Journal of Management Studies 46:6 September 2009 doi: 10.1111/j.1467-6486.2009.00844.x
Do Firms Learn from Alliance Terminations? An Empirical Examination
Nitin Pangarkar National University of Singapore abstract In this paper, drawing from the learning from failure perspective, we argue that firms that have experienced prior terminations are less likely to have their future alliance terminated. Our key argument is that prior terminations will enable firms to design better alliances and adopt more appropriate alliance management strategies to avoid future terminations. We also suggest a more nuanced view of learning by hypothesizing that termination experience will mediate the relationship between alliance formation experience and likelihood of termination. We used the case–control methodology to select a sample of 198 alliances (consisting of 99 terminations and an equal number of surviving alliances) from the global biotechnology industry, and deployed logistic regression analysis to test the hypotheses in a multivariate setting. Our analysis strongly supports both hypotheses.
BACKGROUND Alliances have been commonly deployed by firms to further strategic aims such as new market entry, innovation, and improving market position, among others (Dussauge et al., 2000; Heimeriks and Duysters, 2007; Varadarajan and Cunningham, 1995). Unfavourable alliance outcomes such as terminations are also common (Gudmundsson, 1999; Winkler and Lay, 2003; Zollo et al., 2002) and, regardless of their causes, they might impose significant costs on the alliance partners. Though prior studies have addressed the issue of alliance termination, the literature is impacted by three sets of issues. First, the literature is not as copious as that on specific aspects of alliance management including motivations behind formation (Mariti and Smiley, 1983), the choice of governance structure (Gulati, 1995; Pangarkar and Klein, 2001) and partner selection (Varadarajan and Cunningham, 1995), which is surprising since alliance performance is ‘one of the most interesting and also one of the most vexing questions’ on the research agenda (Gulati, 1998, p. 309). Second, from a conceptual standpoint, many studies on alliance outcomes have assumed a straightforward relationship between experience accumulation (often in terms Address for reprints: Nitin Pangarkar, NUS Business School, National University of Singapore, 1 Business Link, Singapore 117592 (
[email protected]). © Blackwell Publishing Ltd 2009. Published by Blackwell Publishing, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
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of forming a larger number of alliances) and the extent of learning (and consequently outcomes). For complex inter-organizational arrangements such as alliances, however, a more nuanced perspective on learning may be needed. The empirical results of some studies (e.g. Anand and Khanna, 2000; Barkema et al., 1997) also suggest that the experience effects may be present in some types of alliances but not others. Zollo et al. (2002) note that since alliances are less frequent, more heterogeneous and causally ambiguous than several manufacturing processes or administrative tasks for which learning has been observed and demonstrated, it may be useful to ‘unpack’ the general notion of learning in alliances. Moving in this direction, some studies have refined the idea of learning in alliances to include aspects such as partner-specific experience (a proxy for partner specific routines and learning; Dyer and Singh, 1998) or technology (Reuer and Zollo, 2005; Zollo et al., 2002). Our study follows in the tradition of these studies in the sense that we propose a specific type of learning (prior outcome-based) but also importantly differs from these studies, as noted below. Drawing from the learning from failure literature, we hypothesize that firms that have experienced one or more terminations in the past would have learned from their prior terminations and hence are less likely to have their future alliances terminated (Sitkin, 1996). We further hypothesize that there will be an interactive effect between general experience and the outcome-based experience for explaining performance – specifically that termination experience will mediate the relationship between general formation experience and the likelihood of termination. Our study also advances the current empirical approach towards studying alliance outcomes in general, and the relationship between experience level and alliance outcomes, in particular. We adopted the highly-appropriate case–control methodology, which is widely used in epidemiology (Khoury and Yang, 1998), to select a sample of 198 alliances – 99 that were terminated and a matched sample of 99 that survived to the end point of the sampling frame. Our large sample based analysis can be contrasted with some of the case-study based research on the topic of alliance termination (Arino and de la Torre, 1998; Doz, 1996; Kumar and Nti, 1998) and, hopefully, will lead to conclusions that have greater generalizability. Unlike many prior studies that have examined alliance outcomes from the perspective of a single partner (Killing, 1983; Lane and Lubatkin, 1998; Pangarkar and Choo, 2001; Reuer et al., 2002a), we study these phenomena from a dyadic perspective, which should be especially appropriate since the experience levels of both partners will have a bearing on the outcome of an alliance (Dussauge et al., 2000); Olk’s (2002, p. 133) remark that while studying alliance level phenomena ‘it will be important for researchers to focus only on alliance level variables or variables measured for all partners, and not those of a single partner’ lends support to our approach. Third, we believe that our modelling approach might help to address the inconclusive results obtained by prior literature about the experience–performance outcomes relationship – while some studies have found a positive effect (e.g. Barkema et al., 1997), others have found no effect (e.g. Anand and Khanna (2000) for some type of alliances; Merchant and Schendel, 2000). In this regard Merchant and Schendel (2000, p. 725) made the following observation: ‘it is notable just how mixed previous empirical findings © Blackwell Publishing Ltd 2009
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are – and how much these inconsistencies persist.’ By helping to resolve these inconclusive results, we hope to advance the state of the knowledge on the topic. The remainder of this paper is organized as follows. We begin by briefly summarizing the key arguments of the organizational learning perspective and developing the hypotheses to be tested by the study. We discuss the methodological aspects of the study next, followed by the results of data analyses. We conclude the paper by discussing the key results, the contributions and limitations of the study, and the directions for further research. Before we proceed, it may be useful to identify the bounds of the current study. First, we focus on unplanned terminations – that is, alliances that did not have a planned end date at the time of formation (in other words, they are not closed-ended). Second, the terminated alliances in our sample did not lead to a mutually fruitful outcome for the partners such as the filing of a patent or commercialization of a new drug and hence may be considered as failures. The learning perspective would suggest that failures are powerful triggers for firms to shake off inertia and status quo (e.g. prior strategies that may have outlived their usefulness; Mitroff and Anagnos, 2001), which, in turn might lead to avoiding failures in the future (Bazerman and Watkins, 2004; Sitkin, 1996). In other words, while alliance failures may be detrimental in the ‘short term’ (or for the ‘present alliance’), they may help a firm in the long run, especially in industries where alliances are a competitive necessity and not forming them is not an option (Powell et al., 1996). In fact, Coelho and McClure (2005) note: In the evolving and stochastic (world) of business environment failures are statistical certainties. The benefits of failures accrue to the survivors, who may be: (1) the firms within which failure occurs, if failure is recognized in a relatively short order and acted upon; and/or (2) firms that learn from those firms where failure occurs. Surviving firms that have learned from failure tend to be more profitable than they otherwise would have been. The lessons of failure can enable surviving owners and suppliers of capital to allocate and husband their resources more wisely.
