August 2006

Determinants of Success

in R&D Alliances

ADVANCED TE CHNOLOGY P OLOGY P ROGRAM

NISTIR 7323

National Institute of Standards and Technology • Technology Administration • U.S. Department of Commerce

Jeffrey H. Dyer

Benjamin C. Powell

Mariko Sakakibara

Andrew J. Wang

Trust

Communication

Experience

Structu

re

Resources

Go ev nr na

t o M ec

vi t a oi n

Goals

Capabilit

ei

s
About ATP’s Economic Assessment Office

The Advanced Technology Program (ATP) is a partnership between government and private 

industry to conduct high-risk research to develop enabling technologies that promise significant 

commercial payoffs and widespread benefits for the economy.

Since the inception of ATP in 1990, ATP’s Economic Assessment Office (EAO) has performed 

rigorous and multifaceted evaluations to assess the impact of the program and estimate the returns 

to the taxpayer. To evaluate whether the program is meeting its stated objectives, EAO employs 

statistical analyses and other methodological approaches to measure program effectiveness in 

terms of:

• Inputs (program funding and staffing necessary to carry out the ATP mission)

• Outputs (research outputs from ATP supported projects)

• Outcomes (innovation in products, processes, and services from ATP supported projects)

• Impacts (long term impacts on U.S. industry, society, and economy)

Key features of ATP’s evaluation program include:

• Business Reporting System, a unique online survey of ATP project participants, that gathers 

regular data on indicators of business progress and future economic impact of ATP projects.

• Special Surveys, including the Survey of Applicants and the Survey of Joint Ventures.

• Status Reports, mini case studies that assess ATP projects on several years after project 

completion, and rate projects on a scale of zero to four stars to represent a range of project 

outcomes.

• Benefit-cost analysis studies, which identify and quantify the private, public, and social 

returns and benefits from ATP projects

• Economic and policy studies that assess the role and impact of the program in the U.S. 

innovation system

EAO measures against ATP’s mission. The findings from ATP surveys and reports demonstrate that 

ATP is meeting its mission:

• Nine out of 10 organizations indicate that ATP funding accelerated their R&D cycle.

• The existence of a “Halo Effect.” As revealed by EAO surveys, shows that an ATP award

establishes or enhances the expected value in the eyes of potential investors.

• ATP stresses the importance of partnerships and collaborations in its projects. About 85 percent 

of project participants had collaborated with others in research on their ATP projects.

Contact ATP’s Economic Assessment Office for more information:

• On the Internet: www.atp.nist.gov/eao/eao_main.htm

• By e-mail: atp-eao@nist.gov

• By phone: 301-975-8978, Stephanie Shipp, Director, Economic Assessment Office, 

Advanced Technology Program

• By writing: Economic Assessment Office, Advanced Technology Program, National Institute 

of Standards and Technology, 100 Bureau Drive, Stop 4710, Gaithersburg, MD 20899-4710
NISTIR 7323

The Determinants of 

Success in R&D Alliances

Jeffrey H. Dyer

Marriott School of Management

Brigham Young University 

University Provo, Utah 84602

Benjamin C. Powell

Culverhouse College of Commerce and Business Administration

University of Alabama

Tuscaloosa, AL 35487

Mariko Sakakibara

Anderson School of Management

University of California, Los Angeles

Los Angeles, CA 90095

Andrew J. Wang

Advanced Technology Program

National Institute of Standards and Technology

Gaithersburg, MD 20899

August 2006

U.S. DEPARTMENT OF COMMERCE

Carlos M. Gutierrez, Secretary

TECHNOLOGY ADMINISTRATION

Robert Cresanti, Under Secretary for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

William Jeffrey, Director

ADVANCED TECHNOLOGY PROGRAM

Marc G. Stanley, Director

UNITED STATES OF AMERICA

DEPARTMENT OF COMMERCE

Abstract

This study offers an examination of the determinants of innovative success in R&D alliances.

The relative importance of a set of alliance design factors (e.g., alliance structure characteristics such as number and type of partners, and firm-level attributes such as prior alliance experience and existing R&D capabilities) and alliance management factors (e.g., frequency of

communication among partners, effectiveness of governance arrangements). These factors are

hypothesized to influence the alliance partners’ ability to exchange knowledge and collaborate

in R&D, and thereby influence their ability to produce innovations. The study uses a survey

dataset of 397 firms involved in 142 R&D alliances that were supported by the Advanced

Technology Program. Three measures of alliance success at the firm level are utilized: a perceptual measure of overall value, patent application, and financial value realized from technology

commercialization. Alliance designers are largely successful in “optimally” choosing the structure of alliances. The empirical analysis shows that the number of alliance partners and the

presence of competitor firms have little effect on alliance outcomes. Among the alliance management factors, effective governance arrangements and frequent communication are found to

have an important and positive impact on alliance outcomes. Finally, the results show that

more ambitious projects that “reach for the stars” have more successful outcomes.

(Alliances, Innovation, Knowledge Management).

iii

Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vii

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ix

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

II. Theoretical Perspectives on R&D Alliance Success . . . . . . . . . . . . .5

Alliance Design Factors: Alliance Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Alliance Design Factors: Firm Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Alliance Management Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

III. Data and Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Analysis of Outcomes: Firm-level or Alliance-level . . . . . . . . . . . . . . . . . . . . . . . . .16

IV. Measures and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Independent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

V. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

VI. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

About the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

About the Economic Assessment Office . . . . . . . . . Inside front cover

About the Advanced Technology Program . . . . . . . Inside back cover

v
Tables

Table 1. Survey Respondents: Projects and Companies, by Year of Project 

Completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Table 2. Survey Respondents: Projects and Companies, by Technology 

Area Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Table 3. Tabulation of Outcomes Variables: Overall_Value, Patent_Application, 

Commercialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Table 4. Description of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Table 5. Summary Statistics for Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

Table 6. Correlation Matrix for Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

Table 7. Regression Results for Outcomes Variable: Overall_Value . . . . . . . . . . . . . . . . .29

Table 8. Regression Results for Outcomes Variable: Patent_Application . . . . . . . . . . . . .30

Table 9. Regression Results for Outcomes Variable: Commercialization . . . . . . . . . . . . .31
Acknowledgements

The background for this study was the earlier work by Jeffrey Dyer and Benjamin Powell

under a contract with ATP’s Economic Assessment Office (EAO). They interviewed company

participants from ATP joint venture projects in the automotive industry to assess the factors

that contribute to success in R&D collaborations. Their findings are published in the report

Determinants of Success in ATP-Funded R&D Joint Ventures: A Preliminary Analysis Based

on 18 Automobile Manufacturing Projects, GCR-00-803, National Institute of Standards and

Technology, Gaithersburg, MD, December 2001.

ATP’s Economic Assessment Office contracted with Westat, a survey research organization,

to develop and implement survey instruments to collect original data from companies that

have participated in ATP-supported projects. Andrew Wang, economist at ATP/EAO, initiated

and directed the effort to carry out a special survey of company participants in ATP joint venture projects. Jennifer O’Brien, survey methodologist at Westat, oversaw design, development,

and data collection for the Survey of ATP Joint Ventures.

Stephen Campbell, economist at ATP/EAO, provided invaluable support to this research in

constructing data, conducting analysis, and commenting on interpretation of results. Andrew

and Stephen presented analysis and findings related to this research at the American Statistical

Association meeting in August 2004. Andrew presented an early version of this study at the

International Society for New Institutional Economics meeting in October 2004. Jeffrey Dyer

presented a version of this study at the Academy of Management meeting in August 2005.

For the present study, Stephanie Shipp, Director, EAO, provided comments and feedback

on several drafts of the report. Brian Belanger, former ATP Deputy Director, and Lorel Wisniewski, present ATP Deputy Director, reviewed the final draft and provided comments. Dennis Leber, Statistician, NIST, also provided comments on the final draft. Robert Sienkiewicz,

Supervisor, EAO, and Janet Brumby, Business Liaison Specialist, EAO, oversaw the publication

of the report. Joann Brooks designed the cover.

vii

Executive Summary

Innovation is increasingly important to competition in technology intensive industries. In seeking innovation, individual firms often find that external knowledge and research partners are

critical to success. Innovation is often the result of synthesizing or “bridging” ideas from different knowledge domains. Therefore, firms increasingly enter into research and development

(R&D) alliances with other firms to combine complementary knowledge in the pursuit of new

innovative technologies. Indeed, many governments around the world support cooperative

research activities in the expectation that collaborating firms will successfully develop new

technologies that will improve economic competitiveness.

Prior research suggests that alliance success is difficult to achieve, with failure rates around

50 percent. R&D alliances are even more challenging than other types of alliances, because

knowledge sharing is characterized by inherently problematic issues that require careful governance, and because the innovation process itself is characterized by a high degree of uncertainty, which makes success extremely difficult to predict.

The perspective adopted in this study is that success in an R&D alliance is more likely to

occur when the firms initiating the alliance: (a) partner with other firms that possess relevant

complementary knowledge, and (b) effectively share and combine that complementary knowledge. Having the requisite knowledge within the alliance team and having the necessary

processes in place to exchange that knowledge is critical to producing technical innovations.

Effective “knowledge management” is therefore a fundamental driver of success in R&D

alliances. Alliance success depends broadly on three categories of factors that influence the

exchange of complementary knowledge: alliance “design” decisions made during the alliance

formation stage; alliance “management” decisions made during the alliance execution stage;

and “luck”, that is, the playing out of random events under uncertainty. During the alliance

formation stage, the designers of the alliance seek to identify the “win-win” opportunity for

potential alliance members and recruit potential members based on a cost-benefit analysis of

what each potential member might contribute to the alliance objective.

To examine how alliance-design and alliance-management factors influence R&D alliance

success, this study uses a unique survey dataset that includes 397 firms in 142 R&D alliances.

These R&D alliances received funding from the Advanced Technology Program, a U.S. federal

government program that supports innovation and early-stage technology in U.S. industry. In

the analysis, multiple firm-level measures of performance outcomes are utilized, including a

perceptual measure (subjective assessment of overall value to the firm), a patent measure

(patent applications filed by the firm), and a financial measure (revenues or cost savings realized by the firm from commercialization of technology). For 121 out of the 142 R&D

i x
alliances represented in the dataset, survey response information is available from more than

one partner in the alliance.

The empirical analysis produces the following conclusions. Alliance designers are largely

successful in choosing an “optimal” structure for the alliance in terms of the number and type

of alliance partners. The number of alliance partners has a weakly negative effect on patent

application, but no effect on overall value or commercialization. The presence of competitors

in an alliance has no effect on any of the three outcomes measures.

Effective contractual provisions and governance arrangements for alliance management

have a positive effect on alliance success in terms of delivering overall value and generating

patent applications. Goodwill trust among alliance partners has a weakly negative effect on

overall value, and a negative effect on patent application. These results suggest that successful

alliances do not simply depend on goodwill trust, but develop contractual-based trust based on

effective contractual provisions and governance arrangements. In other words, in alliance management, one would do well to “Trust, but verify.”

Frequency of communication has a strong positive effect on all three measures of R&D

alliance performance, including the perceptual measure of overall value, the patent application

measure, and the financial value from commercialization measure. R&D alliance managers can

increase the likelihood of alliance success by establishing routines that encourage frequent

communication. Frequent communication facilitates the knowledge sharing and coordination

that is critical to alliance success.

Finally, “reaching for the stars” is a strong predictor of alliance success. More ambitious

projects with farther reaching goals demonstrate greater success on all three measures of R&D

alliance performance. More ambitious projects have intrinsically greater potential value and

potential impact. In addition, more ambitious projects are likely to mobilize greater commitment and effort on the part of both the partner companies and the individual participants.

The research carried out in this study suggests that there are fundamental differences

between R&D alliances and other types of alliances, and consequently, the factors that influence success may be quite different. Some factors may be determinants of success in some types

of alliances, such as manufacturing or marketing alliances, but not in other types of alliances,

such as R&D alliances where creativity and innovation are central. Though many research

studies combine R&D alliances and other types of alliances as a single subject for analysis, this

study concludes that R&D alliances should be treated as a separate and independent type of

collaborative activity.

x The Determinants of Success in R&D Alliances 
Part I

Introduction

Innovation is an increasingly important dimension of competition in technology intensive

industries. In seeking innovation, individual firms often find that external knowledge and

research partners are critical to success. Innovation is often the result of synthesizing or

“bridging” ideas from different knowledge domains (Hargadon and Sutton, 2000; Burt, 2004).

Therefore, firms increasingly enter into research and development (R&D) alliances with other

firms to combine complementary knowledge in the pursuit of new innovative technologies.

Indeed, many governments around the world support cooperative research activities in the

expectation that collaborating firms will successfully develop new technologies that will

improve economic competitiveness.

Unfortunately, while R&D alliances have become a popular mechanism to pursue innovation, prior research suggests that alliances have failure rates of around 50 percent (Kogut,

1989; Alliance Analyst, 1998; Kale et al, 2002). R&D alliances are presumably even more

challenging than other types of alliances because collaborators must simultaneously share

knowledge while trying to prevent undesired knowledge spillovers (Hamel, 1991; Oxley and

Sampson, 2004). The free exchange of knowledge by partners is critical for ideas and knowledge to be recombined in such a way as to produce innovations (Hargadon and Sutton, 2000).

