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IMPROVING THE PROGRESS OF RESEARCH & DEVELOPMENT (R&D) PROJECTS BY SELECTING AN OPTIMAL ALLIANCE STRUCTURE AND PARTNER TYPE
INTRODUCTION In the past decade it has become common in knowledge-intensive sectors such as telecommunications or biopharmaceuticals to undertake Research and Development (R&D) with diverse alliance partners (Sampson, 2007; Beers & Zand, 2014; Oxley & Sampson, 2004). In designing such partnerships two issues powerfully influence the R&D project performance, (a) the selection of alliance partners (Fey & Birkinshaw, 2005; Belderbos, et al., 2004; Li et al., 2008) and (b) designing an appropriate alliance governance mode (Poppo & Zenger, 2002). While alliances exhibit a “…bewildering spectrum of governance structures…” with a wide range of partner tasks, intensity of interaction, and other agreement provisions (Ebers & Oerlemans, 2013), “surprisingly little is known about how collaborative activities are organized and administered within these governance structures” (Albers Wohlgezogen & Zajac, 2013). However, recent research has begun to drill down to the contractual micro-foundations or micro details of collaboration agreements, to zero in on what each agreement states regarding the degree of interaction and joint tasks performed by each partner (Reuer & Devarakonda, 2016). This paper focuses specifically on R&D alliance governance structure using two critical dimensions of governance mechanism: the intensity of communication between partners and the coordination of tasks that each has to perform, because it is not enough to dichotomize alliances as “non-equity” or contractbased alliances, versus equity joint ventures (EJVs), where the personnel of the partners work closely and have the greatest mutual interaction. Even within the “non-equity” group, there can be considerable interpartner coordination, depending on the tasks and provisions specified in the agreement. Thus, it is not a dichotomy, but a spectrum – ranging from loose interaction between partners at one end to intense coordination in EJVs. This argument calls into a question of previous literature on alliance governance structure. Previously, the Knowledge Based View (KBV) suggests that a more organizationally-intense
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alliance mode such as an EJV would be more effective in sharing and creating tacit/complex knowledge (Kogut & Zander, 1992; Sampson, 2004b; Macher, 2006; Oxley & Wada, 2009). Transaction Cost Economics (TCE) also suggests that a quasi-hierarchy, as in an EJV, is a better alternative when transaction costs increase, because a firm cannot easily envisage and control all future uncertainties through a contract to safeguard their transaction. Too loose an R&D collaboration, with few guidelines or contractual specifications for how the partners are to work together, would not be productive. Nevertheless, in EJVs the benefits of close partner interaction leading to positive knowledge creation can be offset by a much higher up-front investment in finance, personnel, other resources (i.e., higher risk), and coordination costs -- so that an EJV may not always be an optimal choice in R&D collaborations. In complex R&D work, throwing scientists and personnel from two or more companies into one hierarchical organization, may greatly increase information processing costs, especially when the allies have different levels of information-processing capacity (Galbraith, 1977), and when the partners come from significantly different organizational and technological backgrounds. This is what prior research has not emphasized enough: The cost side of greater inter-partner interaction and control/coordination between alliance partners. In this paper, we propose a spectrum, or index, of alliance governance modes, tracking the degree of inter-partner interaction and coordination (covering both contractual or “non-equity” alliances as well as EJVs). We construct this index from an actual reading of alliance agreement provisions in our sample. We then ask, “Which degree or level of interaction and coordination is most conducive to R&D project performance?” In fact, in industries with high uncertainty, radically changing technology and unpredictable directionality of technology development (e.g., biopharmaceuticals - the context of this study) the share of EJV-types of collaborations has already shrunk dramatically, in favor of non-equity or contractual alliances (Frankfort & Hagedoorn, 2016). Clearly, company negotiators and their lawyers have learned how to substitute, instead of EJVs with large resource commitments, more flexible and reversible non-equity arrangements which have low sunk costs in the face of alliance failure (Folta, 1998; Colombo, 2003). While