THEORY AND HYPOTHESES Theory: The Organizational Learning Perspective A key premise of the organizational learning literature is that as firms accumulate experience in undertaking particular kinds of organizational activities, they become more proficient at managing these activities – i.e. as in all human endeavours, practice makes perfect (Arino and de la Torre, 1998; Cyert and March, 1963). Learning occurs in an iterative fashion when firms repeatedly engage in a focal activity, draw inferences from their experiences and are able to store and retrieve the inferred learning for future engagements in the focal activity (Levitt and March, 1988). The efficiency improvements due to learning effects are particularly salient for managing complex activities (Barkema et al., 1997; Lyles, 1988), and have been documented in diverse industries including manufacturing industries such as airframes (aircraft assembly), steel, shipping, and rayon as well as in service industries such as health care, fast food and hotels. © Blackwell Publishing Ltd 2009
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Development of routines is one of the key mechanisms by which learning is believed to occur. Routines codify prior learning by spelling out methods for improving efficiency, correcting errors or refining processes (Miller, 1996). They also ensure reliable performance by imposing consistency in thought and action (Miller, 1996). There is significant prior literature which has advanced the argument that learning from prior failures can help organizations avoid future failure. Sitkin (1996) argued that failure creates a recognition of risk and a motivation for change that otherwise would not exist. He describes this as a ‘learning readiness’ that, without failure, is very difficult to produce in most organizations. He further argues that failure is an essential part of the learning process of organizations and that failures, especially minor ones, should not be concealed or avoided. Bazerman and Watkins (2004) argue that when organizations fail to learn from failures, they become vulnerable to predictable surprises, which occur when organization’s leadership ignores or fails to understand clear evidence that a potentially devastating event can occur. Similarly, Mittelsteadt (2005, p. 287) posits that learning to identify mistakes in an analytic and timely fashion is often the difference between success and failure. The importance of learning from failure has been emphasized by a very diverse group of people including John Keats (poet), Lao Tzu (traditional Chinese wisdom), Soichiro Honda (founder of Honda Motor Company) and Michael Dell (Peebles, 2003). In a recent (2008) issue of Wired magazine, Gardiner (2008) pointed out that Apple has experienced numerous product failures (e.g. Lisa, Newton) over the years and its ability to adapt and learn from prior mistakes differentiates it from other less successful and innovative firms.
Learning and Alliance Management The learning perspective is highly relevant to alliance management. According to Anand and Khanna (2000, p. 295), ‘Alliances are complex organizational forms that can be best viewed as incomplete contracts.’ Hence the ability to anticipate some of the contingencies, and respond to them in an appropriate fashion, which could come from experience and learning are important. Alliances also have an inherent conflict built in them since they are simultaneously collaborative as well as competitive, where firms face significant risks of being de-skilled and hollowed-out (Balakrishnan and Koza, 1993; Kale et al., 2000; Lei and Slocum, 1992). Learning from prior alliances enables firms to balance these conflicting requirements. In addition, learning might help firms to design better alliances (Lyles, 1988), improve their ability to adapt an alliance to a changing environment and reduce the likelihood of misapplying knowledge (Reuer et al., 2002a). As argued by prior literature, repetition in terms of forming ever larger numbers of alliances is useful for developing knowledge and routines, which, in turn, can enhance performance. Recent studies have, however, argued that heterogeneity of experiences may also be critical, especially when firms are dealing with novel situations. Reuer et al. (2002b), for instance, found that heterogeneous experiences are more helpful for predicting the performance of culturally novel international joint ventures (IJVs) than familiar ones. Below, we consider heterogeneity of experiences in terms of outcomes achieved in prior alliances – specifically unplanned alliance terminations or failures. © Blackwell Publishing Ltd 2009
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Hypotheses Development Before we proceed to develop the hypotheses, it may be useful to spell out one of our key assumptions – that firms internalize the learning from prior alliances and transfer it to future alliances, which influence their (future alliances’) outcomes (Gulati, 1995; Khanna et al., 1998). In other words, similar to the prior literature adopting this perspective, we are discounting the possibility that a variety of factors, including key employee turnover and lack of communication between different units/divisions in a company (e.g. one of which has experienced a prior termination but the other has not), will prevent the learning from being internalized. While the role of prior alliance experience in achieving favourable outcomes is well researched (e.g. Barkema et al., 1997; Doz, 1996), the role of learning from prior terminated alliances has not been studied. We believe that prior terminated alliances are a rich source of learning for firms (Arino and de la Torre, 1998), especially given managers’ decision biases. Specifically, managers might have a tendency to adopt standardized responses to reduce the environmental complexity. This standardization, however, means that responses that are valid in one context are misapplied in another context (Baum and Ingram, 1998). Behavioural theorists also argue that time and resource constraints might induce managers to undertake bounded search, incremental experimentation and satisficing behaviour (Miller, 1996). Since prior terminations would typically be manifestations of significant trials and tribulations in the prior alliance (or in other words have some shock value; Khanna et al., 1998), they might induce an unconstrained kind of learning which surfaces critical assumptions (Anand and Khanna, 2000; Miller, 1996) and triggers global search for better strategies. The learning from prior terminations is likely to be reflected in more appropriate alliance structuring (ex ante choices) as well as alliance management (ex post alliance management strategies), as discussed below. A significant body of literature has argued that ex-ante design choices influence alliance outcomes and that even strong relationship building will not be able to overcome poorly configured alliances (Arino and de la Torre, 1998). As a key first step in appropriate alliance design, managers must recognize the mixed motive nature of alliances (simultaneous existence of cooperative and competitive aspects). Khanna et al. (1998) argue that many managers tend to frame alliances as purely cooperative, rather than mixed motive, thus exposing their firms to opportunism hazards (Baughn et al., 1997). We submit that prior terminations might force these managers to incorporate the mixed motives into their alliance design from the outset and thus reduce the likelihood of future termination, say by designing strategies that reduce the likelihood of appropriation of skills by the partner (Khanna et al., 1998). Ex ante choices such as product range and partner selection are also likely to impact alliance outcomes. In Arino and de la Torre’s (1998) study, lack of proper due diligence by partners implied that the initial product focus (ecological cleaners) was inappropriate (due to limited demand in the focal market) and there were significant cultural differences in aspects such as compensation systems and degree of centralization (implying lack of due diligence and inappropriate partner selection). Change in product focus to skin care, in turn, led to issues such transfer pricing, cannibalization and profit sharing. We submit © Blackwell Publishing Ltd 2009