However, various factors—attributes of partners, governance arrangements, communication

processes—may inhibit the exchange of complementary knowledge in an alliance, thereby

decreasing the probability of innovation. Moreover, even when R&D alliance partners are able

to simulate the free flow of knowledge that can occur within a firm, the innovation process

itself is characterized by a high degree of uncertainty, which makes success extremely difficult

to predict.

Understanding how firms can enhance the probability of success in R&D alliances is an

important question for both firms and governments. Researching the determinants of knowledge sharing and innovative success in R&D alliances is especially challenging since innovation

processes are inherently uncertain. Despite these challenges, numerous scholars have examined

a variety of factors that may influence the performance outcomes of R&D alliances.

This study seeks to provide a more complete understanding of the factors that influence

success in R&D alliances. The perspective adopted in this study is that success in an R&D

alliance is more likely to occur when the firms initiating the alliance: (a) partner with other

1
firms that possess relevant complementary knowledge, and (b) effectively share and combine

that complementary knowledge. Having the requisite knowledge within the alliance team and

having the necessary processes in place to exchange that knowledge is critical to producing

technical innovations. Effective “knowledge management” is therefore a fundamental driver of

success in R&D alliances. Alliance success depends broadly on three categories of factors that

influence the exchange of complementary knowledge: alliance “design” decisions made during

the alliance formation stage; alliance “management” decisions made during the alliance execution stage; and “luck”, that is, the playing out of random events under uncertainty. During the

alliance formation stage, the designers of the alliance seek to identify the “win-win” opportunity for potential alliance members and recruit potential members based on a cost-benefit

analysis of what each potential member might contribute to the alliance objective. In this costbenefit analysis, if alliance designers properly optimize their decision, then each member’s contribution to the alliance is balanced against the burden that it imposes on the alliance. As such,

in an ex post analysis, the analyst would not expect to see differential results in actual performance outcomes related to alliance design characteristics.

In real life, however, not all ex ante factors may have been fully optimized. Not all alliance

design decisions and alliance management decisions results are necessarily fully “optimal” in

an equilibrium sense. For example, if ex ante design decisions on the number of firms to

include in an alliance, or whether to include competitor firms, were optimal, then we would

not expect to see these alliance characteristics to be correlated with alliance outcomes in an ex

post analysis. But such alliance design and management decisions are in fact made by actors in

a context of imperfect information, uncertainty, and learning, so we can expect that these decisions are less than perfect, and therefore, we assess whether some decisions turn out to be “less

optimal” than others in an ex post analysis.

To examine how alliance-design and alliance-management factors influence R&D alliance

success, we use a unique survey dataset that includes 397 firms in 142 R&D alliances. These

R&D alliances received funding from the Advanced Technology Program, a U.S. federal government program that supports innovation and early-stage technology in U.S. industry. The

data were collected by the Advanced Technology Program.

In our analysis of the determinants of R&D alliance success, we go beyond prior studies in

several ways. First, we focus exclusively on R&D alliances. Many prior studies include all

types of alliances (e.g., marketing, manufacturing, R&D, etc.) rather than focusing specifically

on R&D alliances. R&D alliances are different from other types of alliances, especially in their

focus on knowledge sharing and innovation, so analyses that pool different types of alliances

are difficult to interpret for R&D alliances. Second, we develop our analysis by drawing upon

multiple theoretical perspectives from economics, organization theory, and strategic management. Prior studies of R&D alliances focus on a narrow set of factors that may influence R&D

alliance success, typically relying on a particular theoretical lens rather than drawing on a

broad set of theoretical perspectives. While using a single theoretical perspective has the

advantage of allowing for deeper theoretical insight, it has the disadvantage of excluding many

2 The Determinants of Success in R&D Alliances 
factors that may be important for empirical understanding. Third, we employ multiple measures of alliance success. Most prior studies have inadequate measures of R&D alliance success.

Firm-level measures such as firm profitability or stock price are only remotely related to R&D

alliance performance outcomes. Alliance survival is a poor measure of R&D alliance success

since most R&D alliances are designed to last for a limited time period. Survey-based perceptual measures capture the degree to which an alliance has achieved broad and diverse goals,

but may also be subject to a variety of response biases. Weaknesses associated with each type

of performance measure suggest that a study of performance outcomes of R&D alliances

would ideally include a combination of objective and subjective measures. We utilize multiple

measures of performance outcomes at the firm-level, including a perceptual measure (subjective assessment of overall value to the firm), a patent measure (patent applications filed by the

firm), and a financial measure (revenues or cost savings realized by the firm from commercialization of technology). Finally, we employ firm-specific measures of alliance outcomes from

multiple members of an alliance. Most prior studies do not have alliance-wide measures of performance outcomes, that is, they do not have data from different members in the alliance. Different firms in an alliance have different objectives, and benefits to each firm may vary

depending on a variety of factors. In order to better understand alliance “success” and factors

that influence success, we use data from more than one alliance partner for 121 out of the 142

R&D alliances represented in the sample. We find substantial variance within an alliance in

terms of individual partner firms’ assessment of the success of the alliance. For example, in 16

percent of the alliances, one alliance partner rated the alliance as “successful” or “very successful” in delivering value to the firm, while another alliance partner rated the alliance as “unsuccessful” or “very unsuccessful” in generating value. Although there is positive correlation in

performance outcomes among alliance partners, our analysis indicates that alliance success is

an individual firm-level phenomenon, so data gathered from only one partner cannot generalize to the “alliance” level.

We examine alliance design factors that are expected to influence alliance success. We consider alliance structure characteristics such as the number of partners, type of partners (e.g.,

presence of competitors), and geographic proximity of partners. We also consider firm-level

attributes such as the firm’s prior experience with alliances in general or with specific alliance

partners, and the firm’s existing stock of R&D knowledge and capabilities. These alliance

design factors (alliance structure characteristics and firm-level attributes) are largely established

at the time of alliance formation, and reflect the decisions made by the alliance designers.

We also examine alliance management factors that are expected to influence alliance success. Alliances that are able to establish effective governance arrangements and institute

processes that build trust are expected to be more likely to share knowledge and achieve innovation success. Alliances that facilitate communication among partners effectively are also

expected to be more likely to achieve innovation success. Alliance partner commitment and

effort devoted to the alliance project, as measured by technical personnel resources allocated,

is also expected to relate to alliance success. These alliance management factors develop during

the course of the project, that is, in the process of alliance execution.

Introduction 3
4 The Determinants of Success in R&D Alliances 

In summary, we examine the relative importance of alliance design factors and alliance

management factors in determining R&D alliance success. We also examine whether partners

in an R&D alliance realize similar, or dissimilar, benefits from participation in the alliance.

Finally, applying insights from our analyses, we explore what both firms and governments

might do to increase the likelihood of success in R&D alliances.
Part II

Theoretical Perspectives on 

R&D Alliance Success

The theoretical perspectives underpinning hypotheses tested in this study are derived from both

our review of prior literature on R&D alliance success, and from our exploratory interviews

with participants in R&D alliances that received funding support from the Advanced Technology Program (ATP) at the National Institute of Standards and Technology (NIST). We conducted semi-structured interviews that focused on the question: What are the factors that

contribute to, or inhibit, alliance success? The participants in these interviews consistently

identified factors that related to knowledge sharing in the alliance, which in turn affected success in achieving technical objectives, generating research outcomes, and commercialization of

technology (Dyer and Powell, 2001). Drawing on prior research and on these interviews, we

developed the theoretical logic and hypotheses presented in the following sections.

Alliance Design Factors: Alliance Structure

Number of Alliance Partners

In alliance design, firms that initiate an alliance aim to optimize the number of partners to

involve in the alliance. Additional partners may bring additional knowledge and resources to

the alliance, but each additional partner also brings additional transaction and coordination

costs (Gulati and Singh, 1998). Each additional partner firm must be included in negotiations

regarding the goals of the collaboration, protection of intellectual property, control and ownership of research output, how to share knowledge and collaborate in R&D, etc. Adding more

partners to an alliance may also hinder knowledge sharing by increasing the risk of unintended

knowledge leakage (Oxley and Sampson, 2004). The greater the number of partners in an

alliance, the more reluctant individual firms may be to share knowledge, fearing greater potential for unintended knowledge spillovers when more firms have access to the knowledge.

With each additional partner, the number of alliance partners increases linearly as N, but

the number of dyadic relationships increases quadratically as N(N-1)/2. With two firms there

is one relationship to manage; with three firms there are three relationships; with four firms,

six relationships, and so on. In our interviews with R&D alliance participants, many observed

5
that knowledge sharing and coordination was more difficult with more members. As one participant stated, “The more people you have, the more people you have to coordinate. It gets

unwieldy at some point.” (Dyer and Powell, 2001, p.14). Above some threshold number of

partners, transaction and coordination costs become significant, and concerns about knowledge leakage inhibit the ability of alliance partners to share knowledge, which is critical to

R&D alliance success. In the cost-benefit calculation for deciding how many partners to

include in an alliance, if alliance designers err in optimizing the number of partners to involve,

we expect that the tendency is to underestimate transaction and coordination costs. Hence, we

expect that alliances with a greater number of partners will have lower performance outcomes.

Hypothesis 1: The greater the number of R&D alliance partners, the lower the performance

outcomes of the R&D alliance.

Presence of Competitors

Prior research suggests that R&D alliances are fraught with risks because firms must simultaneously share knowledge and technology, as well as protect knowledge (Hamel, 1991; Oxley

and Sampson, 2004). Firms must find the right balance between maintaining open knowledge

exchange to further the technological goals of the alliance while also preventing unintended

leakage of knowledge. Preventing opportunism within R&D alliances is a prime concern, and

especially challenging for a number of reasons.

First, joint R&D often requires high levels of investment in complementary assets or

knowledge by the participants. When a firm performs part of a research project, the knowledge it gains may be useless unless combined with the work of partner firms with complementary knowledge. This creates potential for opportunistic behavior on the part of partner firms

that possess the complementary assets or knowledge. In effect, these knowledge assets are

“transaction-specific” assets, and in this transaction relationship there is significant potential

for opportunism (Klein et al, 1978; Williamson, 1985).

Second, R&D alliances are characterized by a high degree of uncertainty regarding both

inputs and outputs. Monitoring inputs and “effort” on the part of one’s partner is extremely

difficult. R&D alliance tasks are largely intellectual in nature and, therefore, third party monitoring is inefficient. Under these conditions, effective self monitoring (Demsetz, 1988) is

required because it is impossible to really know whether an alliance partner is truly sharing its

most relevant knowledge. In short, the high degree of uncertainty regarding inputs and outputs

provides numerous opportunities for opportunistic behavior on the part of alliance partners.

Finally, there are significant information asymmetries among partners in R&D collaborations. Each firm brings different knowledge to the table and may be reluctant to share information due to the desire to prevent unintended knowledge spillovers. Once technological

information is revealed, the receiver of knowledge has no incentive to pay for the information.

Thus, a primary challenge in R&D alliances is to figure out how to openly share knowledge

6 The Determinants of Success in R&D Alliances 
that is relevant to the alliance objectives while preventing undesirable knowledge spillovers.

These challenges to knowledge-sharing in R&D alliances are exacerbated in the case of

competitor collaborations where partners are ultimately engaged in a zero-sum game in the

marketplace (one partner’s commercial success ultimately has a negative impact on the commercial success of another partner). For example, the decision regarding the extent to which a

partner should fully collaborate (sending the most high caliber researchers, sharing proprietary

knowledge, etc.) may be characterized by a Prisoner’s Dilemma game, where despite the fact

that the two firms would be better off by jointly cooperating, both firms individually have the

incentive not to share skills and information. Hamel’s (1991) detailed examination of nine

alliances revealed that firms typically try to internalize their partner’s skills while protecting

their own. As one manager in his study observed, “[Our partner] tries to suck us dry of technology ideas they can use in their own products. Whatever they learn from us, they’ll use

against us worldwide.” (Hamel, 1991, p.87). In our interviews, an R&D alliance manager

stated, “Having direct competitors in the [alliance] definitely inhibited information sharing. I

don’t take guys to the [alliance] meetings if they talk too much; sometimes I have to say to

them ‘That’s enough. You are talking too much.’” (Dyer and Powell 2001, p.13).

Even if the outcome of research effort is not characterized as a zero-sum game, R&D

alliances with direct competitors could lead to a collective reduction of research efforts. Katz

(1986) demonstrates the possibility that when firms cooperate in cost-reducing R&D, but

compete in product markets, the firms might collaborate to conduct less R&D to lessen the

severity of competition in product markets. Branstetter and Sakakibara (2002) empirically

examine Japanese government-sponsored R&D consortia and find that the research productivity of participating firms is lower when the degree of product market competition among participants is higher.

Hypothesis 2: If an R&D alliance involves firms that are direct competitors in product markets, then the performance outcomes of the R&D alliance are lower.

Geographic Distance between Alliance Partners

Prior research shows that geographic proximity plays an important role in facilitating interaction and knowledge-sharing between collaborating firms (Saxenian, 1994; Dyer, 1996;

Almeida and Kogut, 1999). For example, Dyer (1996) finds a strong relationship between geographic proximity of automaker and supplier facilities, and the extent to which the firms

engage in face-to-face interaction. He also finds that greater face-to-face interaction between

supplier-customer engineers leads to fewer defects and higher overall product quality. Geographic distance presumably increases the cost of frequent face-to-face communication, thereby

decreasing knowledge sharing and reducing coordination effectiveness (especially when tasks

are highly interdependent).

Hypothesis 3: The greater the geographic distance between R&D alliance partners, the lower

the performance outcomes of the R&D alliance.

