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Research Policy 40 (2011) 287–296

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Research Policy
journal homepage: www.elsevier.com/locate/respol

Exploration versus exploitation in alliance portfolio: Performance implications of organizational, strategic, and environmental ?t?
Yasuhiro Yamakawa a,? , Haibin Yang b,1 , Zhiang (John) Lin c,2
a b c

Babson College, Arthur M. Blank Center for Entrepreneurship, 231 Forest St., Babson Park, MA 02457, United States Department of Management, City University of Hong Kong, Kowloon, Hong Kong School of Management, University of Texas at Dallas, Richardson, TX 75080, United States

a r t i c l e

i n f o

a b s t r a c t
How does the nature of ?rms’ alliance portfolio in terms of exploration versus exploitation affect performance? Building on prior research grounded in the resource-based view and the relational perspective, we expand ?rms’ boundary to include their inter-?rm relationships and their immediate environment, and develop an extended resource-based framework centering on the concept of ?t. Speci?cally, we propose that whether the exploration versus exploitation orientation of an alliance portfolio may bene?t ?rm performance depends on how such an orientation ?ts the ?rm’s internal organizational characteristics, strategic orientations, and the industry environment. Data from ?ve U.S. industries over eight years largely support our thesis. Overall, our study calls for a holistic approach to consider the importance of organizational, strategic, and environmental ?t in understanding the performance implications of alliance-formation choices. ? 2010 Elsevier B.V. All rights reserved.

Article history: Received 1 August 2009 Received in revised form 26 September 2010 Accepted 15 October 2010 Available online 19 November 2010 Keywords: Alliance portfolio Exploration and exploitation Performance consequences

1. Introduction While strategic alliances often carry positive implications for ?rm performance (Das et al., 1998; Schreiner et al., 2009), how and when such impact may manifest have not yet been systematically examined, in particular when a ?rm can have multiple alliances with different purposes (Lin et al., 2009). Under what conditions does a ?rm’s alliance portfolio lead to superior performance? To address the research question we extend the resource-based view with a relational perspective, and argue that the performance implications of a ?rm’s alliance portfolio need to be considered in terms of its ?t with organizational, strategic, and environmental factors. According to the resource-based view, ?rm behaviors are resource-driven (Barney, 1991; Dierickx and Cool, 1989). Yet, such

? All authors have contributed equally. We thank David Deeds for collaboration on earlier related work, Irem Demirkan for her research assistance, Gregory Dess, Jane Salk, Seung-Hyun Lee, and Sebahattin Demirkan for their constructive comments, and Dung Hua for her editorial help. We also thank Editor Ben Martin and two anonymous reviewers of Research Policy for their helpful suggestions. An earlier version of the paper was presented at the 2006 Academy of Management Annual Meeting, Atlanta, Georgia. ? Corresponding author. Tel.: +1 781 239 4747; fax: +1 781 239 4178. E-mail addresses: yyamakawa@babson.edu (Y. Yamakawa), haibin@cityu.edu.hk (H. Yang), zlin@utdallas.edu (Z. Lin). 1 Tel.: +852 3442 7857; fax: +852 3442 0309. 2 Tel.: +1 972 883 2753; fax: +1 972 883 6029. 0048-7333/$ – see front matter ? 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.respol.2010.10.006

a traditional view tends to treat ?rms with closed boundaries within which resources reside. Meanwhile, more scholars have started to recognize the relational nature of a ?rm and the broad social and economic environment that a ?rm is embedded in. For example, Dyer and Singh (1998), Lavie (2006), and Arya and Lin (2007) have proposed an extended resource-based view to bridge the traditional resource-based view and the relational perspective. In line with this direction, we build on the more recent and extended resource-based view, which expands ?rms’ boundaries to their inter-?rm alliance relationships and the alignment with their external environment (Dyer and Singh, 1998; Lavie, 2006). From such a perspective, we view ?rms’ alliance portfolio (in terms of exploration and exploitation) as their capabilities of accessing and deploying different resources in inter-?rm relations, and further emphasize how important these capabilities need to ?t with ?rm characteristics, strategic orientations, and industry conditions. Our study intends to contribute to the literature in four important aspects. First, while the purpose of a strategic alliance may be for mutual bene?ts, the bene?t of an individual alliance may not always be transferable to the parent ?rm (Baum et al., 2000; Gulati, 1998). In this study we thus move beyond individual alliances to examine the impact of a ?rm’s alliance portfolio. Speci?cally, we ask how alliance-formation choices between exploration and exploitation in a ?rm’s alliance portfolio affect ?rm performance. By viewing alliance formations as ?rms’ strategic choices in terms of exploration and exploitation, we attempt to explore the mechanism through which rents are created from such alliance-formation choices. In this sense, our study also contributes