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that, having gone through a prior termination, partners would give greater attention to the design aspects of their future alliances such as product range and choice of partners (compatibility in corporate culture) to avoid terminations. With regard to alliance management, partner firms that have experienced terminations are more likely to put in place processes to reduce shirking (Anand and Khanna, 2000; Merchant and Schendel, 2000) and to strive to resolve conflicts at an early stage rather than let them (conflicts) escalate (Arino and de la Torre, 1998). In addition, they may also act quickly to show some positive synergies and results to keep the buzz going and garner additional commitment and resources from their parent(s) (Doz, 1996). Having acted slowly in implementing even simple strategies in a prior terminated alliance with Delta and Swissair (the Global Excellence Alliance) and seen interest in the alliance wane within the company itself, Singapore Airlines was quick to implement strategies such as forming a joint purchasing organization in its next alliance with Lufthansa, which was far more successful and was expanded upon later (Pangarkar, 1999). We further argue that firms that have been in prior terminations are likely to be better learners, and their better learning capacity might trigger the virtuous cycle where positive gains from alliances (due to learning) are rewarded with commitment of greater resources by the parent, in turn helping to further boost the gains from the alliance. The higher learning capacity due to prior terminations may be attributed to several reasons, discussed below. Prior literature has acknowledged that learning from experience is difficult, even more so for alliances since they (alliances) are complex, heterogeneous and causally ambiguous (Baum and Ingram, 1998; Inkpen, 2000). Prior studies have also found that it is easier for both individuals and firms to learn from new experiences when there is exposure to a broader array of prior experiences (Bower and Hilgard, 1981; Cohen and Levinthal, 1990; Lane and Lubatkin, 1998). The diversity of experiences will also ensure that managers do not make the error of inappropriate generalization by extrapolating prior experience and learning where it is not applicable (Haleblian and Finkelstein, 1999). We further submit that prior terminations increase the diversity of experiences and thus enhance the learning capacity of firms. Corning Inc. serves as a good example in this regard. Despite having two prior (longstanding and successful) alliances in Asia, the company’s alliance in Indonesia was folded up, with complete write off equity capital, within five years of formation (Mahini and Yoshino, 1981). There were key environmental differences across the prior contexts versus the Indonesian JV with the latter characterized by high governmental intervention (requiring side payments for gaining approvals), high dependence of the economy (and exchange rate) on commodity markets and a fragmented distribution channel for selling dinnerware implying continued dependence on the local partner for access. Corning also made a poor design choice by going in with a partner who was an entrepreneur and hence had no reputation to protect. We submit that this termination, specifically the increased diversity of experiences for a firm that had been quite successful with alliances, would importantly influence Corning’s future alliance strategy in terms of choice of the market (environment), partner and strategy, especially in other emerging markets. In summary, we believe that being involved in a prior termination will help firms (though having too many terminations may be detrimental to reputation) reduce the © Blackwell Publishing Ltd 2009
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likelihood of future terminations through better alliance design as well as more effective alliance management. Hence, Hypothesis 1: Alliances formed by firms that have experienced at least one prior termination will be less likely to terminate than alliances formed by firms which have not experienced prior terminations. Interaction Between Formation and Termination Experience Drawing from the learning perspective, many prior studies have argued that greater alliance experience will translate into positive alliance outcomes (Anand and Khanna, 2000; Merchant and Schendel, 2000; Park and Kim, 1997; Reuer and Zollo, 2005). Under Hypothesis 1, we have also argued that experience of at least one prior termination might be useful for reducing the likelihood of future terminations. We submit that the relationship between alliance formation experience and termination outcomes will be mediated by termination experience. In other words, once we account for specific outcome-based experience (in the form of termination experience), the explanatory power of general alliance formation experience will diminish to insignificance. While general alliance formation experience may be useful for routinized aspects such as choosing a partner or deciding on an organizational form (Anand and Khanna, 2000; Hoang and Rothaermel, 2005; Powell and et al., 1996), specific outcome-based experience may be more useful in avoiding negative outcomes since the latter is less susceptible to negative transfer effect or the error of inappropriate generalization (Haleblian and Finkelstein, 1999). In fact, Reuer et al. (2002b) found that prior IJV experience has a negative impact on wealth creation at the time of announcement but that heterogeneity of experiences positively impacts performance. Hence, we propose the following hypothesis: Hypothesis 2: Termination experience (either of the partners having at least one prior terminated alliance) will mediate the relationship between alliance formation experience of partner firms and the likelihood of termination of the focal alliance. METHODS AND MEASURES Data We used the Actions Database provided by BIOAbility – a company specializing in information services for the biotechnology industry (http://www.bioability.com), as the primary source of information regarding alliance formation and termination events in the biotechnology industry. The database is constructed from articles published in 57 international sources including biotechnology industry journals as well as reputed business sources such as the Wall Street Journal and the Washington Post. The database, which covers the period from 1980 to 1999, contains the following information about each alliance: the announcement date, identity of the partners and their nationalities, technology and products involved, industry classification, a description of the particular © Blackwell Publishing Ltd 2009
