are involved to reduce the

perceived relational risk. In contrast, if the focal firm does not perceive a risk in relation with its

partner, it will not consider paying for monitoring and renegotiation. To conclude, perceived

relational risk may be the missing link between transactional costs and opportunism.

Lastly, asset specificity or idiosyncratic assets may be another important factor inducing

perceived relational risk. Asset specificity indicates the extent to which a firm can transfer or

apply its assets to other situations (Williamson 1985). Idiosyncratic assets create sunk costs to a

firm since the assets it invests in a particular relation are no longer valuable beyond that relation.

Thus, the amount of specific assets invested by the focal firm relative to its partner influences

how it perceives the risk embedded in the relation. In a situation where the focal firm invests

idiosyncratic assets more than its partner, according to the equity theory of motivation (Adams

1963), there exists a sense of inequity whenever firms believe that their ratio of outcome to

inputs is different from those of others. As a result, if the focal firm invests more than its

counterpart, it is expected that the focal firm would have a sense of inequity to engender the

perception of relational risk. Even though the focal firm invests less than its partner in the

relation, the sense of inequity triggers the focal firm to concern the partner (Das and Teng 1996).

19

Perceived reputation risk. Reputation risk relates to the extent of uncertainty that society

or relevant constituents (e.g., shareholders, competitors, suppliers, employees, and customers)

will disapprove of a firm’s relationship with another organization and the negative consequences

associated with such disapproval. A reputation is important and serves to distinguish a firm from

others. Some researchers (e.g., Houston and Johnson 2000) suggest that reputation represents a

firm’s public self. Other scholars (e.g., Dutton and Dukerich 1991) suggest that reputation

indicates how a firm believes outsiders see it and therefore is a self assessed image rather than an

image estimated by the public. Nevertheless, a large body of research suggests that both image

and reputation reflect external appraisals of an organization (Berg 1985; Fombrun 1996; Gioia et

al. 2000). This dissertation, following most studies, views both reputation and organizational

image through a similar lens. That is, both reflect the public self of an organization.

Perceived reputation risk is grounded in the literature of social legitimacy (DiMaggio and

Powell 1983). Legitimacy relates to societal evaluations and approvals of organizational goals.

If the focal firm has to engage in an interfirm alliance, it is believed that it will partner with an

organization which image is socially acceptable at least within the same network (Anderson et al.

1994).

Recently, going on-line is highly regarded by customers in today’s competitive markets

(Frazier 1999; Geyskens et al. 2002) and associating with information technological (IT) savvy

firms is considered socially desirable. Thus, many brick-and-mortar firms (e.g., Toys “R” Us

and Wal-Mart) obtain IT services from giant IT firms (e.g., Microsoft Inc. and American Online

Inc.) rather from other small IT providers to prevent from risking their reputation (Bromley

1993). Despite the fact that partnering with legitimate firms may sometimes reduce the focal

firm’s efficiency (Kogut 1988), the primary purpose of doing so allows the focal firm to gain or

20

at least maintain its reputation among other organizations within a network (Anderson et al.

1994; Scott and Lane 2000). As a result, it suppresses the reputation risk perceived by the focal

firm in association with its partner.

Risk varies from situations to situations and has been found playing a critical role in new

product alliances. In the following section, I will turn to review the literatures on contracting

which is considered to be a tool to manage risk perceptions.

LITERATURE REVIEW ON GOVERNANCE MECHANISMS AND CONTRACTS

Since Williamson’s (1975) governance structure of organizations, i.e., markets and

hierarchies, various classifications of governance mechanisms have been suggested in the

marketing literatures over the last two decades (c.f. Rindfleisch and Heide 1997). Most studies

in the 80s and early 90s, following the domain of Williamson’ work, focus on vertical and

horizontal integrations as two primarily types of governance structure (e.g., Gatignon and

Anderson 1988; Klein 1989; Klein et al. 1990). Researchers view transaction costs as the major

concern in the development of governance mechanisms.

While the transaction cost analysis framework is critical, researchers have recently

switched their attention primarily from transaction costs to social exchange components, such as

organizational ties (Granovetter 1973; Hansen 1999) and dependency (Pfeffer and Salancik

1978), and used these social elements to develop types of governance mechanisms or

relationships (e.g., Cannon and Perreault 1999). Some researchers suggest that governance

mechanisms can take a variety of types, including, but not limited to, market governance,

nonmarket governance, formalization, interfirm cooperation, information exchange, and legal

bonds (Cannon and Perreault 1999; Dahlstrom and Nygaard 1999; Heide 1994).

21

The theoretical roots of these studies can be traced back to Macneil’s (1980) and

Williamson’s (1985) seminal work. Specifically, Williamson proposes three types of contracts

which include classical contracts, neoclassical contracts, and relational contracts. Classical

contract, based on full legal protection for the focal firm’s expectation or bargain interest, can be

expressed completely all the future rights and obligations of each party. The emphasis of a

classical contract is on legal rules, formal documents, and self-liquidating transactions.

Neoclassical contract, on the other hand, involves longer-term contracts where all future

contingencies cannot be identified and where appropriate adaptations may not be evident until

the contingency arises. Formal and legal forms of contracts become less relevant while the

presence of a range of processes designed to create flexibility is critical. Similar to classical

contract, however, neoclassical contract considers arbitration procedures when disputes or

conflicts arise.

Not all transactions will fit into either classical or neoclassical contracting, however. In

some cases, contracts or agreements are designed without arbitration procedures, details of

presentation, or remedies. Williamson (1985) refers to this as relational contracting. A

relational contract develops through mutual understanding between firms. Different from

classical contracting, the presentation, formality, and legal status of relational contracting

becomes irrelevant. More important in relational contracting is the need for flexibility in the

legal enforcement of contractual obligations (Campbell and Harris 1993).

Williamson’s three forms of contracting provide the foundations for the development of

contractual forms in the marketing strategy literature. Extant studies expand from both

Macneil’s (1980) and Williamson’s (1985) work and use different means to derive a framework

of contractual use. Table 1 summarizes the classifications of governance relationships and their

22

definitions as they appeared in the marketing literature in the last decade. A closer examination

on the underlying characteristics of these different classifications found in Table 1, however, are

similar. For example, Heide (1994) is one of the earliest scholars to systematically examine and

develop a typology of governance structure. On the basis of transaction cost analysis, resource

dependence perspective, and relational contracting theory, Heide (1994) proposes three forms of

governance structure, namely, market, unilateral/hierarchical non-market, and bilateral

governance. The author defines market governance as discrete exchange which implies no long-

term exchange intentions (Dwyer et al. 1987). In contrast, non-market governance, be it

unilateral or bilateral, relies on the ability of an exchange partner over another to execute rules

and instructions (Bradach and Eccles 1989).

Though Hiede (1994) attempted to develop a typology of governance form in marketing

channels, his focus was on how specific interfirm processes are carried out in each form of

governance rather than what types of governance structure a firm should adopt. Lusch and

Brown (1996) build on social contract perspectives (Macneil 1980) to develop two types of

contracts in marketing channels. They suggest that explicit or hard contracts and normative or

soft contracts are the two major forms of contracts to govern the relationships between buyers

and suppliers. Dahlstrom and Nygaard (1999), on the other hand, differentiate control structures

into two types: formalization and interfirm cooperation. Formalization relates to the extent to

which interorganizational partners use rules and procedures to govern a relationship, while

interfirm cooperation is the extent to which partners coordinate to each other.

23

TABLE 1

Classification Schemas and Definitions of Governance Structure Used in Marketing

Authors Classification / Levels Definition Heide (1994) Governance typology

(form)

1. Market governance

“synonymous with the concept of discrete exchange” (p. 74)

2. Unilateral/Hierarchical nonmarket governance

“an authority structure that provides one exchange partner with the ability to develop rules, give instructions, and in effect impose decisions on the other” (p. 74)

3. Bilateral nonmarket governance

“the parties jointly develop policies directed toward the achievement of certain goals” (p. 74)

Lusch and Brown (1996)

Channel contracting

1. Explicit (hard) contract

“the extent to which an explicit contract attempts to see into the future and explicitly state today (i.e., in the present) how various situations that might occur in the future would be handled if they were to occur” (p. 20)

2. Normative (soft) contract

“when a mutual understanding exists between parties as to how they will interact and deal with each other, including the handling of future contingencies” (p. 20)

Dahlstrom and Nygaard (1999)

Control structures

1. Interfirm cooperation

“the extent to which the principal and agent coordinate strategies” (p. 162)

2. Formalization “the extent to which rules and procedures govern

relationship between interorganizational partners” (p. 162)

24

TABLE 1 (CONTINUED)

Authors Classification / Levels Definition Cannon and Perreault (1999)

Buyer-seller relationships

1. Information exchange

“expectations of open sharing of information that may be useful to both parties” (p. 441)

2. Operational linkages “the degree to which the systems, procedures, and routines of the buying and selling organizations have been linked to facilitate operations” (p. 442)

3. Legal bonds “detailed and binding contractual agreements

that specify the obligations and roles of both parties in the relationship” (p. 443)

4. Cooperative norms

“expectations the two exchange parties have about working together to achieve mutual and individual goals jointly” (p. 443)

5. Adaptations by sellers

“investments in adaptations to process, product, or procedures specific to the needs or capabilities of an exchange partner” (p. 443)

6. Adaptations by buyers

“investments in adaptations to process, product, or procedures specific to the needs or capabilities of an exchange partner” (p. 443)

Cannon et al. (2000)

Governance forms

1. Legal bonds (legal contracts)

“the extent to which detailed and binding contractual agreements are used to specify the roles and obligations of the parties” (p. 182)

2. Cooperative norms (social norms)

“shared expectations regarding behavior” (p. 183)

3. Plural form (both legal bonds and cooperative norms)

“combinations of market, social, or authority-based mechanisms than on any one category exclusively” (p. 184)

Houston and Johnson (2000)

Relationship structure

1. Buyer-supplier contracts

“a buyer-supplier agreement with no explicit promises of relational continuity (implicit expectations may exist)” (p. 2)

2. Joint ventures “a jointly funded entity … in which partners

agree to a formula for sharing risks and rewards” (p. 2)

25

Cannon and Perreault (1999) integrate literatures from various disciplines and use

clustering approaches to segment buyer-seller relationships into six categories: information

exchange, operational linkages, legal bonds, cooperative norms, relationship-specific adaptations

by sellers, and relationship-specific adaptations by buyers. Cannon and his colleagues (2000)

later empirically test the effect of two relationship types, legal bonds and cooperative norms, on

performance.

Most recently, Houston and Johnson (2000) base on the dichotomy of buyer-supplier

contracts versus joint ventures to examine the choice of relationship structure on performance.

The review of current literature reveals, however, that the use of dichotomy of market vs.

nonmarket or contracts vs. joint ventures appears to be oversimplified (c.f., Das and Teng 2001).

Other factors, such as relational ties, levels of dependency, and environmental dynamisms,

dictate the choice of governance or relationship types (e.g., Cannon and Perreault 1999; Lusch

and Brown 1996; Rindfleisch and Moorman 2001).

Despite the fact that there are various forms of governance mechanisms or contractual

types, a closer examination of their definitions suggests that they are quite similar in nature. For

example, the concept of legal bonds proposed by Cannon and his colleagues’ (2000) is parallel to

the meaning of explicit contracts found in Lusch and Brown (1996) in that both of the legal

bonds and the explicit contracts highlight the importance of presentation or explicitness. In

essence, although various classifications exist in the literature, the common thread lie in their

similarity of shared attributes.

Contracts, as governance mechanisms, vary in how they are written, i.e., what attributes

or contractual arrangements are included. Lusch and Brown (1996, p. 20) suggest that two

different forms of contracts can coexist since “explicit and normative contracts are not opposite

26

ends of a continuum.” It is possible for a firm to simultaneously employ a highly explicit

contract and a contract which is secured by social consensus and mutually understood between

organizations. Cannon, Achrol, and Gundlach (2000) likewise propose that plural forms are

possible when a firm adopts both legal bonds and cooperative norms in its contracts. These

examples point to the conclusion that different contractual arrangements including explicitness

and normative attributes (e.g., flexibility, solidarity, and mutuality) can be observed in a contract

(Macneil 1980; Williamson 1985).

Governance Attributes

A governance attribute is defined as any feature or characteristic that comprises a

contract. The nature or attributes of a contract has long been discussed in extant literature (e.g.,

Dwyer et al. 1987; Macneil 1980). Some researchers have empirically investigated the use of

contracts, more precisely, the governance attributes in interfirm relationships (e.g., Cannon et al.

2000; Lusch and Brown 1996). These researchers have recognized that a contract can be in

different forms including various attributes. For example, Lusch and Brown (1996) identify two

specific natures of contractual arrangements, i.e., explicitness and normativeness. They note that

some contracts are written more explicit than others while some are more normative or even

more in both, suggesting that these two attributes are not mutually exclusive with each other.

Cannon, Achrol, and Gundlach (2000) likewise propose two forms of contractual arrangements,

i.e., legal bonds and cooperative norms. They also argue that a contract can be a combination of

plural forms in order to govern an exchange relationship.

Although there is no consensus on which attributes or contractual arrangements comprise

a contract, extant research suggests that explicitness and cooperative norms are the critical

27

components set in the contract to govern a relationship between the focal firm and its partner

(c.f., Macneil 1980). The notion of explicitness is well defined in the literature (Lusch and

Brown 1996; Macneil 1980). The construct, cooperative norms or normative contracts, although

pertinent, is loosely developed in the marketing literature. Norms should go beyond mutual

understanding and include components such as flexibility, adaptability, relational-base, and

solidarity (Macneil 1980). Thus, I see governance attributes as encompassing one legal attribute:

explicitness and two relational attributes: flexibility and mutuality.

Consistent with extant studies (e.g., Cannon et al. 2000), this dissertation focuses on

governance attributes from the focal firm’s perspective. Two major reasons pertain to the

emphasis from the focal firm’s viewpoint. First, when entering into an agreement with a partner,

it is how the focal firm perceives risks in association with its partner that matters. Second, the

use of various attributes in a contract is a tool that the focal firm can manipulate to suppress its

risk perceptions.

Contract explicitness is a legal arrangement that the focal firm can use in a contract.

Explicitness implies the levels of presentiation (Macneil 1980), which is defined as “the extent to

which an explicit contract attempts to see into the future and explicitly state today how various

situations that might occur in the future would be handled if they were to occur” (Lusch and

Brown 1996, p. 20). It reflects how much detail the focal firm’s contract or agreement is written

to specify its roles and obligations in relation to its partner (Cannon et al. 2000).

Relational attributes are governed by social norms rather than legal forces. The degree of

normative attributes relies upon contractual types or contractual relations. Contractual relations

reflect the spectrum between discrete and relational transactions (Macneil 1980). Discrete

contract, at one end of the continuum, suggests that the relationship between the focal firm and

28

its partner is simply transactional. In reality, most transactions, however, “involve relations apart

from the exchange of goods itself. Thus every contract is necessarily partially a relational

contract, that is, one involving relations other than a discrete exchange” (Macneil 1980, p. 10).

To use a relational contract, the focal firm is hoping that it can create more rooms for it to

develop long-term orientations and obligations, and enhance future cooperation with its partner.

Thus, including different degrees of normative attributes: flexibility and mutuality in addition to

explicitness in a contract with its partner is critical (for details see Macneil 1980) and is

consistent to extant research (e.g., Cannon et al. 2000; Heide and John 1992; Lusch and Brown

1996).

Flexibility refers to the extent that a firm can modify itself above and beyond what has

been stated in a contract for its partner (Macneil 1980). It is a mutual expectation that two firms

in an interfirm relationship will make changes for each other (Heide and John 1992).

Researchers find that flexibility is a critical component in social norms which helps resolve

conflicts, create options, and increase adaptability (Dant and Schul 1992; Lusch and Brown

1996).

Mutuality is a key for keeping firms to continue their exchange relationship. It reflects

the expectations of joint efforts to create positive outcomes and is the degree that two firms stand

by one another and cooperate wholeheartedly when facing difficulties. Mutuality impedes both

firms to use power over their partners which in a way facilitates mutual accommodation and

understanding (Macneil 1980).