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Fig. 1. Theoretical framework.

to the burgeoning literature that delineates the boundary of the exploration–exploitation paradigm. Second, we clarify the relationships among ?rms’ alliance purpose, competitive strategy, and performance. This study builds on the recent stream of research (e.g., Ruiz-Ortega and Garcia-Villaverde, 2008; Vorhies et al., 2009) that extends the resource-based view through meshing it with other perspectives (e.g., competitive strategy, relational perspective). We further advance the extended resource-based view (Dyer and Singh, 1998; Lavie, 2006) by applying it to the study of strategic alliances while considering ?rms as having open boundaries with inter-?rm alliance relations. Third, in implementing the extended resource-based view, we highlight the importance of ?t – organizational, strategic, and environmental ?t that affects ?rm performance – as suggested in other studies (e.g., Kratzer et al., 2008). Speci?cally, we explore whether a ?rm’s alliance portfolio leads to superior performance when its resources are combined effectively with appropriate organizational characteristics, strategic orientations and environmental circumstances (Dickson and Weaver, 1997). Our approach not only addresses endogeneity issues commonly confronted by prior studies in this area, but also examines the impact of ?t among internal and external boundary conditions that affect ?rm performance. To truly understand strategic alliances it is as important as exploring the antecedents to examine the consequences of a ?rm’s allianceformation choices and its boundary conditions (Lin et al., 2009). Fourth, we attempt to make a contribution to the literature by empirically testing our model in a multi-industry setting. While prior research has generally focused on one industry (Park et al., 2002; Rothaermel, 2001a), we employ a multi-industry context to increase heterogeneity within our sample. Overall, our study extends the resource-based framework to include not only ?rm characteristics but also their inter-?rm relations and their immediate environment when investigating the role of a ?rm’s alliance portfolio on ?rm performance (Lavie, 2006; Rothaermel, 2001b). 2. Theory and hypotheses As behavioral players, ?rms are embedded in the broad social and economic environments and must rely on their past experience for future actions (Cyert and March, 1963; March and Simon, 1958). To deal with the uncertainty and ambiguity of the external environment, managerial discretion is often re?ected through the choices of ?exibility and stability (Burgelman, 1991, 2002) or in the

words of March (1991), “exploration and exploitation.” Exploration is associated with terms like search, variation, risk taking, experimentation, discovery, and innovation; while terms like re?nement, production, implementation, and execution are associated with exploitation (Levinthal and March, 1993; March, 1991:71). Koza and Lewin (1998) further extend the concept to strategic alliances and suggest that those with the purpose for discovery and development of new technology including research and development (R&D) alliances and technical alliances are exploratory in nature, while those with the purpose for ef?cient transactions and utilizations of resources including licensing alliances, marketing alliances, and supplying alliances are exploitative in nature. Yet, how such alliance portfolios of different purposes may impact ?rm performance has not received suf?cient investigation until recently (Jiang et al., 2010; Lavie, 2007). One of the constraints may be partly attributed to the fact that prior studies have largely relied on the traditional resource-based view (Barney, 1991; Dierickx and Cool, 1989), which tends to treat ?rms with clearly de?ned boundaries within which resources reside. Consequently, strategic alliances are often considered as the result of ?rms’ internal calculations alone. In this study, we build on the more recent and extended resource-based view, which bridges the relational perspective and expands ?rms’ boundaries with their inter-?rm alliance relationships and the alignment with their external environment (Dyer and Singh, 1998; Lavie, 2006). This is also consistent with the purpose of strategic alliances, which is about pooling partners’ resources together to explore and exploit internal and external resources (Das and Teng, 2000; Noda and Bower, 1996; Park et al., 2004; Rivkin and Siggelkow, 2003). In sum, we argue that ?rm performance can be affected by the choices of exploration/exploitation alliances in its alliance portfolio. Further, this main effect is subject to a joint consideration of its ?t with organizational, strategic, and environmental characteristics as we believe that organizational characteristics such as ?rm age re?ect the level of resource endowments; internal strategic orientations such as cost leadership and differentiation strategies entails the way in which a ?rm deploys its resources; external environmental contexts such as industry growth constrains the supply of resources. Consequently, our research question is: How does the ?t between alliance-formation choices and organizational characteristics, strategic orientation, and industry condition affect ?rm performance? Our theoretical framework is presented in Fig. 1.