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transaction and assignment of partner firms into broad categories such as biotech firms, pharmaceutical firms, diversified firms, education institutes etc. Under the description of the alliance, details regarding the objectives of the alliance as well as the roles performed by each of the alliance partners are listed. The database includes several thousand events (e.g. alliance formations, extensions, modifications, and terminations; mergers; facilities opening; patent filings; and regulatory approvals for new drugs) involving firms from 65 different countries. The Case–Control Methodology and Sample Selection Process Prior studies have often adopted the Cox regression/hazard rate methodology for analysing survival of firms. We submit that it is extremely difficult, if not impossible, to adopt this methodology in the context of the present study. Alliances in the biotechnology industry run into several thousands and information about alliance termination/ failure is not reported consistently (versus, say firm failures). As Mr Michael McCully, Director of consulting at ReCap, which has a database of 18,000 pharmaceutical and biotech alliances, says: [It’s] difficult to establish a true failure or termination rate because companies tend to ‘bury their dead at night’. [We do not have] good numbers on alliance termination rates because we don’t have enough good information. (Lam, 2004) Hence, we believe the information and resource requirements for adopting this methodology to be prohibitively high. We further believe that the case–control methodology, which contrasts alliances that have been terminated (cases) versus alliances that are still continuing (controls), offers an excellent alternative. Unlike other methods like a cohort design that move from cause to effect, the case–control method works from effect to cause – in this case we start by identifying the terminated alliances and their matching controls. A key strength of this method in the current context is that by focusing on a narrower sample of alliances consisting of the cases and the controls (versus the whole population in survival analysis/ Cox regression), it reduces the information requirements. The case–control method has been employed by several researchers in organization theory to study phenomena such as hospital closure (Wertheim and Lynn, 1993), corporate failure (Hambrick and D’Aveni, 1988) and the severance of auditor–client relationships (Seabright et al., 1992). In the context of studies focusing on healthcare issues, Khoury and Yang (1998) note that: The last two decades have witnessed the growth and development of case–control studies in epidemiology. These studies are easier and less costly to conduct than prospective cohort studies, have more power for less frequent type outcomes (most human disease) and can give risk estimates of association (odds ratios) that are equivalent to relative risk measures obtained from cohort studies. In spite of the methodologic discussions and potential limitations of such studies, the case–control method has become standard practice for conducting valid epidemiologic studies of human disease etiology. © Blackwell Publishing Ltd 2009
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As a first step in the analysis, we identified all the alliance terminations in the database. We coded an alliance as a terminated alliance when one partner bought out the interests of the other partner, thus converting the alliance into an acquisition, and other cases where the alliance was completely dissolved.[1] Since our focus is on unplanned terminations, we also excluded the following: • All closed-ended alliances where the partners had put a time limit on duration at the outset (e.g. cooperate on a R&D project for a period of 5 years) and which ended at the predetermined point in time (5 years in the above case) since these do not constitute unplanned terminations. • Alliances that came to an end due to the achievement of a mutually fruitful outcome (e.g. filing/approval of a new drug with the regulatory agencies). • Multi-partner alliances, since it is difficult to arrive at partner-specific experience (a key control variable) for these alliances (Saxton, 1997). Following Seabright et al. (1992), we established the following procedure for selecting the controls. We stratified the ‘controls’ according to the alliance partners involved and the year of formation. We then picked individual ‘controls’ to match the cases in respect of the following criteria: (1) one alliance partner was common to both the case and its corresponding control; (2) the control was formed in the same year as the case; and (3) the partner combination (e.g. a large pharmaceutical firm and a biotech firm) was similar across the case and the control. We used the same year of formation to account for the possibility that the alliance termination process is not a ‘stationary’ series over time (Seabright et al., 1992). In other words, the outcomes achieved by alliances formed during any particular year might be impacted by changes in legislation or other broader environmental conditions outside the control of the partners. In some cases, both partners did not have continuing alliances (potential controls) established in the same year as the terminated alliance. We then selected a continuing alliance that was established within a band of one year on either side of the year of formation. The final sample consisted of 99 cases and 99 matched controls. For further analysis, the cases and the controls were combined to create a sample of 198 alliances. Variables and Measures We coded alliance termination as a binary variable which assumed a value of 1 for terminated alliances (cases) and 0 for surviving alliances (controls). For the alliance formation experience variable, we averaged the alliance formation experience across the two partners. For the termination experience variable, we converted the average experience across the two partners into a binary variable which differentiates between alliances where partners have had some termination experience versus others where partners had none. Our approach of measurement (averaging across partners), which is similar to Anand and Khanna (2000) and Oxley (1999), implicitly assumes that the more experienced partner helps the less experienced partner, rather than exploiting the differential in learning abilities, say, by making it a learning race and dumping the partner © Blackwell Publishing Ltd 2009