Contracts are an important form of governance structures. They provide a means to

manage interfirm relationships. In the remaining sections of this chapter, I will propose a

conceptual model on the basis of the risk typology and contracts discussed in the previous

29

sections. In addition, as mentioned earlier, organizational distance is crucial in determining the

effect of risk perceptions on the use of governance attributes. The conceptual model developed

below will also include a detail discussion on the concept of organizational distance. Further, it

is believed that new product success derives from how firms use governance attributes to manage

their partners. As such, it is interesting to examine the effects of governance attributes on new

product success. I am now turning to the conceptual model and hypothesis development.

CONCEPTUAL MODEL AND HYPOTHESES

The conceptual model proposed here includes three components. As shown in Figure 1,

the first component is the main effect of perceived risk types on the design of contracts to

maintain or govern in a new product alliance. Although it seems clear that direct relationships

do exist between perceived risk types and governance attributes, to date, we still do not know

which facets of perceived risk are more salient than others in the design of a contract to maintain

an alliance relationship.

The second component relates to the organizational distance between two partner firms.

Researchers (e.g., Bucklin and Sengupta 1993) suggest that domain and goal similarity is critical

to the creation of alliances and affects governance mechanisms (Williamson 1981). The role of

organizational distance in affecting the use of contract has not been examined, however. On the

basis of my field interviews, it is suggested that organizational distance plays a significant role

between risk perception and governance mechanisms. Thus, this dissertation proposes to

examine its moderating effect.

The third component is the impact of governance attributes on new product success.

Extant research suggests that the appropriate design of contracts leads to enhanced performance

30

(e.g., Lusch and Brown 1996). This study adds on this stream of research to examine such

relationship in the presence of risk perception on new product success. In short, this study

attempts to answer three questions: (1) which dimensions of perceived risk are critical and what

are the combinatory properties of perceived risk regarding the design of contracts, (2) what is the

moderating role of organizational distance, and (3) what are the performance implications of

different governance attributes?

FIGURE 1

Full Conceptual Model

Implementation

Governance Attributes

H7Relational Attributes

Contract Explicitness

New Product

Knowledge

- Flexibility - Mutuality

Success

Relational

Performance

Perceived Risk

Reputation

H1-5

H6

Organizational Distance

31

EFFECTS OF PERCEIVED RISK TYPES ON GOVERNANCE ATTRIBUTES

Researchers have theorized that perceived risk is a critical element in organizational

buying decisions (Johnston and Lewin 1996; Kohli 1989; Peters and Venkatesan 1973). Puto,

Patton, and King (1985) suggest that gathering information, playing odds, and spreading risk are

three primary strategies to reduce perceived risk. Here, relying on contracting theoretical

perspectives (Macneil 1980; Williamson 1985), I propose that the design of contractual

arrangements is an alternative strategy to handle perceived risk. Since evidence shows that

focusing on a global nature of risk is insufficient (Kohli 1989) and some researchers also

theorize that specific types of risks dictate the governance of structure in an alliance (Das and

Teng 1996), I hypothesize that performance, implementation, knowledge leaking, relational, and

reputation risk perceptions have direct effects on governance attributes as shown in Figure 2.

FIGURE 2

Effects of Perceived Risk Types on Governance Attributes

Reputation Risk

Relational Risk

Knowledge Leaking Risk

Implementation Risk

H1-5Relational Attributes - Flexibility - Mutuality

Governance Attributes

Contract Explicitness

Perceived Risk Types

Performance Risk

32

Although the assumption of risk neutrality has been neglected in the majority of

marketing (c.f., Rindfleisch and Heide 1997), literatures in law and economics (e.g., Allen and

Lueck 1999) suggest that the idea of risk is increasingly important. In fact, risk is such an

important factor that helps predict the choice of governance structure and well as the

arrangement of a contract. For example, in conditions where the focal firm believes that its

partner perceives high levels of risks on it, the firm will most likely design an exhaustive

contract for performance relief, dispute resolution, among others to maintain stability of the

contract with its partner. Similarly, Otsuka, Chuma, and Hayami (1992, p. 2012), in their study

of agricultural economies, found that the exposure of risk “provides the most consistent

explanation for the existence of a share contract.”

The dominance of this risk explanation in economics is promising, yet the empirical

evidence to support its implications is not only scant, but also mixed. Prendergast (1999, p. 21),

an agricultural economist, alludes that “there is some evidence that contracts are designed to

optimally trade off risk against incentives. However, the evidence is hardly overwhelming, with

some studies showing the effect of noise on piece rates while others show little.” In interfirm

research, studies on risk and the use of contracts are inadequate. Since risk has been

acknowledged as a critical component in contracting (Allen and Lueck 1999), I explore the

impact of specific types of perceived risks on governance attributes as shown in Figure 2.

Effects of Perceived Performance Risk

When perceived performance risk is high, which means that the chance that the final

products or services offered by a partner firm cannot function as expected by the focal firm and

33

when the negative consequences associated with unsatisfactory products is significant, the focal

firm may want to design a contract which details all possible future events in order to avoid any

loss. Performance risk perception does arise very likely as the focal firm cannot always observe

the partner firm’s outputs perfectly. Some products or services are simply difficult to measure

their performance accurately (Heide and Miner 1992). For instance, it may be difficult to

ascertain whether a grain supplier provides its buyer the best quality of flour. Thus, when

performance ambiguity exists, the focal firm is exposed to risks (Heide and John 1992).

Imperfect observation of outputs seems to be relevant in most situations. When

Williamson’s (1985) assumption of risk neutrality is abandoned, the imperfect observation of the

output leads to critical consequences on a contract (Lawarrée and Audenrode 1996). Thus, to

mitigate the negative consequences associated with a partner firm’s performance outcomes, the

level of explicitness stated in a contract can be regarded as a means for the focal firm to attenuate

its fear.

In addition, when perceived performance risk arises, the focal firm should develop a

contract with an emphasis on both flexibility and mutuality. Mutuality promotes cooperation. It

means that a firm will put efforts to produce benefits for each other (Macneil 1980). Flexibility

allows the focal firm to make changes if necessary. Thus, when the focal firm perceives high

performance risk on its partner, a contract that includes the components of mutuality and

flexibility will become a useful mechanism to reinforce or encourage win-win situations

(Lawarrée and Audenrode 1996).

H1: The focal firm’s performance risk perception is positively related to the levels of (a)

explicitness, (b) mutuality, and (c) flexibility in a contract.

34

Effects of Perceived Implementation Risk

Implementation risk occurs when the focal firm believes that its partner does not

coordinate and execute projects and plans as expected in their new product alliance. In this case,

the focal firm may fear that its plans will be interrupted if the partner does not carry out its

responsibilities. When the focal firm believes that its partner fears to break down in

implementation processes, it implies that the focal firm should clearly specify the requirements

and conditions in a contract. High levels of explicitness in a contract become very important as a

highly explicit contract implies that all procedures, steps, communications, and other behaviors

or actions that will be taken place are specified in the contract (Williamson 1983).

To reduce a firm’s risk perception associated with implementation breakdown, other

normative attributes in a contract may be necessary (Cannon et al. 2000). As Cannon, Achrol,

and Gundlach (2000, p. 184) allude, normative attributes “involve expectations rather than rigid

requirements of behavior, they create as cooperative as opposed to a confrontational environment

for negotiating adaptations, thus promoting continuity in exchange.” Likewise, Lusch and

Brown (1996) argue that flexible norms allow a partner firm to adapt to changes required by the

focal firm. Since implementation can break down easily when mutual understanding is

inadequate, the attribute of flexibility included in a contract by the focal firm provides such a

means for it to make changes. Flexibility is also regarded as an important normative attribute

against any of deviant behavior during implementation processes. As a result, if the focal firm is

believed to have high implementation risk perception on its partner, it should include flexibility

component in the contract. Similar to the situation when perceived performance risk is high,

mutuality also promotes the beliefs that the partner firm will cooperate and create win-win

35

situations by sharing information about production and marketing schedules, timing, and other

implementation processes with the focal firm (Macneil 1980).

H2: The focal firm’s implementation risk perception is positively related to (a)

explicitness, (b) mutuality, and (c) flexibility in a contract.

Effects of Perceived Knowledge Leaking Risk

Knowledge leaking risk is the risk that a firm may fear that its knowledge will disclose to

other organizations and thereby losing its competitive advantage. Extant research has also

proved that knowledge leaking to a partner firm is detrimental (e.g., Beamish and Banks 1987;

Hamel 1991). In an NPA setting, sometimes, it is unavoidable for a firm to release information,

data, or technical know-how to its partner when joint activities such as new product

developments are taken place. Such fear is understandable. To reduce the potential for

downside informational losses that the focal firm may come across, providing an explicit

contract for hard bargaining may be a useful catalyst (Gundlach et al. 1995). An explicit

contract helps secure the focal firm to have the right to enforce penalties.

In addition, when perceived knowledge leading risk is high, strong mutuality and

flexibility in a contract may serve to suppress such fear. Mutuality projects an image that a focal

firm is willing to work with its partner toward the goal of positive outcomes. It further promotes

the beliefs that both parties will cooperate and create win-win situations by sharing information

on production schedules and shipping information, among others to each other (Macneil 1980).

Flexibility attenuates the feeling that the partner firm will take advantages of the focal firm and it

therefore should positively relate to perceived knowledge leaking risk.

H3: The focal firm’s knowledge leaking perception risk is positively related to (a)

explicitness, (b) mutuality, and (c) flexibility.

36

Effects of Perceived Relational Risk

Relational risk is the risk that a partner firm does not commit to the focal firm (Das and

Teng 1996). To ensure against the feeling that the partner firm will not commit to the

relationship, the focal can design a complete contract to include all future contingencies

(Williamson 1985). Some researchers, for example, Maher (1997), in his study of vertical

integrations and hold-up problems, suggests that a firm should clearly state the terms and

conditions each party would undertake to reduce conflicts when another firm does not commit

itself to the relationship. In contrast, some researchers (e.g., Lyons and Mehta 1997) contend

that a too conscientious contract simply creates negative feeling and induces bad faith that a firm

may see in its partner. I concur with the latter point of view and believe that a detailed contract

only provokes the opportunistic behavior and thereby increases relational risk perception.

Explicitness is not a panacea in all situations and other normative arrangements in a

contract are necessary (Williamson 1993). Heide and his colleagues (1990; 1992) suggest that

the use of relational attributes can cultivate the expectation of long-term commitment which is a

critical suppressor of relational risk perception. To attenuate such risk perception, the focal firm

should include both mutuality and flexibility components in a contract. Mutuality reinforces the

notion of continuance of a relation whereas solidarity maintains that both parties will stand by

each other in case problems occurred. Flexibility reduces the chances that the partner firm will

take opportunities from the focal firm. Thus, high levels of mutuality and flexibility discount the

focal firm’s negative feeling on the partner. .

H4: The focal firm’s relational risk perception is (a) negatively related to the level of

explicitness but (b) positively related to mutuality and (c) flexibility in a contract.

37

Effects of Perceived Reputation Risk

Reputation relates to the extent that a firm is regarded by outsiders (Weiss et al. 1999).

Perceived reputation risk arises when the focal firm fears that the affiliation with its new product

alliance partner will influence its organizational reputation in the eyes of stakeholders (e.g.,

shareholders, customers, competitors, employees, and other partner firms). “Reflected glory and

guilt by association” suggested by Bromley (1993) is an excellent illustration. A firm’s

reputation is tied to its partner through ‘guilt by association’ or ‘reflected glory.’ Evidence also

suggests that firms do watch their reputations and are concerned about managing their

reputations through different means such as the use of sales representatives (Weiss et al. 1999).

This indicates that a firm will detail all possible contingencies. In case the firm’s partner is

involved in some affairs that may affect the focal firm’s reputation, it can terminate the

relationship as specified in a contract.

H5: The focal firm’s reputation risk perception is positively related to the level of

explicitness in a contract.

THE MODERATING ROLE OF ORGANIZATIONAL DISTANCE IN RISK-

GOVERNANCE ATTRIBUTES RELATIONSHIPS

The second component of my conceptual model deals with the moderator, i.e.,

organizational distance. As shown in Figure 3, organizational distance influences the

relationship between risk perceptions and governance attributes. Organizational distance has

been found in extant research that have effects on the formation and management of strategic

alliance partners (Simonin 1999).

38

FIGURE 3

The Moderating Role of Organizational Culture

Perceived Risk Types

Performance Risk Contract Attributes

Contract Explicitness

Reputation Risk

Relational Risk

Knowledge Leaking Risk

Implementation Risk

H6

Organizational Dis

39

Relational Attributes- Flexibility - Mutuality

tance

Organizational distance represents the extent to which two alliance firms are dissimilar to

each other (Simonin 1999). Each firm has its own organizational culture and can be dramatically

different from its partner. Organizational culture denotes “the shared beliefs which pertain to

dominant organizational attributes, leadership styles, organizational bonding mechanisms, and

overall strategic emphasis” (Deshpandé et al. 1993). It has been proposed to have effects on

various marketing practices. For example, Moorman (1995) examined the impact of culture on

the use of marketing information and new product development processes. Likewise, Hewett,

Money, and Sharma (2002) found that internally focused corporate cultures have stronger

association with the positive relationship between relationship quality and buyers’ repurchase

intentions than externally focused corporate cultures. While organizational culture has been

explored in extant studies (e.g., Balakrishnan and Wernerfelt 1986; Hewett et al. 2002), a core

issue of how far apart the organizational culture between the focal firm and its partner has

seldom been examined (for exceptions, see Simonin 1999).

Knowing the organizational distance between two alliance firms, i.e., how far the focal

firm’s organizational culture is different from its partner, is critical (Tyebjee 1988). As Achrol,

Scheer, and Stern (1990) observe, difference in perceived position or status among the

managerial levels at which interaction occurs between firms could lead to cultural and political

conflict. Likewise, Johnson and her colleagues (1996) find that firms sharing similar

organizational structures and business practices are more likely to become successful in their

joint venture. Consistent with their study, I propose that organizational distance which is defined

as the gap between two partner firms in terms of their company philosophy, management style,

40

and other business practices is important and is believed to affect the relationship between risk

perceptions and governance attributes (Figure 3).

When the focal firm’s organizational distance is close to its partner firm, the links

between risk perceptions and levels of explicitness in a contract should be weakened while the

links between risk perceptions and levels of flexibility and mutuality should be strengthened.

The reason is that a small organizational distance between two firms implies that they have

similar philosophies, business practices, and risk taking orientation and thereby requires a less

explicit contract to govern the relationship (Williamson 1975). In line with this argument,

Bucklin and Sengupta (1993) find that domain and goal similarity is important in the creation of

alliances and such organizational compatibility allows a firm to develop positive feelings on its

partner. However, when both of their organizational cultures are different from each other, the

focal firm may see its partner more difficult to communicate (Mosakowski 1997) inducing

stronger risk perceptions. As a result, the focal firm may have a higher level of explicitness in a

contract (Williamson 1981).

To conclude, organizational distance can be regarded as a moderator changing the

relationship between risk perceptions and the use of governance attributes. The closer the

organizational distance between two firms, the relationship between risk perceptions and levels

of explicitness would be weakened while the association between risk perceptions and levels of

flexibility and mutuality would be strengthened. The above discussion leads to the following

hypotheses.

H6a: The positive relationship between risk perceptions and levels of explicitness is

stronger when the organizational distance between the focal firm and its partner is

wide.

41

H6b: The positive relationship between risk perceptions and levels of (a) flexibility and

(b) mutuality is stronger when the organizational distance between the focal firm

and its partner is narrow.

EFFECTS OF GOVERNANCE ATTRIBUTES ON NEW PRODUCT SUCCESS

The third component included in the conceptual model relates to the outcomes of

governance attributes on new product success. Although studies on the impact of governance

attributes on new product development are inadequate, extant research suggests that governance

attributes do affect firm performance (e.g., Cannon et al. 2000; Lusch and Brown 1996). In this

research, I propose that governance attributes are crucial in determining new product success.