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2.1. Alliance-formation choices (exploration vs. exploitation) and ?rm performance The choice between exploration and exploitation in alliance formation is a function of ?rms’ strategic intent, organizational learning, and their expected returns (Koza and Lewin, 1998). Specifically, on the one hand, entering an exploration alliance requires a desire by the ?rm to discover new opportunities through the acquisition of knowledge, skills, and capabilities which are novel to the ?rm, with the aim of creating new resources and competencies to adapt to the environment (Koza and Lewin, 1998). Exploitation alliances, on the other hand, are built to leverage existing ?rm resources and capabilities; the goal is to join existing competencies with complementary assets that exist beyond a ?rm’s boundary (Rothaermel and Deeds, 2004). March (1991) suggests that organizations bene?t from a balance between exploration and exploitation in their choices. Empirical studies, though few, also seem to echo this view regarding alliance formation (e.g., He and Wong, 2004). However, ?rms do have different preferences for their alliances given the limited resources, strategic orientations, and external environmental conditions. Furthermore, there is little empirical evidence to show what should be the balance and whether such a balance will bring ?nancial bene?ts to the ?rm (Lin et al., 2007; Raisch and Birkinshaw, 2008). Given that returns to exploitation alliances are more proximate, predictable, and less risky compared to exploration alliances (Rothaermel, 2001a; Rowley et al., 2000), the performance impact from exploitation alliances is likely to be greater especially in the near term. Vice versa, exploration alliances (as opposed to exploitation alliances) should bring less direct and immediate ?nancial bene?t to the parent ?rm. Thus, H1. A higher ratio of exploration (as opposed to exploitation) alliances in a ?rm’s alliance portfolio will be negatively associated with its immediate ?rm performance. 2.1.1. Organizational ?t: alliance portfolio (exploration vs. exploitation) and ?rm age Firm age is an important indicator for a ?rm’s accumulative resource base. Prior research has consistently found that ?rm age plays a critical role in ?rms’ strategic decisions and performance (Freeman et al., 1983; S?rensen and Stuart, 2000; Sutton, 1997). Drawing on the extended resource-based view, we expect that ?rm performance is affected by a ?t between its alliance portfolio (?rms’ choice between exploration and exploitation) and age. In other words, we predict that a ?t between a ?rm’s alliance-formation choices and age will lead to enhanced performance. Speci?cally, we argue that younger ?rms will bene?t more from exploitation alliances, while older ?rms will bene?t more from exploration alliances in their alliance portfolio. In general, younger ?rms face a liability of newness and smallness (Stinchcombe, 1965) because of their lack of strong connections to resource providers, such as distributors, potential customers, etc., thus having limited internal resources and capabilities (as opposed to more mature ?rms). While technologies pioneered by younger ?rms are likely to have greater impact on their ?eld (S?rensen and Stuart, 2000), their lack of internal resources makes it necessary to cooperate with older ?rms to access complementary assets such as ?nancial capital, marketing, and distribution capabilities (Pisano, 1991; Tushman and Anderson, 1986) and increase their legitimacy and reputation (Stuart et al., 1999). Therefore, while some younger entrepreneurial ?rms are found to be obsessed by their exploratory activities even at the time of environmental shifts (Anderson and Tushman, 2001), exploitation alliances enable younger ?rms to make more ef?cient use of their existing resources and capabilities.