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once its learning objectives are achieved (Khanna et al., 1998; Nakamura et al., 1996). The assumption seems reasonable in light of Anand and Khanna’s (2000) finding that joint or collective experience is a predictor of value creation (joint outcome) rather than value division. We calculated partner-specific experience as the number of prior relationships between the same partners. In all cases, we measured experience right up to the termination of the focal alliance (for terminated alliances) or the end point of the database (for surviving alliances), which we believe to be more appropriate than prior literature which looks at experience gathered at the point of alliance formation (e.g. Reuer et al., 2002a), since alliances formed after a focal alliance can also contribute to the learning of the partner firms and influence the outcome of the focal alliance (Arino and de la Torre, 1998; Doz and Hamel, 1998).[2] Variables operationalized as counts of events, are positively skewed and hence do not exhibit a normal distribution, thus violating a key assumption in regression estimation. To address this issue we performed a logarithmic transformation of these variables. Control Variables Year of formation. Outcomes for alliances may be influenced by broader environmental conditions such as regulatory policies and state of the economy, among other factors. To account for this possibility, we included the Year of Formation as a control. This control is in addition to the year-based matching between individual ‘cases’ (terminated alliances) and their corresponding controls (surviving alliances). We performed a logarithmic transformation of calendar year to account for the positive skewness in the variable. Partners of different types. Similar partners have greater capability to appropriate each other’s skills (Park and Kim, 1997), raising the transaction hazards (Balakrishnan and Koza, 1993) as well as probability of termination due to the occurrence of learning races (Khanna et al., 1998). On the other hand, dissimilarity among partners increases the possibility of conflict due to differences in priorities as well as management styles (Doz, 1996). The results of prior research have been inconclusive, with some studies finding similarity to be beneficial (Doz et al., 2000; Park and Kim, 1997) but others finding the opposite (Park and Russo, 1996). Based on the broad categorization provided in the database about the type of firms (e.g. biotech firm, diversified firm, pharmaceutical firm, etc), and similar to Reuer et al. (2002a), we coded a binary variable that assumed a value of 1 if the partners were of different types – e.g. a diversified firm versus a traditional pharmaceutical firms versus a biotech firm.[3] Joint R&D (purpose/scope of the alliance). When partners undertake joint R&D, there may be some stickiness associated with the relationship (Osborn and Baughn, 1990). In the event of dissolution, partners face the challenge of equitably dividing the intellectual property developed jointly (Anand and Khanna, 2000). Hence we would expect alliances involving joint R&D to exhibit lower likelihood of termination. We operationalized this variable as a binary variable, assuming a value of 1 when the alliance involved undertaking joint R&D. © Blackwell Publishing Ltd 2009
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Cultural distance. Scholars believe that cultural dissimilarities create unique challenges for partners in international alliances, which are not encountered in domestic alliances (Gray and Yan, 1992; Sarkar et al., 2001). Joint ventures between culturally dissimilar partners are often vulnerable to conflicts among key personnel and early dissolution due to difficulties in communication and coordination as well differences in management practices and employee expectations (Arino and de la Torre, 1998; Kogut and Singh, 1988). Since cultural differences create significant challenges in several different aspects of alliance management, they are expected to increase the likelihood of termination (Barkema et al., 1996). For calculating cultural distance, we used Hofstede’s (1980) indices and Kogut and Singh’s (1988) formula. Division of labour in the alliance. Prior literature has argued that the overlap (or lack of it) between the activities performed by the partners could have an impact on alliance outcomes. In alliances with non-overlapping activities (also called as link alliances (Dussauge et al., 2000) or sequential ventures (Park and Russo, 1996)), the occurrence of learning races might lead to a higher likelihood of termination (Nakamura et al., 1996). We operationalized this variable as a binary variable (1 = partners perform different tasks in the alliance). Equity alliances (governance structure). Several studies have examined the choice of governance structure and its role in alliance strategies and outcomes (e.g. Gulati, 1995; Hennart, 1988). A common argument is that equity alliances have high entry and exit costs. Equity relationships are also believed to bring about co-alignment of incentives (Gulati, 1995; Parkhe, 1993), thus reducing the possibility of opportunistic behaviour and, consequently, lowering the likelihood of termination. We operationalized this variable as a binary variable (1 = equity alliances). Partner-specific experience. Several prior studies have argued that partner-specific experience (or repeated relationships) could have an important bearing on alliance outcomes. Parkhe (1993) showed that if the partners had a prior alliance, they perceived each other to be less opportunistic (more trustworthy). Enhanced inter-partner trust might also reduce adjustment costs in repeated exchanges (Williamson, 1979) and allow partners to adopt trust-based governance structures and rely on informal safeguards. Higher trust might also facilitate efficient resolution of conflicts and bring harmony in an alliance (Das and Rahman, 2002). Firms that have had a prior relationship are also likely to have developed specific routines for attaining synergistic cooperation, including those for sharing information, identifying misunderstandings early as well as remedying them, thus benefiting their current alliance (Dyer and Singh, 1998). Firms with prior alliances might also develop stable role definitions for boundary spanners and codify informal commitments (Zaheer et al., 1998). Multiple alliances between the same partners might also make their relationship more stable due to reciprocity (Kogut, 1989). In summary, repeated alliances would be expected to be less susceptible to termination because of greater trust between partners (Granovetter, 1992; Gulati, 1995) as well as established routines for synergistic cooperation (Dyer and Singh, 1998). © Blackwell Publishing Ltd 2009
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RESULTS OF DATA ANALYSES Though some of the early alliances in the database were formed in 1980, most of the alliances in our sample were formed during the late 1980s and 1990s, with 1992 having the greatest number of alliances formed (10.6 per cent) followed by 1996 (9.6 per cent), and 1988 and 1989 (tied at 9.1 per cent each) (see Table I). The year 1997 witnessed the largest proportion of terminations (14.1 per cent) followed by 1994 and 1996 (tied at 11.1 per cent each). As many as 113 alliances (57.1 per cent of the total) in our sample were ‘domestic’ – i.e. the home country of the two partners was identical. Out of these, 106 alliances (53.5 per cent) were between US-based firms. Among international collaborations, USA– Switzerland alliances were the most prevalent (17 alliances, 8.6 per cent of the total). These proportions are roughly similar to those observed by Lerner and Merges (1998). In terms of the purpose behind alliance formation, our sample included 54 (27.3 per cent) licensing agreements, 27 (13.6 per cent) marketing and distribution agreements, 48 (24.2 per cent) joint R&D ventures and 55 (27.8 per cent) research agreements where only one party performed the research with the other party contributing some other skill such as funds or marketing. At the time of formation, the partner’s average experience amounted to 11.5 alliances (80.8 per cent of firms with some experience). By the end point (termination or end of the Table I. Patterns of alliance formation and termination Formations
