Interorganizational Routines and Performance in Strategic Alliances

Interorganizational Routines and Performance in Strategic Alliances Maurizio Zollo • Jeffrey J. Reuer • Harbir Singh Strategy and Management Department, INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France Fisher College of Business, Ohio State University, 2100 Neil Avenue, Columbus, Ohio 43210 Management Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104 [email protected] • [email protected] • [email protected]

Abstract This paper applies evolutionary economics reasoning to the strategic alliance context and examines whether and how routinization processes at the partnering-firm level influence the performance of the cooperative agreement. In doing so, it introduces the concept of interorganizational routines, defined as stable patterns of interaction among two firms developed and refined in the course of repeated collaborations, and suggests that partner-specific, technology-specific, and general experience accumulation at the partnering-firm level influence the extent to which alliances result in knowledge accumulation, create new growth opportunities, and enable partnering firms to achieve their strategic objectives. We also consider how governance design choices at the transaction level shape the effectiveness of interorganizational routizination processes. Based on a sample of 145 biotechnology alliances, we find that only partner-specific experience has a positive impact on alliance performance, and that this effect is stronger in the absence of equity-based governance mechanisms. We interpret these results to support the role of interfirm coordination and cooperation routines in enhancing the effectiveness of collaborative agreements. (Strategic Alliances; Organizational Routines; Evolutionary Economics; Transaction-Cost Economics; Organizational Learning; Knowledge; Biotechnology; Experience Curves)

Introduction In his recent extensive survey of the literature on strategic alliances, Gulati (1998) portrays the problem of understanding the factors influencing alliance performance as “one of the most interesting and also one of the most vexing questions” on the research agenda (p. 309). Indeed, the complexity of this problem has been widely recognized in research over the last decade. The difficulties associated with studying alliance performance have

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been attributed to many factors, including the lack of consensus around a typology of collaborative agreements, diversity in firms’ strategic intents in pursuing alliances, and the lack of objective performance data (e.g., Anderson 1990, Geringer and Hebert 1991, Kogut 1988). These obstacles notwithstanding, the theoretical and practical relevance of identifying antecedents of alliance performance provides a strong motivation for research that moves beyond firms’ initial governance choices or indirect proxies such as alliance survival to study collaborators’ specific alliance outcomes. This paper examines the performance of strategic alliances—defined as cooperative agreements of any form aimed at the development, manufacture, and/or distribution of new products—in the context of the biotechnology industry and develops a set of hypotheses based on evolutionary economics to explain differences in alliance performance. To this end, the notion of interorganizational routines is developed and used to provide a novel theoretical explanation for the performance implications of prior ties among partnering firms. The present analysis aims to contribute to our current understanding of strategic alliances in several ways. First, we develop a direct, theoretically grounded measure of alliance performance using insights from learning- and options-based approaches to alliance research. Following various learning views on alliances (e.g., Hamel 1991, Inkpen and Beamish 1997, Khanna et al. 1998) and other perspectives noting that alliances provide firms with valuable stepping stones in order to commit sequentially in uncertain investment contexts (e.g., Balakrishnan and Koza 1993, Kogut 1991), we focus on a firm’s overall achievement of its objectives in the alliance as well as the degree to which the collaboration contributes to the firm’s accumulation of knowledge and to the creation of new opportunities for the firm. Second, the paper provides a theoretical basis for the ORGANIZATION SCIENCE, 2002 INFORMS Vol. 13, No. 6, November–December 2002, pp. 701–713

MAURIZIO ZOLLO, JEFFREY J. REUER, AND HARBIR SINGH Interorganizational Routines and Performance in Strategic Alliances

study of the different types of learning processes firms undergo through the accumulation of experience in the management of strategic alliances (Reuer et al. 2002). We contrast three stocks of collective experience that approximate how firms involved in these kinds of cooperative ventures learn about (a) how to handle the complexities of the alliance process (general alliance management knowledge), (b) the technology and the specific knowledge required to develop new products in the specific area of interest (technological or product-specific knowledge), and (c) the partnering firms themselves (partner-specific knowledge). Interorganizational routines are posited to develop through this last type of experience trajectory. Although firms’ serial investments in alliances potentially provide learning opportunities, experiential learning should not, however, be taken for granted. Alliances are in fact considerably more infrequent (e.g., March et al. 1991), heterogeneous, and causally ambiguous (Lippman and Rumelt 1982) than manufacturing processes and other administrative tasks for which experience effects have been documented (see Argote 1999 for a review). By considering alternative alliance experience trajectories, we hope to unpack the general learning problem in this type of corporate development process and assess the degree to which the various components of organizational knowledge accumulated by partnering firms actually translate into improved performance. Finally, the paper considers the interaction effect between partner-specific experience and alliance governance design to provide a direct test of how the performance effects of prior ties differ across alternative alliance governance structures. Previous research has shown that partnering firms tend to choose nonequity structures in the presence of prior ties and equity structures otherwise (e.g., Gulati 1995, Gulati and Singh 1998), but has not tested the performance outcomes of prior ties and the potential interaction with governance design. In an initial effort to identify the boundaries of the effectiveness of interorganizational routines, we offer the argument that the development and refinement of such routines will be more important in shaping the performance of nonequity-, as opposed to equity-based, collaborations because the former lack the coordination potential and alignment of incentives available through equity governance.