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the premise of R&D alliances is to seek and combine complementary knowledge and capabilities, thus creating technological value, at the same time, being flexible and reducing coordination costs is also important. This leads to a research question on which we focus in this study: “What alliance governance structure helps balance the benefits and costs of interaction and coordination, and best contribute to R&D project performance?” The second aspect of this study is to ask how R&D performance depends on alliances with diverse or organizationally dissimilar partners (e.g., between biotechnology firms, pharmaceutical companies, universities and research institutes (McCutchen & Swamidass, 2004; Sampson, 2007). Alliance partner diversity is a potential source of complementary resources, and a driver of better collaboration performance (Jiang, Tao & Santoro, 2010). We focus on two different types of partner diversity: (1) Organizational (e.g., university, non-profit research institute, or contract research organizations as opposed to competing firms) and (2) Technological base diversity (how similar or distinct are their technology bases?) (Beers & Zand, 2014; Belderbos, Carree & Lokshin, 2004; Sampson, 2007). We then examine how the partner diversity enhances, or moderates the relationship between the degree of inter-partner interaction and coordination in a given alliance mode, and R&D performance. As a general conclusion, we hypothesize that allies can achieve the best R&D outcomes with governance specification that entails intermediate (neither too low nor too high) levels of interaction and coordination on a spectrum that ranges from very loose non-equity alliances to EJVs on the right side of the spectrum of inter-organizational relationships. Non-equity based alliances with a reasonable degree of task specification and contractual detail, nevertheless remain more flexible and reversible than EJVs and may constitute a good intermediate position, aligning incentives and inter-partner interaction, versus the greater resource investment, risks and overly burdensome governance costs of EJVs. As such, this paper enriches KBV, TCE and Real Options theory (ROT) of alliance governance mode choice and partner selection, by focusing on the importance of organizational flexibility/reversibility in the face of uncertainty. THEORY AND HYPOTHESES Benefits and Costs of Interactive and Administrative Governance Mechanisms
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In companies, as Frankfort & Hagedoorn (2016) show, the share of EJVs has greatly shrunk, being replaced with lengthier and complex alliance contracts, stipulating roles/responsibilities, dispute resolution and intellectual property rights – specifications that can align incentives, effectively monitor and control partners, and mitigate the risk of technological leakage/ with opportunistic behaviors. Academic research has recently begun to follow practice and probe the anatomy of alliance contracts which specify governance, coordination, and communication mechanisms (Albers et al., 2013; Reuer & Devarakonda, 2016), and development of the inter-partner relationship (Poppo & Zenger, 2002; Agarwal, Croson & Mahoney, 2010) – thereby optimizing joint tasks, resources, and the exchange of know-how and skills. However, there is also a cost side to increasing the contractual details specifying inter-partner governance mechanisms. Increased inter-partner interaction/interdependence promotes access to complementary technologies and tacit know-how – up to a point. But there will also be higher informationprocessing costs, particularly when the complexity of R&D activity increases and when the partners are coming from diverse technological bases. This can significantly increase the frequency and costs of interactions (Kumar & Seth, 1998) and could lead to undesirable gridlock and technology spillovers in some cases (Oxley & Sampson, 2004). A more bureaucratic form of alliance governance (e.g., an EJV) with joint steering committees or a board, allows alliance partners to effectively coordinate and control future contingencies – but with much greater coordination costs (Sampson, 2004a). Hence the issue is one of seeking a balance between the benefits and costs of partner interaction and coordination that best achieves R&D performance. Hypothesis 1: R&D Alliance Governance Structure and Performance Successful management of interactional and formal governance which allows partners to effectively coordinate idiosyncratic resources, manage tasks, and mitigate the risk of technological leakage/ opportunistic behavior (Hoetker & Mellewigt, 2009) is difficult when R&D alliances involve in complex tasks (Mowery et al., 1996; Dyer et al., 2007). Moreover, successful R&D performance is even more difficult to achieve, when the technology is rapidly changing and there are multiple stages of discovery, testing and development. As illustrated in Figure 1, the value-creation activity in the pharmaceutical