Such hypothetical relationships will be examined in the following sections.

Developing new products with other firms is increasingly important and has been

suggested to provide strategic value to the focal organization (Sivadas and Dwyer 2000). A

review of the current literature further suggests that the choice of governance modes is critical to

new product success (e.g. Rindfleisch and Moorman 2001; Robertson and Gatignon 1998).

Forming alliances to develop new products seem to be promising. However, Gates (1993) found

that most alliances are unstable and many of them fail even before their ultimate goals are

fulfilled. By some estimation, the failure rate is as high as about 70% of total alliances (c.f.

Sivadas and Dwyer 2000). I contend that the choice of governance modes is important, yet the

attributes included in contracting with partner firms is even more critical. That is, what

components incorporated in a contract determine the success of an alliance relationship,

specifically, new product outcomes.

42

Both legal (explicitness) and relational (flexibility and mutuality) attributes are expected

to have positive effects on new product outcomes as illustrated in Figure 4. Specifically, using

explicit contracts to specify a firm’s responsibilities and obligations reduces role ambiguities

(Lusch and Brown 1996), suppresses its risks and uncertainties (Cannon et al. 2000), and is

believed to enhance new product success. For example, Cannon, Achrol, and Gundlach (2000, p.

183) find that a clearly specified and detailed contract helps both suppliers and buyers to

“visualize potential contingencies and devise appropriate safeguards” resulting in enhanced

performance. Thus, levels of explicitness should positively relate to new product success. When

a contract is written in a high level of explicitness, it implies that all possible details such as time

of response and enforcement are included. By having the partner firm to follow the explicit

details in a contract, the focal firm should be able to deliver its new product within a planned

time schedule.

FIGURE 4

The Effects of Governance Attributes on New Product Success

H7

New Product Success

Governance Attributes

Contract Explicitness

I

circums

contract

Relational Attributes - Flexibility - Mutuality

n addition, a high level of flexibility in a contract allows firms to adapt to changed

tances. It therefore should have a positive effect on new product success. A flexible

means the ease of changing organizational decision processes and structures (Harris et

43

al. 1998) and reacting to new demands (Li and Kouvelis 1999). A flexible contract can also

speed up the development of new products (Clark and Fujimoto 1991; Eisenhardt and Tabrizi

1995).

Mutuality indicates that two firms in a contract must stand by each other to become

successful (Macneil 1980). The importance of mutuality is that it encourages firms to create

mutual benefits and share information when their partner firms need so. It is suggested that

mutuality should enhance new product success. Such expectation can be explained in terms of

social ties and relational embeddedness suggested elsewhere in interfirm studies (e.g., Hansen

1999; Moorman et al. 1992; Rindfleisch and Moorman 2001; Uzzi 1999). For example, Hansen

(1999) finds that lacking mutuality or weak ties among team members hinders the transfer of

knowledge in new product development activities resulting in longer project completion time.

Without mutuality, new product development activities may suffer (Uzzi 1999).

H7: There is a positive relationship between (a) explicitness, (b) mutuality, and (c)

flexibility and new product success.

SUMMARY

This chapter has detailed the theoretical framework and has proposed a set of testable

hypotheses. Table 2 presents a summary of the relationships between risk perceptions and

governance attributes and how governance attributes in turn affect new product success and the

moderating role of organizational distance.

44

TABLE 2

Summary of Hypothesized Relationships

H1: The focal firm’s performance risk perception is positively related to the levels of (a)

explicitness, (b) mutuality, and (c) flexibility in a contract.

H2: The focal firm’s implementation risk perception is positively related to the levels of (a)

explicitness, (b) mutuality, and (c) flexibility in a contract.

H3: The focal firm’s knowledge leaking perception is positively related to the levels of (a)

explicitness, (b) mutuality, and (c) flexibility in a contract.

H4: The focal firm’s relational risk perception is (a) negatively related to the level of

explicitness but positively related to (b) mutuality and (c) flexibility in a contract.

H5: The focal firm’s reputation risk perception is positively related to the level of explicitness in

a contract.

H6: The positive relationship between risk perceptions and levels of explicitness is stronger

when the organizational distance between the focal firm and its partner is wide.

H6b: The positive relationship between risk perceptions and levels of (a) mutuality and (b)

flexibility is stronger when the organizational distance between the focal firm and its

partner is narrow.

H7: There is a positive relationship between (a) explicitness, (b) mutuality, and (c) flexibility

and new product success.

45

CHAPTER THREE

RESEARCH METHOD

To the people who see the possibilities, and who, sometimes with courage, sometimes with faith and hope – but always with effort, perseverance, and energy – strive to make the possible become reality.

Mohr 2001

This chapter presents the research procedures including the sampling frame, data collection,

operationalization of constructs, scale development, questionnaire design, and analysis plan. The

proposed research procedures are employed to investigate the following areas:

1. The effects of perceived risk types on governance attributes.

2. The moderating role of organizational distance on the relationship between risk

perceptions and governance attributes.

3. Performance implications (i.e., new product success) of governance attributes.

RESEARCH CONTEXT

High technology industries are known to be full of complexity and volatility, which is the

source of risk perception created on both manufacturers and their partners (Heide and Weiss

1995; Krishnan and Bhattacharya 2002; Ruyter et al. 2001). Specifically, firms in high

technology industries must cope with the fact that they are exposed to different types of risk

when they develop new products with other organizations. In this dissertation, I chose to study

electronics industries including computers, facilities, materials, and equipments,

telecommunications, and transportation in the US. These industries are characterized by rapid

technological changes (Mohr 2001), which provide an excellent research context to examine

perceived risk types and the design of contracts.

46

PRELIMINARY FIELDWORK

Before conceptualizing the model for this dissertation, extensive preliminary fieldwork

was conducted to (1) uncover various types of risk in interfirm relationships and (2) identify an

appropriate research context. Ten telephone interviews with senior executives were conducted

between February and March 2002. These managers represented different industries such as

electronics, software, textile and clothing, and pharmaceuticals and were knowledgeable about

inter-organizational relationships.

The interviews were conducted in an unstructured manner. Managers were asked to

describe some of the situations they had come across in reality when dealing with their partners,

be they suppliers, customers, or partners.

To demonstrate perceived performance risk, consider what a merchandising manager at a

large trading company said to me:

“Sometimes, we are thinking of switching to another supplier who can

produce the same products at a lower price. But we never make it happen

because we are not sure whether the product quality of a new supplier is as

good as reliable as our current supplier.”

To illustrate perceived implementation risk, a merchandising manager of a sports good

company with $5 million in sales describes his experience:

“Every time when I go to a trade fair, I am looking for potential suppliers

who can replace some of the current suppliers. I am not quite happy with them

because I always have to spend a whole lot of time to email or fax to them to ask

for a production status, samples, or shipping schedules. They never get back to

47

me in time. I just find very difficult to implement my own plans when I really need

their samples to show to my clients.”

A senior merchandiser at a backpack trading and manufacturing company shares her

experience of perceived relational risk with me:

“Our company has contracted out our production to another factory in

China. We provide them materials that they need for production. Those

materials such as fabrics are imported from Korea and Japan and are very

expensive. We pay for the production materials. But then we were told that they

have been stealing the materials to make money out of extra production.

Although we don’t have any proof, we really think that the factory is not fully

committed to us.”

An owner of a small manufacturer summarizes what we learned about perceived

reputation risk.

“We lost about 10,000 dollars to Wal-Mart in our last shipment. We

really want to terminate our relationship with Wal-Mart. However, we just

cannot afford to do so because we are afraid of losing other customers. Some

customers, they probably may think that we cannot meet the order or we may do

something wrong and therefore Wal-Mart terminates its relationship with us.”

To conclude, these examples drawn from the preliminary field interviews which provide

a base for the development of risk typology. It is believed that one type of risk perception can

be more salient than others depending upon the situations a firm is facing. However, various

types of risk perceptions can coexist in any interfirm relationships.

48

INSTRUMENT DEVELOPMENT PROCEDURES AND MEASURES

Multiple indicators were used to represent unobservable constructs to increase their

validity (Bagozzi et al. 1991; Churchill 1979). Wherever possible, existing scales were used in

this study. Risk perceptions, however, are new and require extensive work to develop an

instrument for each of the risk types. For new constructs (e.g., perceived implementation risk

and perceived reputation risk), I followed recommended procedures (Churchill 1979; Gerbing

and Anderson 1988) to develop multi-item measures.

An initial pool of new scale items was generated based on the review of literatures and

discussions with academic experts in this area. Subsequently, another wave of qualitative

fieldwork was conducted. Six interviews were set up initially with the assistance from the

College of Engineering and Architecture at Washington State University. The College of

Engineering and Architecture has extensive business connections with high tech firms. Finally,

four interviews out of six were conducted with senior executives to verify the

comprehensiveness of the perceived risk typology, to assess the clarity of scale items, and to

improve the overall flow of the research instrument. Appendix A summarizes each of the

indepth interviews.

Respondents were asked to fill out a questionnaire and give comments and feedback on

the questionnaire. Based on the comments received from the field interviews, questions were

restructured and the layout of the survey was modified in the following manners. First, the

instruction of asking key informants to identify a new product alliance partner as a reference was

moved from the cover page to the beginning of the questions. Managers commented that they

normally did not pay attention on the cover page. Second, four demographic-type questions

were moved from the end to the beginning of the questionnaire. The purpose is to get

49

respondents involved in the alliance partner they have chosen to make references to for this

study. Third, a sentence, “Please be sure to keep focusing on the partner firm you have

identified,” was used wherever appropriate to remind respondents to continually make references

to the partner firm they had selected at the beginning of the study. Fourth, the ownership of new

product brands and technology seems to be critical in affecting respondents’ risk perceptions. As

such, four questions, i.e., “With regard to the brand of the new product, what kind of

arrangement has been made?,” “ Who owns the new product brand?,” “Who owns the

technology?,” and “Who will be responsible for introducing and marketing this new product?,”

were added in the second section of the questionnaire. Finally, some items were removed or

modified after receiving the feedback from the managers and extensive discussions with

academic experts.

Measures

The measure development procedures involve developing multiple items for each

construct in the proposed model. The following sections describe each of the measures.

Perceived Risk Types

Following the framework developed in consumer behavior research (e.g., Bettman 1973)

and organizational studies (e.g., Das and Teng 1996), five different types of risk perceptions

were investigated. Perceived performance risk relates to the technology, services, or

components provided by a firm that will cause its partner firm to lose. A total of six completely

new items were created. Sample items to capture the domain of perceived performance risk

include, “we feel uncertain whether our partner firm can contribute to our new product

development,” “we are worried about the adequacy of technology offered by our partner firm,”

50

and “the chance that our new products will fail in the market is high if the partner firm does not

deliver required technology.” Table 3 provides the sample items and construct definitions.

TABLE 3

Constructs and Definitions

Construct Definition and Sample Questions and Items Scale Development

Independent Variables Perceived Performance Risk

The extent of uncertainty associated with jointly developed products and services failing to function as expected and the negative consequences associated with the failure. We feel uncertain whether our partner firm

can contribute to our new product development.

We are worried about the adequacy of technology offered by our partner firm.

New scale, anchored on a seven-point Likert scale

Perceived Implementation Risk

The level of uncertainty and negative consequences associated with the interruption of strategic execution processes when the focal firm’s partner does not implement as expected. We feel uncertain whether we can effectively

coordinate with the partner in developing new products.

We are afraid that we cannot get the partner firm to buy into what needs to be done in developing new products.

New scale, anchored on a seven-point Likert scale

Perceived Knowledge Leaking Risk

The extent to which the focal firm’s proprietary knowledge will be disclosed to other organizations through the development of new products with its partner firm and the negative consequences associated with such disclosure. We fear that too much proprietary

knowledge may be disclosed to this partner firm.

We could suffer a loss if our new product specifications and designs are released to this partner.

New scale, anchored on a seven-point Likert scale

51

TABLE 3 (CONTINUED)

Constructs and Definitions

Construct Definition and Sample Questions and Items Scale Development

Perceived Relational Risk

The extent to which the focal firm is uncertain about its partner firm’s commitment and the negative outcomes when the partner firm is not committed fully to the new product development alliance. We are afraid that this partner firm is not as

dedicated as they should be to make our joint new product development work.

Our firm is at risk if we break up with this partner firm and have to find a new partner

New scale, anchored on a seven-point Likert scale

Perceived Reputation Risk

The extent to which the society or relevant constitutes will disapprove of a firm’s relationship with another organization and the negative consequences associated with such disapproval. If our partner firm does not have a good

reputation, there is some chance our reputation will suffer also.

Our partner’s reputation is so strong and positive, associating with them could only increase our own reputation.

New scale, anchored on a seven-point Likert scale

Moderator Organizational Distance

The extent to which the focal firm’s domain, philosophy, and business practices is different from or similar to its partner. Company culture Operating philosophies

Adapted from Johnson et al. (1996), anchored on a seven-point scale

Mediators

Explicitness The extent to which a contract is presented to its partner In dealing with this partner, your contract or agreement precisely defines The role of each party. How disagreements will be resolved.

Adapted from Lusch and Brown (1996), anchored on a seven-point Likert scale

52

TABLE 3 (CONTINUED)

Constructs and Definitions

Construct Definition and Sample Questions and Items Scale Development

Flexibility The extent to which a firm can adapt to changed circumstances Your agreement with the partner firm allows both of your company and your partner to Make changes when necessary. Make adjustments in the ongoing

relationship with the partner to cope with changing circumstances.

Heide and John (1992), anchored on a seven-point Likert scale

Mutuality The extent to which a firm can adapt to changed circumstances Your agreement with the partner firm allows both of your company and your partner to Work together to be successful. Access information each of us need.

Heide and John (1992), anchored on a seven-point Likert scale

Outcome Variable

New Product Success

The extent to which the new product developed jointly with the partner is successful The overall performance of our new product

program has met our objectives. From an overall profitability standpoint, our

new product development program has been successful.

Adapted from Sivadas and Dwyer (2000) and Song, Sounder, Dyer (1997), anchored on a seven-point Likert scale

Perceived implementation risk reflects the level of uncertainty and negative consequences

associated with the interruption of strategic execution processes when the focal firm’s partner

does not implement as expected (McDermott and Boyer 1999). Extant literature (e.g., Noble and

Mokwa 1999) suggests that implementation relates to the coordination between firms. On this

basis, seven items were developed to measure perceived implementation risk. Sample items

include: “we feel uncertain whether we can effectively coordinate with the partner in developing

53

new products,” “we are afraid that we cannot get the partner firm to buy into what needs to be

done in developing new products,” and “we stand to lose our competitive advantage if our

partner does not execute and implement according to requirements” (Table 3).

Perceived knowledge leaking risk is described as the extent to which the focal firm’s

proprietary knowledge will be disclosed to other organizations through the development of new

products with its partner firm and the negative consequences associated with such disclosure.

Following the literature on organizational learning and interfirm relationships (Beamish and

Banks 1987; Hamel 1991) and field interviews, seven new items were developed. For instances,

“we fear that too much proprietary knowledge may be disclosed to this partner firm,” “we could

suffer a loss if our new product specifications and designs are released to this partner,” and “we

fear that we will lose our competitive advantage if our proprietary product knowledge is

disclosed to our partner firm.”

Perceived relational risk has been discussed in the management literature (Das and Teng

1996). Using related literature in management (Das and Teng 1999) as a guide and with a

specific emphasis on the context of new product alliances, seven new items were created.

Sample items illustrated in Table 3 include: “we are afraid that this partner firm is not as

dedicated as they should be to make our joint new product development work,” “our firm is at

risk if we break up with this partner firm and have to find a new partner/ally,” and “our firm has

taken a risk in trusting and committing to the relationship with this partner.”

Perceived reputation risk relates to the extent to which the society or relevant constitutes

will disapprove of a firm’s relationship with another organization and the negative consequences

associated with such disapproval. Drawing from the literature on organizational image and

reputation (Gioia et al. 2000; Houston and Johnson 2000), seven new items were developed.