Theory and Hypotheses The performance of strategic alliances has been researched directly, as well as indirectly through reducedform models, from a variety of perspectives such as transaction-cost theory (e.g., Balakrishnan and Koza

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1993, Hennart and Reddy 1997, Oxley 1997), real options theory (Kogut 1991), agency theory (Reuer and Miller 1997, Wild 1994), and organizational learning theory (e.g., Barkema et al. 1997). Recently, a number of scholars have also advanced explanations of alliance performance based on the partnering firms’ abilities to manage the postformation dynamics of their interaction (Arinõ and de la Torre 1998, Doz 1996, Doz and Hamel 1998). Similarly, current research on interfirm collaboration has posed the question as to whether an alliance capability exists and how firms might cultivate it (e.g., Zajac 1998). Our specific objective is to contribute to this stream of work by offering a theoretical treatment and an empirical test of the performance effects of different types of alliance experience. General Collaborative Experience The rationale for hypothesizing that a firm’s accumulation of experience in an organizational activity will translate into improved performance for the activity has been discussed at length in several streams of research. The learning curve literature in the operations management area has investigated the phenomenon of decreasing unit costs with the accumulation of production experience (Dutton and Thomas 1984, Epple et al. 1991, Yelle 1979). More recently, this stream of work has inquired into the shapes of learning curves and the factors affecting the shapes of learning curves (e.g., Lapre and Van Wassenhove 1998, Lapre et al. 1998). The behavioral school in organizational studies has approached the problem with a broader view of the learning process, considering the entire range of activities being performed within the boundaries of an organization (Cyert and March 1963, Levitt and March 1988, March and Simon 1958). Further, the evolutionary economics school has developed theory on how organizations change in time and space based on the evolution, adaptation, and replication of routinized behavior (Cohen and Bacdayan 1994; Nelson and Winter 1982; Winter 1987, 1995). Firms accumulate a collective understanding about the execution of organizational tasks, which is tacitly (i.e., without explicit articulation or codification) updated and refined to achieve continuous marginal improvements in performance. All of these theoretical traditions suggest a positive relationship between a firm’s general alliance experience and the performance implications of the focal agreement. They also provide the foundations for arguments pointing to the importance of mastering complex alliance processes in order to enhance the likelihood of alliance success (e.g., Doz 1996, Doz and Hamel 1998). Anand and Khanna (2000) used event study methods and found initial evidence for experience effects for joint ventures, but

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not licensing agreements. The performance effects of JV formation are larger for firms with greater experience in joint ventures, particularly research JVs. Also, Barkema et al. (1997) report that domestic joint venture experience, but not international joint venture (IJV) experience, promotes IJV longevity. Thus, there appears to be a substantial theoretical basis as well as some emerging empirical support for the following general hypothesis: HYPOTHESIS 1. The greater the number of previous alliances established by a firm, the better the performance of the focal alliance. While the vast majority of empirical analyses on experiential learning study internal manufacturing or R&D activities as the types of tasks subject to organizational learning processes, we are interested in the application of these theoretical insights to the strategic alliance context. Whether or not experience effects will be manifest in the alliance setting is ultimately an empirical question, however, since several factors may be in operation that mitigate the potential benefits of alliance experience. First, alliances are typically less frequent and more heterogeneous than many manufacturing and R&D processes. The lower frequency and greater heterogeneity of alliance activity increases the probability that lessons learned from previous experiences will be applied to a context which is superficially similar but inherently different, a problem cognitive psychologists refer to as negative transfer effects (Cohen and Bacdayan 1994, Gick and Holyoak 1987). Haleblian and Finkelstein (1999) have shown that some of these problems might arise in the context of corporate acquisitions, which share many of the traits of strategic alliances from a learning point of view. Second, as administrative processes, alliances are characterized by significant ambiguity and behavioral uncertainty, making it difficult to attach time, quality, and cost dimensions to interfirm collaboration (e.g., Anderson 1990, Geringer and Hebert 1991). Third, the paucity of performance metrics is a related obstacle to the firm’s attempts to incrementally adapt its routines and thereby improve its performance in future collaborations. Thus, the performance measurement problem not only makes alliances difficult for researchers to study, but also makes them hard for firms to learn. These issues make an empirical test of Hypothesis 1 interesting because these factors may partially or wholly offset the theorized benefits of experience accumulation. These issues also lead us to examine more specific types of alliance experience, as discussed below. Technology-Specific Experience The accumulation of expertise from previous alliances completed in similar technical domains is the second

learning process that we take into consideration in our performance model. Apart from the benefits of alliance experience in general that were discussed above, technology-specific alliance experience potentially has a positive impact on alliance performance for two additional reasons. First, Cohen and Levinthal (1990) have theorized and empirically shown that firms engaged in creative efforts accumulate an absorptive capacity proportional to the amount of previous discovery made in similar domains. If this is true, then prior alliances in the same or highly related technological areas should build a proportionally higher degree of absorptive capacity at the firm level, which will enhance the probability of success of new exploratory ventures. Second, firms’ experiences specific to a given technological area will likely be less heterogeneous than alliance experience in general. While positive technology-specific alliance experience effects can still potentially be mitigated by factors such as low frequency, ambiguity, and the lack of performance metrics, the likelihood of negative transfer effects should be lower for alliance experience derived from similar areas.1 We hypothesize: HYPOTHESIS 2. The greater the number of previous alliances established by a firm in the same technological area as the focal alliance, the better the performance of the focal alliance. Partner-Specific Experience The process of alliance implementation produces a wide range of knowledge spillovers, over and above the improved understanding of the managerial challenges specific to the alliance process or the alliance’s technological domain. One key body of knowledge accumulated during alliance activity concerns the partnering firms themselves. As they work through the operational details of the collaborative agreement, both partners develop a more refined understanding of each other’s cultures, management systems, capabilities, weaknesses, and so forth. Thus, by engaging in multiple alliances with each other over time, partners might tacitly develop a set of routines which undergird the way they interact among themselves. Every time partners add another collaborative agreement, they have an opportunity to reinforce and adapt these interorganizational routines, which can progressively smoothen their interaction patterns. The fact that the two groups of individuals cooperating across firm boundaries develop this form of understanding of each other’s behaviors and beliefs tends to help mitigate coordination, conflict resolution, or information-gathering problems, which in turn facilitates iterative learning and adjustment cycles (Doz 1996). For example, consider two groups of scientists having