Theoretical Perspectives on R&D Alliance Success 7
Alliance Design Factors: Firm Attributes

General and Partner-Specific Alliance Experience

Prior research generally suggests that firms with greater partnering experience develop “relational capabilities” that enhance their ability to extract value from subsequent alliances

(Anand and Khanna, 2000; Kale et al, 2002). Partner experience can be either “general

alliance experience” or “partner-specific alliance experience.” Whereas the former refers to

experience gained from all prior alliances, the latter refers to prior alliance experience with a

specific partner. Firms that engage repeatedly in an activity are able to draw inferences from

their experiences, and store and retrieve such inferred learning for use in subsequent engagements in the activity (Levitt and March, 1988). In the alliance context, firms with substantial

experience in alliances often have dedicated personnel charged with capturing, codifying, and

communicating best practices in managing alliances. Similarly, when firms have repeated

alliances with specific partners, the partnering firms may be induced to invest in relation-specific assets that reduce transaction and coordination costs. Moreover, learning accumulated

through partner-specific experience may lead to the emergence of stable and efficient interorganizational knowledge-sharing routines (Dyer and Singh, 1998; Zollo et al, 2002).

Most studies have found results consistent with the expectation that general and partnerspecific experience lead to superior alliance performance. However, a recent study by Hoang

and Rothaermel (2005) calls into question the relationship between partner-specific experience and R&D alliance performance. In their study of R&D alliances for new drug development, they find that general partnering experience has a positive effect on performance for

small biotechnology firms, but not for their larger pharmaceutical firm partners. They suggest

that larger firms have already acquired significant experience and capabilities at alliances, so

there is little difference in capabilities of these firms. Interestingly, contrary to their expectations, they find that partner-specific experience has a weakly negative effect on successful

drug development. Hoang and Rothaermel suggest that the reason for this counterintuitive

finding may be that partners inappropriately generalize from their prior experience with that

partner—but the next drug development project is not like the last one. The logic for this

conclusion is similar to that in Haleblian and Finkelstein’s (1999) study of acquisition and

performance, which concludes that firms with a moderate amount of experience in acquisitions may be less successful in subsequent acquisitions if the new acquisition is significantly

different in nature from prior acquisitions. Another plausible reason for this result may be

that for firms pursuing innovation objectives in alliances, repeated transactions with the same

partner—while promoting efficiency—may not result in novelty. Thus, previous findings of a

positive relationship between partner-specific experience and alliance performance in general

may not hold for R&D alliances in particular. Our study offers the opportunity to examine

the effects of both general and partner-specific experience on R&D performance, using multiple performance measures.

8 The Determinants of Success in R&D Alliances 
Hypothesis 4a: The greater the general alliance experience of the firm, the better the 

performance outcomes of the R&D alliance for the firm.

Hypothesis 4b: The greater the partner-specific experience of the firm with its alliance 

partners, the better the performance outcomes of the R&D alliance for the firm.

R&D Capability

Generally, we expect that the overall R&D capability and total stock of knowledge of an

R&D organization would have a positive impact on performance outcomes of an R&D

alliance that the organization participates in. A proxy measure for an organization’s overall

R&D capability and stock of knowledge is the total number of R&D personnel at the organization. To realize benefits from participating in alliances, firms must identify, assimilate, and

commercialize useful knowledge developed through collaboration. A firm’s ability to take

advantage of externally generated knowledge—its absorptive capacity—depends upon the

stock of related knowledge accumulated by the firm (Cohen and Levinthal, 1989; 1990). Relatively few empirical studies have examined the impact of absorptive capacity on the benefits

that firms obtain from participation in alliances. Mowery et al. (1996) find that pre-alliance

technological overlap with alliance partners enhances a firm’s absorption of technological

capabilities, but a firm’s R&D intensity has no effect. We also expect that economies of scale

and scope in R&D characterize the R&D capability of a firm. We expect that “knowledge

spillovers” between personnel within a firm enhance the R&D capability of the firm. Within 

a firm, R&D personnel working on any given project are able to learn from other technical

personnel at the firm working on different projects. We hypothesize that firms with a greater

R&D capability and stock of knowledge, as proxied by total R&D employment, are able to

benefit more from R&D alliances, and therefore have better alliance performance outcomes.

Hypothesis 5: The greater the total R&D employment of the firm, the better the performance

outcomes of the R&D alliance for the firm.

Alliance Management Factors

Number of Technical Personnel

A key management decision for a firm participating in an R&D alliance is to determine the

number of technical personnel to allocate to the effort. The level of technical personnel

resources that a firm devotes to an alliance project affects both the direct R&D output and the

R&D learning benefits that the firm can expect to receive from participating in the alliance.

For the firm, the allocation of R&D personnel is firstly related to direct innovation outputs as

a result of the effort, and secondly related to R&D absorptive capacity and R&D learning

(Cohen and Levinthal, 1989).

Theoretical Perspectives on R&D Alliance Success 9
In regard to absorptive capacity, Lane and Lubatkin (1998) compare firm-level and firm

dyad-specific measures of absorptive capacity and find that the latter is better in explaining

learning outcomes from alliances. Thus, prior research indicates that alliance-specific measures

of absorptive capacity, i.e. pre-alliance technological overlap (Mowery et al, 1996), or similarity of knowledge stock and management practices (Lane and Lubatkin, 1998), accounts for

alliance benefits better than traditional firm-level measures of absorptive capacity, i.e., R&D

spending or R&D intensity.

Prior research has emphasized that gatekeeping or boundary-spanning roles are important

for absorbing external knowledge (Cohen and Levinthal, 1990). The more people that a firm

positions at the “gate”, the more receptive the firms become to external or alliance knowledge.

By involving more R&D personnel in an alliance collaboration, a firm is able to transfer more

individual and interpersonal knowledge, and is able to widen the conduit for knowledge flows

from the R&D alliance to itself. Agrawal (2006) examines the impact of total amount of time

that professors, graduate students, and research scientists work in collaboration or close communication with firms that license university inventions, and finds that as total collaboration

time increases, both the likelihood of commercialization and the degree of commercialization

success increases. Thus, we expect that firms that allocate more R&D personnel resources to

an R&D alliance effort will receive greater benefits from the alliance, both in innovation outcomes and learning outcomes.

Hypothesis 6: The greater the number of technical personnel allocated to an R&D alliance 

by the firm, the better the performance outcomes of the R&D alliance for the firm.

Frequency of Communication

Prior studies suggest that when firms collaborate on complex problems, they are more likely to

be successful if they develop processes that facilitate frequent communication (Clark and Fujimoto, 1991; Dyer, 1996). Frequent communication results in greater knowledge-sharing

between alliance partners, which increases the likelihood of success in collaborative efforts.

Mohr and Nevin (1990) define communication as the process by which partner firms in an

alliance transmit information, coordinate activities, prompt participatory decision-making, and

encourage commitment and loyalty to the alliance. Some research suggests that partners that

develop relation-specific know-how through frequent communication are less likely to misunderstand or misinterpret information (Nishiguchi, 1994; Clark and Fujimoto, 1991). More

efficient communication and coordination should result in better performance. With greater

complexity of collaborative tasks, direct face-to-face communication and work interaction is

believed to be of greater importance relative to other forms of communication, such as email,

telephone, or even video conference. Face-to-face interaction is described as having high

knowledge carrying capacity because it presents immediate feedback opportunities and makes

use of both visual and audio modes of communication (Daft and Lengl, 1986; Dyer, 1996).

1 0 The Determinants of Success in R&D Alliances 
Interestingly, the relationship between communication and performance has rarely been

empirically tested in R&D alliances, and, based upon our review of the R&D alliance literature, the relationship between face-to-face interaction and performance has never been tested.

Our hypothesis is that more frequent communication and interaction will result in greater

knowledge sharing and better performance outcomes in R&D alliances.

Hypothesis 7a: The more frequent the communication among R&D alliance partner 

personnel, the better the performance outcomes of the R&D alliance.

Hypothesis 7b: The more frequent the face-to-face interaction among R&D alliance partner

personnel, the better the performance outcomes of the R&D alliance.

Governance Arrangements

Governance arrangements play an important role in creating a transaction environment

where alliance partners can cooperate and share knowledge or resources with assurance that

they will also share equitably in the benefits from the collaboration. Prior studies on alliance

governance and alliance success have examined the conditions under which collaborating

firms prefer contractual versus equity ownership governance arrangements (Oxley, 1997;

Oxley and Sampson, 2004; Sampson, 2004). These studies generally find that when hazards

of opportunism are high in an alliance, equity governance arrangements are preferred. Conversely, when hazards are low, contractual governance is preferred (Sampson, 2004). R&D

alliances are often viewed as alliances where the hazards of opportunism are high because of

the challenges associated with both sharing, and protecting, knowledge. However, in an

empirical study of contractual and equity governance arrangements in a sample of R&D

alliances, Sampson (2004, p.486) concludes that “Contractual governance appears to be more

efficient in all but the most extreme cases” due to the “excessive bureaucracy” associated

with managing equity-based governance arrangements. In fact, ATP research “joint venture”

projects are contractual joint ventures or alliances, organized under a contractual agreement,

and not equity joint ventures where partner firms create a separate legal entity (with joint

ownership by the parent companies).

Alliance partners have to rely on safeguards (e.g., contractual provisions, governance procedures) in order to prevent opportunism (Williamson, 1985). Williamson (1993) argues that

contractual safeguards are necessary whenever there is the potential for opportunism within a

transaction relationship. Since R&D collaborations entail risks with regard to intellectual

property rights and knowledge spillover, they are more likely to be successful when the partners have crafted effective contractual provisions to protect intellectual property rights, monitor task performance, and resolve disagreements. Without satisfactory contractual protections

or governance procedures, alliance partners may be unwilling to share knowledge that is critical to the venture. For example, without satisfactory protections one party may use or modify

Theoretical Perspectives on R&D Alliance Success 1 1
technology acquired during the alliance in ways that were not intended and which are injurious to a partner. Naturally, an unwillingness to share knowledge would severely hamper the

ability of the collaborating firms to jointly develop valuable new knowledge.

Hypothesis 8: The more satisfied are R&D alliance partners with contractual provisions and

governance procedures for protecting intellectual property rights and resolving disagreements,

the better the performance outcomes of the R&D alliance.

Goodwill Trust

Although prior research on the relationship between governance arrangements and alliance

success has typically focused on forms of contractual or equity ownership arrangements (Oxley

and Sampson, 2004; Sampson, 2004), sociologists have long emphasized that informal social

controls—e.g., goodwill trust—often substitute for formal social controls (Macaulay, 1963;

Granovetter, 1985; Black, 1976; Ellickson, 1991). Prior work generally suggests that “contractual trust” and “goodwill trust” are substitutes (Sako, 1991). Thus, alliance partners may rely

on personal trust relations as a primary governance mechanism in an alliance relationship.

We draw on prior literature in defining trust as one party’s confidence that the other party

in the exchange relationship will not exploit its vulnerabilities (Barney and Hansen, 1994;

Zaheer et al., 1998; Dyer and Chu, 2003). This confidence, or trust, is expected to emerge

where the “trustworthy” party in the exchange relationship: (1) shows good will and behaves

in ways perceived as “fair” by the exchange partner; and (2) does not take advantage of an

exchange partner even when the opportunity is available (Mayer et al., 1995). Our definition

therefore characterizes interfirm trust as a construct based on goodwill or benevolence, and

has frequently been referred to as “goodwill trust” (Sako, 1991; Dyer and Chu, 2003). Conceptually, organizations are not able to trust each other—trust is a phenomenon that has its

basis in individuals. Trust can be placed by one individual in another individual or in a group

of individuals (e.g., within an organization). However, individuals in an organization may

share an orientation toward individuals within another organization. In this perspective,

“interorganizational trust describes the extent to which organizational members have a collectively-held trust orientation toward the partner firm” (Zaheer et al, 1998, p.142).

In this study, we consider goodwill trust, this collective orientation of a company toward

its alliance partners. Trust is likely to be important in R&D alliances, which represent situations of risk where there is potential for undesirable knowledge spillovers and opportunistic

behavior on the part of partners. Higher levels of trust are expected to improve R&D alliance

performance by lowering transaction costs or increasing knowledge sharing.

Trust lowers transaction costs and improves the effectiveness of coordination by reducing

both bargaining and monitoring costs. When alliance partners trust that payoffs will be fairly

divided, they do not have to plan for all future contingencies. They can be confident that

1 2 The Determinants of Success in R&D Alliances 
equitable adjustments will be made as market conditions change. Trust therefore promotes

negotiating efficiency by enabling each party to be more flexible in granting concessions

because of the expectation that the partner will reciprocate in the future (Dore, 1983). Furthermore, negotiations are more efficient because partners have confidence that information

provided by the other organization is not misrepresented. As observed by Zaheer et al. (1998,

p.144), “Trust reduces the inclination to guard against opportunistic behavior (i.e. deliberate

misrepresentation on the part of the exchange partner).”

Trust also influences the extent to which alliance partners are willing to share knowledge—especially proprietary knowledge. A company will share this information if it trusts that

the partner will not steal its ideas or use them in a way that would be inappropriate or damaging. Without goodwill trust, alliance partners are less likely to share knowledge, which is critical to success in R&D alliances.

Hypothesis 9: The greater the goodwill trust among partners in an R&D alliance, the better

the performance outcomes of the R&D alliance.