When ?rms age, successful exploitative practices tend to be retained, reinforced, and diffused through different organizational levels, while organizational memories and routines are built (Macpherson and Holt, 2007). This repetition and ?ne-tuning of existing resources and capabilities may lead ?rms to organizational simplicity (Miller, 1993), competency traps (Levitt and March, 1988), and core-rigidity (Leonard-Barton, 1995). Such organizational inertia can limit ?rms’ ability to absorb and act on knowledge developed beyond their boundaries (Hannan and Freeman, 1984). Under this condition, older ?rms’ existing resources and capabilities become obsolete and no longer match with the demands of the current environment (Eisenhardt, 1989; Thompson, 1967). An important mechanism that older ?rms use to adapt to changing environmental conditions is entry through alliances (Hill and Rothaermel, 2003). Alliances provide them with a learning opportunity to retool obsolete resources and capabilities (Rothaermel, 2001a). However, exploitation alliances by their nature – exploiting what already exists – does not provide signi?cant learning opportunities as exploration alliances do. Entry into exploration alliances enables older ?rms to access cutting-edge thinking on product and process development for rejuvenating their technology base and discovering new opportunities (Hagedoorn and Schakenraad, 1994). Thus, H2. Younger ?rms will bene?t more from a higher ratio of exploitation alliances, while older ?rm will bene?t more from a higher ratio of exploration alliances, in their alliance portfolio. 2.1.2. Strategic ?t: alliance portfolio (exploration vs. exploitation) and strategic orientation The choice of alliance as well as the performance consequences of that particular choice can be driven by ?rm strategy, the way how a ?rm deploys its resources. While there are a number of different conceptualizations of strategies in the literature (e.g., Miles and Snow, 1978; Rumelt, 1974; Barlow, 2000), we examine the impact of the classic strategies of cost leadership and differentiation (Porter, 1980). Cost leadership emphasizes ef?ciency and low cost relative to competitors (Dess and Davis, 1984). Cost leaders are successful in markets primarily because their products cost less than competitors’ equivalent products. Differentiation strategy, on the other hand, involves value creation that is perceived as unique (Hill, 1988). As a result of the difference in resource deployment, it may be useful to examine the relative emphasis of a ?rm’s strategic orientation.1 Exploitation alliances are an important means for ?rms to maximize the bene?ts of existing resources and capabilities. As noted by March (1991), exploitation exhibits returns that are positive, proximate, and predictable, while those of exploration are uncertain, distant, and often negative. In this study, we argue that the strategy re?ected in a ?rm’s choice of resource allocation can in?uence the performance consequences of its choice of exploration or exploitation alliances—namely, in terms of strategic ?t. Strategic ?t among many activities is fundamental not only to competitive advantage but also to the sustainability of that advantage (Porter, 1996). Strategic ?t is the consistency among a ?rm’s

1 We would like to re-emphasize here that we are not advocating the mutually exclusive understanding of strategic orientation and alliance portfolio; it would be too extreme to characterize that ?rms with differentiation orientation will only engage in exploration alliances while those with cost leadership orientation will only engage in exploitation alliances. Cost leadership ?rms may be forced to hunt for new processes and methods to make them more ef?cient under certain circumstances, and alliances may simply be one means by which they achieve this. Rather, what we argue here is the relativity in their portfolio that ?rms with differentiation focus will tend to have more exploration alliances whereas those with cost leadership focus will tend to have more exploitation alliances.