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industry (the context of this study), for instance, is highly diversified (e.g., drug discovery and three stages of clinical trial), and thus encompasses a broad scope of activities such as exchanging technological knowledge (even tacit knowledge or experience), learning partners’ expertise, and creating innovative technologies/ drug products. Hence, it is critical to closely interact with alliance partners while effectively coordinating complex tasks for a better alliance performance. ======================= Insert “FIGURE 1” Here ======================= Increasing communication and coordination mechanisms in an alliance should – up to a point -positively affect R&D performance (Gulati & Singh, 1998; Poppo & Zenger, 2002) -- especially where tacit knowledge (often the core asset of a firm) is involved (Dyer et al., 2007; Jiang & Li, 2009). The KBV suggests that learning and transferring knowledge in R&D alliances requires more intensive interaction and detailed coordination, and enhances the common understandings of technology applications and transformation (Kogut & Zander, 1992; Oxley & Wada, 2009). The TCE perspective is congruent: more organizationally intertwined, or hierarchical, structures reduce transaction costs by facilitating observability and monitoring, which lowers appropriability hazards or unintended technology spillovers (Oxley, 1997; Gulati & Singh, 1998; Kuittinen et al., 2009). However, any desirable thing may be overdone. Too much intensive interaction, complex joint tasks and bureaucracy can also be sub-optimal beyond a point. According to organization design scholars (Galbraith, 1977; McCann & Galbraith, 1981), interaction and complex coordination between partners – beyond an optimal level -- can greatly increase information-processing costs, possibly resulting in conflict and communication failure, and a negative impact on performance (Pondy, 1970). For example, technology transfer and sharing in a highly knowledge intensive industry (e.g., biopharmaceutical industry) is a multidisciplinary activity dealing with a diverse scope of knowledge and skills. The integration of pharmacology, genomics, biotechnology and other medical sciences makes it more complex to collaborate with multidisciplinary partners. Interactions between multidisciplinary partners is more challenging due to the lack of shared information and knowledge. It can also increase communication frequency and the
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likelihood of interpersonal conflict, and thus reduce group outputs (Taylor & Greve, 2006). ======================= Insert “FIGURE 2” Here ======================= Taken together (benefits and costs), we propose a spectrum based on an increasing degree of interaction and contractual complexity (coordination), as shown in Figure 2 (i.e., from market to hierarchy). The left side of the spectrum, say in the case of pure unidirectional licensing where knowledge flow is one way (mainly from a licensor to a licensee), task interaction can be minimal, and the contract can be uncomplicated which reduces the possibility of partner conflict and information processing costs. By contrast, in more complex agreements, specifying detailed workflows in more interactive R&D alliances, where the partners are also engaged in multiple tasks or have additional contractual duties such as crosslicensing plus a material supply chain linking one partner to the other, this can dramatically escalate technical and management complexity and uncertainty. Increasingly complicated agreements for interaction, can ultimately increase the possibility of miscommunication, misunderstanding and errors between partners, and lower the likelihood of R&D performance. EJVs, on the extreme right side of the spectrum in Figure 2, are not just quasi-hierarchies, in the sense that there is joint share-holding. In the case of EJVs, the overall degree of coordination and communication tends to be the greatest. Recent academic literature addresses a fact known for a long time to practitioners and negotiators, that EJVs are also covered by some of the most highly detailed, long agreements specifying partner roles, timelines, decision-making processes, joint steering committees, safeguarding of technology, veto powers, and real option clauses giving rights to buy or sell shares, or block or terminate, contingent upon achieving (or failing to achieve) certain thresholds or criteria (Reuer & Devarakonda, 2016; Reuer et al., 2011; Bérard & Perez, 2014; Ragozzino, Reuer & Trigeorgis, 2016). As part of an EJV, there is often an auxiliary provision covering the licensing of intellectual property or a supply chain materiel flow between the EJV and one or both partners. The EJV partners’ engineers and personnel work together daily, on a long term basis, under joint routines, and supervision of joint steering committees (Sampson, 2004a). There are also informal communication mechanisms (Kale et al., 2000; Lee