Theoretical Perspectives on R&D Alliance Success 1 3

Part III

Data and Sample

The Advanced Technology Program (ATP) at the National Institute of Standards and Technology (NIST), U.S. Department of Commerce, supports technology innovation in the United

States through competitive funding awards to companies pursuing high-risk R&D. A key mission of the ATP is to promote collaborative R&D in U.S. industry. Since 1990, ATP has

funded over 200 research joint ventures involving almost 1000 companies, universities, and

other organizations. An ATP research “joint venture” must include at least two for-profit companies, and may also include universities and other nonprofit organizations. ATP joint venture

project awards do not have a maximum award amount, but the project participants must provide at least 50 percent of the total project cost. ATP joint venture projects typically last for

three to five years. The ATP targets high-risk R&D projects with potential for broad economic

impact, and supports innovative technologies in any field—biotechnology, chemistry and

advanced materials, electronics, information technology, manufacturing.

We note that ATP research “joint venture” projects are not equity joint ventures where

partner firms create a separate legal entity (with joint ownership by the parent companies) to

pursue collaboration objectives. Instead, they are contractual joint ventures or alliances, organized under a contractual agreement. For the purpose of this paper, we use the terms “R&D

alliance” and “research joint venture” interchangeably to refer to these R&D project collaborations that receive funding support from the ATP.

This study of R&D alliances began with interviews of ATP program managers responsible

for supervising ATP-funded research joint venture projects, and then interviews with company

participants in ATP-funded R&D alliances. The interviews focused on investigating the determinants of success in R&D alliances in order to build internal validity for the subsequent

research (Dyer and Powell, 2001).

To build external validity, we used the interview findings to contribute to the development

of a special survey, the Survey of ATP Joint Ventures, which was fielded in 2003. The survey

collected information at the firm level (e.g., project benefits to the respondent’s company), and

also at the firm-dyad level (e.g., characteristics of the relationship between the respondent’s

company and specific partner companies participating in the same R&D project). All companies in ATP research joint venture projects funded between 1990 and 2001, with project completion by 2004, were included in the survey. Altogether, 486 companies were eligible to

respond to the survey.

1 5
The survey used a mixed-mode methodology that included an internet web survey, and a

follow-up phone interview for those that did not respond to the web survey. Survey design and

data collection was carried out by a leading survey research firm. Following standard survey

procedures, multiple contact attempts were made in order to maximize survey response rates.

An advance letter describing the purpose of the survey was mailed to each company representative. An email then provided the representative with the web survey link and login information. Reminder emails were sent to non-respondents over the course of several weeks. After

eight weeks, remaining non-respondents were contacted by telephone to collect the survey

data. While these telephone contacts were intended to allow respondents the opportunity to

complete the interview over the telephone, the vast majority of these calls actually served to

prompt the respondent to complete the web survey. Thus, although technically the administration of the survey was mixed-mode, only fourteen telephone interviews were actually conducted while 383 completed the web survey. The overall response rate (proportion of survey

responses relative to the within-scope population) was 89%.

The survey yielded a dataset with a total of 397 companies and 142 R&D alliances. At

least one firm is represented from each of 142 R&D alliances. For 121 R&D alliances, two or

more firms are represented in the dataset. The survey data were supplemented by firm-level

and project-level archival data. The archival data included information such as the amount of

project award funding provided by the ATP, the project participants’ cost-share contribution,

and other project and company descriptive information. Table 1 shows the distribution of

projects and companies in the survey dataset, by year of project completion. Table 2 shows the

distribution of projects and companies, by the technology area of the project.

Analysis of Outcomes: Firm-level or Alliance-level

For 121 R&D alliances, we have survey data responses from two or more companies participating in the alliance. Therefore, we address an issue that has not been resolved in the alliance

literature: Should alliance outcomes be assessed at the firm-level or at the alliance-level?

Anderson (1990) argues that joint ventures should be evaluated as independent entities seeking

to maximize their own rather than their parents’ performance, in order to minimize parent

politics and parochial viewpoints, foster harmony among parents, and facilitate learning and

innovation. Anderson’s argument suggests that the appropriate level of analysis for studying

alliance performance is the alliance level. Glaister and Buckley (1998) criticize Anderson’s perspective as naive and impractical because alliances are embedded within their parents’ alliance

networks and thus politically inseparable from the power structure of those networks. These

opposite views reflect a difference of opinion in the alliance literature regarding whether the

appropriate level of analysis is that of the alliance or the participating companies.

The typical approach to making alliance performance an operational measure is to equate

the company and alliance levels of analysis by viewing alliance performance from the vantage

1 6 The Determinants of Success in R&D Alliances 
Data and Sample 1 7

TABLE 2

Survey Respondents: Projects and Companies, by Technology Area Project 

Projects Companies

Technology Area Number Percent Number Percent

Biotechnology 15 11% 27 7%

Chemistry/Materials 43 30% 102 26%

Electronics 41 29% 106 27%

Information Technology 17 12% 38 9%

Manufacturing (Discrete) 26 18% 124 31%

Total 142 100% 397 100%

Source: Advanced Technology Program, Survey of ATP Joint Ventures

TABLE 1

Survey Respondents: Projects and Companies, by Year of Project Completion

Project 

Completion Projects Companies

Year Number Percent Number Percent

1995 4 3% 11 3%

1996 6 4% 11 3%

1997 13 9% 29 7%

1998 14 10% 25 6%

1999 17 12% 53 13%

2000 30 21% 101 25%

2001 10 7% 24 6%

2002 18 13% 43 11%

2003 18 13% 62 16%

2004 12 8% 38 10%

Total 142 100% 397 100%

Source: Advanced Technology Program, Survey of ATP Joint Ventures
point of a single participating company. For example, Arino (2003) collected responses from

83 Spanish companies participating in alliances in order to evaluate the construct validity of

measures of alliance performance. While responses from more than one participating company

were received in the case of four alliances, Arino (2003) randomly dropped company responses

in order to keep only one response per alliance, thereby equating the company and alliance

level of analysis. Geringer and Hebert (1991) justify their use of a single response per alliance

by arguing that participants are aware of their collaborators’ assessment of the alliance, and

the assessments of their collaborators are in effect incorporated into their own assessment.

While using a single response per alliance is generally accepted in alliance studies in part

because of the difficulty of obtaining multiple responses, there are clearly potential problems

with viewing alliance performance from the perspective of a single participating company. In

particular, this is problematic in cases where Geringer and Hebert’s (1991) assertion regarding

the interdependence of alliance partners’ performance assessments may not hold.

Our data enable us to assess empirically whether companies participating in the same

alliance have similar assessments of the alliance performance. If responses are sufficiently similar, then they can be aggregated to form an alliance-level construct. If they cannot be aggregated, then the appropriate level of analysis is that of the participating companies. Procedures

used to assess whether individual data can be aggregated to form group measures (Ostroff,

1993; James, 1982) can be applied to responses from companies participating in an alliance.

Intraclass coefficients (ICC) provide a means of assessing whether data may be aggregated

(Bliese, 2000). ICC(1) compares the proportion of variance accounted for by membership in a

particular group—or in our analysis a particular alliance. An ICC(1) of 1 would indicate perfect agreement among firms in an alliance. Our data yields an ICC(1) of 0.12. While there is

no standard cutoff for ICC(1), a value of 0.12 indicates a low level of agreement. ICC(2) is an

estimate of the reliability of group (alliance) means; the ICC(2) for our data set is 0.28, well

below the accepted cutoff of 0.70. These low values indicate that aggregation would be inappropriate for our data and that the appropriate unit of analysis for our study is that of participating companies rather than the alliance.

1 8 The Determinants of Success in R&D Alliances 
Part IV

Measures and Results

Dependent Variables

To provide a robust and comprehensive assessment of success, this study incorporates three

distinct dimensions of the benefits to firms collaborating in R&D alliance—perceptual measures of success, patent measures, and financial measures. First, perceptual measures allow participants to qualitatively assess the extent to which the alliance achieved success in R&D

collaboration. Perceptual measures have been found to be important for measuring success in

R&D alliances because success in R&D alliances often means development or acquisition of

knowledge as the collaborating firms pursue technology objectives (Anderson and Narus,

1990; Tuchi, 1996; Hebert and Beamish, 1997; Sakakibara, 1997). Also, additional in-house

R&D is usually necessary to fully benefit from the outcomes of R&D alliances, making it difficult to evaluate alliances from their tangible results alone (Sakakibara, 2001). Use of managerial assessments of alliance performance received some initial criticism on concerns about

potential bias and inaccuracy. But research by Geringer and Hebert (1991) shows that there is

high correlation between subjective assessments of performance and objective financial measures based on accounting data. Thus, if properly conducted, managerial assessments of alliance

performance are a reasonable way to measure alliance performance (Anderson and Weitz,

1989; Anderson, 1990; Child and Yan, 1999; Das and Teng, 2000). We use a survey-based

measure for the firm’s assessment of the overall success of the R&D alliance in terms of delivering value to the firm (Overall_Value).

Patents have been widely used as a measure of R&D output. We use patent application by

the firm (Patent_Application) as a measure of outcomes from the research joint venture project. With indicator variables to control for differences in the propensity to patent across technology fields, patents are a good measure of the creation and formal ownership of intellectual

property. Finally, we use an indicator of financial value from technology commercialization or

implementation that the firm has already realized as a result of the research joint venture project. This measure (Commercialization) indicates whether the firm has received actual revenues

from sales or licensing, or achieved actual cost savings, from commercializing or implementing

technology developed in the R&D alliance. During interviews we were told that the most successful projects not only achieve technical objectives but also result in commercialized technology that generated some financial value for the company. While the amount of value may vary

with a variety of factors (e.g., strength of competition, willingness of customers to adopt new

1 9
technology, emergence of alternative technologies, etc.), we consider any positive value created

from the research joint venture project to be an indicator of the success of the project.

Table 3 presents a cross-tabulation of the three outcomes variables used in this study. The

five response categories for Overall_Value are shown in the header column, and the four possible response combinations for Patent_Application and Commercialization are shown in the

header row. From the top three rows of the table, we see that firm respondents may have a poor

or neutral perceptual assessment of overall value of the alliance, even while the firm may have

generated patent applications or financial value from technology commercialization. From the

bottom two rows, we see that firm respondents may have a positive perceptual assessment of

the overall value of the R&D alliance, even if the firm has not (yet) generated tangible outcomes

such as patent applications or financial value from technology commercialization.

Independent Variables

To empirically test the hypotheses outlined earlier, we construct variables to be employed in

regression analysis. The independent variables include both project-level (i.e., alliance-level)

variables and firm-level variables. We use both administrative data and survey data in constructing these variables. From administrative data, we construct the following project-level variables:

• Proj_Company_Count is the number of company partners in the alliance;

• Proj_Company_Distance is a measure of the geographic distance between company

partners;

2 0 The Determinants of Success in R&D Alliances 

TABLE 3

Tabulation of Outcomes Variables: Overall_Value, Patent_Application, Commercialization

Patent_Applica- Patent_Applica- Patent_Applica- Patent_Application = No tion = No tion = Yes tion = Yes

Commerciali- Commerciali- Commerciali- Commerciali- Row

Overall_Value zation = No zation = Yes zation = No zation = Yes total

(1) Very Unsuccessful 10 0 0 0 10

(2) Unsuccessful 29 2 9 2 42

(3) Neither successful 

nor unsuccessful 53 2 15 6 76

(4) Successful 75 34 48 25 182

(5) Very Successful 16 25 24 19 84

Column total 183 63 96 52 394

Source: Advanced Technology Program, Survey of ATP Joint Ventures 
• Proj_Total_Budget is the total project budget for the research joint venture project;

• Proj_Tech_Bio, Proj_Tech_Chem, Proj_Tech_Elec, Proj_Tech_IT are indicator variables for the technology area of the project, i.e., Biotechnology, Chemistry/Materials,

Electronics, Information Technology;

• Proj_Endyear_00_02, Proj_Endyear_03_04 are indicator variables for the end year of

the project, i.e., 2000-2002, 2003-2004.

Firm-level variables constructed from administrative data include:

• Lead_Company is an indicator for the formal lead company for the research joint venture;

• Small_Company is an indicator that the company has fewer than 500 employees.

From survey data, we construct the following project-level variables:

• Proj_Ambitiousness is a measure of the ambitiousness of the R&D alliance project

goals relative to other R&D initiatives in the industry;

• Proj_Current_Competitor is an indicator for whether the alliance includes companies

that are competitors in product markets;

• Proj_Contract_Governance is a measure of satisfaction with the formal alliance agreement and governance procedures developed in the alliance;

• Proj_Goodwill_Trust is a measure of goodwill trust among the alliance partners.

Firm-level variables constructed from survey data include:

• General_Alliance_Experience is a measure of company project personnel’s previous

experience with inter-firm R&D collaborations in general;

• Partner_Specific_Experience is a measure of company project personnel’s prior experience working with personnel from selected alliance partners in the current collaboration;

• Frequency_Communication is a measure of the frequency of communication by company project personnel with personnel from alliance partners;

• Joint_Work_Interaction is a measure of company project personnel’s interaction with

personnel from alliance partners to carry out joint work on project tasks;

• RD_Employment is the number of R&D employees at the company location;

• FTE_Technical is the number of company full-time equivalent technical staff that

work on the project, on average per year.