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system of activities, and their ability to reinforce and create synergy among each other so as to support the ?rm’s strategy and create a competitive position in the marketplace. Classic examples include the myriad of ways in which Southwest Airlines works to drive out costs and improve reliability, Wal-Mart’s continuous effort to drive down delivery costs, and 3M’s intricate system of rewards and recognition of individual innovation. In each of these cases, success is not achieved simply due to the overall strategy – be it cost leadership or differentiation – but rather the “?t” between the chosen strategy and the system of activities undertaken by the ?rm to implement the chosen strategy. A choice between exploration and exploitation alliances is one activity in a ?rm’s system of activities and the proper ?t with the strategy will be critical for the ?rm to realize returns from its alliances. We argue that exploitation alliances provide a better ?t with a cost leadership strategy. It is common to observe that ?rms with a cost leadership strategy will be more likely to focus on costsaving and ef?ciency in alliances, while reducing their expenses for experimentation and exploration activities. Exploitation alliances act as an important means for ?rms to implement their cost strategy by maximizing the bene?ts of existing resources. In contrast, a differentiation strategy places a high premium on uniqueness, which differentiates a ?rm from its rivals. A constant search for new technologies and new opportunities is needed for ?rms that adopt a differentiation strategy. Moreover, a differentiation strategy requires ?rms to be sensitive to the changing markets and places high demands on creating new capabilities to meet such changes (Koza and Lewin, 1998). Thus, ?rms with a differentiation strategy will more likely see the value of exploration alliances (Gilsing and Nooteboom, 2006). In other words, for ?rms pursuing a differentiation strategy with demand for continuous innovation and introduction of new products and services, exploration alliances become a better ?t. Thus, H3(a). Firms with a focus on cost leadership strategy will bene?t more from a higher ratio of exploitation alliances, while H3(b): Firms with a focus on differentiation strategy will bene?t more from a higher ratio of exploration alliances, in their alliance portfolio. 2.1.3. Environmental ?t: alliance portfolio (exploration vs. exploitation) and industry context While scholars have alerted us to the importance of understanding the nature of exploration and exploitation, questions remain regarding the role of the environmental context (Gibson and Birkinshaw, 2004; O’Reilly and Tushman, 2004). The critical impact of the external environment on ?rms’ strategic decisions and subsequent performance has been widely acknowledged in the management literature (Eisenhardt, 1989; Provan, 1989; Soh and Roberts, 2003). From an extended resource-based view which regards ?rms’ boundaries as including inter-?rm relations, the external environment where ?rm and their alliances operate should be even more important. Indeed, studies suggest that environmental condition plays an important role in the effect of strategic alliances (Lin et al., 2007; Robertson and Gatignon, 1998). Exploring the ?t between a ?rm’s alliance portfolio (choice of exploration vs. exploitation) and the environment will enhance our understanding of the boundary conditions, and extend the theoretical insights of the exploration and exploitation framework, which is fundamentally about how to utilize external resource opportunities. Among the various dimensions of an organizational environment theoretically delved in the literature (Castrogiovanni, 1991; Dess and Beard, 1984), we focus on an important and well-researched aspect that captures the environmental resource condition: industry growth. Given that industry growth re?ects the resource supply and opportunities of the ?rm’s environment, it will in?uence the impact of a ?rm’s alliance choices of exploration vs.

exploitation on subsequent ?rm performance (Spanos et al., 2004). Low levels of industry growth are associated with greater competitive stability and continued emphasis on strategies that acknowledge mutual interdependence and complementarity (Rajagopalan and Deepaic, 1996). Managers are likely to have low pressure for high-risk searches in new domains, and push their bias towards exploiting existing resources and capabilities, and reaping the positive, proximate and predictable returns to exploitation. Moreover, in a low growth industry where entry barriers are typically high, exploitation alliances allow a ?rm to build stable relationships with the main players of the industry and extract greater ?nancial bene?t through the exercise of coordination and market power (Koza and Lewin, 1998). The presence of scale economies enables ?rms to concentrate on cost reduction and get the most out of their existing resources (Porter, 1980). Therefore, forming exploitation alliances enables the ?rms in a low growth industry to exploit their existing resources and capabilities as well as enhance coordination and market power. In contrast, rapid increases in demand, high turnover of rivals, and volatile mappings of market share characterize high growth industries (Anderson and Zeithaml, 1984; Eisenhardt and Schoonhoven, 1996). Firms in an industry with a high growth rate face a more competitive environment and must differentiate themselves and their products in order to survive (Soh and Roberts, 2003). As noted earlier, differentiation depends on doing something unique, valuable, and dif?cult to imitate, which biases ?rms towards exploratory search. An industry’s growth is an indicator of market attractiveness and industry evolution (Scherer and Ross, 1990). High growth markets provide ?rms with new technological and market opportunities, but also tend to make current technological advantages obsolete more quickly (Rawski, 1994). Driven by the need to explore new technologies and opportunities in a booming market, ?rms in a high-growth market will reap more bene?ts by entering more exploration alliances as opposed to exploitation alliances. Furthermore, the high growth rate in an industry increases ?rm opportunities, which in turn, tends to reward a ?rm’s risk-taking behaviors (March and Shapira, 1987, 1992). In other words, when a ?rm with an emphasis on exploration alliances has the aim of building new competencies and creating new skills, such bene?ts may be ampli?ed when the ?rm is in a high-growth industry environment where opportunities are ample and incentives for new product development are high (Danneels, 2002; Rothaermel, 2001a). Thus, H4. In a high-growth industry ?rms will bene?t more from a higher ratio of exploration alliance, whereas in a low-growth industry ?rms will bene?t more from a higher ratio of exploitation alliances in their alliance portfolio.

