Part 1 - 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 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 cost-benefit 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 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.

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.

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Date created: August 29, 2006
Last updated: September 11, 2006

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