Measures and Results 2 1
Regression Results

The variables employed in regression analysis are displayed in Table 4, which also provides additional details on the definition and construction of the variables. Table 5 provides summary statistics for the variables. The average firm in the sample has a perceptual measure of the

Overall_Value of the alliance outcomes that is above the neutral value of “3”, which represents a

survey response that the alliance was “neither successful nor unsuccessful” in delivering overall

value to the company. The mean values of Patent_Application and Commercialization indicate

that 37 percent of firms in the sample filed a patent application, and 29 percent of firms achieved

revenues or cost savings from commercialization of technology developed in the alliance.

2 2 The Determinants of Success in R&D Alliances 

TABLE 4

Description of Variables

Overall_Value The respondent’s overall assessment of the success of the alliance in

delivering value to the company. An ordered categorical variable from a

survey item with a 5-point Likert scale.

Patent_Application Indicator for whether the company has filed any patent applications as

a result of the alliance. A binary variable derived from a survey item.

Commercialization Indicator for whether the company has achieved actual revenues or

cost savings from commercialization of technology as a result of the

alliance. A binary variable derived from survey items.

Proj_Company_Count The number of companies in the alliance. A project-level integer variable derived from administrative records.

Proj_Ambitiousness The ambitiousness of the alliance objectives relative to other R&D initiatives in the industry. A project-level numerical variable derived from

a survey item with a 7-point Likert scale. Responses from all project

respondents are averaged to form a project-level variable.

Proj_Current_Competitor Indicator for whether the alliance includes any companies that are current competitors. A project-level binary variable derived from a survey

item indicating whether a selected alliance partner is a competitor in a

product market. Responses from all project respondents regarding

selected alliance partners are incorporated.

General_Alliance_Experience The extent to which company R&D project personnel have previous

experience with inter-firm R&D collaborations. An ordered categorical

variable from a survey item with a 4-point Likert scale.

CONTINUED
Measures and Results 2 3

TABLE 4

Description of Variables (Continued)

Partner_Specific_Experience The extent to which company R&D project personnel have prior experience working with R&D project personnel from selected alliance partners. A numerical variable derived from a survey item with a 4-point

scale. Responses regarding selected alliance partners are averaged.

Proj_Company_Distance The average of bilateral geographic distances between company locations calculated for each pair of company partners in the alliance. A

project-level numerical variable derived from administrative records.

Measured in 1000s miles.

Frequency_Communication The frequency of communication of company R&D project personnel

with alliance partner staff by telephone, email, or video-conference. An

ordered categorical variable from a survey item indicating frequency of

communication as: several times a week, weekly, biweekly, monthly,

quarterly.

Joint_Work_Interaction The numbers of days per year, on average, that company R&D project

personnel meets with alliance partner staff to carry out joint work on

project tasks. An integer variable derived from a survey item.

Proj_Contract_Governance The extent to which project respondents are satisfied with the alliance

agreement and other governance procedures with regard to: protection

of contributed intellectual property, ownership of new intellectual property, resolution of disputes or disagreements, and verification of work

task performance. A project-level numerical variable derived from four

survey items each with a 5-point Likert scale. Responses from all project respondents are averaged to form a project-level variable.

Proj_Goodwill_Trust The extent to which project respondents trust alliance partners to show

goodwill and not take unfair advantage. A project-level numerical variable derived from two survey items each with a 4-point Likert scale.

Responses regarding selected alliance partners are averaged.

Responses from all project respondents are averaged to form a projectlevel variable.

R&D_Employment The number of R&D employees at the company location. An integer

variable derived from a survey item. Measured in 1000s persons.

FTE_Technical The number of company full-time equivalent technical staff that work

on the R&D alliance project, on average per year. An integer variable

derived from a survey item.

CONTINUED
TABLE 4

Description of Variables (Continued)

Proj_Total_Budget The total budget of the R&D alliance project, including funding from the

government award as well as cost-share contribution from alliance

partners. A project-level numerical variable from administrative

records. Measured in 1,000,000s dollars.

Lead_Company Indicator for whether the company is the formal lead company for the

R&D alliance project. A binary variable from administrative records.

Small_Company Indicator for whether the company is a small company with fewer than

500 employees. A binary variable from administrative records.

Proj_Tech_Bio Indicator for whether the R&D alliance project represents a biotechnology area. A project-level binary variable from administrative records.

Proj_Tech_Chem Indicator for whether the R&D alliance project represents a

chemistry/materials technology area. A project-level binary variable

from administrative records.

Proj_Tech_Elec Indicator for whether the R&D alliance project represents an electronics

technology area. A project-level binary variable from administrative

records.

Proj_Tech_IT Indicator for whether the R&D alliance project represents an information technology area. A project-level binary variable from administrative

records.

Proj_Endyear_00_02 Indicator for whether the R&D alliance project ended during the time

period 2000-2002. A project-level binary variable from administrative

records.

Proj_Endyear_03_04 Indicator for whether the R&D alliance project ended during the time

period 2003-2004. A project-level binary variable from administrative

records. 

2 4 The Determinants of Success in R&D Alliances 

Table 6 presents the matrix of correlation coefficients for the variables used in the regression analysis. The three outcomes variables Overall_Value, Patent_Application, and Commercialization are correlated with each other. The three outcomes variables are each positively

correlated with Proj_Ambitiousness, Frequency_Communication, and FTE_Technical. Two of

the outcomes variables—Overall_Value and Patent_Application—are negatively correlated with

Proj_Company_Count and Proj_Current_Competitor, and positively correlated with Proj_Contract_Governance and Lead_Company.
Measures and Results 2 5

TABLE 5

Summary Statistics for Variables

Variable N Mean Std Dev Min Max

Overall_Value 394 3.73 0.996 1 5

Patent_Application 397 0.375 0.485 0 1

Commercialization 397 0.292 0.455 0 1

Proj_Company_Count 397 5.53 4.42 2 22

Proj_Ambitiousness 397 5.64 0.770 1 7

Proj_Current_Competitor 397 0.086 0.280 0 1

General_Alliance_Experience 396 2.71 0.787 1 4

Partner_Specific_Experience 391 2.17 0.907 1 4

Proj_Company_Distance 397 0.763 0.601 0.001 2.600

Frequency_Communication 390 3.47 1.22 1 5

Joint_Work_Interaction 382 21.3 30.4 0 360

Proj_Contract_Governance 393 14.9 1.93 10 20

Proj_Goodwill_Trust 392 6.52 0.892 2.5 8

R&D_Employment 380 0.237 0.351 0 1.000

FTE_Technical 384 4.04 3.95 0 25

Proj_Total_Budget 397 13.1 9.92 1.5 63

Lead_Company 397 0.247 0.432 0 1

Small_Company 397 0.358 0.480 0 1

Proj_Tech_Bio 397 0.068 0.252 0 1

Proj_Tech_Chem 397 0.257 0.437 0 1

Proj_Tech_Elec 397 0.267 0.443 0 1

Proj_Tech_IT 397 0.096 0.295 0 1

Proj_Endyear_00_02 397 0.423 0.495 0 1

Proj_Endyear_03_04 397 0.252 0.435 0 1

Source: Advanced Technology Program, Survey of ATP Joint Ventures 

Notes:

Variables with name prefix Proj_ are defined as project-level variables (i.e., all members of a project/alliance have

the same value for the variable).

For the Proj_Tech_ variables indicating project technology area, the omitted category is the Manufacturing technology area.

The value for R&D_Employment may be zero if no company employees are designated as R&D employees, or if

the company no longer has R&D employees at the specified location at the time of the survey.

The value for FTE_Technical may be zero if the company provides less than one full-time equivalent technical

staff person to the project per year.
2 6 The Determinants of Success in R&D Alliances 

TABLE 6

Correlation Matrix for Variables

Variable 1 2 3 4 5 6 7 8 9 10

Overall_Value 1

Patent_Application 2 0.21***

Commercialization 3 0.33*** 0.11**

Proj_Company_Count 4 -0.13*** -0.29*** 0.01

Proj_Ambitiousness 5 0.23*** 0.23*** 0.10** -0.28***

Proj_Current_Competitor 6 -0.10** -0.13** -0.04 0.01 -0.12**

General_Alliance_Experience 7 0.17*** 0.01 0.03 -0.16*** 0.18*** -0.04

Partner_Specific_Experience 8 0.02 -0.05 -0.01 0.03 -0.01 0.06 0.23***

Proj_Company_Distance 9 0.04 0.08 -0.03 -0.21*** -0.04 0.10** -0.08 -0.12**

Frequency_Communication 10 0.23*** 0.26*** 0.13** -0.05 0.08* -0.12** 0.06 0.07 0.00

Joint_Work_Interaction 11 0.06 -0.01 0.02 0.12** 0.06 0.00 0.13** 0.11** -0.15*** 0.33***

Proj_Contract_Governance 12 0.21*** 0.19*** -0.02 -0.15*** 0.17*** -0.05 0.12** -0.02 -0.17*** 0.14***

Proj_Goodwill_Trust 13 -0.01 -0.09* 0.03 0.21*** 0.00 -0.16*** 0.00 -0.10** -0.20*** 0.11**

R&D_Employment 14 -0.02 0.15*** -0.07 -0.14*** 0.18*** 0.08 0.02 0.13** 0.05 0.11**

FTE_Technical 15 0.23*** 0.35*** 0.19*** -0.30*** 0.20*** -0.05 0.04 0.05 0.11** 0.24***

Proj_Total_Budget 16 0.04 -0.01 0.03 0.14*** -0.03 0.14*** -0.05 -0.04 0.09* 0.03

Lead_Company 17 0.23*** 0.42*** 0.07 -0.34*** 0.15*** -0.11** 0.12** 0.10* 0.21*** 0.21***

Small_Company 18 0.00 -0.07 0.12** 0.08 -0.12** 0.02 0.00 -0.11** -0.02 -0.12**

Proj_Tech_Bio 19 0.02 0.12** 0.16*** -0.18*** 0.09* -0.05 0.05 -0.11** -0.01 -0.02

Proj_Tech_Chem 20 0.05 0.08 0.00 -0.21*** 0.07 0.05 -0.04 -0.01 0.03 0.11**

Proj_Tech_Elec 21 0.13** 0.11** -0.04 -0.19*** -0.03 -0.02 0.11** 0.05 0.18*** -0.06

Proj_Tech_IT 22 -0.08* -0.08 -0.06 -0.05 -0.01 0.08* -0.05 -0.10** 0.14*** 0.14***

Proj_Endyear_00_02 23 -0.06 0.01 0.04 0.14*** -0.03 -0.10* -0.03 0.06 -0.01 -0.01

Proj_Endyear_03_04 24 0.19*** 0.04 -0.12** -0.03 0.18*** -0.12** 0.22*** -0.10* -0.01 -0.02

Notes:

* indicates statistical significance at the 0.10 level

** indicates statistical significance at the 0.05 level

*** indicates statistical significance at the 0.01 level

Source: Advanced Technology Program, Survey of ATP Joint Ventures 

Among the independent variables, we find that Frequency_Communication is negatively

correlated with Proj_Current_Competitor, and positively correlated with Lead_Company and

FTE_Technical. We find that Proj_Contract_Governance is negatively correlated with

Proj_Company_Count and Proj_Company_Distance, and positively correlated with

General_Alliance_Experience and Frequency_Communication. We find that

Proj_Goodwill_Trust is negatively correlated with Proj_Current_Competitor, Partner_Specific_Experience, and Proj_Company_Distance, and positively correlated with Proj_Company_Count, Frequency_Communication, and Proj_Contract_Governance.
Measures and Results 2 7

11 12 13 14 15 16 17 18 19 20 21 22 23

0.09*

0.04 0.26**

0.03 0.11** -0.06

0.00 0.05 -0.04 0.23***

-0.01 0.00 -0.04 0.07 0.25***

0.04 0.13*** -0.02 0.12** 0.49*** -0.11**

-0.05 -0.11** 0.02 -0.46*** -0.05 -0.04 0.01

-0.02 -0.14*** -0.09* -0.02 0.16*** 0.01 0.12** 0.22***

-0.05 0.03 0.08* 0.03 0.05 -0.17*** 0.12** -0.15*** -0.16***

-0.04 0.28*** -0.05 0.07 0.10* 0.06 0.06 0.02 -0.16*** -0.35***

0.05 -0.09* -0.08* 0.05 0.10** 0.39*** -0.03 -0.03 -0.09* 0.19*** -0.20***

-0.03 -0.01 -0.03 -0.01 0.04 0.11** -0.02 -0.01 -0.03 0.03 -0.06 0.00

0.02 0.09* 0.06 -0.03 -0.06 -0.04 0.00 -0.01 -0.06 -0.17*** 0.21*** -0.07 -0.50***

Tables 7, 8, and 9 present the results from multiple regression analysis used to test the

hypotheses presented earlier. The three tables present results for the three outcomes variables

Overall_Value, Patent_Application, and Commercialization, respectively. Equations (1abc) provide alternative specifications for testing Hypothesis (3) and (7ab) relating to geographic distance of partner companies, and the frequency of communication and interaction. Equations

(2ab) provide alternative specifications for testing Hypothesis (4ab) relating to general and

partner-specific alliance experience. Equations (3) and (4) present alternative specifications for

testing Hypothesis (5) and (6), respectively. The other variables of interest for testing the

hypotheses are included in all of the regression specifications.

Table 7 presents regression results for the outcome variable Overall_Value. The coefficient for Frequency_Communication is consistently positive, supporting the hypothesis that

frequent communication is a key alliance management factor for success. Equations (1abc)

show that geographic distance and face-to-face work interaction have no effect, and therefore
the frequency of communication is independent of geography and face-to-face interaction.