54

Sample items include: “if our partner firm does not have a good reputation, there is some chance

that our reputation will suffer also,” “our partner’s reputation is so strong and positive,

associating with it could only increase our own reputation,” and “it is risky for us to develop new

products with this firm because it seems that their reputation with our customers is not all that

good.”

Organizational Distance

It is defined as the extent to which the focal firm’s domain, philosophy, orientation, and

business practices is different from or similar to its partner firm. The measure was modified on

the basis of Johnson et al. (1996) with emphases on new product development. Respondents

were asked to what extent their companies were different from or similar to their partner firms on

the following items: “risk taking,” “innovativeness,” and “new product development processes,”

for instances. All items were anchored on a seven-point scale in which 1 represents very similar

to partner firm and 7 represents very different from partner firm.

Governance Attributes

Explicitness, flexibility, and mutuality are included in this research. The measure of

explicitness is adapted from Lusch and Brown (1996). Since this research focuses on new

product alliances, a specific component relating to intellectual property was added. Respondents

were asked the extent to which their contracts were presented to their partners. Items include

“the role of each party,” “what intellectual property remains with each partner,” and “the

responsibilities of each party” and are anchored on a seven-point Likert scale. Flexibility and

mutuality are two governance attributes borrowed from Heide and John (1992). Items to capture

55

flexibility include “make changes when necessary,” “make adjustments in the ongoing

relationship,” “apply rules and policies loosely,” and “grant exceptions to meet special requests.”

Items for mutuality include “work together to be successful,” “access information each of us

need,” and “create mutual benefits.” All items are anchored on a seven-point Likert scale.

Outcome Variable

This research views new product success as the focal outcome variable. The measure

was adapted from Sivadas and Dwyer (2000) and Song, Sounder, and Dyer (1997). Sample

items include “the overall performance of this new product alliance program has met our

objectives” and “from an overall profitability standpoint, our new product alliance development

program has been successful. Again, the items are anchored on a seven-point Likert scale.

Control Variables

The primary focus of this research is to examine the impact of risk perceptions on the use

of governance attributes and how different nature of these governance attributes influence new

product alliance outcomes. As a result, two variables were included to control for individual

firm differences that might have effects on the proposed relationships.

Relationship age corresponds to the length of business relationships between the focal

firm and its partner. Past studies demonstrate that relationship age influences interfirm

cooperation (Morgan and Hunt 1994; Smith and Barclay 1997). To assess the relationship age,

respondents were asked to specify the number of business experience with their partner firm.

Alliance experience relates to the past experience or history of a firm involving in other

alliance activities. Extant research suggests that past experience has a strong effect on risk

56

perceptions (Sitkin and Weingart 1995). To my best knowledge, alliance experience has never

been studied. However, its effect on the relationship between risk perceptions and governance

attributes is likely. To assess alliance experience, five new items anchored on a seven-point

Likert scale were developed. They include: “our company has been involved in other alliances,”

“we are experienced in managing alliance partners,” “we are familiar with the practice of

forming alliances,” “this is our first time working with other firms to develop new products,” and

“we are very new at figuring out how alliances work.”

SAMPLE FRAME AND PROCEDURES

The sample frame for this research is firms that have recently participated in new product

development alliance activities. Firms in electronic industries such as semiconductors, software,

telecommunications, and medical equipments are included (Mohr 2001). As a sampling base, all

firms that had been involved in any form of alliance activities and such activities were publicized

(e.g., Wall Street Journal, Business Week) between 1999 and 2003 were included. A total of

1,252 firms were identified. Figure 5 outlines the sampling procedures.

57

FIGURE 5

Respondent Identification Procedures and Results

Prescreen and qualify informants through

telephone Qualified Respondents = 404 firms

Pilot (N=50)

Main – 1st Wave(N = 252)

Main – 2nd Wave(N = 102)a

Identify firms that have NPAs and their addresses

N = 801 firms

Identify publicized alliance activities from secondary sources N = 1,252 firms

Note: a Only mailing addresses were verified

58

As shown in Figure 5, identifying key informants involves the following three steps.

Step 1: Identifying firms that had alliance activities between 1999 and 2003

This step involves an extensive review of secondary sources listed on ProQuest

database. 1,252 firms were found to have alliance activities between 1999 and 2003. These

firms could be involved in various sorts of alliance activities not limited to new product

development. In other words, not all alliance activities involved in this stage had new product

alliances.

Step 2: Identifying company contact information

This step is to go to the website of each of the companies identified in Step 2 and to

obtain their telephone numbers and mailing addresses. 801 firms out of 1,252 were found to

have individual company website and contact information.

Step 3: Prescreening and qualifying key informants

This study is primarily interested in new product alliances. With this objective in mind,

only firms that had new product development activities with other companies would be selected.

In this stage, two research assistants were involved in qualifying and prescreening key

informants. Prescreening was conducted in early February through early March. As mentioned

before, not all firms that had alliance activities between 1999 and 2003 dealt with new product

development. Thus, the purpose of prescreening is to identify firms that have new product

alliances. In addition to it, precontacting each key informant by telephone can (1) assess the key

informant’s ability to serve as a key informant by asking if he or she was knowledgeable about

the alliance in question, (2) obtain cooperation, and (3) verify his/her mailing address. The

procedures of prescreening and qualifying gave this study 302 firms that met the above three

59

criteria and 102 firms which met the third criterion only. Key informants were mostly holding

key positions such as chief executive office, president or vice president of engineering or

marketing.

PILOT STUDY

Sample and Procedures

Thirty-four firms were randomly selected from the sampling frame and a list of 16 firms

was provided by the College of Engineering and Architecture at Washington State University.

The College of Engineering and Architecture has an extensive connection with companies in

high-tech industries and places students regularly in those companies. Consequently, a total of

50 firms were included in the pilot study. The purpose of conducting the pilot study is to

examine the validity and reliability of each of the measures employed in the questionnaire and to

assess the nomological net.

A packet of survey materials was sent to each key informant. As detailed by Campbell

(1955), the key informant approach enables researchers to obtain information about a firm by

collecting data from selected people within that organization who are highly knowledgeable

about the phenomena under study. This approach has been used by many other researchers in a

similar type of research (e.g., Lusch and Brown 1996; Morgan and Hunt 1994; Rindfleisch and

Moorman 2001). A senior executive (e.g., vice president, chief officer) in the area of

engineering, research and development, new product development, marketing, and/or operations

is appropriate for this study. Respondents were asked to identify the most recent partner firm

with whom they have jointly developed a new product(s). Questions focus on the attributes they

60

used in contracting with their partners, their risk perceptions of the partners, the organizational

distance from their partners, and new product outcomes.

Prenotification letters were sent on February 11. A week after sending the prenotification

letters, each informant was mailed a packet including a cover letter, a survey, and a postage-paid

reply envelope. A sample of the prenotification letter and cover letter can be found in Appendix

B.

All survey packets were serialized for tracking purposes. There are two reasons to

include a tracking number on each of the survey packets: (1) perspective respondents who have

not responded to the initial mailing can be identified and (2) responses from key informants can

be verified through secondary data resulting in attenuating common method variances found in

many firm-level research. The tracking number was printed on a return-mailing label. Given

that all packets were serialized, informants who did not reply within two weeks could be

identified and were mailed a second set of survey materials (Dillman 1978). This time, a

handwritten Post-it Note and a one-dollar financial incentive were attached on to a cover letter.

The handwritten note is a plea to request that informants to help me with my dissertation.

As a result, four firms replied that they were not appropriate to participate in the survey at

that point in time mainly because their alliance activities were still on going and two firms did

not have time for this research. This left an effective sampling frame of 44 firms. Ten responses

were received from the first mailing and 16 from the follow-up mailing. In total, twenty-six

firms were returned with complete information, for a 59% response rate for the pilot study.

61

Measure Purification and Construct Validation

The pilot sample responses were used to purify measures and provide evidence of the

validity and reliability of both the new and adapted scale items. Following Churchill’s (1979)

recommendations, all measures were purified using the data collected from the pilot study.

Exploratory factor analysis is useful at the earliest stage to assess the validity in particular of new

measures (Anderson and Gerbing 1988; Nunnally 1978). Any items in a construct having a cross

factor loading above .4 would be removed. In other words, if an item with high factor loadings

across factors, the unidimensionality of a construct would be a concern (Churchill 1979).

After purification, all the scales were examined for internal consistency and

unidimensionality. To maintain the unidimensionality of a construct, a clean factor structure is

expected to obtain from exploratory factor analyses. As a result, any items that had a loading

below .4 were removed. Table 4 shows the factor loadings and the reliability of each construct

after eliminating items with low factor loadings. Measures of explicitness, flexibility, and

mutuality were developed on the basis of Lusch and Brown’s (1996) study and all of them

perform reasonably well. Factor loadings of explicitness range from .76 to .94. Both flexibility

and mutuality measures also perform as expected with factor loadings range from .56 to .73 and

.66 to .89 respectively. The measure of new product success was drawn from both Sivadas and

Dwyer’s (2000) and Song, Sounder, and Dyer’s (1997) work. The reliability of this scale is as

high as .89 and the factor loadings are above .6.

Wherever possible, existing measures are used for this study. However, as the scales for

perceived risk types (i.e., performance risk, implementation risk, knowledge leaking risk,

relational risk, and reputation risk) and organizational distance are new and do not have

62

precedence in the literature, these new scales may not perform as well as expected. Six items

were included in the original scale of perceived performance risk. Two of them had factor

loadings less than the acceptable cut-off point of .4 and were removed. After eliminating the two

items, the remaining four items loaded nicely together and the reliability is above .83 which is

higher than the acceptable level of .6 for a new scale (Vogt 1999). Similar to performance risk,

two items out of seven on the implementation risk scale were removed due to their low factor

loadings. The remaining five items loaded reasonably well. Two items of knowledge leaking

risk scale were dropped due to low factor loadings. The remaining items have factor loadings

between .84 and .97. With respect to relational risk, the factor loadings range from .59 to .92

after three items out of seven were dropped. Reputation risk was the most problematic scale.

Only three items out of seven were retained. Although the factor loading of the first item is as

low as .31, I decided to keep it on the scale following Bagozzi and Yi’s (1988) recommendation

of 3 to 5 items.

The scales were modified after all constructs except for perceived reputation risk showed

reliabilities over the minimum recommended level, .7. Perceived reputation risk is a new

measure and the reliability is .62 after four items were removed. Original items are provided in

Appendix C.

63

TABLE 4

Item Factor Loadings and Reliability Analysesa

Construct Items Factor Loading Reliability (Alpha) Explicitness EX1

EX3 EX4

.76

.94

.84

.88

Flexibility FLEX1 FLEX2 FLEX4

.56

.66

.73

.78

Mutuality MUTUAL1 MUTUAL2 MUTUAL3

.66

.87

.89

.83

Organizational Distance DIST1 DIST2 DIST3 DIST4 DIST5

.72

.63

.89

.75

.56

.84

Perceived Performance Risk PERF_R3 PERF_R4 PERF_R5 PERF_R6

.61

.81

.91

.70

.83

Perceived Implementation Risk IMP_R3 IMP_R4 IMP_R5 IMP_R6 IMP_R7

.46

.59

.50

.57

.69

.88

Perceived Knowledge Leaking Risk KNOW_R1 KNOW_R2 KNOW_R3 KNOW_R4

.85

.84

.87

.97

.93

Perceived Relational Risk REL_R1 REL_R3 REL_R4 REL_R7

.59

.92

.87

.64

.84

Perceived Reputation Risk REPU_R1 REPU_R5 REPU_R7

.31

.92

.85

.62

New Product Success NPS1 NPS2 NPS4 NPS5

.66

.90

.75

.93

.89

an = 26

64

Nomological Net and Discriminant Validity

Nomological net involves demonstrating that the pattern of correlations with other

measures of other constructs adheres to theoretical expectations. Table 5 shows the correlation

matrix for all the focal measures used in this study. The correlations between the constructs

provide a strong support for the specified nomological network, thereby establishing

nomological validity of the constructs. In addition, examining the correlation matrix can also

assess the constructs’ discriminant validity. Zero order correlations between variables indicate a

high level of discriminant validity. However, as shown in Table 5, the correlations among

perceived risk types can be as high as .53. Discriminant validity may be a concern among some

of the perceived risk types. Similarly, the correlation between flexibility and mutuality reaches

.57. Such high correlation is not unexpected. As Gundlach and his colleagues (1995) comment

on the literature of social contracting, flexibility and mutuality are two components which are

closely related to each other.

65

TABLE 5

Correlation Matrix of Variables after Purification 1 2 3 4 5 6 7 8 9 10 1. Explicitness 1.00 2. Flexibility .22 1.00 3. Mutuality .39* .57** 1.00 4. Performance Risk .33 .30 .30 1.00

5. Implementation Risk .05 -.08 .07 .53** 1.00

6. Knowledge Leaking Risk .03 -.21 -.28 -.35 .02 1.00

7. Relational Risk -.08 -.22 -.20 -.04 .51** .43* 1.00 8. Reputation Risk .01 .19 -.25 .24 .12 .12 .11 1.00 9. Organizational Distance .33 -.16 .19 .04 .13 .53** .20 -.01 1.00

10. New Product Success .19 .48* .70** .27 -.06 -.33 -.35 -.48* .06 1.00

Mean 5.38 4.98 5.69 4.61 4.46 3.41 3.57 2.85 3.99 5.35 Standard Deviation 1.13 1.04 1.08 1.46 1.24 1.81 1.35 1.03 1.26 1.22

n=26 *correlation is significant at the .05 level. ** correlation is significant at the .01 level.

MAIN STUDY

The main study consisted of two waves. For the first wave, 252 packets of survey

materials were sent on March 10. Unlike the pilot study where a prenotification letter was sent

to each individual firm, no prenotification letters were sent to the respondents of the main study.

The major reason is that respondents were precontacted through telephone and were notified that

they would receive a survey packet shortly. So, the prenotification step was unnecessary.

Similar to the pilot study, a packet including a cover letter, a postage return envelope, and

the survey, was sent to each firm. A sample of cover letter and the survey instrument can be

66

found in Appendix D. In addition, a handwritten plea was attached on the cover to ask

informants to help with this research. Also, as an incentive to participate, informants were told

that they would be provided with an executive summary report of the research findings.

Out of 252 firms, 10 replied that they neither had NPAs nor enough knowledge to

participate in this study. Data from thirty-three firms were received. Three of which were not

completed. Thus, the effective response rate for the major survey was 12.6% (=30/239). Given

that the effective response rate for the pilot study was 59.1%, the effective response rate for the

first wave of the main study was far below my expectation.

Two approaches were adopted in an attempt to increase the response rate and the total

number of responses. First, a follow-up survey was mailed on April 2. Following Dillman’s

(1978) recommendations, a follow-up letter with a questionnaire was sent to the respondents as a

reminder three weeks after the initial mailing. A one-dollar-incentive together with a hand-

written plea for help with this research was attached onto a cover letter. Four were returned

indicating that they did not have NPAs. Thirty-four completed questionnaires were received.

Together with the 30 usable responses received from the initial mailing, the effective response

rate for the first wave of the main study was 27%.

Second, as described previously, an additional 102 firms were identified to have NPAs.

However, the research assistants were not able to contact the key informants in person after

calling three times in different time periods. Despite the fact that no consent was received from

this list of respondents, a packet including a cover letter, a postage return envelope, and the

survey was sent to each firm. It was believed that these firms had new product alliance activities

and were therefore eligible for this study. Four were returned because of incorrect addresses.

Another three indicated that they did not have enough knowledge. A total of twelve completed

67

survey was received. Similar to the first wave of the main study, a follow-up survey was

conducted. Again, following Dillman’s (1978) recommendations, a follow-up letter with a

questionnaire was sent to the respondents as a reminder two weeks after the initial mailing. This

time, in addition to a hand-written plea for help with this research, a one-dollar-incentive was

attached onto a cover letter. Five usable questionnaires were received. In total, 17 completed

questionnaires were received from the second wave, giving a 17.9% response rate. Given that no

commitment was obtained from the respondents of this wave, the response rate was comparable

to other recent studies to this population (e.g., Isobe et al. 2000). The study was terminated on

April 22. A total of 107 usable questionnaires were received, for a 28.5% response rate. Table 6

summarizes the response rates for both the pilot and the main studies.