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Total Note:
a
Terminations
Frequency
Per cent a
Frequency
Per cent a
1 1 3 8 9 12 6 14 18 18 17 13 21 14 12 10 19 2 0 198
0.50 0.50 1.5 4.0 4.5 6.1 3.0 7.1 9.1 9.1 8.6 6.6 10.6 7.1 6.1 5.1 9.6 1.0 0 100.0
0 0 0 0 0 1 1 5 9 6 5 6 6 9 11 9 11 14 7 99
0 0 0 0 0 1.0 1.0 5.0 9.1 6.1 5.0 6.1 6.1 9.1 11.1 9.1 11.1 14.1 7.1 100.0
Percentages do not add up to 100% due to rounding. © Blackwell Publishing Ltd 2009
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sampling timeframe), this had almost doubled to 22.82 alliances. The experience levels for our sample are comparable to Heimeriks and Duysters’ (2007) sample where each firm had an average of 17 relationships. Termination experience was not as common or extensive. At the time of formation, partners had been involved in 0.55 terminations, with 25 per cent of the cases having positive values. By the end point, however, this had increased to 1.78 terminations (besides the focal alliance if it was terminated). In terms of distribution of this key variable across the whole sample, 32 observations had 0 value (neither partner had termination experience); another 40 cases had an average value of 0.5 (one of the partners had a prior termination but not the other) and the remaining cases had average value of more than 1. At the time of formation, the average partner-specific experience stood at 0.20 alliances, with 14.6 per cent of the cases having positive values, which is very similar to Dussauge et al.’s (2000) and Reuer et al.’s (2002a) studies. By the endpoint, however, this had gone up to 0.50 alliances. Similar to Park and Kim (1997) and Hagedoorn and Sadowski (1999), we find that only a small minority of alliances (15/ 99 = 15.15%) end in acquisitions. A small proportion (6/99 = 6.06%) of (equity) alliances also ended when one partner divested its equity stake in the other partner. Non-equity alliances accounted for approximately 70 per cent of the total alliances in our sample versus 59 per cent in Reuer et al.’s (2002a) study. Among the equity alliances, 11 per cent were joint ventures, with the remaining 19 per cent involving purchase of an equity stake. This suggests that earlier studies focusing on outcomes of joint ventures may have been excluding a large proportion of total alliances – especially those focused on marketing functions (Townsend, 2003) or forged in high tech industries (Hagedoorn, 1993). To test whether there is any endogeneity caused by prior alliance terminations, which would imply that firms that had several terminations would find it difficult to forge future alliances, we performed the following analysis.[4] First, we identified three firms that had experienced multiple terminations (Genentech (5 terminations), Amgen (4 terminations) and Boehringer Ingelheim (3 terminations)) and constructed alliance formation history for each of these firms. For ease of analysis, we focused on successive, non-overlapping, five-year time windows (e.g. 1981–85, 1986–90, etc) In almost all cases (except Amgen for post-1995 versus the preceding period (1991–95)), the average number of alliances formed by each of these firms increased over time, suggesting that prior terminations are not hindering the future alliance formation of these firms. We suggest that these firms are not less successful in their alliances – it may be that they are more active than most their peers in forming alliances and if, similar to innovation, there is a small stochastic element in alliance success ( Jovanovic and MacDonald, 1994), it could explain their terminations. The positive and significant correlation coefficient between formation and termination experience also supports this argument. We present the correlation matrix for all the variables included in the study in Table II. It is apparent that other than two control variables (division of labour, and joint R&D) which have a high bivariate correlation coefficient, we have no cases of severe multicollinearity. The correlation coefficients between the various experience variables are modest and range from 0.20 to 0.45. Despite the low bivariate correlation coefficients, we will estimate the variance inflation factors (VIFs) to ensure that our results (especially the significance levels of coefficients) are robust. © Blackwell Publishing Ltd 2009
1 -0.035 -0.036 0.232** -0.165* -0.127 -0.246** -0.164* -0.354** 0.104
(1)
(3)
1 -0.149* 1 -0.111 -0.121 0.001 -0.059 -0.040 -0.055 0.051 0.240** -0.025 0.102 0.091 0.087 0.095 0.079
(2)
(5)
(6)
(7)
(8)
(9)
(10)
1 -0.034 1 0.020 0.013 1 -0.083 0.066 -0.237** 1 0.055 0.123 -0.053 0.454** 1 -0.162* 0.022 -0.079 0.399* 0.203** 1 -0.227** -0.732** 0.028 -0.029 -0.135 0.117 1
(4)
Notes: * Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed). † Mean and standard deviation values are for the raw (untransformed) variables. The regression estimation, however, is based on transformed (logarithmic transformation) variables. ‡ Mean and S.D. are reported for the termination experience variable before conversion into binary variable.
Terminated (1) 0.50 (0.50) Year of formation† (2) 1989.95 (3.99) Partners of different types (3) 0.77 (0.42) Equity alliance (4) 0.30 (0.46) Joint R&D (5) 0.24 (0.43) Cultural distance (6) 1.64 (3.33) Alliance formation experience† (7) 25.26 (24.82) Partner-specific experience† (8) 0.500 (1.01) Termination experience‡ (binary) (9) 1.54 (2.16) Division of labour in the alliance (10) 0.63 (0.49)
Mean (S.D.)
Table II. Correlations and descriptive statistics
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Table III. Results of logistic regression analysis Dependent variable = Alliance terminated (1)
Constant Year of formation (log) Partners of different types Equity alliance Joint R&D Cultural distance Division of labour in the alliance Alliance formation experience Partner-specific experience Termination experience (binary) Chi-squared/degrees of freedom Correct classification rate (%) Nagelkerke R squared
Dependent variable = Termination experience of partners (1/0)+
Model I
Model II
Model III
30.273 (622.184) -3.787 (81.921) 0.181 (0.406) 1.248** (0.388) -0.230 (0.579) -0.140** (0.053) 0.541 (0.532) -0.693** (0.234) -0.317 (0.399) n.a.
1.789 (674.127) 0.169 (88.758) 0.105 (0.427) 1.235** (0.411) 0.054 (0.630) 0.178** (0.064) 0.994 0.589 -0.374 (0.248) -0.257 (0.406) 3.031*** (0.837) 57.230/9*** 69.2 0.339
-387 (893.884) 50.855 (117.692) -0.440 (0.547) -0.547 (0.530) 1.202 (0.794) 0.006 (0.060) 1.282 (0.705) 0.129*** (0.037) 0.761 (0.647) n.a.
35.983/8*** 67.0 0.223
47.855/8*** 87.7 0.373
Notes: * Significant at 5% level; ** Significant at 1% level; *** Significant at 0% level. n.a. → variable not yet introduced into the model/not applicable + → Exact same result about the significance of the coefficient for the key variable was obtained when we used a non-binary dependent variable (and OLS estimation method) for termination experience.