Equations (2ab) show that the prior alliance experience variables do not have an effect on the

perceptual measure of overall value. The coefficient for Proj_Contract_Governance is consistently positive, which supports the hypothesis that contractual provisions and governance

procedures are important to alliance success. Somewhat surprisingly, we find that Proj_Goodwill_Trust has a marginally significant negative coefficient in some equation specifications.

Also contrary to expectations, we find in Equation (3) that R&D_Employment has a negative

coefficient. In Equation (4), we find no effect of FTE_Technical on the perceptual measure of

outcomes. We find in all specifications that the number of companies in the alliance, and the

presence of competitors, do not have an effect on the perceptual measure of overall value.

This finding suggests that alliance designers are successful in structuring the alliance to have

the appropriate number and types of partners. The coefficient for Proj_Ambitiousness is positive in all specifications, which indicates that projects with ambitious goals deliver greater

perceived value to the participants. The coefficient for Lead_Company is also generally positive, indicating that more active or leading members of an alliance are more likely to experience successful outcomes.

Table 8 presents regression results for the outcome variable Patent_Application. Equations

(1abc) show that frequency of communication has a positive effect on the outcomes variable,

while geographic distance and face-to-face interaction do not have any effect. Contrary to

expectations, we see in Equations (2ab) that General_Alliance_Experience and Partner_Specific_Experience have negative coefficients. Proj_Contract_Governance has a marginally statistically significant positive effect in some specifications, and Proj_Goodwill_Trust has a

statistically significant negative effect in most specifications. Equation (3) shows that

R&D_Employment has no effect, and Equation (4) shows a marginally significant positive coefficient for FTE_Technical. The number of companies in the alliance, indicated by Proj_Company_Count, has a marginally significant negative effect in most specifications, while the

presence of competitors has no effect. Proj_Ambitiousness has a statistically significant positive

effect in most specifications, and Lead_Company has a positive effect in all specifications.

Table 9 presents regression results for the outcome variable Commercialization. Again, we

find the coefficients for Frequency_Communication and Proj_Ambitiousness to be consistently

positive. In Equation (4), we find that FTE_Technical has a positive coefficient, indicating that

greater allocation of technical personnel to the project increases the likelihood of successful

outcomes. We do not find the other main variables of interest to have any effect on the commercialization outcomes variable.

2 8 The Determinants of Success in R&D Alliances 
Measures and Results 2 9

TABLE 7

Regression Results for Outcomes Variable: Overall_Value

Dependent variable: Overall_Value

Estimation method: OLS

(1a) (1b) (1c) (2a) (2b) (3) (4)

Proj_Company_Count 0.00978 0.00198 0.00430 0.00229 0.00222 0.00278 0.00571

Proj_Ambitiousness 0.18676*** 0.18658*** 0.16959** 0.17274** 0.19500*** 0.19491*** 0.18187***

Proj_Current_Competitor -0.23801 -0.16669 -0.28411 -0.17219 -0.16862 -0.04968 -0.19033

General_Alliance_Experience 0.07428

Partner_Specific_Experience 0.02099

Proj_Company_Distance 0.08143

Frequency_Communication 0.15701*** 0.15709*** 0.15846*** 0.17436*** 0.14349***

Joint_Work_Interaction 0.00154

Proj_Contract_Governance 0.07620*** 0.05954** 0.06560** 0.05638** 0.05778** 0.05655** 0.06118**

Proj_Goodwill_Trust -0.07421 -0.09294 -0.09838 -0.09659* -0.09176 -0.09825* -0.10788*

R&D_Employment -0.30891**

FTE_Technical 0.02166

Proj_Total_Budget 0.01101** 0.01283** 0.01339** 0.01340** 0.01145** 0.01457** 0.00969

Lead_Company 0.41462*** 0.31356*** 0.40648*** 0.29505** 0.32519*** 0.33497*** 0.21346

Small_Company 0.08453 0.10215 0.06162 0.09065 0.12313 0.02468 0.08681

Proj_Tech_Bio 0.10771 0.10211 0.14458 0.09439 0.10086 0.14713 0.08658

Proj_Tech_Chem 0.21405 0.15927 0.25231* 0.15183 0.16169 0.19130 0.16147

Proj_Tech_Elec 0.13903 0.17111 0.19499 0.16256 0.17678 0.19718 0.18011

Proj_Tech_IT -0.22935 -0.35857* -0.23289 -0.36503* -0.27980 -0.33607 -0.33642*

Proj_Endyear_00_02 -0.00578 0.01430 -0.00509 0.01120 0.03050 0.04579 0.03959

Proj_Endyear_03_04 0.34377** 0.35036*** 0.34501** 0.32892** 0.35669*** 0.36679*** 0.37208***

Constant 1.48630** 1.42069** 1.91670*** 1.37698** 1.33465** 1.41793** 1.51206***

N (number of observations) 394 389 381 388 386 375 380

Notes:

* indicates statistical significance at the 0.10 level

** indicates statistical significance at the 0.05 level

*** indicates statistical significance at the 0.01 level
3 0 The Determinants of Success in R&D Alliances 

TABLE 8

Regression Results for Outcomes Variable: Patent_Application

Dependent variable: Patent_Application

Estimation method: Logit

(1a) (1b) (1c) (2a) (2b) (3) (4)

Proj_Company_Count -0.0624 -0.0819* -0.0617 -0.0956** -0.0785* -0.0834* -0.0722

Proj_Ambitiousness 0.3721** 0.3861** 0.3853** 0.3962** 0.4103** 0.3393* 0.3356*

Proj_Current_Competitor -0.7963 -0.5608 -0.7253 -0.4915 -0.5123 -0.6558 -0.5385

General_Alliance_Experience -0.3965**

Partner_Specific_Experience -0.3647**

Proj_Company_Distance 0.239

Frequency_Communication 0.4679*** 0.4929*** 0.4974*** 0.4677*** 0.4132***

Joint_Work_Interaction -0.00113

Proj_Contract_Governance 0.1680** 0.1033 0.1381* 0.1252 0.1020 0.1117 0.1148

Proj_Goodwill_Trust -0.2761* -0.3382** -0.2676* -0.3590** -0.3850** -0.3315** -0.3423**

R&D_Employment 0.379

FTE_Technical 0.0817*

Proj_Total_Budget 0.0179 0.0280* 0.0270* 0.0280* 0.0258* 0.0301* 0.0188

Lead_Company 1.7691*** 1.5879*** 1.7294*** 1.6195*** 1.7473*** 1.5275*** 1.2764***

Small_Company -0.3119 -0.2496 -0.3447 -0.2618 -0.2731 -0.1360 -0.2404

Proj_Tech_Bio 0.7278 0.7995 0.8007 0.8442 0.5882 0.6625 0.6092

Proj_Tech_Chem 0.1999 0.1077 0.1882 0.0324 0.0039 0.0383 0.0221

Proj_Tech_Elec 0.1693 0.1985 0.1872 0.1999 0.1823 0.0139 0.0662

Proj_Tech_IT -0.7277 -1.2427** -0.7745 -1.3675** -1.2942** -1.3037** -1.3367**

Proj_Endyear_00_02 0.1001 0.2783 0.1913 0.3479 0.3133 0.2945 0.2817

Proj_Endyear_03_04 0.1196 0.3085 0.1855 0.5014 0.2038 0.3257 0.3375

Constant -3.9403** -4.2304*** -3.5712** -3.4948** -3.3088** -4.1481** -3.9932**

N (number of observations) 397 390 382 389 387 376 381

Notes:

* indicates statistical significance at the 0.10 level

** indicates statistical significance at the 0.05 level

*** indicates statistical significance at the 0.01 level
Measures and Results 3 1

TABLE 9

Regression Results for Outcomes Variable: Commercialization

Dependent variable: Commercialization

Estimation method: Logit

(1a) (1b) (1c) (2a) (2b) (3) (4)

Proj_Company_Count 0.0306 0.0250 0.0355 0.0309 0.0280 0.0311 0.0377

Proj_Ambitiousness 0.4702** 0.4646** 0.4786** 0.4840** 0.4779** 0.5143*** 0.4230**

Proj_Current_Competitor -0.3034 -0.1843 -0.2950 -0.2087 -0.1775 -0.1245 -0.0801

General_Alliance_Experience 0.1070

Partner_Specific_Experience -0.0651

Proj_Company_Distance -0.04

Frequency_Communication 0.2745*** 0.1066** 0.2728** 0.2724** 0.1956*

Joint_Work_Interaction 0.00106

Proj_Contract_Governance -0.0321 -0.0583 -0.0380 -0.0630 -0.0629 -0.0659 -0.0222

Proj_Goodwill_Trust 0.0989 0.1082 0.1362 0.1240 0.1028 0.1253 0.1060

R&D_Employment -0.44

FTE_Technical 0.1033***

Proj_Total_Budget 0.0166 0.0208 0.0197 0.0207 0.0191 0.0277* 0.0050

Lead_Company 0.2839 0.1614 0.3073 0.1888 0.1982 0.2098 -0.2583

Small_Company 0.4975** 0.5895** 0.5370** 0.6107** 0.5982** 0.4546 0.6150**

Proj_Tech_Bio 0.8210 0.9704* 0.9176* 0.9538* 0.9244* 0.9132* 0.7382

Proj_Tech_Chem 0.0315 -0.0480 -0.0202 -0.0026 -0.0655 -0.1995 -0.1652

Proj_Tech_Elec 0.0809 0.0599 0.0001 0.0675 0.0619 0.0252 -0.1338

Proj_Tech_IT -0.5799 -0.8945 -0.7544 -0.8702 -0.8209 -1.0999* -0.9478*

Proj_Endyear_00_02 -0.1881 -0.2064 -0.2474 -0.2512 -0.1838 -0.3075 -0.2438

Proj_Endyear_03_04 -0.8989*** -0.9322*** -1.0299*** -1.0124*** -0.9519*** -1.0667*** -0.9738***

Constant -4.0812** -4.6587*** -4.2515*** -5.0625*** -4.4840*** -4.7422*** -4.7167***

N (number of observations) 397 390 382 389 387 376 381

Notes:

* indicates statistical significance at the 0.10 level

** indicates statistical significance at the 0.05 level

*** indicates statistical significance at the 0.01 level

Part V

Discussion

Our results show that R&D alliance success is influenced by a combination of alliance design

factors and alliance management factors. These may include alliance structural characteristics

such as the number of partners or the presence of competitors; alliance partner attributes such

as R&D capabilities or prior alliance experience; alliance management factors such as allocation of technical personnel, frequency of communication, or effectiveness of governance

arrangements.

When designing an alliance, firms may systematically underestimate the challenges of collaborating with multiple partners as well as competitors. A number of respondents we interviewed cited “coordination” problems associated with having too many alliance partners.

Stated one participant, “From my perspective, there were just too many [participants]. First, it

took too long getting to know everyone. In addition, scheduling meetings was a nightmare; we

had to schedule meetings a year in advance. It was just too difficult to coordinate.” (Dyer and

Powell 2001, p.14). Of course, at the alliance formation stage, the alliance partners evidently

believe that each partner brings something worthwhile to the alliance—but they may misjudge

the extent to which coordination and transaction costs increase as the number of partners

increase. In our data, we find that the likelihood of patent application declines when the

alliance has three or more members, but the perceptual measure of overall value does not fall

until the alliance includes eight or more members. While this negative correlation of outcomes

with number of alliance partners is evidenced in the simple correlation coefficients, the multiple regression analysis provides less support for the hypothesis that alliance designers tend to

include too many partners in the alliance at the time of forming the alliance.

Similarly, while the simple correlation coefficient indicates that alliance outcomes are negatively correlated with the presence of competitors, the multiple regression analysis does not

find evidence for the hypothesis that competitor alliances perform less well than alliances that

do not involve competitors. But the simple correlation coefficients do indicate that competitor

alliances are characterized by less frequent communication and lower levels of goodwill trust.

As one participant noted, “With vertical [supplier-customer alliances], there are no direct competitors and everyone is in a win-win situation. As a result, everyone is more likely to lay their

cards on the table. But that’s not true with horizontal [competitor alliances]. When you are

direct competitors, you are more guarded and keep your cards close to the vest.” (Dyer and

Powell, 2001, p.12). While some participants indicated that having competitors in an alliance

is a problem, at least one said that he didn’t think it was a problem working with competitors:

3 3
“We’ve developed a good working relationship with our [competitors]. We all share information and do what we can to make the project successful. I don’t see this as a problem.” (Dyer

and Powell, 2001, p.13).

Prior studies have found that alliance experience is correlated with alliance success. As a

simple correlation, we find that general R&D alliance experience is positively correlated with

the perceptual measure of overall value, but estimates in multiple regression show that there is

no effect. For patent application as the measure of outcomes, while there is no simple correlation with either general alliance experience or partner-specific experience, the multiple regressions show that both general alliance and partner-specific experience have a negative effect on

the likelihood of patent application. Since many of the firms in our study are large firms, they

may have reached the point of diminishing returns to general alliance experience as Hoang and

Rothaermel (2005) suggest. In theory, we expect partner-specific experience to increase trust,

information sharing, and the effectiveness of coordination among alliance partners. This is

supported by interviewed respondents who claim that pre-existing relationships among the key

participants is helpful in getting the alliance started. However, while partner-specific experience

may be helpful in getting an alliance started, for R&D alliances where innovation is the key

objective, prior partner-specific experience may imply greater knowledge overlap and less novelty. Thus, prior partner-specific experience, while helpful for alliances where value is achieved

through working together efficiently, may not be as helpful when the task objectives are creativity and innovation. Indeed, some participants felt that first time collaborations are just as

likely to produce a “creative” or “novel” research outcome as a third or fourth collaboration.