TABLE 6

Sampling Frames and Response Rates

Study Sampling Frame Effective Sampling Frame

Usable Responses

Effective Response Rate

Pilot (Initial) 50 44 10

Pilot (Follow-up) 34 33 16 59.1%

Main – 1st Wave (Initial) 252 239 30

Main – 1st Wave (Follow-up) 209 205 34

27%

Main – 2nd Wave (Initial) 102 95 12

Main – 2nd Wave (Follow-up) 83 83 5

17.9%

Total 404 376 107 28.5%

68

RESPONSE BIAS AND VALIDITY CHECKS

Following Armstrong and Overton’s (1977) recommendations, potential nonresponse

bias was assessed through a series of t-tests to compare early with late respondents in terms of all

key constructs. In this study, responses from the initial mailing were used to compare with the

responses received from the follow-up mailing. A series of t-test on key constructs were

performed. The results in Table 7 suggest that there is no significant difference between early

and late respondents in terms of key variables.

TABLE 7

Comparison of Early Respondents to Late Respondents

Construct t-value Significance Level

Perceived Performance Risk 1.38 .17

Perceived Implementation Risk 1.50 .14

Perceived Knowledge Leaking Risk .99 .34

Perceived Relational Risk .34 .73

Perceived Reputation Risk 1.57 .12

Explicitness .04 .97

Flexibility 1.53 .13

Mutuality 1.62 .11

New Product Success .93 .36

In addition, potential nonresponse bias was assessed by comparing nonresponding firms

with responding firms in terms of their demographics. Since a majority of the firms in this study

69

are the members of the American Electronics Association (AeA), their demographic information

such as annual sales, number of employees, and years of establishment can be found on the AeA

website (www.aea.org). If not available, other means such as individual company websites were

used to identify the required information. The results illustrated in Table 8 further suggest that

there is no significant difference between respondent firms and non-respondent firms in terms of

their age, revenues, and number of employees. This provides evidence that the responding firms

were comparable to the sample, and any findings in this study had sufficient external validity to

be generalizable across firms within the same high-tech industries.

TABLE 8

Comparison of Respondents to Non-Respondents

Indicator t-value Significance Level

Age .94 .32

Sales .56 .61

Number of Employees .44 .59

Responses obtained from the pilot study were compared to responses from the main

study. As demonstrated in Table 9, results indicate that there is no significant difference these

two groups of responses in terms of the key constructs. Data were therefore combined to test the

hypotheses.

70

TABLE 9

Comparison of Pilot to Main Responses

Construct t-value Significance Level

Perceived Performance Risk .11 .92

Perceived Implementation Risk .3 .76

Perceived Knowledge Leaking Risk .46 .65

Perceived Relational Risk .01 .99

Perceived Reputation Risk .77 .44

Explicitness 1.08 .28

Flexibility .36 .72

Mutuality 1.18 .24

New Product Success .21 .83

Validity Check

As a validity check, respondents were asked to provide their information regarding their

positions, the number of years they had worked for their firm, and their level of involvement and

influence in alliance activities. Results suggest that respondents had worked for their company

for an average of 9 years. About 90% respondents were presidents, vice presidents, or senior

executives who were highly involved in their alliances (5.8 on a seven-point scale) and did have

significant influence on new product development alliances (5.9 on a seven-point scale). The

findings provide evidence that the sampling approach was successful in identifying key

informants.

71

SUMMARY

In this chapter, a comprehensive discussion of research method was provided.

Specifically, it detailed the measurement development and the sampling procedures. The next

chapter presents the results of this research including the respondent characteristics and

hypothesis testing results.

72

CHAPTER FOUR

DATA ANALYSIS AND RESULTS

A nice adaptation of conditions will make almost any hypothesis agree with the phenomena. This will please the imagination but does not advance our knowledge.

J. Black 1803, p. 193.

Chapter 4 presents the analyses of the survey data and the results of hypothesis testing.

This chapter is organized into three major sections. The first section presents respondent

characteristics and the patterns of alliance activities. The second section relates to measure

validation. The final section presents the hypothesis testing procedures and the results.

RESPONDENT CHARACTERISTICS AND DESCRIPTIVE STATISTICS

From the responses received, respondents represented four industries including

computers and office equipments, semiconductors, software, and medical devices with a range in

size by revenues of $300 thousand to $60 billion and size by number of employees ranged from

5 to 30,000. All respondents in the sample were high-tech firms and informants for each firm

were at the senior vice president level or higher with the majority holding the chief executive

position (90% of total respondents). These respondents have been employed at their firms

approximately 9 years on average (Table 10).

73

TABLE 10

Characteristics of Firms and Key Informants

Respondent/Firm Characteristics Minimum Maximum Mean

Respondent’s Length of Employment with Firm (years) 1 30 9

Firm Age (years) 1 87 25

Firm Size (number of employees) 5 30,000 124

Alliance Patterns

Seventy percent of the firms have alliance partners located in the US. About 30% of

firms, however, have international alliance partners in Canada, Germany, India, China, or Japan,

among others. It is not surprising as forming alliances across borders increased dramatically in

the last two decades (Johansson 1995).

Among 107 firms, 70 of them have partners, be they suppliers or customers, in the same

industry. As illustrated in Table 11, forming alliances with competitors in the same industry is

uncommon. Only four firms said that they partnered with their competitors to produce new

products. An interesting observation found in here is that 12 responding firms had an alliance

partner with multiple identities, i.e., supplier, customer, and/or competitor.

74

TABLE 11

Composition of Partner Firms

Different Industry

Same Industry

Competitor 0 4

Customer 15 20

Supplier 18 32

Multiple Forms 2 10

Others (e.g., industry friends, L-T joint ventures) 2 4

Total 37 70

Table 12 summarizes the characteristics of partner firms. The average size of partner

firms by number of employees was 7,277 (range from 2 to 100,000) and by sales was $13 billion

(range from 1 to 500,000 million). In general, the partner firms had been in business for 22.5

years (range from 1 to 137 years) and the responding firms had been involved doing business

with their partner firms for an average of 5.3 years (range from 1 to 40).

75

TABLE 12

Characteristics of Partner Firms

Characteristics Minimum Maximum Mean

Firm Age (years) 1 137 22.5

Firm Size (sales in million) 1 500,000 12,655

Firm Size (no. of employees) 2 100,000 7,277

Length of Relationship (years) 1 40 5.29

MEASURE VALIDATION

On the basis of the results from the pilot study, measures were modified (results can be

found in Chapter 3). Here, as recommended by Churchill (1979) and Gerbing and Anderson

(1988), unidimensionality and discriminant validity were both examined by means of a series of

confirmatory factor analysis models using LISREL 8 (Jöreskog and Sörbom 1993). Given that

the sample size of this study is small (n=107), relevant constructs were split into subsets of

theoretically related variables in order not to violate recommended minimal sample size to

parameter estimate ratios (Bentler and Chou 1988). Splitting relevant constructs into subsets of

variables allows me to test for construct convergence within maximally similar sets of variables

and have been used in past research (e.g., Grewal and Tansuhaj 2001; Moorman and Miner

1997).

In the following, I will lay out the measure validation procedure.

76

Domain Examination

All the key constructs in this study are of multiple items. For the multi-item scales, each

set of items was initially subjected to an examination of item-to-total correlations to identify

items that did not belong to the specific domain. None of the items was found to be a concern.

High-Order Factor Examination

In most cases, constructing high-order factors must be guided by theories. However,

there exist certain situations when no empirical work has been done in the same area. In this

research, both performance risk and implementation risk have never been studied in the past.

Although my field interviews suggested that performance risk was different from

implementation risk in that the latter focused on implementation processes while the former

emphasized outcomes, an examination of their correlation suggests that they are correlated at .91,

which indicates that the two variables are highly correlated with each other. Such high

correlation indicates that a second-order factor may exist. Conceptually, these two variables

should be correlated since implementation processes should influence performance outcomes.

Thus, to examine whether a single second-order factor is superior than a two first-order factors, I

followed the procedures which have been used in extant research (e.g., Heide and John 1992).

The proposed factor structure and its parameter estimates can be found in Table 13. The

fit indices for the model indicate a satisfactory fit to the data (χ2 (64) = 156.77, p = .00, GFI =

.88, RMSR = .08, Bentler and Bonnett’s index ∆ = .82, Bentler’s comparative fit index = .84).

Although Bentler and Bonnett’s index does not exceed the ideal point of .90 and Bentler’s

comparative fit index is also below .90, the relevant first- and second-order factor loadings are

large and statistically significant (Table 13). In particular, the factor loadings of the two first-

order factors, performance risk and implementation risk, are .96 and .93 respectively.

77

TABLE 13

First-Order and Second-Order Loadings of Performance Risk and Implementation Risk

First-Order Loadingsa

Indicator Performance Risk (PER) Implementation Risk (IMP)

PERF_R3 .52

PERF_R4 .82

PERF_R5 .85

PERF_R6 .91

IMPL_R3 .71

IMPL_R4 .80

IMPL_R5 .71

IMPL_R6 .66

IMPL_R7 .80

Second-Order Loadingsa

First-Order Factor Default Risk (DEFAULT)

PER .96

IMP .93 aAll factor loadings are significant at p < .05

Given the high factor loadings, the assertion that a second-order factor structure underlies

the factors of performance and implementation risk was supported. Further, for the sake of

simplicity, both performance risk and implementation risk were combined into an equally

78

weighted composite score representing the high order factor of default risk for the hypothesis

tests.

The constructs of mutuality and flexibility were examined for two reasons. First,

theoretically speaking, mutuality and flexibility have been proposed to underline different

conceptual meanings (Macneil 1980). Macneil (1978) has made a very compelling argument

suggesting that mutuality corresponds to the mutual understanding between partners while

flexibility implies how a firm adapts to environmental changes. Empirically speaking, however,

some researchers argue that mutuality and flexibility are essentially two similar factors

underlying the notion of relational norms (Heide and John 1992), normative (soft) contracts

(Lusch and Brown 1996), interfirm coordination (Dahlstrom and Nygaard 1999), and

cooperative norms (Cannon and Perreault 1999). Given that past studies have proposed a

second-order factor structure, an effort was made to examine whether mutuality and flexibility

comprise a high-order factor.

The proposed factor structure and its parameter estimates can be found in Table 14. The

fit indices for the model indicates a satisfactory fit to the data (χ2 (3) = 8.8, p = .00, GFI = .85,

RMSR = .08, Bentler and Bonnett’s index ∆ = .80, Bentler’s comparative fit index = .81).

Although Bentler and Bonnett’s index does not exceed the ideal point of .90 and Bentler’s

comparative fit index is also below .90, the relevant first- and second-order factor loadings are

large and statistically significant (Table 14). In particular, the factor loadings of the two first-

order factors, flexibility and mutuality, are .71 and .75 respectively.

79

TABLE 14

First-Order and Second-Order Loadings of Flexibility and Mutuality

First-Order Loadingsa

Indicator Flexibility (FLEX) Mutuality (MUT)

FLEX1 .78

FLEX2 .80

FLEX4 .58

MUTUAL1 .73

MUTUAL2 .74

MUTUAL3 .79

Second-Order Loadingsa

First-Order Factor Default Risk (DEFAULT)

FLEX .71

MUT .75

aAll factor loadings are significant at p < .05

Given the high factor loadings, the assertion that a second-order factor structure underlies

the notion of flexibility and mutuality was supported. It should be noted that despite the fact that

Macneil’s (1980) argument is theoretically compelling, the assessment of high-order constructs

appears in Table 14 is consistent to past empirical studies (e.g., Heide and John 1992). As a

result, for the sake of simplicity, both flexibility and mutuality were combined into an equally

weighted composite score representing relational attributes for the hypothesis tests.

80

Confirmatory Factor Analyses

After constructing the two high-order constructs, confirmatory factor analyses as

suggested by Gerbing and Anderson (1988) were used to assess the convergent and discriminant

validity for three measurement models: (1) perceived risk types including default risk (i.e.,

performance risk and implementation risk), knowledge leaking risk, relational risk, and

reputationrisk, (2) governance attributes including explicitness and relational components (i.e.,

mutuality and flexibility), (3) new product success, organizational distance, and alliance

experience.

Table 15 shows the results from confirmatory factor analysis models. Results indicate

that a reasonably good fit for each measurement model. All factor loadings are greater than the

.4 cutoff (Nunnally and Bernstein 1994) and are statistically significant.

TABLE 15

Results from Confirmatory Factor Analysis Models

Measurement Model

Range of Standardized

Factor Loadings

NNFI CFI RMSEA χ2 (d.f., p-value)

1. Governance Attributes .76 - .89 .81 .86 .11 81.54 (26, p = .12)

2. Risk Types .49 - .94 .86 .88 .09 294.71 (146, p = .00)

3. New Product Success, Organizational Distance, Alliance Experience

.43 - .95 .87 .89 .09 172.72 (74, p = .00)

81

With respect to discriminant validity, two approaches are commonly used to assess it

(Anderson and Gerbing 1988). The first approach is to examine the 95% confidence intervals (+

or –1.96 standard errors) around all possible pairwise factor correlations. The phi coefficient (Φ)

plus and minus the standard error of Φ should not include 1 to establish discriminant validity

between any two variables. The second approach is to conduct a series of two-factor

confirmatory factor analysis models for each of all possible pairs of constructs. Specifically, the

phi coefficient (Φ) is constrained to unity for the first model of each pair of constructs and in the

second model, the (Φ) is freed. A chi-square test is then performed to compare the first model to

the second model. If the second model is found to be superior, it then provides an evidence of

discriminant validity. In this research, the former approach was adopted. As Bentler and Chou

(Bentler and Chou 1988) suggest, the assessment of discriminant validity based on an inspection

of confidence intervals is entirely complementary to the latter approach. The matrix described in

Tables 16a – 16c show that none of the confidence intervals encompasses +/-1 suggesting that

discriminant validity is established.

82

TABLE 16a

Confidence Intervals, Average Construct Variance Extracted, and Construct Reliability –

Risk Types

Confidence Intervals

1 2 3

Average Variance Extracted

Construct Reliability

1. Default Risk .54 .91

2. Knowledge Leaking Risk

-.05 – -.03 .68 .89

3. Relational Risk .31 – .33 .43 – .45 .59 .84

4. Reputation Risk .11 – .13 .51 – .53 .34 – .36 .68 .81

TABLE 16b

Confidence Intervals, Average Construct Variance Extracted, and Construct Reliability –

Governance Attributes

Confidence Intervals

2

Average Variance Extracted

Construct Reliability

1. Explicitness .26 – .28 .71 .88

2. Relational Attributes .43 .82

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TABLE 16c

Confidence Intervals, Average Construct Variance Extracted, and Construct Reliability –

New Product Success, Organizational Distance, Alliance Experiences

Confidence Intervals

1 2

Average Variance Extracted

Construct Reliabilities

1. New Product Success .66 .89

2. Alliance Experiences .17 – .19 .78 .95

3. Organizational Distance -.09 – -.11 -.13 – -.11 .36 .73

Reliability

Reliability is the correlation between a measure and itself. If the correlation is high, then

most of the variance in a measure would be thought to be systematic (Nunnally 1978). Hence,

reliability indicates the degree to which measures are free from random error.

Coefficient alpha is the basic statistic used to determine the reliability of a measure based

on internal consistency. Coefficient alpha, however, does not adequately estimate errors caused

by factors external to an instrument such as differences in testing situations and respondents over

time. In this research, two indicators, average variance extracted and construct reliability, were

used to assess reliability (Bagozzi and Yi 1988). The average variance extracted indicates the

reliability of a set of measures of a latent variable and the values greater than .5 are considered

adequate (Bagozzi and Yi 1988). Tables 16a-c suggest that except for relational attributes and

organizational distance, the measures of average variance extracted suggested satisfactory

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reliabilities. Construct reliability further supports that the measures are internally consistent.