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to coordinate their sequential testing activities on new compounds, the execution of which is assigned to each group depending on their areas of specialty. During the first alliance experience, one could expect several inefficiencies in the swapping of the testing activity to occur simply because each group does not know how the other operates and cannot predict when they need to prepare for their turn to act. Patterns and modes of communication might not have been established, and the result might be both “idle” time waiting for one’s turn to spring into action, as well as “rush” activities when the turn to act comes up unexpectedly. Similar benefits from the existence of prior cooperation experiences can be expected to arise from the tacit development of procedures for troubleshooting (e.g., “if test X does not work, tell the senior manager Smith and then try an alternative joint test Y in collaboration with Mr. Jones of the other department of the partnering firm”) or for resolving disputes (“don’t worry if Mr. Smith complains, he does that all the time; just make sure Mr. Scott has a word with him, and he will come around”). Related evidence in the supply-chain management literature suggests that the development of interfirm coordination routines partially accounts for the performance of Japanese automotive manufacturers (Dyer 1997). Furthermore, Dyer and Singh (1998) reason that relationship-specific knowledge stemming from frequent and intense partner interactions leads to a relational capability that can translate into improved transactional outcomes as well as firm-level competitive advantages. It is worth noting that the above arguments rest primarily on collaborators’ development of interorganizational routines through multiple alliances. A second theoretical mechanism supporting positive partner-specific alliance experience effects is the development of interpersonal trust among the members of the two organizations. According to this view, collaborating firms would benefit from frequent prior interactions with the same partner, and the hazards of opportunistic behavior would be reduced (Gulati 1995). Both arguments might play a distinctive role in the explanation of alliance performance, and are unlikely to be mutually exclusive. Trust, however, requires stronger conditions than mere frequency of interaction to develop (Ring and Van De Ven 1992). Consequently, one might infer that in an alliance context where two firms have accumulated a history of cooperation, the presence of interorganizational routines can be considered highly probable, while the development of trust can be taken much less for granted. As an illustrative example, consider Hewlett Packard, which had a total of 18 alliances with Cisco Systems and more than 30 with Microsoft. In a series of interviews that we performed in the context of a different project, managers

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responsible for the overall relationship with these key partners acknowledged an improvement in the quality of the ongoing interactions over time, but denied that they would expect their partners to forgo opportunities to hold them up. In other words, familiarity might breed trust (Gulati 1995), but it most probably breeds cooperation and coordination routines among partners, which might in turn generate positive learning effects according to evolutionary economics. HYPOTHESIS 3. The greater the number of previous alliances established by a firm with the same partner, the better the performance of the focal alliance. Partner-Specific Experience and Governance Design The arguments set forth above suggest that partnerspecific alliance experience accumulation will be beneficial to collaborators because of the advantages stemming from the development of interpartner cooperative routines. To further our analysis of the performance implications of partner-specific experience trajectories, it is important to take into consideration contingencies that might enhance (or weaken) the strength of this causal linkage. One important contingency identified by transactioncost theory is the governance structure put in place by the two partners at the time of the alliance’s formation. A standard method of distinguishing these governance structures is to categorize collaborative agreements into nonequity and equity arrangements. The former are governed solely through a contract, while the latter employ equity ownership as well. For the purposes of this paper, an equity-based alliance can involve one partner owning a minority stake in the other partnering firm, or the two firms can create a new entity whose ownership is shared among them (i.e., a joint venture). Because equity and nonequity alliances have different underlying governance properties that affect their functioning (e.g., Gulati and Singh 1998, Oxley 1997, Pisano 1989), we expect that the performance implications of the routines developed through partner-specific experience will differ across equity and nonequity alliances. The alignment of partners’ incentives through ownership and the enhanced coordination through joint control are the two most important distinctions between equity and nonequity alliances (e.g., Borys and Jemison 1989, Chi 1994). In equity alliances, ownership creates a situation of residual claimancy that serves to align the partner’s incentives more closely than a contract alone can (e.g., Hennart 1988). Monitoring rights also may be more extensive if, for example, a separate business entity is created or a new board is in place to control a joint venture’s activities. This suggests that the development of