We present the results of the logistic multiple regression analysis in Table III. To estimate the effect of the proposed variables on the likelihood of termination, we estimated two models – Model I with control variables only and Model II with the key variable of interest (termination experience). Model I also served as a baseline model for testing the mediation effect for Hypothesis 2. We estimated Model III to complete our analysis of the mediation effect. The bottom rows in Table III show the classification accuracy of each of the models. All our models (classification accuracy ranging from 67 to 87 per cent) perform better than random proportional chance models. The outperformance is especially noteworthy for Models I and II where random models would be expected to have a classification accuracy of 50 per cent (Gulati, 1995).[5] The Nagelkerke R-squared for the full model is 0.339, suggesting that the variables, taken together, can explain the phenomenon of © Blackwell Publishing Ltd 2009
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alliance termination quite well. All the VIFs in our estimation were below 2.5, suggesting that our estimates of coefficients were not impacted by multicollinearity. For testing Hypothesis 1, we will focus on Model II since this model contains the key variable of interest. We received strong support for the hypothesis since the coefficient for the termination experience is highly significant. There was a large improvement in the chi-squared statistic (35.983 to 57.230) as well as the Nagelkerke R-squared (0.223 to 0.339) from Model I to Model II – supporting the above conclusion. The impact of termination experience is over and above that of alliance formation/partnering experience, thus providing some indirect support to our argument about the impact of diversity of prior alliance outcomes on likelihood of termination of a focal alliance. Since some firms have multiple terminated alliances, we also had a potential concern that the observations in the dataset may not be independent of each other.[6] To account for this possibility, we tracked down all the firms that had more than one termination. We then removed all terminations except the earliest one, for each of these firms and re-estimated the regression analyses. The results were exactly similar to the overall model – in fact, the termination experience variable had a stronger impact (results not included in the paper). We also repeated the analysis by retaining the latest termination and excluding the earlier ones. The results were similar, however. Turning to the results regarding the control variables, we find that the year of formation has no impact on the likelihood of termination. Alliances between dissimilar firms are also no more likely to be terminated than other alliances. There could be several explanations for this result. First, many alliances in the biotechnology industry involve a pairing of entrepreneurial biotech firms with large and established pharmaceutical firms (different types) (Lerner and Merges, 1998; Rothaermel, 2001). These alliances are characterized by a strong complementarity between the partner firms since, despite their larger size, cash resources and strong marketing networks, large pharmaceutical firms ‘have been unable to create internally the kind of research environment that fosters constant innovation and discovery’ (Powell et al., 1996, p. 123). The strong complementarity between the different types of partners may be neutralizing the negative impact (if any) of differences in corporate culture and priorities. The coefficient for the joint R&D variable had the expected sign but was not significant. Alliances where partners perform non-overlapping activities (division of labour) are, also, no more susceptible to termination. The coefficient for the partner specific experience variable (repeat alliances) had the correct sign but was not significant. There were two surprising findings regarding the control variables. First, we find that equity alliances are more likely to be terminated – contrary to the arguments of prior literature (e.g. Gulati, 1995). The constitution of our sample might explain this result. We found that the subsample of ‘cases’ (terminated alliances) contained a higher proportion of equity alliances (40.4 per cent) than the subsample of ‘controls’ (19.2 per cent). Second, we found that higher cultural distance leads to lower likelihood of termination. Prior empirical results regarding the impact of cultural distance have been inconclusive, with some studies finding cultural distance to be a negative influence (Barkema et al., 1996; Li and Guisinger, 1991) but others finding it to be a positive influence (Luo, 1999; Park and Ungson, 1997). Another explanation might lie in the fact that national/cultural differences could be synergistic in this industry due to variation in the national systems of © Blackwell Publishing Ltd 2009
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innovation. Japanese biotechnology firms, for instance, have used R&D alliances to access leading edge research and to learn about how the American research system operates and how scientific knowledge is converted into commercial products (Bartholomew, 1997). We tested Hypothesis 2 by estimating three separate regressions as recommended by Baron and Kenny (1996). The coefficients lend strong support to Hypothesis 2 since the coefficient for the formation experience variable which was significant without the mediation (Model I) became insignificant after the inclusion of the mediating variable (Model II). We also find that formation experience has a significant impact on termination experience (measured as a binary or as a discrete variable). Prior empirical studies examining the relationship between alliance formation experience and outcomes have obtained mixed results, with some studies finding a positive effect (Anand and Khanna, 2000; Barkema et al., 1997), others finding no effect (Merchant and Schendel, 2000; Reuer et al., 2002b) and some even finding a negative effect (Reuer et al., 2002b). We submit that prior studies hypothesizing a straightforward relationship may have unwittingly omitted some mediating factors which might explain the mixed findings. DISCUSSION We believe that our paper advances the state of the research on alliance outcomes, on both theoretical and empirical fronts. Theoretically, we argued that learning from prior failures might enable firms to avoid future terminations. Prior literature has often argued that alliances are terminated because learning races render the ‘slower’ partner in the alliance redundant (Dussauge et al., 2000; Nakamura et al., 1996). In the learning race perspective, alliances may be considered as being similar to Trojan horses and one of the partners in the alliance must lose out. Our results suggest that alliances may be terminated because partners have not experienced failures prior to the focal alliance – that is, terminations might occur even in the absence of strategic/opportunistic behaviour by one of the partners. The conceptual as well as managerial implications of the two arguments would be rather different. Conceptually, learning races need to be viewed through a game theoretic lens while our perspective would deploy a nuanced learning view. Managerially, learning races would imply that firms must be on-guard in their interactions with the partners. Our results would suggest that firms internalize the learning from their prior terminations, and also look out for partners that have prior termination experience, while avoiding those with poor reputations. Similar to the recent literature on alliance experience, our study suggests that a nuanced view of learning in alliances based on specific types of learning is useful for explaining alliance outcomes. Anand and Khanna’s (2000) research showed that different types of alliances (e.g. R&D, marketing or production) offer varying degrees of learning opportunities. Extrapolating from this, the learning available through forging multiple alliances is likely to be of a different nature versus the learning from terminated alliances. For instance, by forging multiple alliances, firms might learn about negotiations strategies and choosing appropriate partners as well as governance form. Terminated alliances, on the other hand, might lead to valuable insights regarding interacting with the partner and avoiding escalation of (as well as resolving) conflicts. In our analysis, © Blackwell Publishing Ltd 2009