None of the measures has reliability below .7 (Bagozzi and Yi 1988).

HYPOTHESIS TESTING AND RESULTS

Before testing hypotheses, the construct scales were formed by summing and averaging

the resultant values for all items in a scale. All scales had multiple indicators with a minimum of

three items per scale. Using multi-item scales can make relatively fine distinctions among

respondents and specificity of items can be averaged out with an increase in reliability as the

measurement error is reduced. In other words, several items were combined for the purpose of

averaging out random errors in this study (Nunnally 1978). Table 17 provides the correlation

matrix and summary statistics.

TABLE 17

Correlation Matrix and Summary Statistics

1 2 3 4 5 6 7 8

1. EXPLICIT 1.00

2. REL_ATT .26* 1.00

3. DEFAULT .10 .10 1.00

4. KNOWRISK .03 .05 -.04 1.00

5. RELRISK -.014 -.05 .33* .42* 1.00

6. REPRISK .12 .12 .15 .33* .24* 1.00

7. DIST .01 -.12 -.03 .02 .11 -.04 1.00

8. NPS .33* .44* -.01 -.24* -.43* -.27* -.04 1.00

Mean 5.46 5.45 4.42 3.16 3.52 2.05 4.12 5.35 Standard Deviation 1.19 .89 1.30 1.63 1.31 1.23 1.11 1.02

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Hierarchical regression analyses were employed to test the first two components of the

model, i.e., (1) the direct effects of risk perception on governance attributes and (2) the

moderating effect of organizational distance as shown in Figure 6. Interaction terms between the

measure of organizational distance and each of the perceived risk types were generated. Before

creating interaction terms, all variables were mean-centered to avoid the problem of

multicollinearity (Aiken and West 1991; Mason and Perreault 1991). The first model, i.e., the

restricted model, includes control variables only. The second model includes both control

variables and the hypothesized direct effects of perceived risk types. The final model is a full

model encompassing all variables in the second model and the interaction terms.

FIGURE 6

Revised Model (Main Effects and Moderating Effects)

Contract Explicitness

Relational Attributes (Mutuality and

Flexibility

H6

Organizational Distance

H1-H5

Governance Attributes

Reputation Risk – H5

Relational Risk – H4

Knowledge Leaking Risk – H3

Default Risk (Performance and Implementation Risk) – H1-H2

Perceived Risk Types

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Table 18 shows the results of the hierarchical regression models used to test Hypotheses

1-6. Specifically, Model 1 includes two control variables, alliance experiences and years of

business relationship with partner firms. Alliance experience (ALLYEX) was expected to have

direct positive effects on levels of explicitness was supported (b=.19, p<.01). Number of

relationship years (R_AGE) was also proposed to have direct positive effects. Unexpectedly, the

results indicate a negative relationship between the number of relationship years and explicitness

(b=.-.04, p<.05). Similarly, both alliance experience and relationship age were expected to have

positive effects on relational attributes. None of the assertion was supported.

In Model 2, all main effects were included in the hierarchical regression analysis. The R-

square changed significantly (∆R2 = .10, p<.05) indicating the existence of main effects on

explicitness. However, the R-square did not have a significant change (∆R2 = .04, p>.1) with

respect to the main effect of risk types on relational attributes. The following sections detail the

main effect hypothesis testing results.

Hypotheses 1 and 2

H1a and H2a proposed that there is a positive relationship between perceived default risk

(i.e., performance risk and implementation risk) (DEFAULT) and explicitness (EXPLICIT). The

coefficient associated with default risk is positive and significant (b=.16, p<.05) providing

support for the hypotheses. Also, H1b and H2b suggested a positive relationship between

perceived default risk and relational attributes (i.e., flexibility and mutuality). As expected, the

results lend support to the hypotheses (b=.10, p<.1).

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Hypothesis 3

H3a suggested that perceived knowledge leaking risk (KNOWRISK) is positively related

to explicitness. No empirical support was found for this hypothesis (b=.02, p>.1). Results also

did not support H3b which proposed a positive relationship between knowledge leaking risk and

relational attributes (b=.05, p>.1).

Hypothesis 4

H4a contended that perceived relational risk (RELRISK) was negatively associated with

explicitness. The results in Table 18 lend support to this hypothesis (b=-.27, p<.01). In contrast,

findings did not provide evidence to support H4b. Perceived relational risk was found to have a

negative effect on relational attributes (b=-.10, p>.1) which was in contrast to the hypothesis.

Hypothesis 5

H5 suggested that perceived reputation risk (REPRISK) is positively related to

explicitness. This hypothesis is supported as the results indicate that the coefficient is positive

and statistically significant (b=.18, p<.05).

Hypothesis 6

To examine the second component of the model, i.e., the moderating role of

organizational distance, all variables in the second model and the interaction terms were included

in the final model. The R-square did not change significantly (∆R2 = .03, p>.10 for explicitness

and ∆R2 = .04, p>.10 for relational attributes) indicating an absence of moderating effects.

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TABLE 18

Hierarchical Regression Modelsa

Model 1 Model 2 Model 3

EXPLICIT REL_AT EXPLICIT REL_AT EXPLICIT REL_AT

Model Summary

R2 = .11*** ∆R2 = .11***

F = 6.49

R2 = .02 ∆R2 = .02 F = 1.08

R2 = .19*** ∆R2 = .10**

F = 3.67

R2 = .06 ∆R2 = .04 F = .99

R2 = .22 ∆R2 = .03 F = 2.48

R2 = .09 ∆R2 = .04 F = .96

ALLYEX .19*** .04 .21*** .02 .22*** .03

R_AGE -.04** -.02* -.04*** -.02* -.05*** -.02*

DEFAULT .16* .10* .13* .11*

KNOWRISK .02 .05 .03 .03

RELRISK -.27*** -.10 -.29*** -.08

REPRISK .18** - .18* -

DIST*DEFAULT -.01 -.01

DIST*KNOWRISK -.07 .01

DIST*RELRISK -.06 -.09

DIST*REPRISK -.12 -

Note: One-tail tests, *p < .1, **p<.05, ***p<.01. For simplicity, constant terms and organizational distance were not included in the table.

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Ordinary least square regression was used to test the final component of the model as

illustrated in Figure 7. In other words, the hypothesis of having positive relationships between

governance attributes and new product success was examined.

FIGURE 7

Revised Model (Effects of Governance Attributes on New Product Success)

H7 New Product Success

Relational Attributes (Flexibility and

Mutuality)

Contract Explicitness

Governance Attributes

Hypothesis 7

Results in Table 18 show that the positive relationship between explicitness (EXPLICIT)

and new product success (NPS) was established (b=.20, p<.01). As expected, relational

attributes (REL_AT) were also found to have direct positive effects on new product success

(b=.43, p<.01).

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TABLE 19

Regression Model Results

Independent Variable Dependent Measure: NPS

Constant 1.90***

EXPLICIT .20***

REL_AT .43***

R-Square .24***

Two-tailed tests: *p < .1, **p<.05, ***p<.01

Finally, to examine whether there exists a mediating effect of governance attributes on

the relationship between risk perceptions and new product success, I followed the procedures

established by Baron and Kenny (1986). A mediator represents the generative mechanism

through which an independent variable is able to influence a dependent variable of interest. It

speaks to how or why such effects occur. In this research, governance attributes were proposed

to mediate the effects of risk perceptions on new product success. To establish the fact that

governance attributes do play a mediating role, according to Baron and Kenny (1986), they

should have the following two properties: (1) governance attributes are correlated with both

perceived risk types and new product success and (2) the relationship between perceived risk

types and new product success approaches zero if governance attributes are held constant.

Again, following Baron and Kenny’s (1986) recommendations, three regression

equations were included: (1) regressing governance attributes on perceived risk types; (2)

regressing new product success on perceived risk types; (3) regressing new product success on

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both governance attributes and perceived risk types. Given the moderating effects of

organizational distance were not supported (Table 18), organizational distance was not included

to test mediating effects. Coefficients were compared to see if there existed any mediating

effects. Results in Table 20 show that explicitness does play a mediating role. Explicitness was

found to have mediating effects in that the beta of explicitness on equation 3 is smaller than that

on equation 2 (b = .18 < b = .20). In particular, levels of explicitness mediate the effects of (1)

default risk, (2) relational risk, and (3) reputation risk on new product success. In contrast,

relational attributes were found to have no mediating effect of risk perceptions on new product

success. Specifically, the beta of relational attributes (REL_AT) on equation 3 is greater than the

beta of REL_AT on equation 2 (b = .45 > b = .43) indicating that no mediating effects.

TABLE 20

Regression Models for Testing Mediating Effects

Equation 1 Equation 2 Equation 3

EXPLICIT REL_ATT NPS NPS

DEFAULT .16** .11* - .06

KNOWRISK .08 .07 - -.03

RELRISK -.25*** -.10* - -.25***

REPRISK .13 - - -.24***

EXPLICIT - - .20*** .18***

REL_AT - - .43*** .45*** Two-tailed tests: *p<.1, **p<.05, ***p<.01

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CHAPTER FIVE

DISCUSSION AND CONCLUSION

This dissertation provides a first step in the direction toward understanding the roles of

perceived risk, organizational distance, and governance attributes in new product alliances. A

typology of perceived risk in interfirm relationships was developed and used to investigate its

effect on the use of governance attributes in new product alliance activities. More important, the

moderating role of organizational distance has seldom been explored in the marketing literatures.

This research studied how organizational distance influenced the effect of perceived risk on

governance attributes. Further, some researchers (e.g, Lusch and Brown 1996) have called for an

attention to the impact of governance attributes on firm performance. To answer this call, the

outcome of governance attributes on new product success was examined.

In the subsequent sections, I will turn to highlight the key implications of the findings of

this study, discuss the limitations, and identify future research directions.

IMPLICATIONS

The basic conclusion that can be drawn from this study is that risk perceptions are

multidimensional and yet their impact on the use of attributes in a contract are critical.

Governance attributes were found to have significant impact on new product success and the

effect of relational attributes is even stronger than contract explicitness on new product success.

Specifically, the empirical findings presented in this support the contention that firms’

perceptions of risk influence the way they design a contract, i.e., what components should be

included in a contract per se. Default risk (performance and implementation risk) was found to

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have direct impact on the level of explicitness. The stronger the default risk, the higher is the

level of explicitness in a contract used to govern a partner firm. Relational risk, as expected, was

found to negatively influence the level of explicitness. The findings, however, do not support the

hypothesis that knowledge leaking risk is positively related to explicitness. Knowledge leaking

is a tricky question. The insignificant result may suggest that the respondent firms are able to

protect their proprietary knowledge from disclosing to their partners. Alternatively, as suggested

by Reed and DeFillipi (1990), firms may believe that their partners are unable to identify their

proprietary knowledge components as a result of causal ambiguity. As a result, contract

explicitness becomes irrelevant when a firm believes that its partner cannot acquire its

knowledge easily.

The hypotheses of direct relationships between risk types and relational attributes (i.e.,

mutuality and flexibility) were found mostly unsupported. With an exception of default risk,

none of the risk types has any influence on the use of relational attributes in a contract. The

reason may be due to the fact that relational attributes are socially embedded and the use of such

attributes relies on a social context. In this research, 70% of the respondents have domestic

partners while 30% of the respondents have international alliance partners in countries such as

Germany, France, India, Japan, China, Taiwan, and Hong Kong. Prior research suggests that

international alliances may be systematically different from domestic alliances (e.g, Gulati 1995;

Harrigan 1985; Kogut and Singh 1988). As a consequence, firms that have alliance partners in

the US vs. China, for example, may have different considerations in the use of relational

attributes.

Organizational distance has seldom been investigated in the marketing literature. In this

research, organizational distance was proposed to play a moderating role between risk

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perceptions and governance attributes. However, no moderating effect was found. Perhaps, the

conceptualization and the measures of organizational distance are as problematic as cultural

distance. Shenkar (2001, p. 520) comments that “the appeal of the CD construct is,

unfortunately, illusory. It masks serious problems in conceptualization and measurement, from

unsupported hidden assumptions to questionable methodological properties, undermining the

validity of the construct and challenging its theoretical role and application.”

Both explicitness and relational attributes were found to have positive effects on new

product success. Thus, firms can effectively manage their alliance partners if they include

explicitness and well as relational attributes in their contracts. Finally, although governance

attributes were not hypothesized to have mediating effects in this study. The conceptual model,

in fact, suggested that governance attributes are critical. An effort was made to examine such

relationship and the findings lend support to the assertion that levels of explicitness but not

relational attributes intervene the effects of perceived risk on new product success. Perhaps, the

results explain why so many alliances fail in a year. Many alliances are established with or

without formal contractual agreements. As such, having an explicit contract may be the key to

the success of alliance activities.

CONTRIBUTIONS

Theoretical Contributions To Existing Literature

The theoretical contributions lie in the examination of risk neutrality assumption in

interfirm relationships concerning new product alliances (NPA) that is embedded in transaction

cost analyses. Prior research has mostly focused on the assumptions of opportunism and

bounded rationality to predict the choice of governance structures, while overlooking the

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assumption of risk neutrality which is an important component in Williamson’s (1975)

transaction cost economics (Chiles and McMackin 1996; Rindfleisch and Heide 1997). The

notion of risk has been studied in other areas such as consumer behavior (e.g., Bauer 1967;

Campbell and Goodstein 2001) and strategy (e.g., Kohli 1989), and yet has not been empirically

examined in NPA research (for exceptions, see Kotabe and Swan 1995; Rindfleisch and

Moorman 2001; Robertson and Gatignon 1998; Sivadas and Dwyer 2000). This dissertation

takes the first step to empirically study the notion of risk in NPA.

Further, this research contributes to the development of risk typology in NPA which is

generalizable to other interfirm relationships. In strategy literature, the preponderance of

research on risk perceptions has focused on its broad-based definition. In this stream of research

(e.g., Forlani and Mullins 2000; Kohli 1989; Sitkin and Weingart 1995), risk perception is

viewed as a global concept involving all sorts of risk characteristics. The primary problem of

this perspective is that it overlooks specific dimensions of risk perceptions that may contribute

differently at different situations. As Kohli (1989, p. 60) noted, “the measurement of perceived

risk may have been too global to capture its moderating effect. In future studies, it may be useful

to … ask more directly about the specific nature of the uncertainty faced by the members.”

Recently, some strategy researchers have suggested an alternative perspective on risk perceptions

(e.g., Das and Teng 1996). For example, Das and Teng (1996), in concert with Kohli’s (1989)

assertion, propose that risk perceptions should consist of two dimensions, i.e., performance and

relational, and suggest that these two dimensions are interwoven with one another in strategic

alliances. I contend that risk that appears in NPA should involve more than two dimensions. On

the basis of my in-depth interviews with senior managers and my comprehensive review of

literatures (e.g., management, decision science, psychology, and consumer behavior), a set of

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risk types including default risk (performance and implementation risk), knowledge-leaking risk,

relational risk, and reputation risk is developed. It is believed that the risk typology can enhance

our understanding of risk in interfirm research.

In addition, some researchers have called for examining the impact of contracts on

performance (e.g., Lusch and Brown 1996). This research building upon previous studies on

contracting issues (e.g., Cannon et al. 2000) found that both relational attributes and explicitness

(one form of legal attributes) have strong and positive effects on new product success.

Practical Contributions

Any organizational decision must involve some kind of risk, which is likely to be

perceived by a decision-maker. Risk is a concept as important as other constructs such as

resources and capabilities. Knowing how to reduce risk can help firms create and maintain

sustainable competitive advantage (Chatterjee et al. 2003). As noted by Wang, Barney, and

Reuer (2003, p. 58), “risk management should be incorporated into the company’s strategic

planning.” Thus, it is believed that the examination of perceived risk is particularly relevant to

practitioners, academia, and researchers. My focus is on perceived risk rather than objective risk

suggested elsewhere in finance or decision science literature (for detailed discussion, see Das and

Teng 1996). Although objective risk, such as the measure of return variance and excessive

returns, provides insights on an estimation of a decision outcome, it does not explain how

decision behavior, such as evaluation of alternatives, is made (Pablo et al. 1996). I believe that

understanding risk perception is important since it dictates how managers process and interpret

information (Tyler and Steensma 1998) and thereby shape their decision-making behavior (e.g.,

Kohli 1989; Sitkin and Weingart 1995).