interorganizational routines will be particularly important in nonequity agreements because they lack the incentive alignment and control properties associated with equity alliances. By contrast, the beneficial effects of interorganizational routines developed through prior collaborations with the partner firm will be less pronounced when equity-based structures and their associated governance mechanisms (e.g., greater incentive alignment, control, etc.) are in place. HYPOTHESIS 4. The positive performance effect of partner-specific experience will be greater for nonequity alliances than for equity alliances. It is important to note that the arguments supporting this hypothesis stem from the combined application of evolutionary economics and transaction-cost economics reasoning. The opportunity to intersect these two theoretical domains, typically acting at two different levels of analysis (i.e., the firm for the former, the transaction for the latter), is provided by the fact that partner-specific experience accumulation is a natural bridge between these two levels. By definition, the construct is the result of accumulated patterns of interactions among firms in a given dyad, which will influence the way the two firms will negotiate future agreements among themselves due to the path-dependent nature of repeated behavior within a given boundary (March and Simon 1958, Nelson and Winter 1982, March 1994), or dyad in this case. Based on this reasoning from evolutionary economics, these arguments would not apply to the potential interaction of the other experience trajectories with governance choices. The accumulation of general experience in alliance processes, as well as in specific technological domains, act above the dyadic level of analysis. In addition to the variables discussed above, we sought to control for specific features of the alliance and changes in the relationship over time. We first controlled for alliances’ relevance in terms of partnering-firms’ resource contributions because commitment-intensive alliances will attract greater attention and receive more involvement by top managers (Ocasio 1997) and can supply firms with greater incentives to make the best of the collaborative agreement (Williamson 1983). We also controlled for the alliance’s division of labor because collaborative agreements with a clearer allocation of responsibilities will be likely to experience fewer uncertainties and coordination difficulties (Borys and Jemison 1989, Oxley 1997). Alliance research also stresses the importance of designing alliances so that partners bring complementary capabilities to the collaboration (Geringer 1988). Coordination can be achieved not only by clearly assigning responsibilities to partners or by instituting equity to align

incentives, so we controlled for whether firms had a committee in place to coordinate the execution of the collaboration. We also introduced a control for whether the alliance had an R&D component, because such alliances expose firms to greater contractual hazards. Pisano (1989, p. 114) suggests that in the biotechnology industry, “the type of transaction-specific assets most likely to affect collaborations are those related to human capital or knowhow.” Thus, although we examined only one dimension of asset specificity (Williamson 1983), it is apt to be the most important one in this industry context. Finally, we examined whether partnering firms altered the collaborative agreement itself (i.e., Contract Alterations) and whether firms introduced or formalized monitoring mechanisms for the collaboration (i.e., Monitoring Changes), because such adaptations have recently been emphasized as important to alliance performance (Arinõ and de la Torre 1998, Doz and Hamel 1998).

Methodology Sample and Data The data utilized in this study comes from a survey of biotech and pharmaceutical firms engaged in strategic alliances. While most of our predictions extend to alliances in other industry settings, the biotechnology industry is an interesting context for this study due to the strong rise in the use of collaborative agreements as key tools to implement firms’ growth strategies. The University of North Carolina’s (1993) database on biotech alliances was first used to identify a relevant target population of alliances. This initial search generated a total of 753 collaborative agreements in the human diagnostic and therapeutic treatments and equipment subfields, completed by a total of 655 companies as one of the partners, in the period 1982–1994. One of the two partners for each alliance (378 firms) was then randomly selected to be a potential recipient of the survey, and the BioScan database as well as other library sources were then used to obtain the addresses of as many firms as possible involved in the identified agreements. The resulting dataset included 262 firms involved in 445 alliances for which a complete address and contact person was still available at the time of data collection.2 The survey we designed was pretested using five industry experts. These experts completed and critiqued a draft survey. Following modifications, the two-page questionnaire was faxed or mailed to the CEOs of the targeted sample of firms. An accompanying letter explained the study’s aims and promised a report on the principal findings. This letter to the CEO also requested that the questionnaire be forwarded to the individual who was most


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knowledgeable about the agreement indicated on the survey. After two rounds of follow-up calls, 81 firms completed questionnaires for 145 agreements, which corresponds to a 30.9% response rate (81/262).3 This response rate was considered satisfactory given the seniority of respondents as well as the heavy surveying activity in this industry. CEOs make up 26.9% of the respondents, 33.7% are business development staff, and 39.5% are alliance managers. The sample was formed by an almost even share of alliances between two biotech firms or research institutes (52.9%) vis-a`-vis alliances formed between a biotech firm and a large pharmaceutical company (47.1%). Seventy-six of the alliances in the sample were terminated, and the overall duration of the agreements (up to termination or the time of the survey) was 54 months (std. dev. 35.3, ranging from 2 to 164 months). The final sample of agreements covers 32.6% of the total number of observable transactions (i.e., 145/445), and no response biases could be statistically detected with respect to the experience levels of respondents versus the total sample of firms. To further explore the possibility of nonresponse bias, we assessed possible differences in all of the variables across early and late respondents under the assumption that late respondents are more similar to nonrespondents than early respondents are to nonrespondents (Armstrong and Overton 1977). Two-sample t-tests and chi-square tests on all of the continuous and categorical variables in this study, respectively, indicated that early and late respondents do not differ from one another, with one exception: Late respondents are more likely to have introduced or formalized monitoring mechanisms in the alliance (v2 11.10, p 0.01). Thus, we conclude that there is little evidence of nonresponse bias for our study. Because information on alliance performance as well as on features of the alliance and partnering firm were obtained from the same respondent, we sought to address the possibility of consistency artifacts. The independent variables are objective measures rather than opinions, and qualitative performance assessments are positioned later in the survey. While this enhances the potential for common methods bias, we used Harman’s (1967) singlefactor test to examine whether a significant amount of common variance exists in the data (e.g., Doty and Glick 1998). If so, a factor analysis of all of the variables will generate a single factor or a general factor that accounts for most of the variance (Podsakoff and Organ 1986). However, an unrotated factor analysis using the eigenvalue-greater-than-one criterion revealed five distinct factors, and the first factor captured only 18.7% of the variance in the data. Furthermore, the alliance performance measure loaded on one factor, and the independent variables loaded on two others.