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& Cavusgil, 2006; Pisano, 1989) with cross-cultural overtones. In the extreme case, there can be a substantial increase in information processing costs and possibilities of misunderstanding because managers and engineers are thrown together from different organizational cultures and practices, in a new organizational setting. Taken all together, ceteris paribus, an appropriate degree of overall communication and coordination in a given alliance mode is necessary for R&D performance. However, too much interaction and too complicated control/coordination – beyond an optimal point -- might hinder the R&D performance. Hence, we propose the following: Hypothesis 1: The likelihood of achieving R&D alliance performance will be highest for those R&D alliances adopting a governance mode with a moderate or intermediate degree of overall communication and coordination, rather than those with a low or a high degree of overall communication and coordination. Hypotheses 2 and 3: Selecting Partners to Improve R&D Performance The Moderating Effects of Partner Diversity: (1) Organizational and (2) Technological Base Diversity Accessing each partner’s complementary technology is critical to enhanced R&D performance. Alliances with the complementary resources pooled jointly by each partner will be superior to what each firm can do on its own (Hitt, et al., 2000). Each partner brings to the alliance, distinct and relatively difficult to imitate resources that combine to increase value (Das & Teng, 2000; Parkhe, 1991). But the effectiveness of the combination depends on the degree of diversity/difference between the partners, or strategic fit (Parkhe, 1993; Lin et al., 2009; Lavie et al., 2012). We examine partner diversity along two dimensions: (1) Organizational diversity and (2) Technological base diversity/difference between allies. Organizational Diversity Organizational diversity refers to the degree to which types of organization in an alliance are different; whether an alliance is formed between potentially competing firms, or between firms and universities, research institutes or contract research organizations (CROs). Organizational diversity can moderate the relationship between the overall degree of communication and coordination in an R&D
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alliance and R&D performance in two ways, (i) by providing novel concepts and resources (Jiang et al., 2010), and (ii) because firms, can be direct competitors, whereas universities, research institutes or contract research organizations are much less threatening types of partners. When a partner is a university or non-profit, two considerations promote R&D performance. First, fears of opportunism and unintended knowledge spillovers are greatly reduced. From the TCE perspective, studies such as Faems, et al. (2010) suggest that even with protective agreement clauses, when the partners are direct competitors, there is reason to fear misappropriation of knowledge, or shirking. By contrast universities (or contract research organizations) are much less of a threat because their mission is noncommercial, and thus tend not to directly compete with collaborating firms. Moreover, their ability to exploit spillover knowledge in the marketplace is very limited. This may possibly lead to a more comfortable collaboration and inter-partner interaction in an alliance. Second, non-profit institutes or universities“…are important sources of knowledge…and innovation.” (Belderbos, Carree & Lokshin, 2004), and can contribute novel technical insights that firms within an industry have not yet considered or pursued (Fey & Birkinshaw, 2005). In addition, research institutes and contract research organizations have cumulative experiences in conducting a specific research or clinical trials, and therefore are deeply expert in their area of specialty. And allying with these specialized organizations is important especially when the R&D project is highly complex and uncertain which increases the level of difficulty in communicating and coordinating for complex project. Universities and research institutes have performed specific R&D projects overtime with various partners. Their technology legitimacy obtained from cumulative experiences may help reduce project uncertainty and miscommunication/errors, and enhance the R&D performance. The argument in Hypothesis 1 still applies: overly-intensive
and
too
close
an
interaction
between
partners
increases
the
cost
of
interaction/communication. However, the overall benefits (synergetic effects) of organizational type diversity should positively moderate the inverted-U relationship in Hypothesis 1. Given this, Hypothesis 2: Organizational diversity in R&D alliances (e.g., those with a university, (non-profit) research institute or CRO) positively moderates (or marginally enhances) the aforementioned