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In addition, this study focuses particularly on alliances, since using this form of interfirm

relationship to develop new products is increasingly popular. Alliances provides a means for a

firm to access resources they lack and also facilitate them to achieve various objectives

(Varadarajan and Jayachandran 1999). For example, Hwelett-Packard and Microsoft have

teamed up to create a new modular communications station (CRN 2002). Likewise, Nokia and

Trend Micro formed an alliance to produce a corporate e-mail security appliance (Network

World 2002). However, not all alliance activities are successful. Results indicate that about 70

percent alliances fail or terminate prematurely (c.f., Sivadas and Dwyer 2000). Such high failure

rates may be due to the fact that alliances are “inherent instability arising from uncertainty

regarding a partner’s future behavior and the absence of a high authority to ensure compliance”

(Parkhe 1993, p. 794). Such “inherent instability arising from uncertainty” is the tenet of risk

perceptions.

My emphasis on NPAs is crucial to managers because different types of risk perceptions

can arise out of concerns about knowledge sharing between partners, cultural differences, among

others. Specifically, in a NPA, partners may have to integrate their own technology to create a

product novel to the marketplace. However, as reflected in my field interviews, the alliance

firms may feel uncertain whether the technology provided by their alliance partners can perform

as expected. Further, developing new products jointly with an alliance partner involves sharing

of knowledge. In such cases firms may fear that their proprietary knowledge maybe disclosed to

their partners, who can act opportunistically. In other situations, the status of a firm’s alliance

partner may also cause risk to the firm. Nike is a case in point. Nike has been accused of

partnering with factories that employ child labors. Having alliance relationships with such

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factories will definitely hurt Nike’s brand image, especially when the factories are not well

accepted by Nike’s stakeholders resulting in social disapproval or rejection (Arminas 2001).

As a practitioner, it is therefore important to understand the nature of risk and its various

dimensions in NPAs. Risk varies, so does the use of contracts (e.g., soft vs. hard) and

governance modes (e.g., joint ventures vs. partnerships). In Nike’s case, in order to reduce the

risk in association with sweatshops, using an explicit contract may be a means to deal with such

situation. In short, a practitioner should be aware of what types of risk can arise and what means

s/he can use in order to mitigate different risk types that s/he encounters in collaborating with

other organizations and what kind of outcomes (i.e., new product success) can be expected by

using a specific combination of governance attributes.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

Limitations

This research, similar to extant organizational research (e.g., Rindfleisch and Moorman

2001), employs a key informant approach (Campbell 1955). Although using a single informant

to represent a firm has long been debatable (Phillips 1981), it is believed that this approach is

valid and appropriate for the current research. Campbell (1955) alludes to that if a key informant

is knowledgeable about the subject in questions and is carefully identified, using a key informant

should be appropriate. In fact, results of validity checks on key informants in terms of their

levels of influence on and involvement in new product alliances and their positions and work

experience suggest that these informants “are surprisingly robust” (Griffin 1993, p. 120).

Another potential limitation of the study is that it is cross-sectional and, thus, cannot

capture the dynamic aspects of a firm’s decision process. This study only examines the direct

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effect of perceived risk on governance attributes. However, how different types of risk

perceptions arise or are structured are not considered in this study.

Further, the overall effect size in terms of variance explained by risk perceptions on

governance attributes is relatively small. Except default risk, none of the effects of risk

perceptions on relational attributes was found significant. Since this research is the first study to

develop the typology of risk perceptions in interfirm relationships and empirically test its impact

on governance attributes, it is not surprising that the hypothetical relationships are not

established. Insufficient empirical support for the hypotheses may be the primary constraint or

limitation of the study.

The final limitation goes to measurement issues. Insignificant findings may be due to the

fact that risk measures are new and yet unable to capture the domain comprehensively. Further,

risk measures are problematic and researchers have not had consensus even on the

conceptualization of and the definition of risk For example, in Kohli’s (1989) study, perceived

risk is measured by the negative outcomes and the level of importance of a decision. The

product term of negative consequences and importance of a decision reflect perceived risk.

Similarly, relational attributes were found to underline various domains in past research and the

conceptualization varies from studies to studies (e.g., Cannon, Gundlach, and Archol 2000;

Heide and John 1992; Lusch and Brown 1996). Organizational distance is another construct that

needs further examination. None of the moderating effects was found in this research. The

major reason may be due to the fact that the domain of the construct has not been captured.

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Future Research Directions

This dissertation takes an important step to develop the typology of risk perceptions in

interfirm relationships. Future research is encouraged to use the typology to investigate its

impact on other aspects in interfirm settings. For instance, risk perceptions may influence

relationship quality, entry mode decisions, partner selections, among others. One type of risk

perceptions may be more salient than others depending upon situations or research contexts. As

such, it may be interesting to explore a particular type of risk perceptions in a specific setting.

One of the significant findings of this study is that governance attributes are positively

related to new product success. Future research may follow this line to develop a list of

governance attributes and examine which attributes are critical to the success of new product

development and other performance outcomes. In addition, since the mediating role of relational

attributes was not established in this study, it may suggest that the effects of perceived risk types

on new product success may be moderated instead of mediated by relational attributes. Although

my field interviews suggested that relational attributes played a mediating role, future studies

may look into the moderating role of relational attributes on performance (Brown et al. 2000).

Finally, as Gulati (1995) suggests, international alliances are different from domestic

alliances. Future studies may make an effort to investigate into international alliance settings

which may provide results different than this research.

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APPENDICE

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APPENDIX A

Summary of Indepth Interviews

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Interview #1 Date: 2/12/2003 Time: 11:00am – 12:00 noon Location: Penn State (telephone interview) Informant: Mr. K, Senior Manager of Engineering Department The informant is a manager in a large software company on the east coast. Forming alliances to produce new products is not very common within the software industry but is very common between industries. Horizontal alliances are hardly found because new programs developed by a firm can be easily appropriated by its partners. “When we want to have the new platforms developed by other firms, the only way our company does is through acquisition. There are many small firms which are very smart. We will just buy the whole firms if we want to have their new platforms,” the senior manager said. Vertical alliances are more common in the software industry. The company works with its customers to develop a platform which is unique to them. The company normally owns the technology while its customers own the final products. The company does not have any particular concerns when working with its customers to develop new products. However, developing new products with other software firms is unlikely to happen in this company. Knowledge leaking is a real concern. Besides, small software firms may steal away customers from the company if they know which customers the company is going to sell to. Profits from selling a new platform to a customer are not much but the money from providing maintenance services to the customer is significant. That is the major reason the company does not ally with other software companies to develop new products.

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Interview #2

First telephone interview – Date: 2/12/2003 Time: 12:40pm – 1:15pm Second telephone interview – Date: 2/17/2003 Time: 11:00am – 11:20am Informant: Ms. Y, Product Development Manager The informant is a product development manager of a business equipment manufacturer in Oregon. Developing new products with alliance partners happens fairly often in this firm. Partners are difficult to identify, however. Past experiences with the company’s partners are critical. The company only chose to develop new products with other firms whom it had extensive business relationships. “We don’t have to worry about their technology. We know how well they do,” Ms. Y said.

The informant said that the contents of contracts the company had been used to manage its partners varied from one firm to another. It is difficult to say which kind of contracts is more effective. It depends upon the partners. If the partners are new to the firm, more comprehensive contracts will be employed. Meanwhile, the most important thing in a contract is to clearly define the ownership of the new product in terms of its patent, technology, and brand. There are many situations in which the firm owns a new product’s technology while its partner owns the patent.

The success of a new product really relies on a partner’s cooperation. The primary

concern of the informant in any of the alliance activities she has come across is whether a partner can implement as expected. “Formal and frequent meetings are necessary to keep control over our partner … meeting frequently is the only way to ensure success,” she concluded.

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Interview #3

Date: February 14, 2003 Time: 2:00pm – 3:00pm Location: Washington State Informant: Mr. T, President of a computer chip company The third interview is with a small computer chip company. The company has been in business since 1988. Forming alliances to product new products is fairly often in this company. Some of its alliance partners are large firms including Sony, Xerox, Konica, and HP. The company chose to ally with large firms as Mr. T believed that it was the most effective way to create a right product to the market. Depending upon the needs of its alliance partners, the company will customize solutions for them. It normally takes the company several months to develop a new chip with its alliance partner. Occasionally, the company will partner with small firms since it is more efficient and a lot easier to work with than large firms. However, it is more profitable and less risky to ally with large companies to create new products. Working with big names such as Sony can enhance the company’s self image.

Risks do exist when developing new products with other firms. When the company is looking for an alliance partner, it tries not to disclose its new technology to its prospective partners. The only thing the company does is to convince its prospective alliance partners how well the new technology can help improve their products’ speed, efficiency, and an overall performance. Also, the company will develop an algorithm which Mr. T would believe having a high market potential and then search alliance partners. A new algorithm developed by the company is always unique to the market and is under protections by patents. The company will also ask its alliance partners to sign a non-disclosure agreement (NDA) to prevent its new technology from being stolen by the partners.

The company has come across with a situation that its alliance partners had an intention

to steal its technology. Forming alliances must really have to be careful. Before the company is engaged with its partner, a comprehensive contract is needed. The typical length of a contract is 25 pages which details all possible contingencies.

Further, creating new products with other firms is a very common business practice in

this industry. However, the informant estimated that at least nine out of ten alliance relationships fail. There are two major reasons causing alliance failure. First, according to the informant, the new products developed with his partners must be the first to the market. If the new products are not the pioneer but the second to the marketplace, the company will lose its competitive advantage and there is no point to keep the alliance partners. Second, uncertainties in technological environments are a real concern. When the technology provided by a partner is no longer needed by customers, alliance relationships are dissolved.

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Interview #4

Date: 2/20/2003 Time: 10:30am – 12:15pm Location: Washington State Interviewee: Mr. C, a vice president of an investment company, a business consultant, and a chief operation officer of a manufacturing company According to the informant, alliance activities usually start when firms recognize that they need external resources and capabilities to help finance or materialize their new product ideas. Many firms are very entrepreneurial. They are innovative but they lack marketing sense. These companies can invest all their personal savings to develop a product which does not have a market. Thus, alliances are formed after a firm has gone through the following three stages: (1) recognizing it hurts, (2) having an urge to fix a problem, and (3) willing and able to buy in other firm’s resources and capabilities. Many firms do not recognize that the market is not large enough for their new product ideas. They just believe what they believed. Many of these firms are technology-based and do not have any marketing knowledge. As a result, the primary thing the company does is to educate and re-educate its prospective partners that knowing their customers’ needs and their customers’ customers’ needs is critical to the success of new products.

Building a trustful relationship with a prospective partner is important to the success of new product alliances. Without trust, a prospective partner will never share its new product ideas or technologies with the company. Many partner firms are technological-based and always afraid that their new “babies” (i.e., new ideas) will be stolen. In order to gain a trust from a prospective partner, as mentioned earlier, the prospective partner must recognize it hurts, want to fix it, and be willing and able to search help.

Matching if an alternative means to increase the chance of alliance and new product

success. Matching works out in a way similar to an arranged marriage. It means to collaborate two companies’ core competencies to develop new products. The informant will first identify two firms on the basis of his experiences with each of them and then arrange a meeting for these two firms to meet with each other. Sometimes, if the informant sees a high potential in an alliance venture, an investment will be made in it. An example given by the informant is that his firm has invested in an alliance venture which is located in Xiamen, China. It is his first project in China.

There are various concerns and uncertainties in an alliance venture. In order to overcome

those concerns, the informant said that he would design a contract that could allow him to make changes over time.

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APPENDIX B

Prenotification Letters and Cover Letter for Pilot Study

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Date: Dear:

Here in the Department of Marketing at Washington State University we are conducting a

study on New Product Development (NPD) in high tech settings. We have identified your firm as one who might be interested and willing to participate in this important study.

The goal of the study is to understand the business practices between NPD alliance

partners. At some time, a company like yours may have joined with another firm(s) to develop a new product(s). Forming alliances to create new products not only provides access to valuable resources, but also reduces costs. However, reports indicate that many alliance activities fail. Through this study, we are attempting to find out the concerns of the firms in participating into NPD alliances and how they handle those concerns. In order to get a comprehensive picture, we will contact as many firms as possible in North America that have NPD alliance activities.

A few days from now you will receive in the mail a request to fill out a brief questionnaire for an important research project. I am writing in advance because we have found many executives like to know ahead of time that they will be contacted.

We expect the findings of this research to be extremely useful to managers, and therefore we would like to make them available to you. We will provide a space in the questionnaire for you to indicate whether or not you would like to receive an executive summary of the results. In addition, should you decide to help us by completing the survey, I want to assure you of complete confidentiality. Our concern is with information aggregated over a large number of firms, not with any individual firm or manager. Neither the identity of the firms nor managers participating in the study will be disclosed in any form at any point. We will use the information in summary form only.

Thank you for your time. We very much hope that you agree with us about the potential value of this project and that you will help us with it by completing the survey. Sincerely, Jean L. Johnson Associate Professor of Marketing

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Date Dear:

Recently we contacted you by phone about our research project and that time you kindly agreed to participate. A few days ago we sent you a letter further explaining the study and letting you know that the questionnaire would soon be sent to you. As you may recall, our project involves the business practices between new product development (NDP) alliance partners. By filling out and mailing in the questionnaire included in this packet, you can help us understand how firms can make effective NPD alliances.

We expect the findings of this research to be extremely useful to managers, and therefore we would like to make them available to you. We will provide a space in the questionnaire for you to indicate whether or not you would like to receive an executive summary of the results. In addition, should you decide to help us by completing the survey, I want to assure you of complete confidentiality. Our concern is with information aggregated over a large number of firms, not with any individual firm or manager. Neither the identity of the firms nor managers participating in the study will be disclosed in any form at any point. We will use the information in summary form only.

Thank you for your time. We very much hope that you agree with us about the potential value of this project and that you will help us with it by completing the survey. We have included a postage paid return envelope for your convenience. If you think you are not in the right position to participate in our study, please forward the enclosed questionnaire to someone you think may be appropriate. If you have questions with respect to this survey, please do not hesitate to contact me at 509-335-1877 or email johnsonjl@wsu.edu.

Sincerely, Jean L. Johnson Associate Professor of Marketing

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APPENDIX C

Original Items and Removed Items after Purification

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Original and Removed Items in Scales after Purification Construct/ Acronym Items

Explicitness

EX1 The role of each party

EX2* What intellectual property remains with each partner

EX3 The responsibilities of each party

EX4 How each party is to perform

Flexibility

FLEX1 Make changes when necessary

FLEX2 Make adjustments in the ongoing relationship with the partner to cope with changing circumstances

FLEX3* Apply rules and policies loosely

FLEX4 Grant exceptions to meet special requests

Performance Risk

PERF_R1* We feel uncertain whether our partner firm can contribute to our new product development.

PERF_R2* We are worried about the adequacy of technology offered by our partner firm.

PERF_R3 Our partner firm’s technology is important to the success of our new products.

PERF_R4 We are at risk if our partner firm does not perform according to requirements.

PERF_R5 We would take a big loss if our partner firm were not reliable in delivering required technology.

PERF_R6 The chance that our new products will fail in the market is high if the partner firm does not deliver required technology.

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Implementation Risk

IMP_R1* We feel uncertain whether we can effectively coordinate with our partner in developing new products.

IMP_R2* We are afraid that we cannot get our partner firm to buy into what needs to be done in developing new products.

IMP_R3 We are worried about interruptions in our new product development plans if our partner firm does not cooperate with us.

IMP_R4 We are at risk if our partner firm does not execute and implement what is required.