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Measures Alliance Performance. The relatively small number of studies on alliance performance can be partially attributed to the significant challenges researchers face in measuring alliance performance.4 We used three perceptual indicators of alliance performance to directly gauge the implications of alliances for partnering firms. In choosing these indicators, we sought to capture alliance outcomes that partnering firms would value in a high-tech sector such as the biotechnology industry. First, respondents indicated on a five-point Likert scale their satisfaction with the knowledge accumulated from participating in the collaborative agreement. Alliance research identifies knowledge accumulation as a key organizational outcome of interfirm collaboration (e.g., Hamel 1991, Inkpen and Beamish 1997, Khanna et al. 1998). Second, respondents indicated the extent to which the alliance created new opportunities for the firm. Many alliances evolve beyond partnering firms’ initial expectations, and the real option view of alliances in particular emphasizes the creation of new, often unexpected, opportunities as an important source of value from collaboration (e.g., Kogut 1991). Finally, to capture other elements of firms’ strategic intents in engaging in alliances, respondents were asked to rate the degree to which the alliance satisfied the partnering firm’s initial objectives. These three indicators were standardized and summed to construct a global measure of alliance performance (i.e., alliance performance), which had a satisfactory Cronbach alpha of 0.83 (Nunnally 1978).5 Explanatory Variables. To measure each firm’s experience with alliances, we asked respondents to report the number of prior agreements with any partner on any subject (i.e., General Collaborative Experience), the number of prior agreements with any partner in product areas similar to the one of the focal agreement (i.e., technologyspecific experience), and the number of prior agreements with the same partner (i.e., partner-specific experience). Examination of each of these variables’ distributions indicated significant positive skewness, so we redefined these three variables using the natural log transformation “new variable” ln(1 “old variable”).6 As discussed above, the second set of independent variables serves to characterize the collaborative agreement at the time of its formation. A dummy variable indicated whether or not the collaborative agreement was an equity alliance (i.e., equity). To assess the relative importance of the alliance, respondents were asked to describe the relevance of the alliance for the overall business of the partnering firm in terms of the relative size of available resources committed. The variable can take on values ranging from 1 to 4, which correspond to “marginal,” “normal,” “important,” and “critical.” To construct a



measure for the alliance’s division of labor, we asked respondents to indicate partners’ responsibilities by allocating 100 percentage points between the collaborators across six functionally defined categories. The division of labor in the collaboration was then measured as follows: 1 Division of labori ni

ni

兺

|P1ij P2ij|,

(1)

j1

where ni is the number of project activities undertaken by alliance i, P1ij is the percentage representing Firm 1’s responsibility for task j, and P2ij is the percentage indicating Firm 2’s responsibility for task j (i.e., P1ij P2ij 100% for all j). The larger the variable, the greater the alliance’s division of labor. Prior research has used dichotomous measures to distinguish alliances’ division of labor such as X-type vs. Y-type collaborations (Porter and Fuller 1986), sequential versus integrative alliances (Park and Russo 1996), and scale vs. link collaborative agreements (Dussauge et al. 2000). Our measure has the advantages of being continuous and being more fine grained in getting to allocations of specific task responsibilities. We also introduced a binary indicator to account for the presence or absence of a committee that facilitates the execution of collaborative activities (i.e., coordination committee), and included a dummy variable to indicate whether the alliance had an R&D component (i.e., R&D). The final two control variables address changes in the alliance’s governance structure after the collaboration has been set up. First, we controlled for possible ex post changes in the collaborative agreement using a dummy variable to account for the existence of contract alterations in the alliance (i.e., contract alterations). Second, we incorporated a dummy variable to indicate whether the partners introduced or formalized monitoring mechanisms after the alliance’s establishment (i.e., monitoring changes). In the Appendix* we report on the key questions included in the survey used to operationalize the constructs included in our model.

Results Table 1 presents descriptive statistics and a correlation matrix. The table indicates that firms’ experiences with alliances were very heterogeneous. The firms’ average number of prior alliances with any partner in any technological domain was 12.6 and ranged from 0 to well over 100. Similarly, the number of prior alliances on related technological subjects averaged 1.2 and ranged from 0 to 20. Finally, the average number of prior alliances with the partner in question was 0.2 and ranged from 0 to 5. The correlations among the independent variables, as well as the inclusion of the partner-specific experience •

equity multiplicative term, raise the possibility of multicollinearity problems. However, the maximum variance inflation factor (VIF) for variables in all of these models was 3.6, which is below the rule-of-thumb cutoff value of 10 for multiple regression models (Neter et al. 1985, p. 392). Regression diagnostics revealed the presence of several outlying observations. We eliminated outliers from the analysis when their DFFITS values exceeded 2冪p/n in absolute value, where p is the number of estimated parameters and n is the sample size (Belsley et al. 1980). Table 2 presents the results from the multiple regression analyses of alliance performance. Comparisons of the three models in the table were made using hierarchical F-tests. Model 3 provides a significant improvement in explanatory power over Models 1 and 2 (i.e., F(3),(2) 6.54, p 0.05; F(3),(1) 3.42, p 0.05). Model 2 appears to explain the variance in alliance performance marginally better than Model 1 (i.e., F(2),(1) 2.23, p 0.10). Drawing upon evolutionary economics, Hypotheses 1 and 2 posited that general collaborative experience and technology-specific experience would positively influence alliance performance. The results presented in Table 2 provide no support for these two hypotheses. In considering the functional relationship between alliance experience and performance, it is instructive to consider work in cognitive psychology that has uncovered a U-shaped relationship between experience and performance in individual tasks (e.g., Cormier and Hagman 1987). The reasoning advanced by this stream of research is that at low levels of experience, agents inappropriately apply experience to a task that is superficially similar, yet fundamentally different, from prior ones. Based on Haleblian and Finkelstein’s (1999) evidence for a Ushaped experience-performance relationship in the M&A context, we sought to test whether such processes might account for the insignificant linear effect of alliance experience. We modeled alliance performance in Models 1 through 3 as a function of general collaborative experience and its square term, both of which were not transformed, but we found no evidence of a U-shaped relationship indicative of negative transfer effects. The results do indicate, however, that partner-specific experience fosters better alliance performance. Model 2 indicates that the greater the firm’s prior alliance experience with the partner, the better the performance of the focal alliance (p 0.05). The beneficial effects of partner-specific alliance experience are qualified in Model 3, which indicates that the performance implications of partner-specific experience differ across equity and nonequity alliances. Consistent with the predictions


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Table 1

Descriptive Statistics and Correlation Matrixa

Variable

Mean S.D.