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curvilinear relationship between the likelihood of achieving R&D performance and the overall degree of coordination and communication in a given R&D alliance mode. Technological Base Diversity Technological base diversity here refers to the difference between the technology/product domains (areas of technological competence or expertise) of the partners. When companies perform R&D alone, path-dependence theory suggests it is harder to change technological trajectories and transform existing technological patterns into innovative ones (Letterie et al., 2008). It is easy for a firm to remain in its product domain (Kogut & Zander, 1992). In fact, what drives alliances in high technology sectors such as biopharmaceuticals is that internal R&D spending, however large, often fails to result in commercially viable products – sometimes called the “dry R&D pipeline” problem. After spending billions and years through the testing and certification process, if internal R&D yields no fruits, this can be dangerous to the health of the firm. Moreover, the growing complexity of innovations demands inputs from a widening variety of sources. Even the largest firms feel internally inadequate, and seek alliance partners who can fill in the gaps of technology not present within the firm’s own boundaries (Contractor & Lorange, 2002). Technological diversity – or a sufficient differentiation between the previous core technological domains between partnering firms -- is therefore needed to access new and unique external technologies for better innovation and collaboration performance (Lin, 2007; Lin et al., 2009). Prior studies have shown mixed results regarding the effect of technical diversity on alliance performance. Some found positive effects of knowledge base similarity between alliance partners, on performance (Lane & Lubatkin, 1998; Ahuja, 2000) while others found non-linear relationships (Ahuja & Katila, 2001; Nooteboom et al., 2007). This may imply that the link between technological diversity and R&D alliance performance may depend upon how well allies interact and coordinate for R&D (Letterie et al., 2008). When alliance partners pool similar domains of technology, coordination complexity is lower and, ceteris paribus, this reduces conflicts, miscommunication, information processing costs and coordination
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complexity. However, the very closeness of the allies’ technological domains might not be so synergistically beneficial to innovation performance. On the other hand, when the partners’ technological base domains are very distinct, synergistic combination of knowledge becomes more difficult due to cognitive distance (Nooteboom et al., 2007), technical routines that are very distinct, and coordination costs. Too great a knowledge base gap between partners may be a disincentive. The negative effect of too great technological base ‘distance’ between allies in joint R&D is balanced against the innovation-fostering benefits of combining idiosyncratic technologies (Sampson, 2007). An intermediate degree of technological distance allows the partners to set-up a slightly challenging goal, incentivizes their collaboration. Hence, we suggest that an intermediate degree of technological base diversity between allies positively moderates the curvilinear relationship between the alliance performance and the overall degree of inter-partner task interaction and coordination in a given R&D alliance mode. Hypothesis 3: The earlier posited curvilinear relationship between the alliance performance and the overall degree of coordination and communication in a given R&D alliance mode will be positively moderated by an intermediate degree of technological base diversity between allies. METHODS Sample and Data The research sample consists of strategic alliances announced between 2000 and 2004 (5- year window) in the biopharmaceutical industry (i.e., U.S. SIC code: 2833 through 2836). The biopharmaceutical industry is very knowledge intensive and many firms engage in strategic alliances for innovation. In addition, discrete sequential steps in pharmaceutical R&D allow us to measure a more focused, project-level alliance performance. As shown in Figure 1, R (research) activity focuses on in-vitro drug discovery, whereas D (development) is associated with human-based clinical trials. And it is a timeconsuming activity taking up to 15 years to develop one drug product. In order to be able to measure R&D alliance collaboration performance, we need to see whether each R&D project goes through to the next stage in the R&D process. For instance, the performance outcome of an alliance formed in the research stage (i.e., drug discovery) in 2003 will be seen 4 years later (or more in some cases) in 2007. And the
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performance outcome of alliances formed in the phase II clinical in 2008, for instance, may still be ongoing, and thus might not be able to be counted. Hence, we tracked alliance deals announced in a 5-year period (i.e., 2000 ~ 2004) to get a comprehensive view of alliance performance. We use a data source, Current Agreements Database, which covers a broad range of global alliance deals, from equity joint ventures to detailed non-equity contracts. The database, like others such as Recap (by Thompson Reuters), compiles data on alliances from various sources such as news articles and SEC filings.