3. Method 3.1. Sample Our main ?nancial data source is Standard and Poor’s COMPUSTAT (SPC), which we further complemented with Moody’s FIS Online. We focused on 95 ?rms from ?ve industries (pharmaceutical, computer, food, steel, and paper) over eight years (1988–1995 inclusive) given that these industries have distinctive resource growth conditions and alliance activities over this time period, which can offer useful contrast and variance, but less alternative explanations for our analysis such as dotcom bubble and ?nancial crisis. For each ?rm, alliance data during the time period (including the purpose of each alliance and the history of each alliance) were retrieved from SDC platinum. We found little cross-industry alliances. The data for industry growth rate was obtained from

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census data for manufacturers from Bureau of Census, United States Department of Commerce. Covering the ?ve different industries enabled us to account for ?ve different environmental contexts in terms of industry growth. 3.2. Measures Firm performance is our dependent variable. We used return on assets (ROA) obtained from the year-end report in COMPUSTAT to capture the magnitude of ?rms’ economic performance (Bettis and Hall, 1982; King et al., 2004). Exploration alliance ratio. This variable represents the ratio of newly formed exploration alliances (as opposed to exploitation alliances). In other words, we focus on how exploration-heavy a ?rm’s alliance portfolio is (as opposed to exploitation-heavy). Following the works by Koza and Lewin (1998), Rothaermel (2001a), and Beckman et al. (2004), we adopted a strict measure of exploration/exploitation. Speci?cally, we coded alliances based on both alliance purpose and partner history. For the “purpose” aspect, we utilized Lavie and Rosenkopf’s (2006) approach, as they provide a scheme for coding exploration/exploitation based on the announced purpose of each alliance. For the “history” aspect, we relied on March’s (1991) approach to capture whether the alliance relationship is new or old in the past ?ve years. We examined the descriptions of all alliances formed by a ?rm in a given year. Those alliances that were focused on marketing and resource utilization such as licensing alliances, marketing alliances, and supplying alliances and those with existing partners were considered exploitative. In contrast, those alliances that were focused on discovery and development of new technology such as R&D alliances and technical alliances as well as those with new partners were considered exploratory in nature (Koza and Lewin, 1998). Alliances that have natures of both exploitation and exploitation were assigned partial weights accordingly (Lavie and Rosenkopf, 2006). To obtain the exploration alliance ratio, the simplest way was to use the ratio—the total number of exploration alliances over the total number of alliances formed. However, in order to have a positive value to indicate a greater exploration alliance ratio (and a negative value to indicate a greater exploitation alliance ratio) for a given year, we used the following formula: Exploration alliance ratio =