(1)

1.Alliance Performance 0.32 2.44 — 2. Collaborative Experience 1.76 1.27 0.08 3. Technological Experience 0.49 0.68 0.10 4. Partner-Specific Experience 0.14 0.34 0.26** 5. Partner-Specific Experience • Equity 0.07 0.27 0.09 6. Equity 0.37 0.49 0.19* 7. Alliance Relevance 2.13 1.07 0.37*** 8. Division of Labor 0.72 0.30 0.15 9. Coordination Committee 0.44 0.50 0.08 10. R&D 0.87 0.34 0.30** 11. Contract Alterations 0.21 0.41 0.21* 12. Monitoring Changes 0.14 0.35 0.17†

(2)

(3)

— 0.28** 0.36*** 0.25** 0.02 0.15 0.06 0.02 0.06 0.06 0.10

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

— 0.04 — 0.01 0.72*** — 0.01 0.11 0.34*** — 0.16 0.11 0.11 0.01 — 0.03 0.11 0.28** 0.06 0.01 — 0.14 0.11 0.01 0.12 0.04 0.11 — 0.08 0.01 0.09 0.18† 0.20* 0.22* 0.18† — 0.07 0.05 0.14 0.07 0.32*** 0.14 0.17† 0.13 — 0.17† 0.28** 0.08 0.02 0.13 0.15 0.07 0.00 0.01

N 108; †p 0.10; *p 0.05; **p 0.01; ***p 0.001.

a

Table 2

Multiple Regression Estimation Results for Alliance Performanceb

Independent Variable

(1)

(2)

(3)

5.12*** 4.83*** 4.33*** (0.75) (0.80) (0.80) Equity 1.02* 0.95* 1.38** (0.40) (0.38) (0.41) Alliance Relevance 0.65** 0.60** 0.60** (0.20) (0.20) (0.19) Division of Labor 1.85* 1.76* 1.19† (0.73) (0.72) (0.73) Coordination Committee 0.01 0.06 0.14 (0.40) (0.40) (0.39) R&D 1.62* 1.78** 1.48* (0.65) (0.66) (0.65) Contract Alterations 0.74 0.64 0.77 (0.50) (0.50) (0.49) Monitoring Changes 1.73** 1.17† 0.62 (0.59) (0.64) (0.65) General Collaborative Experience — 0.17 0.14 (0.17) (0.17) Technology-Specific Experience — 0.16 0.22 (0.29) (0.27) Partner-Specific Experience — 1.59* 3.30*** (0.64) (0.92) Partner-Specific Experience • Equity – – 3.12** (1.21) Model F value 8.76*** 7.06*** 7.42*** R-square 0.41 0.45 0.49 N 99 99 99 Intercept

Standard errors appear in parentheses. †p 0.10; *p 0.05; **p 0.01; ***p 0.001.

b

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of Hypothesis 4, the performance effects of partnerspecific experience are magnified for nonequity collaborations (p 0.01). The large negative parameter estimate for the partnerspecific experience • equity interaction term raises the question of whether partner experience significantly enhances alliance performance for both equity and nonequity collaborations. A hierarchical F-test comparing Models 2 and 3 confirmed that partner-specific experience does have a significant overall effect on alliance performance (i.e., F 6.52, p 0.01). Taking the derivative of Model 3 with respect to partner-specific experience yields the following: alliance performance partner-specific experience 3.30 3.12 • equity

(2)

This equation indicates that the partner-specific experience parameter estimate for nonequity alliances is 3.30 and is 0.08 (i.e., 3.30 3.12) for equity alliances. We find that partner-specific experience has a significant positive effect on alliance performance for nonequity alliances (i.e., t 3.98, p 0.001) but not for equity alliances (i.e., t 0.09) (Jaccard et al. 1990).

Discussion In this paper, we consider the role of different alliance experience trajectories and find that partner-specific experience, rather than other types of alliance experience, influences the performance of collaborative agreements in the biotechnology industry. In developing a theoretical linkage between partner-specific experience and alliance