Hoang and Rothaermel (2005) also find a negative relationship between partner-specific experience and successful outcomes in drug development R&D alliances. It appears that prior partner-specific experience is valuable when success depends on efficiency (low transaction and

coordination costs) but not when success depends on creativity and innovation.

Our results indicate that “contractual trust” has a positive effect on R&D alliance success,

whereas “goodwill trust” appears to have a negative effect on R&D alliance success. We

define “goodwill trust” as confidence that an alliance partner will not take advantage of situations opportunistically. Protection against malfeasance can also be generated through “contractual trust” based on contractual agreements. We find that R&D alliances where alliance

partners have developed effective contract provisions and governance procedures are more

likely to deliver overall value to the company. Participants expressed in interviews that confidence in intellectual property protections was critical for knowledge-sharing to occur. Indeed,

our interviews reveal that governance issues are especially important at the start of an

alliance—some firms are highly satisfied with the agreements, while others are less comfortable

from the beginning.

3 4 The Determinants of Success in R&D Alliances 
Our results give strong support to the idea that R&D alliances must be organized to facilitate frequent interaction. We find that frequency of communication is strongly associated with

all three outcomes measures. To facilitate face-to-face interaction, some companies send their

R&D personnel to meet for a period of time at the premises of another partner firm. The participants we interviewed felt that this coordination routine enhanced interaction and improved

coordination, as the alliance partners worked together to solve complex technical problems. As

stated by one company representative,

On one project we learned much more by meeting for long periods of time at each other’s facilities. For example, our partner had assured us that moisture sensitivity would not be a problem.

This was something that we had been concerned about. When we co-located our personnel for a

period of time, we learned that they thought that preventing moisture problems for an hour was

a long time, while we thought preventing it for a week was a long time. We sorted this out in a

moment once we got together [in the same location]. (Dyer and Powell, 2001, p.16)

While we find that frequency of communication is a strong predictor of success, we do not

find that face-to-face joint work interaction has an effect on success, nor do we find geographic distance of alliance partners to have any effect on success. We do find that joint work

interaction is negatively correlated with geographic distance, and positively correlated with frequency of communication. Since regular project review meetings provide opportunity for faceto-face interaction between alliance partners, and establish a context for ongoing

communication, it appears that face-to-face joint work interaction is not critical to success.

Finally, we find that the ambitiousness of overall goals, as a characteristic of the R&D

alliance project, is strongly associated with all three outcomes measures. R&D alliances with

compelling goals are likely to have strong individual commitment to the effort, which may be

most important to success. Related to project commitment and effort, we find that being the

lead company for the research joint venture project is associated with success in terms of delivering overall value and generating patent applications. The firm’s allocation of technical personnel resources to the project is also associated with success in terms of generating patent

applications and producing financial value through commercialization of technology.

Discussion 3 5

Part VI

Conclusion

This study examines a complete set of alliance design factors and alliance management factors

that may influence R&D alliance success. Relative to the hypotheses presented above, we

derive the following conclusions from our empirical analysis:

(1) Number of alliance partners. We find that the number of alliance partners has a

weakly negative effect on patent application, and no effect on overall value or commercialization. The evidence suggests that alliance designers are largely successful in

achieving an “optimal” number of alliance partners, balancing the marginal benefit of

adding an additional partner to the marginal cost associated with an additional partner.

(2) Presence of competitors. Simple correlations show that alliances with competitor firms

have less frequent communication and lower levels of goodwill trust. But in multiple

regression analysis we find that the presence of competitors in an alliance has no effect

on alliance outcomes. Again, this suggests that alliance designers are largely successful

in “optimizing” the structure of the alliance.

(3) Geographic distance between alliance partners. Simple correlations show that alliances

with greater geographic distance between partners have less joint work interaction,

and lower levels of governance effectiveness and goodwill trust. But in multiple regression analysis, we do not find that geographic distance has any effect on alliance outcomes. The simple correlations also show that frequency of communication between

alliance partners has no correlation with geographic distance, so it appears that geographic distance does not affect the communication, knowledge sharing, and coordination necessary for successful alliance outcomes.

(4) General and partner-specific alliance experience. We find that general alliance experience and partner-specific experience have a negative effect on patent application as a

performance outcome. This may suggest that creativity and invention are more likely

when new partners come together in new collaborations to combine ideas, different

approaches, and complementary knowledge.

(5) R&D capability. We find that R&D employment at the company location has a negative effect on overall value as a performance outcome, and no effect on either the

patent application or commercialization measures. We use R&D employment as a

proxy measure of the R&D capability of the firm. One explanation for the finding of

3 7
negative or no effect of R&D employment on outcomes is that R&D employment is

also a measure of firm size, and R&D personnel in large firms have greater institutional hurdles to overcome in order to realize the potential of their research results.

(6) Number of technical personnel. We find that the number of technical personnel

resources has a positive effect on patent application and on commercialization of technology. This result supports the view that innovation and learning outcomes depend

on the number of technical personnel engaged in the effort.

(7) Frequency of communication. We find that frequency of communication has a strong

statistically significant positive effect on all three outcomes measures for R&D alliance

performance. The finding suggests that alliance managers can increase the likelihood

of alliance success by establishing routines that encourage frequent communication.

Frequent communication facilitates the knowledge sharing and coordination that is

critical to alliance success.

(8) Governance arrangements. We find that effective governance arrangements have a

positive effect on the perceptual measure of overall value, and a weakly positive effect

on the patent application measure. This finding highlights the importance of establishing contractual provisions and governance procedures as a factor in alliance success,

for example, in protecting intellectual property rights, monitoring task performance,

and resolving disputes.

(9) Goodwill trust. We find that goodwill trust among alliance partners has a weakly negative effect on the perceptual measure of overall value, and a negative effect on the

patent application measure. The results suggest that successful alliances do not depend

only on goodwill trust, but develop contractual-based trust that is generated through

contractual provisions and effective governance arrangements. In other words, in

alliance management, one would do well to “Trust, but verify.”

Finally, we find that “reaching for the stars” is a strong predictor of alliance success. More

ambitious projects with farther reaching goals demonstrate greater success on all three measures of R&D alliance performance. More ambitious projects have intrinsically greater potential value and potential impact. In addition, more ambitious projects are likely to mobilize

greater commitment and effort on the part of both the partner companies and the individual

participants.

For strategy and innovation scholars, this study demonstrates the usefulness of employing

multiple measures of success in the analysis of R&D alliances. Each type of measure—perceptual measure of overall value, patent application, and financial value from commercialization—has drawbacks, but by considering them together we are able to gain a richer

understanding of the factors that explain R&D alliance success. Researchers may gain additional insight into the determinants of alliance success by analyzing which factors are common

to various outcome measures, and which factors influence only a specific outcome. Some fac3 8 The Determinants of Success in R&D Alliances 
tors, such as prior experience or goodwill trust, may well be determinants of success in some

types of alliances, such as manufacturing or marketing alliances, but not in other types of

alliances, such as R&D alliances where creativity and innovation are central. Our research

suggests that there are fundamental differences between R&D alliances and other types of

alliances, and consequently, the factors that influence success may be quite different. For example, R&D alliances involve high risk and uncertainty as a fundamental characteristic, and so

there are inherent limitations to applying past experience in future projects. Though many

research studies combine R&D alliances and other types of alliances as a single subject for

analysis, we conclude that R&D alliances should be treated as a separate and independent type

of collaborative activity.

For companies engaging in R&D alliances, and government agencies that support collaborative R&D, this study shows that alliance management is important for alliance success.

While outcomes of research projects are inherently uncertain and determined in part by

chance, there are managerial factors which can increase the odds of success. In particular, managers can increase the likelihood of success by: (a) carefully considering the composition and

structure of alliances, in terms of number of partners, involvement of competitors, attributes of

alliance partners, etc.; (b) establishing effective contractual provisions and governance procedures; and (c) managing R&D alliances to ensure frequent communication among participants.

This study includes only government-sponsored R&D alliances in the United States. One

might ask whether the results of this study generalize to other R&D alliances that do not have

government support, or to R&D alliances in different institutional settings. In order to evaluate whether findings from this study also hold for privately-funded R&D alliances or R&D

alliances in other settings, further research in broader samples may be conducted.

Conclusion 3 9

References

Agrawal, A. 2006. Engaging the inventor: Exploring licensing strategies for university inventions and the role of latent knowledge. Strategic Management Journal, 27(1): 63-79.

Alliance Analyst. 1998. Measuring alliance performance. Philadelphia, PA: NewCap Communications.

Almeida, P. and Kogut, B. 1999. Localization of knowledge and the mobility of engineers in

regional networks. Management Science, 45(7): 905-912.

Anand, B.N. and Khanna, T. 2000. Do firms learn to create value? The case of alliances.

Strategic Management Journal, 21(3): 295-316.

Anderson, E. and Weitz, B. 1989. Determinants of continuity in conventional industrial channel dyads. Marketing Science, 8(4): 310-323.

Anderson, E. 1990. Two firms, one frontier: On assessing joint venture performance. Sloan

Management Review, 31(2): 19-30.

Anderson, J.C. and Narus, J.A. 1990. A model of distributor firm and manufacturer firm

working partnerships. Journal of Marketing, 54(1): 42-58.

Arino, A. 2003. Measures of strategic alliance performance: An analysis of construct validity.

Journal of International Business Studies, 34(1): 66-79.

Barney, J.B. and Hansen, M.H. 1994. Trustworthiness as a source of competitive advantage.

Strategic Management Journal, 15 (Winter Special Issue): 175-190.

Bliese, P.D. 2000. Within-group agreement, non-independence, and reliability: Implications for

data aggregation and analysis. In K.J. Klein and S.W.J. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations: 349-381. San Francisco: Jossey-Bass.

Bizan, O. 2003. The determinants of success of R&D projects: Evidence from American-Israeli

research alliances. Research Policy, 32(9): 1619-1640.

Black, D. 1976. The behavior of law. New York, NY: Academic Press.

Bleeke, J. and Ernst, D. 1995. Is your strategic alliance really a sale? Harvard Business Review,

73(1): 97-105.

4 1
Branstetter, L.G. and Sakakibara, M. 1998. Japanese Research Consortia: A Microeconometric

Analysis of Industrial Policy. Journal of Industrial Economics, 46(2): 207–233.

Branstetter, L. and Sakakibara, M. 2002. When do research consortia work well and why?

Evidence from Japanese panel data. American Economic Review, 92(1): 143-159.

Burt, R.S. 2004. Structural holes and good ideas. American Journal of Sociology, 110(2): 

349-399.

Campbell, S.L., and Wang, A.J. 2004. Industry R&D Collaboration — Project Outcomes 

from Research Joint Ventures in the Advanced Technology Program. 2004 Proceedings 

of the American Statistical Association, 1481-1488.

Caloghirou, Y., Hondroyiannis, G., and Vonortas, N.S. 2003. The performance of research

partnerships. Managerial and Decision Economics, 24(2-3), 85-99.

Chan, S.H., Kensinger, J.W., Keown, A.J., and Martin, J.D. 1997. Do strategic alliances 

create value? Journal of Financial Economics, 46(2), 199-221.

Child, J. and Yan, Y. 1999. Investment and control in international joint ventures: The case 

of China. Journal of World Business, 34(1): 3-15.

Clark, K.B. and Fujimoto, T. 1991. Product Development Performance. Boston, MA: 

Harvard Business School Press.

Cohen, W.M. and Levinthal, D.A. 1989. Innovation and learning: The two faces of R&D.

Economic Journal, 99(397): 569-596.

Cohen, W.M. and Levinthal, D.A. 1990. Absorptive capacity: A new perspective on learning

and innovation. Administrative Science Quarterly, 35(1): 128-152.

Daft, R.L. and Lengel, R.H. 1986. Organizational information requirements, media richness

and structural design. Management Science, 32(5): 554-571.

Das, T.K. and Teng, B.S. 2000. Instabilities of strategic alliances: An internal tensions 

perspective. Organization Science, 11(1): 77-101.

Demsetz, H. 1988. The organization of economic activity. Oxford, UK: Blackwell.

Dore, R. 1983. Goodwill and the spirit of market capitalism. British Journal of Sociology,

34(4): 459-482.

Dyer, J.H. 1996. Specialized supplier networks as a source of competitive advantage: 

Evidence from the auto industry. Strategic Management Journal, 17(4): 271-291.

4 2 The Determinants of Success in R&D Alliances 
Dyer, J.H. and Singh, H. 1998. The relational view: Cooperative strategy and sources of

interorganizational competitive advantage. Academy of Management Review, 23(4): 

660-679.

Dyer, J.H. and Powell, B. 2001. Determinants of success in ATP-funded R&D joint ventures:

A preliminary analysis based on 18 automobile manufacturing projects. GCR-00-803.

National Institute of Standards and Technology. Gaithersburg, MD.

Dyer, J.H. and Chu, W. 2003. The role of trustworthiness in reducing transaction costs and

improving performance: Empirical evidence from the United States, Japan, and Korea.

Organization Science, 14(1): 57-68.