values from positive to negative since the greater the focus on cost leadership, the lesser the value. The differentiation strategy was measured by the ratio of a ?rm’s general selling and administration expenses over total sales, which captures a ?rm’s willingness to spend on marketing and selling related activities per unit of sales in an effort to differentiate itself from its rivals by crafting its product image and strengthening its post-sale services. It is also manifested by the ratio of R&D expenses over total sales, which measures a ?rm’s product differentiation through innovation (Berman et al., 1999; David et al., 2002). The higher these two ratios, the more likely the ?rm is pursuing differentiation strategy either in the form of service and/or brand name marketing or in the form of new product innovation and functionality. Similar to the cost leadership strategy variable, a composite measure was created by summing up the above two ratios since they are all divided by total sales and measure different aspects of differentiation strategy. Industry growth was measured by calculating the growth rate of product shipments in a given year as compared to the previous year at the industry level. We controlled for several factors. For year dummies, since there are multiple years involved in our study, we coded all eight years into eight dummy variables to control for yearly ?uctuations. We also controlled for ?rm size measured as the log form of employee number. In addition, we controlled for total (cumulative) number of alliances, measured as log form of the total number of alliances formed until a certain year for the ?rm. The log forms were used to address the skewness. We also controlled for a ?rm’s knowledge stock, measured by the cumulative number of patents the ?rm holds until the alliance event year. To avoid the left censoring problem, we traced back the patent stock to the year of 1985 for each ?rm. Furthermore, we controlled for industry concentration measured by four-?rm ratio in the industry shipment. Finally, we controlled for the inverse Mills ratio obtained from a regression model exploring the “antecedents” of alliance formation to avoid the potential bias due to endogeneity (Heckman, 1979). Following the Heckman procedure, we entered a variable of industrial alliance trend in the ?rst regression model (antecedents of alliance formation) but not in the second regression model (consequence of alliance formation). The

Total number of (exploration alliances ? exploitation alliances) formed by the ?rm in year t Total number of exploitation alliances formed by the ?rm in year t

(1)

Firm age was measured by subtracting the incorporated year from the alliance event year. More speci?cally, it is calculated by: the year of observation (for speci?c alliance events) minus the year the ?rm was incorporated, plus one. Strategic orientation: cost leadership vs. differentiation. Following the work by Hambrick (1983) we operationalized cost leadership strategy by a ?rm’s cost ef?ciency (the degree to which costs per unit of output are low) and asset parsimony (the degree to which assets per unit of output are low). Cost ef?ciency is one important measure of cost leadership (Porter, 1980) and is measured by calculating the ratio of cost of goods sold over total sales. Asset parsimony is measured by two variables: capital intensity and capital expenditures (Nair and Filer, 2003). Capital expenditure is the net expenditure for plant and equipment over total sales, while capital intensity is measured by the ratio of total assets to total sales. We created a composite variable for cost leadership strategy by summing up the above three variables since they have the same denominator. There are two advantages for using a composite measure. First, we are concerned with the overall strategy a ?rm adopts rather than strategy in a speci?c area. A composite variable measures ?rms’ resource allocation in the above three areas. Second, all the three indexes share a same denominator – total sales – which made it possible for us to combine them. We reversed the signs of

variable of industrial alliance trend (measured as the total number of alliances in the industry at each year) was expected to affect the alliance formation, while not directly in?uence the performance consequences of alliance formation.

3.3. Analysis We adopted a cross-sectional time series feasible generalized least squares (FGLS) regression model to analyze our panel data. This technique provides reliable estimates in the presence of heteroskedasticity and autocorrelation than ordinary least squares regression (Wooldridge, 2002). We entered the variables sequentially. First, control variables were entered into the regression model. Second, the direct effects were entered. Last, we added the interaction terms into the regression model. To test our hypotheses, we used ?rm performance as our dependent variable, and exploration alliance ratio, ?rm age, cost leadership, differentiation, and industry growth as independent variables, while controlling for year dummy variables, ?rm size, total number of alliances, knowledge stock, industry concentration, and inverse Mills

292 Table 1 Descriptive statistics and correlation matrices. No. 1 2 3 4 5 6 7 8 9 10 11 Variable Firm performance Firm size Total number of alliances Knowledge stock Industry concentration Inverse Mills ratio Exploration alliance ratio Firm age Cost strategy Differentiation strategy Industry growth Mean .05 2.49 2.14 .17 .42 1.39 ?.82 48.47 ?.80 .47 .09