performance, we draw upon evolutionary economics and suggest that partner-specific experience facilitates the development of interorganizational routines, or stable patterns of behavior aimed at the interaction and cooperation across the two organizations. These routines may contribute to the performance of the alliance by facilitating the information gathering, communication, decisionmaking conflict resolution, and the overall governance of the collaborative process. While our focus is on predictions derived from evolutionary economics, our results on the relative importance of partner-specific experience also speak to the debate, central to network theory, on the roles of the breadth and depth of ties in influencing performance (Burt 1992, Coleman 1990, Gulati 1998). Similar to Walker et al.’s (1997) findings from their analysis of the formation of alliance networks, our evidence underscores the importance of the depth of ties with respect to the breadth of the network. While our modeling does not explicitly characterize the structure of the interfirm network, the results do highlight the benefits of relational embeddedness (Coleman et al. 1966) in obtaining desirable outcomes from interfirm collaboration. The effects of partner-specific experience on alliance performance ought to be qualified, however. Its positive impact is in fact significantly stronger in the case of nonequity alliances. Equity alliances seem to be relatively insensitive to the development of interorganizational routines between the two collaborators. This finding might indicate that interorganizational routines and the more formal coordination mechanisms provided by equity governance can be viewed as substitute coordination mechanisms. More specifically, firms that have developed an alliance history with a partner and a corresponding set of routines have less need to turn to equity structures to align incentives, provide monitoring rights, and institute formal controls in the collaborative relationship. By contrast, firms lacking prior alliance experience with a partner will find equity structures helpful in facilitating coordination and control of the collaborative undertaking. This linkage between evolutionary and transaction-cost economics might be of relevance for further theoretical development in both organizational learning and governance research. It is important to note that the development of interorganizational routines can be facilitated by the emergence of interpersonal trust among managers and employees in both organizations, but their beneficial effects for alliances are not necessarily dependent upon trust building (e.g., Koza and Lewin 1998). In fact, as we noted, the use of a trust perspective rather than a combination of evolutionary economics and transaction-cost theory would yield the same prediction regarding the way

partner-specific experience and alliance governance interact to influence performance. However, whereas trust stems from interpersonal relations, we emphasize the development of routines at an interorganizational level and suggest that interorganizational routines are likely to explain this result because of the tacit and semiautomatic nature of the routinization process, compared to the more deliberate efforts to assess the likelihood of opportunistic behavior, typical of trust processes. Thus, our theoretical arguments and fieldwork suggest that Gulati’s (1995) findings might be interpreted from an evolutionary economics perspective as support for the influence of routinized behavior across organizational boundaries on governance decisions in cooperative agreements. However, since we also do not measure trust (or routines) explicitly, we need to leave definitive conclusions on their relative importance and potential interplay to future studies. Research that directly measures trust and routines in the alliance context would therefore be particularly valuable to address this problem. Our other hypotheses were in relation to firms’ accumulation of experience with alliances in general or within a technological domain. These predictions stem from several different research traditions relating to experiential learning, yet are not borne out empirically in our data. Whereas the learning curve phenomenon is commonplace in manufacturing contexts, the relationship between the stock of past experience and performance appears to be more complex when the objective is to effectively manage interorganizational cooperative processes. Likewise, firms have been hypothesized to develop absorptive capacity through experience in a technological domain, yet our findings reveal that technology-specific alliance experience does not translate into improved effectiveness for future alliances. Taken together, these findings support the conceptual need to differentiate stocks of alliance experience with respect to the different knowledge domains that they can potentially contribute to, and the even more pressing need to develop new theory on alternative learning mechanisms above and beyond the well-known “learning-by-doing” processes. More deliberate forms of collective learning processes, such as knowledge articulation and codification activities, might be particularly adept at the task of explaining how firms develop capabilities specific to the management of strategic alliances (Zollo and Winter 2002). Managerial Implications From the theoretical arguments and the empirical evidence presented above, one can identify several important


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implications for firms engaged in alliances or planning collaborations. First and foremost is the importance of the firm’s assessment of its level of relational experience as a guide for its governance design choices. If we set all variables in our full model appearing in Table 2 to their means except partner-specific experience and equity, it can be shown that firms with no prior ties benefit from an equity alliance, and a nonequity alliance becomes preferable as soon as the firms have one prior relationship. We have discussed how prior partner-specific alliance experience and equity governance can be viewed as substitute coordination mechanisms. Another implication of these findings underscores the importance of partner selection. If partner-specific experience plays an important role in determining an alliance’s degree of success, then a careful observation of the quality of the firm’s existing interorganizational routines developed from past alliance experiences should be given due consideration at the alliance formation stage. For inexperienced firms, the selection of early alliance partners takes on special relevance inasmuch as opportunities to deepen relationships with those partners will influence the benefits obtained from future alliances. This appears to caution against adopting a broad-based portfolio approach that emphasizes the scattering of partnerships. If the development and refinement of relational capabilities is of central concern to firms engaged in alliances (Dyer and Singh 1998), then an important managerial issue becomes how firms can invest in such capabilities. The complexities of this endeavor lie in the tacitness of interorganizational cooperative routines. Thus, specific investments of managerial time and effort to track down, discuss, and codify key lessons learned from past cooperative experiences may be important to cultivate relational capabilities. For example, Hewlett Packard writes extensive postmortem analyses of alliance partnerships, including an explicit assessment of the quality of the postformation interaction (Booth et al. 1995). The development of these documents might be useful for future alliance processes, not only because they might improve the selection of future partners and the structuring of future alliance agreements, but because the process through which firms go in order to develop these reflective analyses and behavioral guidelines can result in an improved understanding of the causal linkages between decisions, actions, and performance outcomes. Limitations It is important to note several limitations that readers should keep in mind in relation to the study’s contributions. First, the application of evolutionary economics to the alliance context is an important addition to the literature on strategic alliances, but one that also awaits a