Each
agreement
included
several
provisions
such
as
technology
licenses,
joint
research/development, loans, auxiliary supply of materials, and passive equity purchases. Initially the number in the sample was 357, but we had to eliminate those without a final report for their R&D outcome (i.e., whether it proceeded to the next stage or failed/discontinued) in 10-K or 10-Q reports and those with over two alliance partners (since our unit of analysis is an alliance or a dyad). And we had to drop some duplicates in order to be able to perform the random-effects probit estimation, so that the final sample size covered 255 alliance contracts. Measures Dependent Variable Our dependent variable is ‘R&D alliance performance (i.e., R&D Progress)’ measured unambiguously by whether a particular phase of R&D was followed by a decision to proceed to the next stage. Therefore, R&D performance reflects the progress in R&D process. Each R&D phase is a discrete effort (McCutchen, Swamidass & Teng, 2004) under a separate agreement, often with different alliance partners1. In Figure 1, moving from left to right, each successive stage involves higher expense and risk. Hence, each of the four sequential decision points – as to whether to progress an R&D project to the next stage (under a new agreement and/or with a different partner), is a serious indicator or measure of R&D performance.
This is quite common in the pharmaceuticals (Reepmeyer, 2006); a variety of alliance portfolio strategies can be seen (forming an alliance in a drug discovery research stage, and forming another alliance with different partners in a later drug development stage is a common trend) 1
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We coded ‘1’ if an R&D project was progressed to the next stage in R&D process, or ‘0’ for otherwise: The four decision points shown as arrows in Figure 1 (1) Decision to Progress from Research Stage to Development Stage: The creation of, or any application for a new chemical compound, or any decision to go on to Phase I clinical trials (i.e., an application filed for an Investigational New Drug application, IND). (2) Decision to Progress from Phase I to Phase II Development Stage: Small number human subject trials (3) Decision to Progress from Phase II to Phase III Development Stage: Larger numbers of trials. (4) Decision to Progress from Phase III to Commercialization: FDA Application The decision to move from one phase to another is an “acid test” and an appropriate measure for performance since each next stage involves (i) a commitment to risk further tens of millions of dollars, and (ii) involves a fresh alliance agreement – with new governance structure, inter-partner coordination and communication, and obligations – often with a new partner. Moreover, (iii) the nature of the actual R&D tasks varies significantly over all the phases as illustrated in Figure 1. As noted above, we used in this paper an unambiguous measure for R&D performance (i.e., phaseby-phase R&D Progress). This micro-level criterion is far better than company-wide measures of R&D performance. Because more diffused measures of performance such as patent grants or patent citations, profitability, or Tobin’s Q, which apply to a firm as a whole, cannot be correlated to the outcome of each specific R&D project (and its agreement micro-details) which is the focus of our study. Independent Variables Overall degree of coordination and communication (ODCC) in a given R&D alliance mode is our major independent variable, as seen in the lower portion (horizontal axis) of Figure 2. We took several steps to measure this variable. First we tracked non-equity based (contractual) alliances using two critical alliance structural dimensions (i.e., Communication: the degree of task interaction, and Coordination: the degree of contract complexity, see the Appendix for details), and then performed a discriminant analysis to classify them into three different alliance categories: (1) Low integration (2) Moderately integrated, and (3) High