Ellickson, R.C. 1991. Order without law: How neighbors settle disputes. Cambridge, MA:

Harvard University Press.

Geringer, J.M. and Hebert, L. 1991. Measuring performance of international joint ventures.

Journal of International Business Studies, 22(2): 249-263.

Glaister, K.W. and Buckley, P.J. 1998. Measure of performance in UK international alliances.

Organization Studies, 19(1): 89-118.

Glaser, B.G. and Strauss, A.L. 1967. The discovery of grounded theory; strategies for 

qualitative research. Chicago, IL: Aldine Pub. Co.

Granovetter, M. 1985. Economic action and social structure: The problem of embeddedness.

American Journal of Sociology, 91(3): 481-510.

Gulati, R. 1995a. Does familiarity breed trust? The implications of repeated ties for 

contractual choice in alliances. Academy of Management Journal, 38(1): 85-112.

Gulati, R. 1995b. Social structure and alliance formation patterns: A longitudinal analysis.

Administrative Science Quarterly, 40(4): 619-652.

Gulati, R. and Singh, H. 1998. The architecture of cooperation: Managing coordination 

costs and appropriation concerns in strategic alliances. Administrative Science Quarterly,

43(4): 781.

Gulati, R. and Wang, L.O. 2003. Size of the pie and share of the pie: Implications of 

structural embeddedness for value creation and value appropriation in joint ventures.

Research in the Sociology of Organizations, 20: 209-242.

Hagedoorn, J., Link, A.N., and Vonortas, N.S.. 2000. Research partnerships. Research 

Policy, 29(4-5): 567-586.

References 4 3
Haleblian, J. and Finkelstein, S. 1999. The influence of organizational acquisition experience

on acquisition performance: A behavioral learning perspective. Administrative Science

Quarterly, 44(1): 29-56.

Hamel, G. 1991. Competition for competence and inter-partner learning within international strategic alliances. Strategic Management Journal, 12(Special Issue: Global 

Strategy): 83-103.

Hannan, M.T. and Freeman, J. 1984. Structural inertia and organizational change. American

Sociological Review, 49(2): 149-164.

Hargadon, A. and Sutton, R.I. 1997. Technology brokering and innovation in a product 

development firm. Administrative Science Quarterly. 42(4): 716-749.

Hargadon, A. and Sutton, R.I. 2000. Building an innovation factory. Harvard Business

Review. 78(3): 157-166.

Harrigan, K.R. 1985. Strategies for joint ventures. Lexington, MA: Lexington Books.

Hebert, L. and Beamish, P.W. 1997. Characteristics of Canada-based international joint 

ventures. In P.W. Beamish and J.P. Killing (Eds.), Cooperative strategies: North American

perspectives: 403-427. San Francisco, CA: New Lexington Press.

Hoang, H. and Rothaermel, F.T. 2005. The effect of general and partner-specific alliance 

experience on joint R&D project performance. Academy of Management Journal, 

48(2): 332-345.

Irwin, D.A. and Klenow, P.J. 1996. High Tech R&D Subsidies. Journal of International 

Economics, 40(3–4), 323–344.

James, L.R. 1982. Aggregation bias in estimates of perceptual agreement. Journal of Applied

Psychology, 67(2): 219-229.

Kale, P., Dyer, J.H., and Singh, H. 2002. Alliance capability, stock market response, and 

long-term alliance success: The role of the alliance function. Strategic Management 

Journal, 23(8): 747-767.

Kale, P., Singh, H., and Perlmutter, H. 2000. Learning and protection of proprietary assets 

in strategic alliances: Building relational capital. Strategic Management Journal, 21(3):

217-237.

Katz, M.L. 1986. An analysis of cooperative research and development. Rand Journal of 

Economics, 17(4): 527-543.

4 4 The Determinants of Success in R&D Alliances 
Klein, B., Crawford, R.G., and Alchian, A.A. 1978. Vertical integration, appropriable rents,

and the competitive contracting process. Journal of Law and Economics, 21(2): 297-326.

Kogut, B. 1988. Joint ventures: Theoretical and empirical perspectives. Strategic Management

Journal, 9(4): 319-332.

Kogut, B. 1989. The stability of joint ventures: Reciprocity and competitive rivalry. Journal 

of Industrial Economics, 38(2): 183-198.

Lane, P.J. and Lubatkin, M. 1998. Relative absorptive capacity and interorganizational 

learning. Strategic Management Journal, 19(5): 461-477.

Larson, A. 1992. Network dyads in entrepreneurial settings: A study of the governance of

exchange relationships. Administrative Science Quarterly, 37(1): 76-104.

Levitt, B. and March, J.G. 1988. Organizational learning. Annual Review of Sociology, 

14(3): 319-340.

Macaulay, S. 1963. Non-contractual relations in business: A preliminary study. American 

Sociological Review, 28(1): 55-69.

March, J.G. and Simon, H.A. 1958. Organizations. New York, NY: Wiley.

Mayer, R.C., Davis, J.H., and Schoorman, F.D. 1995. An integrative model of organizational

trust. Academy of Management Review, 20(3): 709-734.

Merchant, H. and Schendel, D. 2000. How do international joint ventures create shareholder

value? Strategic Management Journal, 21(7): 723-737.

Mohr, J. and Nevin, J.R. 1990. Communication strategies in marketing channels: A theoretical

perspective. Journal of Marketing, 54(4):36-51.

Mora-Valentin, E.M., Montoro-Sanchez, A., and Guerras-Martin, L.A. 2004. Determining 

factors in the success of R&D cooperative agreements between firms and research 

organizations. Research Policy, 33(1): 17-40.

Mowery, D.C., Oxley, J.E., and Silverman, B.S. 1996. Strategic alliances and interfirm 

knowledge transfer. Strategic Management Journal, 17(Winter Special Issue): 77-91.

Nelson, R.R. and Winter, S.G. 1982. An evolutionary theory of economic change. 

Cambridge, MA: Belknap Press.

Nishiguchi, T. 1994. Strategic industrial sourcing: The Japanese advantage. New York, NY:

Oxford University Press.

References 4 5
Ostroff, C. 1993. Comparing correlations based on individual-level and aggregated data. 

Journal of Applied Psychology, 78(2): 569-582.

Ouchi, W.G. and Bolton, M.K. 1988. The logic of joint research and development. 

California Management Review, 30(3): 9-33.

Oxley, J.E. 1997. Appropriability hazards and governance in strategic alliances: A transaction

cost approach. Journal of Law, Economics and Organization, 13(2): 387-409.

Oxley, J.E. and Sampson, R.C. 2004. The scope and governance of international R&D

alliances. Strategic Management Journal, 25(8-9): 723-749.

Powell, W.W., Koput, K.W., and Smith-Doerr, L. 1996. Interorganizational collaboration and

the locus of innovation: Networks of learning in biotechnology. Administrative Science

Quarterly, 41(1): 116-145.

Sakakibara, M. 1997. Evaluating government-sponsored R&D consortia in Japan: 

Who benefits and how? Research Policy, 26(4-5): 447-473.

Sakakibara, M. 2001. The diversity of R&D consortia and firm behavior: Evidence from

Japanese data. Journal of Industrial Economics, 49(2): 181-196.

Sakakibara, M. 2002. Formation of R&D consortia: Industry and company effects. 

Strategic Management Journal, 23(11): 1033-1050.

Sakakibara, M. and Branstetter, L. 2003. Measuring the impact of US research consortia.

Managerial and Decision Economics, 24(2-3): 51-69.

Sako, M. 1991. The role of “trust” in Japanese buyer-supplier relationships. Ricerche 

Economiche XLV, 45(2-3): 449-474.

Sampson, R.C. 2004. The cost of misaligned governance in R&D alliances. Journal of Law,

Economics and Organization, 20(2): 484-526.

Sampson, R.C. 2005. Experience effects and collaborative returns in R&D alliances. 

Strategic Management Journal, 26(11): 1009-1032.

Saxenian, A. (1994). Regional Advantage. Cambridge, MA: Harvard University Press.

Scherer, F.M. and Harhoff, D. 2000. Technology policy for a world of skew-distributed 

outcomes. Research Policy, 29(4-5): 559-566.

Siegel, D. S. 2003. Data requirements for assessing the private and social returns to strategic

research partnerships: Analysis and recommendations. Technology Analysis & Strategic

Management, 15(2): 207-225.

4 6 The Determinants of Success in R&D Alliances 
References 4 7

Spence, A.M. 1984. Cost Reduction, Competition, and Industry Performance. Econometrica,

52(1): 101–21.

Stuart, T.E. 2000. Interorganizational alliances and the performance of firms: a study of

growth and innovation rates in a high-technology industry. Strategic Management 

Journal, 21(8): 791-811.

Szulanski G. 1996. Exploring internal stickness: Impediments to the transfer of best practice

within the firm. Strategic Management Journal, 17(Winter Special Issue): 27-43.

Teece, D.J., Pisano, G., and Shuen, A. 1997. Dynamic capabilities and strategic management.

Strategic Management Journal, 18(7): 509-533.

Tripsas, M., Schrader, S., and Sobrero, M. 1995. Discouraging opportunistic behavior in 

collaborative R & D: A new role for government. Research Policy, 24(3): 367-389.

Tuchi, C. 1996. Firm heterogeneity and performance of strategic alliances. Working paper,

MIT Sloan School of Management. Cambridge, MA.

Williamson, O.E. 1985. The economic institutions of capitalism: Firms, markets, relational

contracting. New York, NY: Free Press.

Williamson, O.E. 1993. Calculativeness, trust, and economic organization. Journal of Law 

and Economics, 36(1): 453-486.

Zaheer, A., McEvily, B., and Perrone, V. 1998. Does trust matter? Exploring the effects of

interorganizational and interpersonal trust on performance. Organization Science, 9(2):

141-159.

Zaheer, A. and George, V.P. 2004. Reach out or reach within? Performance implications of

alliances and location in biotechnology. Managerial and Decision Economics, 25(6-7):

437-452.

Zollo, M., Reuer, J.J., and Singh, H. 2002. Interorganizational routines and performance in

strategic alliances. Organization Science, 13(6): 701.

About the Authors

Jeffrey H. Dyer is the Horace Pratt Beesley Professor of Global Strategy at the Marriott 

School of Management, Brigham Young University. His research focuses on strategic alliances,

creativity and innovation, and interorganizational learning and knowledge management. 

He serves on the editorial board of Strategic Management Journal, Organization Science, 

and Strategic Organization. He has consulted at companies such as Motorola, Ford, Navistar,

Bang & Olufsen. He received his Ph.D. in Management from the University of California, 

Los Angeles. 

Benjamin C. Powell is Assistant Professor of Management at the Culverhouse College 

on Commerce and Business Administration, University of Alabama. His research focuses on 

organizational formation and knowledge processes, interorganizational learning and R&D

alliances. He received his Ph.D. in Management from the University of Pennsylvania.

Mariko Sakakibara is Associate Professor of Policy at the Anderson School of Management

at the University of California, Los Angeles (UCLA). Her research interests include innovation,

alliances, multinational enterprises, and global competition. Prior to coming to the United

States as a Fulbright Scholar, she was Deputy Director at the Ministry of International Trade

and Industry, Japan. She received her Ph.D. in Business Economics from Harvard University. 

Andrew J. Wang is Economist in the Economic Assessment Office at the Advanced 

Technology Program, National Institute of Standards and Technology (NIST). He conducts

research relevant to program evaluation and policy. He has managed economic studies and

other projects, and has led development of special surveys of program participants. He

received his Ph.D. in Economics from Harvard University.

4 9
About the Advanced Technology Program

The Advanced Technology Program (ATP) is a partnership between government and private

industry to conduct high-risk research to develop enabling technologies that promise significant commercial payoffs and widespread benefits for the economy. ATP provides a mechanism

for industry to extend its technological reach and push the envelope beyond what it otherwise

would attempt.

Promising future technologies are the domain of ATP:

• Enabling or platform technologies essential to development of future new products,

processes, or services across diverse application areas

• Technologies where challenging technical issues stand in the way of success

• Technologies that involve complex “systems” problems requiring a collaborative

effort by multiple organizations

• Technologies that will remain undeveloped, or proceed too slowly to be competitive in

global markets, in the absence of ATP support

ATP funds technical research, but does not fund product developmentæ that is the responsibility of the company participants. ATP is industry driven, and is grounded in real-world

needs. Company participants conceive, propose, co-fund, and execute all of the projects costshared by ATP. Most projects also include participation by universities and other nonprofit

organizations.

Each project has specific goals, funding allocations, and completion dates established at

the outset. All projects are selected in rigorous competitions that use peer review to identify

those that score highest on technical and economic criteria. Single-company projects can have

duration up to three years; joint venture projects involving two or more companies can have

duration up to five years.

Small firms on single-company projects cover at least all indirect costs associated with the

project. Large firms on single-company projects cover at least 60 percent of total project costs.

Participants in joint venture projects cover at least half of total project costs. Companies of all

sizes participate in ATP-funded projects. To date, nearly two out of three ATP project awards

have gone to individual small businesses or to joint ventures led by a small business.

Contact ATP for more information:

• On the Internet: www.atp.nist.gov

• By e-mail: atp@nist.gov

• By phone: 1-800-ATP-FUND (1-800-287-3863)

• By writing: Advanced Technology Program, National Institute of Standards and 

Technology, 100 Bureau Drive, Stop 4701, Gaithersburg, MD 20899-4701