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direct test for the presence and development of coordination, control, and trouble-shooting routines across the two partnering organizations. The uncovered correlational evidence between prior ties and alliance performance needs to be validated in longitudinal studies before it can be described as causal in nature, although the implicit time lag between the two variables provides some level of comfort in this sense. Second, any empirical study can only be as good as the measurement of its key constructs, the dependent variable in particular. Alliance performance has been here proxied with three perceptual assessments derived from different received theories, and it is subject to the usual disclaimers on self-reported performance assessments. In addition, the performance perceptions are one-sided in that they represent the assessments of only one of the two partnering firms, with a single respondent for each firm. Common methods bias is also of concern, but the low level of common methods variance, the objective nature of the experience measures, and the complex logic required by respondents to form hypotheses about the interaction effect between partnerspecific experience and alliance governance provide assurances. Finally, given that the research design focuses on only one industry, biotechnology, extensions of the present study could also explore the generalizability of our findings in non-high-tech sectors. Future Research Directions The contributions and limitations of the present study indicate several additional avenues for future research. The study of the performance implications of alliances remains one of the more important yet difficult areas of research on interfirm collaboration (Anderson 1990, Geringer and Hebert 1991, Gulati 1998, Parkhe 1991), and future research similarly needs to move beyond alliance patterns or firms’ governance choices to study the ways that alliances affect participating organizations. While we tailored our performance scale to the empirical context of high-tech alliances in the biotechnology industry, studies developing alternative scales for alliance performance suited to other research designs would also be valuable in this effort. Also, there is plenty of room to explore the effects that environmental conditions (industry growth rates, regulatory changes, etc.), as well as partnering-firm-level characteristics (strategic orientation, structural and cultural features, etc.) might have on the performance of the collaboration. Closer to the evolutionary economics research agenda, future research also needs to address further the boundary conditions for the effectiveness of experience accumulation, and interfirm routinization processes in strategic alliances. We noted the mixed findings that currently exist



on alliance experience effects, and underscored the importance of differentiating alliance experience trajectories. In light of this mixed empirical evidence, challenges in extending prior research on experiential learning to the alliance setting, and inherent theoretical interest in exploring whether and how firms can develop alliance capabilities, more research on alliance experience effects in different contexts and for different forms of collaboration is warranted. We collected relatively fine-grained data by moving beyond archival data and separating alliance experience trajectories, but future work may differentiate alliance experience in other ways, considering, for example, the heterogeneity in characteristics of prior experiences (duration, scope, content, etc.), and assess contingencies other than alliance governance (e.g., Parkhe 1993). As noted above, evolutionary economics treatments of alliances might also be enhanced by more direct measurement of organizational and interorganizational routines. Finally, it is likely that alliance experience is not the only mechanism responsible for the creation and evolution of organizational capabilities in the alliance domain. Further research is needed to understand how firms accumulate knowledge about managing alliances other than via learning-by-doing processes. Activities involving partnering firms’ deliberate efforts to extract valuable lessons from their own past alliance experience appear to be worthy of explicit investigation. For instance, future research might consider how firms articulate, codify, and diffuse their understanding of the do’s and don’ts in alliance processes through brainstorming sessions, implementation manuals, knowledge management tools (i.e., databases, intranets, etc.), internal training programs, and so on. Such activities have been highlighted in recent writings for practitioners (Harbison and Pekar 1997), but have only recently begun to receive academic attention (Kale 1999). Research in directions such as these are likely to take on more importance as scholars attempt to better understand the performance implications of alliances and their drivers. Acknowledgments The authors wish to thank Erin Anderson, Tim Devinney, Hubert Gatignon, Bill Glick, Bruce Kogut, Arie Lewin, and two anonymous reviewers for helpful comments on previous drafts of this paper. Helpful suggestions were also received from participants at the MESO Conference at Duke University. The Mack Center at the Wharton School and INSEAD’s R&D Department are also acknowledged for providing financial support of this research. Thanks also to Alessandro Zollo for assistance with data collection.

Endnotes *Appendix available from authors. 1 It is worth noting that the concept of similarity across alliances in the

same technological domain needs to be construed as a continuum. In this paper, however, it will be reduced to a dichotomous variable to allow its operationalization and construct a measure of technologyspecific alliance experience. 2 Given the high mortality rate, the acquisition activity and the small size of many of the biotech start-ups, it is reasonable to expect a significant loss of contacts (about 30% of the searched set, in this case). 3 Because the final sample consists of firms with multiple alliances, we examined whether the potential nonindependence of variables influenced our findings. Specifically, for all firms with more than one alliance, we selected one alliance at random and reran the full model presented in Table 2. Using this subsample of 57 alliances, we found that all of the findings presented in the paper continued to hold (results available from the authors). 4 Researchers attempting to examine either the organizational effects of alliances or the performance of the alliance itself have used different approaches. For instance, many studies have used alliance longevity or survival as an indicator of collaborative effectiveness (e.g., Barkema et al. 1997, Li 1995, Park and Russo 1996). An alternative technique employed by several studies is to examine the corporate valuation effects of firms’ alliance investment decisions using event study methods (e.g., Koh and Venkatraman 1991). Other research has turned to patenting data or subjective indicators of parent firm or alliance managers’ satisfaction to examine specific dimensions of alliance performance (see Geringer and Hebert 1991 and Gulati 1998 for reviews). 5 To avoid survival bias, we did not exclude from the sample 53 alliances that had terminated. Because alliances’ termination outcomes may be more objective than our qualitative indicators and offer an opportunity to check the validity of our measure, we compared respondents’ performance assessments across alliances ending due to success, failure, or a more neutral reason such as expiration of the contract or unilateral withdrawal. For alliances terminating due to failure, the mean value for our dependent variable was 1.97. For alliances ending due to unilateral withdrawal or contract expiration it was 1.47, and for alliances terminating due to success it was 0.82. The F-value from an ANOVA testing whether alliance performance differed across these termination outcomes was 4.67 (p 0.05). 6 Skewness can inflate the risk of Type I and Type II errors in multivariate models (Tabachnick and Fidell 1996), and the natural log transformation has been shown to remedy this problem. The transformation “new variable” ln(1 “old variable”) was used because the untransformed measures can equal zero and the natural log of zero is undefined. Inspection of the transformed variables’ distributions revealed that this transformation corrected for skewness.

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