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integration. And as shown in Figure 2, we then added (4) EJVs on the right hand side as a fourth category on top of three non-equity based groups, since EJVs are universally accepted as having the highest degree of coordination and communication 2. This follows decades of studies (e.g., Contractor & Reuer, 2014; Tallman & Phene, 2014) concluding that when the partners create a separate JV firm, jointly staffed and operated with a more organizationally intertwined structure, the degree of overall communication and coordination will be the highest. But the actual degree of overall communication and coordination in a given alliance mode is an empirical question (which we will test in this study) and may vary depending upon industries and the motivations for alliance formations (e.g., knowledge-intensive R&D or manufacturing activities). Thus, the ODCC variable (overall degree of communication and coordination in a given alliance) ranges from 1 (Low) to 4 (High). Partner Diversity (1) Organizational Diversity: We coded this variable ‘0’ for alliances between firms, and ‘1’ for alliances between firms and universities, research institutes or contract research organizations. (2) Technological Base Diversity Even the biggest firms’ technical capabilities are limited to certain technical areas. In pharmaceuticals, different firms cover different disease areas. One may focus on oncology, whereas their partner may focus on circulatory diseases. Medicines increasingly straddle disease domains. Hence the value of alliances across companies that synergize different technological competencies. We used IMS health data and its USC (the Uniform System of Classification) code for therapeutic classification of commercialized drugs. In order to calculate technological base diversity between partnering firms, we first made drug lists of each firm (approved by either the US FDA or European Medicines Agency), and then tabulated the 3-digit USC therapeutic classification to which each drug
2
However, this could potentially raise reliability and validity issues. A referee suggested that category 2A and 2B in Figure 2 should be treated separately. To address this, we, later in the robustness test section, test this by splitting ODCC into two components: Degree of task interaction and Degree of contract complexity.
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belongs. And finally, we used the following calculation method (Sampson, 2007; Van de Vrande et al., 2009). Technological Base Diversity = 1-
𝑇𝑖 𝑇𝑗′ √(𝑇𝑖 𝑇𝑖′ )(𝑇𝑗 𝑇𝑗′ )
where 𝑇𝑖 (j) is a multidimensional vector representing the distribution of firm i (j)’s number of drugs across therapeutic classification. The technological base diversity variable thus will have a value ranging from 0 to 1, with a value of 1 indicating the greatest possible technological base diversity between two partnering firms. This measurement for the technological base diversity between partnering firms is likely better than using patent data, because a patented chemical compound can be used for multiple diseases that may dilute the measurement of the boundary of the technological specialization of a firm (Zimmermann et al., 2007). Control Variables We incorporated several control variables to isolate the effects of levels of communication and coordination on alliance performance. First, we control for ‘Firm Size and Age’, because bigger and older firms tend to have better capabilities and longer experience (Rothaermel & Deeds, 2004; Lin et al., 2012). Firm size can be measured by the number of employees, and firm age by the years between the year of founding of the firm and the year the firm allied with the partner. However, what we actually measure is the size and age gaps between partnering firms since the unit of analysis of our study is an alliance deal – or a dyad. Second, we tracked ‘Cultural Difference’ between partnering firms to control for the effects of culture on the communication (e.g., knowledge transfer) and coordination in an alliance (Garcia-Canal et al., 2008; Lavie et al., 2012) -- using Hofstede’s Five Cultural dimensions, and calculated it through the formula proposed in Kogut & Singh3 (1988). Third, we controlled for ‘Prior Alliance Experience’ (with the same partners) which could have a positive effect on interactions and coordination of multiple tasks (Lin, 2007; Dyer et al., 2007). We counted the number of prior alliances with the same partner. Fourth, ‘Absorptive Capacity’ has been recognized as one of critical factors affecting R&D collaborations such as
∑5i=1{(IndexiX – IndexiY )2 /Vi } /5 where Index ix (iy) stands for the score of country X (or Y) in ith year and Vi stands for the variance of ith year. 3