IMP_R5

We are afraid that there would be serious problems if

our partner firm does not follow our instructions in this

new product development project.

IMP_R6 We are not sure we can be successful in developing this new product if our partner does not implement as required.

IMP_R7 We stand to lose our competitive advantage if our partner does not execute and implement according to requirements.

Knowledge Leaking Risk

KNOW_R1 We fear that too much proprietary knowledge may be disclosed to this partner firm.

KNOW_R2 We could suffer a loss if our new product specifications and designs are released to this partner.

KNOW_R3 It is risky for our company to disclose too much proprietary product knowledge to this partner firm.

KNOW_R4 It is important to keep our new product knowledge inside the company and away from our partner.

KNOW_R5* The chance that our proprietary knowledge will be disclosed to our partner firm is high.

KNOW_R6* Our company has a lot at stake in leveraging our product knowledge to our partner firm.

KNOW_R7* We fear that we will lose our competitive advantage if our proprietary product knowledge is disclosed to our partner firm.

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Relational Risk

REL_R1 We are afraid that this partner firm is not as dedicated as they should be to make our joint new product development work.

REL_R2* Our firm is at risk if we break up with this partner firm

and have to find a new partner/ally.

REL_R3 We worry whether our partner firm has any true sense of obligation to us and our new product development project with them.

REL_R4 It is risky for us to commit totally to this partner firm when we are not sure of their commitment to us.

REL_R5* Our firm could stand to lose a lot if our partner firm is not fully committed to their relationship with us.

REL_R6 We stand to lose if our partner firm decides to be opportunistic in their relationship with us.

REL_R7 Our firm has taken a risk in trusting and committing to the relationship with this partner.

Reputation Risk

REP_R1 If our partner firm does not have a good reputation, there is some chance our reputation will suffer also.

REP_R2* Our partner’s reputation is so strong and positive, associating with them could only increase our own reputation.

REP_R3* We stand to lose if our partner does anything at all that reflects badly on our image and good name.

REP_R4* We believe it is important to develop new products with only allies that have a strong positive image with the public.

REP_R5 Ever since we began the association with this partner, we have had to worry about the damage to our reputation.

REP_R6* Our company could end up looking bad if our partner firm did something irresponsible and in some way harmful to the public.

REP_R7 It is risky for us to develop new products with this firm because it seems that their reputation with our customers is not all that good.

Organizational Distance

DIST1 Technologies

DIST2 New product strategies

DIST3 Innovativeness

DIST4 Risk taking

DIST5 New product development processes

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New Product Success

NPS1 The overall performance of this new product alliance program has met our objectives.

NPS2 From an overall profitability standpoint, our new product alliance development program has been successful.

NPS3 Compared with our major competitors, our overall new product alliance program is far more successful.

Alliance Experience

ALLYEX1 Our company has been involved in other alliances ALLYEX2 We are experienced in managing alliance partners ALLYEX3 We are familiar with the practice of forming alliances ALLYEX4 The is our first time working with other firms to develop new products

(reverse coded) ALLYEX5 We are very new at figuring out how alliances work (reverse coded) *Items removed after purification

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APPENDIX D

Cover Letters and Questionnaire for the Main Study

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Date: Dear:

We want to take this opportunity to thank you for your interest in our research study. The study, being conducted here in the Department of Marketing at Washington State University, involves New Product Development (NPD) in high tech settings. In particular, we hope to better understand the business practices between NPD alliance partners.

More and more often, companies like yours join with other firm(s) to develop new

products. Forming alliances to create new products not only provides access to valuable resources, but also reduces costs. However, reports indicate that many alliance activities fail. Through this study, we are attempting to understand the problems that firms have in NPD alliances and how they handle those problems to make the alliances more effective. To get a comprehensive picture, we are contacting firms like yours that have NPD alliance activities.

We expect the findings of this research to be extremely useful to managers, and therefore

we would like to make them available to you. We will provide a space in the questionnaire for you to indicate whether or not you would like to receive an executive summary of the results. In addition, should you decide to help us by completing the survey, we want to assure you of complete confidentiality. Our concern is with information aggregated over a large number of firms, not with any individual firm or manager. Neither the identity of any firm nor any manager participating in the study will be disclosed in any form at any point. We will use the information in summary form only.

Thank you for your time. We very much hope that you agree with us about the potential value of this project and that you will help us with it by completing the survey. We have included a postage paid return envelope for your convenience. If you think you are not in the right position to participate in our study, please forward the enclosed questionnaire to someone you think may be appropriate. If you have questions with respect to this survey, please do not hesitate to contact Dr. Jean Johnson at 509-335-1877 or email johnsonjl@wsu.edu.

Sincerely, Sincerely, Jean L. Johnson, Ruby Lee, Associate Professor of Marketing Ph.D. Student

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New Product Development Alliances*

*Thank you for taking time to complete this questionnaire. We assure you of complete confidentiality on all of your responses.

World Class. Face to Face.

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SURVEY OF NEW PRODUCT DEVELOPMENT ALLIANCES

Thank you for participating in our study. This questionnaire should take about 10 minutes to complete. The information you provide will be used in summary form only. Neither the identity of the firms nor managers participating in the study will be disclosed in any form at any point. SECTION 1 – General Business Practices with New Product Development Partners/Allies At some time, your company may have joined with another firm(s) to develop a new product(s). We are interested in your business practices with your new product alliance partner. If you have more than one alliance partner, please identify one that came to your mind most recently. In which country is your partner firm located? ______________ Is your partner firm in the same industry as you are? Yes / No Is your partner firm your

1. Competitor 2. Customer 3. Supplier 4. Other; please specify _________________

Does your company have any equity in this alliance venture?

No _____ Yes _____. If yes, approximate _______ %

In dealing with this partner, your contract or agreement precisely defines (please circle your response) ……..

Your contract or agreement with the partner firm allows both of your company and your partner to (please circle your response) ……

Strongly Disagree Strongly

Agree the role of each party. 1 2 3 4 5 6 7 what intellectual property remains with each partner. 1 2 3 4 5 6 7 the responsibilities of each party. 1 2 3 4 5 6 7 how each party is to perform. 1 2 3 4 5 6 7 the legal remedies for failure to perform. 1 2 3 4 5 6 7 what will happen in the case of events occurring that were not planned. 1 2 3 4 5 6 7 how disagreements will be resolved. 1 2 3 4 5 6 7 the time of response. 1 2 3 4 5 6 7

Strongly Disagree Strongly

Agree make changes when necessary. 1 2 3 4 5 6 7 make adjustments in the ongoing relationship with the partner to cope with changing circumstances. 1 2 3 4 5 6 7

apply rules and policies loosely. 1 2 3 4 5 6 7

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Strongly Disagree Strongly

Agree grant exceptions to meet special requests. 1 2 3 4 5 6 7 work together to be successful. 1 2 3 4 5 6 7 access information each of us need. 1 2 3 4 5 6 7 create mutual benefits. 1 2 3 4 5 6 7 Please circle a number to indicate your response on each of the following statements.

Strongly Disagree Strongly

AgreeOur technology development is enhanced because we are always reworking and reviewing specifications with our partner firm. 1 2 3 4 5 6 7

Working with this partner firm enhances our capacity to learn. 1 2 3 4 5 6 7

We are able to deploy the skills and knowledge that we learn from this partner firm in developing new products. 1 2 3 4 5 6 7

Through the association with this partner/ally, we learn better ways to develop new products. 1 2 3 4 5 6 7

In the process of this joint product development, we have absorbed significant amounts of knowledge from our partner. 1 2 3 4 5 6 7

Our ability to advance and upgrade our technologies has greatly increased because of what we have learned in this joint new product development. 1 2 3 4 5 6 7

We look for ways to internalize and retain skills and capabilities from our partners. 1 2 3 4 5 6 7

We actively engage with our partner in order to understand and assimilate the knowledge to develop new products. 1 2 3 4 5 6 7

The joint new product activities improved as we learned with our partner in the product development process. 1 2 3 4 5 6 7

Together, we and our partner firm have made huge strides in our understanding of the technologies involved in this new product. 1 2 3 4 5 6 7

Please use the following scale and write down your response on the space provided.

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree

Our relationship with this partner is a big factor in achieving our strategic objectives. Our firm’s long-term product development strategy depends on maintaining a good, healthy

relationship with this partner. A strong cooperative relationship must be maintained between our company and the ally for

us to remain competitive in our industry. When developing our firm’s new product development strategy, we depend on the

participation of our partner/ally. We do not think about our own firm’s long-term strategy when we make plans with our

partner. If our partner went out of business, our firm would immediately have to change our

competitive strategy.

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To what extent is your company different from or similar to your partner on the following? Please circle your response.

Very similar to partner firm

Very different from partner

firm Company culture 1 2 3 4 5 6 7 Goals regarding growth 1 2 3 4 5 6 7 Operating philosophies 1 2 3 4 5 6 7 Organizational structure 1 2 3 4 5 6 7 Management style 1 2 3 4 5 6 7 Resources 1 2 3 4 5 6 7 Technologies 1 2 3 4 5 6 7 New product strategies 1 2 3 4 5 6 7 Innovativeness 1 2 3 4 5 6 7 Risk taking 1 2 3 4 5 6 7 New product development processes 1 2 3 4 5 6 7

Please be sure to keep focusing on the partner firm you have identified. Use the following scale and write down your response on the space provided.

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree We feel uncertain whether our partner firm can contribute to our new product development. We are worried about the adequacy of technology offered by our partner firm. Our partner firm’s technology is important to the success of our new products. We are at risk if our partner firm does not perform according to requirements. We would take a big loss if our partner firm were not reliable in delivering required technology. The chance that our new products will fail in the market is high if the partner firm does not

deliver required technology.

Please circle your response on each of the following statements

Strongly Disagree Strongly

Agree We feel uncertain whether we can effectively coordinate with our partner in developing new products. 1 2 3 4 5 6 7

We are afraid that we cannot get our partner firm to buy into what needs to be done in developing new products. 1 2 3 4 5 6 7

We are worried about interruptions in our new product development plans if our partner firm does not cooperate with us. 1 2 3 4 5 6 7

We are at risk if our partner firm does not execute and implement what is required. 1 2 3 4 5 6 7

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Strongly Disagree Strongly

Agree We are afraid that there would be serious problems if our partner firm does not follow our instructions in this new product development project.

1 2 3 4 5 6 7

We are not sure we can be successful in developing this new product if our partner does not implement as required. 1 2 3 4 5 6 7

We stand to lose our competitive advantage if our partner does not execute and implement according to requirements. 1 2 3 4 5 6 7

Please use the following scale and write down your response on the space provided.

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree We fear that too much proprietary knowledge may be disclosed to this partner firm. We could suffer a loss if our new product specifications and designs are released to this

partner. It is risky for our company to disclose too much proprietary product knowledge to this partner

firm. It is important to keep our new product knowledge inside the company and away from our

partner. The chance that our proprietary knowledge will be disclosed to our partner firm is high. Our company has a lot at stake in leveraging our product knowledge to our partner firm. We fear that we will lose our competitive advantage if our proprietary product knowledge is

disclosed to our partner firm. Please be sure to keep focusing on the partner firm you have identified and circle your response on each of the following statements.

Strongly Disagree Strongly

Agree We are afraid that this partner firm is not as dedicated as they should be to make our joint new product development work. 1 2 3 4 5 6 7

Our firm is at risk if we break up with this partner firm and have to find a new partner/ally. 1 2 3 4 5 6 7

We worry whether our partner firm has any true sense of obligation to us and our new product development project with them. 1 2 3 4 5 6 7

It is risky for us to commit totally to this partner firm when we are not sure of their commitment to us. 1 2 3 4 5 6 7

Our firm could stand to lose a lot if our partner firm is not fully committed to their relationship with us. 1 2 3 4 5 6 7

We stand to lose if our partner firm decides to be opportunistic in their relationship with us. 1 2 3 4 5 6 7

Our firm has taken a risk in trusting and committing to the relationship with this partner. 1 2 3 4 5 6 7

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Use the following scale and write down your response on the space provided.

Strongly Disagree 1 2 3 4 5 6 7 Strongly Agree If our partner firm does not have a good reputation, there is some chance our reputation will

suffer also. Our partner’s reputation is so strong and positive, associating with them could only increase

our own reputation. We stand to lose if our partner does anything at all that reflects badly on our image and good

name. We believe it is important to develop new products with only allies that have a strong positive

image with the public. Ever since we began the association with this partner, we have had to worry about the damage

to our reputation. Our company could end up looking bad if our partner firm did something irresponsible and in

some way harmful to the public. It is risky for us to develop new products with this firm because it seems that their reputation

with our customers is not all that good. In this section, we are interested in the outcomes brought by this alliance relationship. Please circle your response.

Strongly Disagree Strongly

Agree The alliance is characterized by a strong and harmonious relationship between the alliance partners. 1 2 3 4 5 6 7

We have achieved our primary objective(s) in forming this alliance. 1 2 3 4 5 6 7 We have been successful in learning some critical skill(s) or capabilities from this alliance partner. 1 2 3 4 5 6 7

The overall assessment of this alliance is satisfactory. 1 2 3 4 5 6 7 The overall performance of this new product alliance program has met our objectives. 1 2 3 4 5 6 7

From an overall profitability standpoint, our new product alliance development program has been successful. 1 2 3 4 5 6 7

Compared with our major competitors, our overall new product alliance program is far more successful. 1 2 3 4 5 6 7

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The following questions relate to your firm’s alliance experiences. Please circle your response. Strongly

Disagree Strongly

Agree Our company has been involved in other alliances. 1 2 3 4 5 6 7 We are experienced in managing alliance partners. 1 2 3 4 5 6 7 We are familiar with the practice of forming alliances. 1 2 3 4 5 6 7 This is our first time working with other firms to develop new products. 1 2 3 4 5 6 7 We are very new at figuring out how alliances work. 1 2 3 4 5 6 7

In terms of your firm’s external environment …….

Strongly Disagree Strongly

Agree The technology in our industry is changing rapidly. 1 2 3 4 5 6 7 It is very difficult to forecast where the technology in this industry will be in the next five years. 1 2 3 4 5 6 7

Rapid technology changes in our industry necessities frequent product modifications. 1 2 3 4 5 6 7

Technological developments in our industry are frequent. 1 2 3 4 5 6 7 Technological changes in our industry provide major opportunities. 1 2 3 4 5 6 7 SECTION 2 – Facts about Your Company and Your Partner Firm How long has your partner firm been in business? ____________ years What is the approximate size of your partner firm?

_____________ (in number of employees) _____________ (last year’s sales volume)

How many years have you been doing business with this partner? ________ years How did you know this partner? Please check the appropriate box.

□ We were introduced to this partner through another firm (e.g., consultancy). □ We identified this partner by ourselves □ Other, please specify _______________________

With regard to the brand of the new product, what kind of arrangement has been made? Please check the appropriate box.

□ A new brand was created that was not connected or associated with either partners. □ The new product’s brand was associated with our firm. □ The new product’s brand was associated with the partner firm. □ Other; please specify ________________

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Who owns the new product brand? Please check the appropriate box.

□ Both partners jointly □ Our firm

□ Partner firm □ Other; please specify ________________

Who owns the technology? Please check the appropriate box.

□ Both partners jointly □ Our firm

□ Partner firm □ Other; please specify ________________

Who will be responsible for introducing and marketing this new product? Please check the appropriate box.

□ Both partners jointly □ Our firm □ Partner firm □ Other; please specify ________________

How long have you been with this company? __________ years

How much involvement do you have in this new product development alliance?

Not at all 1 2 3 4 5 6 7 Very much How much influence do you think you have in this new product development alliance?

Not at all 1 2 3 4 5 6 7 Very much

Thank you for your time and we appreciate your effort.

If you want to get a report summarizing the findings of this research, please provide us with your name and address OR attach a business card with your response. Company Name ___________________________________________ Address __________________________________________________ Telephone ________________________________________________ Fax ______________________________________________________ E-mail address _____________________________________________ Respondent Name and Position ________________________________

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