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termination experience emerged as a more powerful predictor of the likelihood of termination than general alliance formation experience, suggesting a possible omission of key variables by prior research. These key differences in the type of learning might explain the mixed results obtained by prior studies for other outcome variables (e.g. only a few studies have observed significant positive effects; Merchant and Schendel, 2000; Reuer et al., 2002a). Our arguments and results also suggest that the learning effect (more specifically, the significance of general versus specific types of learning) may be contingent on the industry. Since alliance formation is rather commonplace, especially in industries such as biotechnology (more than 80 per cent of the firms in our sample had prior alliance experience before the formation of the focal alliance; Powell et al., 1996), alliance formation experience may have limited ability to explain the highly-variable alliance outcomes – Heimeriks and Duysters (2007) observed that firms considered only 52 per cent of their alliances to be successful, on average. We might also speculate that partner-specific experience is less valuable in industries where firm skills (e.g. technological skills) are widely dispersed. In these industries forging alliances with the same partner could have significant opportunity costs – not choosing a partner with a different capability. Though the learning potential for alliance terminations is great since they force introspection and re-examination of strategies, as with other types of experience, this learning may not accrue without conscious efforts by firms (Bazerman and Watkins, 2004; Sitkin, 1996). In fact, the infrequent nature of the termination phenomenon implies that firms have to make deliberate attempts towards capitalizing on this learning before it is forgotten. We also find that termination experience mediates the relationship between alliance experience and likelihood of termination. Our results, together with Heimeriks and Duysters’ (2007) results, suggest that the relationship between alliance formation experience and alliance outcomes is not a straightforward one. Models testing for a direct effect might have led to inconclusive results, possibly because they have not included important mediating factors such as outcome-based experience or alliance capability. To our knowledge, our study is the first attempt to bring in the arguments from the learning from failure perspective into examining alliance performance. Our focus on examining whether learning from terminations gets transferred across alliances may also be considered distinctive. The two other studies addressing the issue of learning from failed alliances focus on the dynamics within a particular alliance rather than across alliances (Arino and de la Torre, 1998; Doz, 1996). As noted earlier, several prior studies have also focused on races to learn and asymmetric learning abilities across partners, again implying an alliance-level analysis where the partners try to outdo each other (Khanna et al., 1998). Our approach, on the other hand, looks at whether outcomes of a particular alliance influence the outcomes of other alliances in a firm’s portfolio. In this regard, we might further propose that future studies would do well to incorporate inter-alliance effects in addition to intra-alliance dynamics such as learning races.[7] With regard to the broader implications, our results lend support to the arguments that though the learning motive is commonly attributed to alliances, the arguments and the level of empirical analysis tend to be too broad (Inkpen, 2002). Many prior studies focus on the repetitive aspect of learning by doing which, sometimes, may not be © Blackwell Publishing Ltd 2009
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observed in complex interfirm arrangements such as alliances due to factors such as causal ambiguity (Reuer et al., 2002b). Many studies do not also take into account possibilities of learning besides getting better through repetition – e.g. learning through failure as argued in the current paper. In this regard, we believe that scholars might do well to come up with narrower, more specific conceptions of learning – for instance Haleblian and Finkelstein’s (1999) behavioural-learning based arguments about inappropriate generalization (and the consequent non-linear effects between experience accumulation and performance) as well as Reuer’s et al. (2002b) heterogeneity/novelty based arguments. LIMITATIONS AND FURTHER RESEARCH We acknowledge several limitations to the current study. First, our measures based on counts of alliances do not capture the conceptual richness of some of the variables. Participation in alliances merely provides partners opportunities to learn. Whether the learning actually takes place, or not, will depend on a number of other factors – including the type of the alliance (which would influence the intensity of interaction), the duration of the relationship, as well as internal processes adopted by the partners (Kumar and Nti, 1998). Our study also adopts a very positive view of experience. There may also be a negative side to experience – development of core rigidities (e.g. an extremely sceptical or nontrusting attitude towards partners based on prior terminations which undermines harmonious relationships in future alliances), or negative reputation due to too many terminations. Given our operationalization of termination experience as a binary variable, we did not explore non-linear effects, but future research might wish to examine this issue, possibly in another industry where alliance formation is less of a competitive necessity (Powell et al., 1996). We also do not take into account the possibility that when one of their key relationships gets terminated, firms may shun forming future alliances. While these firms might have arguably learnt from the costly terminations, this effect is unlikely to be observed in the pharmaceutical/biotech industry where alliances are a competitive necessity rather than choice – as we observed in the Results section, even the firms experiencing the most number of terminations in our sample did not slow down the pace of alliance formation. In other industry contexts, this remains a possibility, however, which may be accounted for by future studies. Our models are also incomplete in the sense that they do not include some firmspecific factors, such as financial difficulties of one or more partners. We submit that these effects may be more fruitfully tested in another industry context since the biotechnology industry is populated by firms at an early stage of development for whom financial losses are commonplace, and may not provide a strong reason for a change in strategic direction such as terminating alliances. Finally, we infer the occurrence of learning by observing outcomes such as reduced likelihood of termination. In this respect, our approach is similar to a large body of literature which has inferred the presence of constructs, such as trust and learning, from secondary data (e.g. Dussauge et al., 2000; Gulati, 1995; Nakamura et al., 1996). The © Blackwell Publishing Ltd 2009
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alternative approaches of observing learning directly through detailed case studies of individual firms or alliances, or gathering primary data from decision makers (e.g.Arino and de la Torre, 1998),[8] however, pose their own set of issues such as lack of generalizability (for case studies) and high costs (for gathering primary data whether on a standalone basis or in combination with secondary data) especially in a global industry. This study suggests several avenues for further research. Our key result that prior termination experience reduces the likelihood of future terminations needs further empirical validation in a diverse range of industries. In other industries where terminations are more frequent than the biotechnology industry relative to the pace of alliance formation, it may be worthwhile to examine whether the negative reputational effects outweigh the benefits from learning. It would also be instructive to empirically examine our conclusion that the relative influence of different types of experience may be contingent on the industry context. Empirical examination of crossover effects of different types of experience may be another fruitful direction to pursue. For instance, does prior termination increase the likelihood of successful alliances in the future, or vice versa? To conclude, drawing from the learning from failure literature, we argued that prior alliance terminations will reduce the likelihood that the partners will experience future alliance terminations. We also predicted, and found, that termination experience is a stronger predictor of alliance termination than general formation experience. Our large sample based analysis and the rigorous case–control methodology should inspire great confidence in the validity and generalizability of our results, which suggest that future literature take a more specific (or nuanced) view of learning in alliances. NOTES [1] To address concerns about whether acquisitions represent unplanned terminations, we will examine the robustness of our results by excluding the acquisitions from the sample and re-estimating our results. [2] The following hypothetical example discusses a simple situation where our measurement approach is more appropriate. Consider firms A and B, which have not had a prior relationship, forming an alliance in time period t. Between time t and t + 5 they form additional alliances with each other. In this case, the termination outcome of the focal alliance will most likely be influenced by the alliances formed after time t, in addition to the initial conditions (experience levels). Prior literature’s approach to measurement of experience based on the initial conditions, however, ignores the formation of alliances between firms A and B after the focal alliance. [3] Doz (1996) uses a similar idea (and operationalization) while arguing that pairings between large and small firms are likely to face problems. [4] We are grateful to one of the anonymous reviewers for pointing out this possibility to us. [5] A random proportional chance model would correctly classify events with an ‘event hit rate’ of p2 + (1 - p)2, where ‘p’ refers to the probability of an event occurring (Gulati, 1995). In our sample, p = 0.50 and therefore, a random model would be able to correctly classify events with 50% accuracy. [6] We are grateful to an anonymous reviewer for pointing out this possibility to us. [7] We treat the network literature as an entirely separate area since it has a different focus in terms of research questions (e.g. centrality in a network and performance), datasets, as well as methodologies. [8] Whether on a standalone basis or combined with secondary data.
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