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learning, knowledge transfer and interaction (Lane & Lubatkin, 1998; Tsai, 2009). Absorptive capacity of a firm, or university or research institute was measured by the accumulated number of patents from the year of inception to the year the alliance was formed. Therefore, it reflects information-processing capability of organization which may affect R&D collaborations. However, many firms in the sample alliances are multinational corporations that have many different subsidiaries in multiple locations. And those subsidiaries do not always assign a patent to their own subsidiary in which the innovation took place. In order to count all patents filed by subsidiaries, we first viewed the name of subsidiaries, and then traced the parent firm to which the subsidiaries belonged. For this process, we used Who Owns Whom and/or FACTIVA which provides company profiles. In addition, we also researched the name of the firms to make sure that the patent is correctly assigned to the right firm, because firms sometimes change their name for various reasons. Fifth, we incorporated ‘R&D Uncertainty’ as a variable to control for the a priori general, industrywide level of (technical) difficulty, for each phase of research and development. According to DiMasi et al., (2010) and PhRMA (2011), approximately 6 out of 5,000 to 10,000 compounds enter clinical trials, and one out of six drugs that enter clinical trials eventually obtain approval for marketing in the U.S. The average general success rate of three clinical trial stages varies; Phase I-64%, Phase II-39% and Phase III-66% (DiMasi et al., 2010). Therefore, the measurement for R&D Uncertainty is based on the level of difficulty of each stage in the research (i.e., drug discovery) and development (i.e., three clinical trials). We measured it by the failure rate of each stage as percentage of total drug compound entered into the stage. MODEL In our analysis, we estimate the role of alliance governance structure (i.e., ODCC: overall degree of communication and coordination of an alliance) on the likelihood of R&D Progress (Performance), and also examine the moderating role of partner diversity and technological base diversity on the relationship between ODCC and performance. And using STATA xtprobit command, we performed a Random-Effects Probit Model rather than a fixed-effect model for a couple of reasons. First, since the dependent variable, ‘R&D alliance performance’, is a dichotomous variable taking the value of 1 or 0, we use a Probit model to estimate the likelihood of R&D progress. Second, the random-effects model is appropriate to address
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concerns of individual unobserved heterogeneity caused by alliance-specific characteristics omitted in the model (Nieto & Santamaria, 2010; Martin & Salomon, 2003). Each alliance is independently involved in different R&D activity ranging from drug discovery to phase III clinical trial, and thus its performance outcome is task-specific. In addition, given the fact that the unit of analysis is alliance dyad, some of alliances in our sample have the same partners over time making their motivation for alliance collaboration not independent. In this case, the random-effects model is preferable since the error term might not be independent across observations. Third, some of covariates (e.g., R&D uncertainty) in the model are timeinvariant. Since the fixed-effects model cannot include time-independent variables, we adopt the randomeffects probit model. In addition to this, we also employed year dummies (2000 ~ 2004) to take into account unobservable year effects on alliance performance. RESULTS Table 1 represents descriptive statistics and a correlation matrix of all variables employed for the empirical test. We checked the VIF (Variance Inflation Factor) score of all variables. And it shows an average “1.08” which is low, and there does not seem to be a multicollinearity problem. ======================= Insert “TABLE 1” Here ======================= Table 2 shows the results of random-effects probit estimation for R&D alliance performance. Since the coefficient of Table 2 does not represent the probability, we also report the marginal effects in Table 3. In Model 1, control variables such as firm age, size, prior alliance experience, absorptive capacity, cultural difference and R&D uncertainty were entered first. However, no control variables showed a direct effect on alliance performance. Model 2 tests the main explanatory variable of our study estimating the effect of overall degree of coordination and communication (ODCC) in a given R&D alliance mode on the R&D alliance performance. ======================= Insert “TABLE 2 & 3” Here ======================= Hypothesis 1 proposed that intermediate levels of task interaction and coordination complexity will
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contribute more to the likelihood of R&D progress. The Model 2 result provides a strong support for this inverted-U-shaped argument (P