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Std Dev. .18 2.12 1.31 .45 .16 .09 .30 32.93 2.92 .27 .03

1 .44 .06 .05 .02 .13 ?.17 .21 .28 ?.59 .00

2

3

4

5

6

7

8

9

10

.29 .28 ?.03 .17 ?.09 .61 .20 ?.42 ?.31

.38 ?.05 ?.03 .13 .14 .02 .01 .20

?.09 ?.16 .12 .23 .03 .02 .27

.31 ?.08 .13 .05 ?.25 ?.35

?.39 .21 ?.02 ?.25 ?.47

?.08 ?.39 .10 .20

.10 ?.17 ?.48

.21 ?.02

.27

N = 760, p < .05 for r > |.12| and p < .01 for r > |.17|. Table 2 Cross-sectional time-series FGLS estimation for alliance consequences. Variables Step 1: Control variables Firm size Total number of alliances Knowledge stock Industry concentration Inverse Mills ratio Step 2: Main variables Exploration alliance ratio (H1) Firm age Cost strategy Differentiation strategy Industry growth Step 3: Interactions Firm age × exploration alliance ratio (H2) Cost strategy × exploration alliance ratio (H3a) Differentiation × exploration alliance ratio (H3b) Industry growth × exploration alliance ratio (H4) Log-likelihood Wald 2 Model 1 .03 (6.51)*** ?.01 (?1.88)? .03 (.80) .04 (.71) .06 (.48) Model 2 .01 (1.47) ?.01 (?2.18)* .01 (.24) .01 (.30) .12 (1.26) .04 (1.90)? .00 (1.44) .34 (8.80)*** ?.35 (?12.33)*** 1.09 (3.31)** Model 3 ?.003 (?.81) ?.004 (?.98) .004 (.18) .03 (.91) ?.02 (?.27) ?.68 (?7.42)*** .001 (2.56)* .20 (3.26)** ?.25 (?4.14)*** 2.48 (3.54)*** .001 (2.51)* ?.53 (?6.23)*** .29 (4.45)*** 2.18 (2.90)** 128.35 51.79 203.33 342.41 249.99 680.09

Note: Total sample size is 760. t-Statistics are in parentheses. Year dummies are included in the model but not listed here. Dependent variable is “Firm performance.” ? p < .10. * p < .05. ** p < .01. *** p < .001.

ratio. To examine the effect of these variables, we calculated all the above measures of control variables and our main variables based on the data prior to the event year, when the new alliances are announced. In other words, we used a one-year lag effect to investigate the role of these driving factors to examine the causal effect.

4. Results Table 1 presents the means, standard deviations, and correlations for the variables in our models. Table 2 reports the results of the hierarchical regression models. To assess the potential threat of multicollinearity problems associated with high correlation, we estimated the variance in?ation factors (VIFs) and condition indexes for our hierarchical regression model. The highest VIF was 2.23, and the average VIF was 1.35, which are well below the recommended ceiling of 10 (Kleinbaum et al., 1988), suggesting no concern of multicollinearity. Hypothesis 1 investigates the effect of a ?rm’s alliance portfolio on its performance. We ?nd that exploration alliance ratio is signi?cant (p < .01) and negatively associated with ?rm performance (Model 3, Table 2) which suggests that ?rms having more newly formed exploration alliances does not improve the short-

term economic returns as compared to entering more exploitation alliances.2 Thus, Hypothesis 1 is supported. Hypothesis 2 examines the effect of organizational ?t (between alliance portfolio and ?rm age). We argue that it is bene?cial for younger ?rms to form more exploitation alliances, while older ?rms bene?t by forming more exploration alliances in their alliance portfolio. This argument is supported by the signi?cant (p < .05) and positive coef?cient of the interaction term. The result indicates that ?rms whose alliance formation choices ?t their age will see greater short-term performance. The signi?cant in?uence of ?rm age on the relationship between exploration alliance ratio and ?rm performance is depicted in Fig. 2A. H3(a)Hypotheses 3a and 3b explore the performance implications of strategic ?t (between alliance portfolio and strategic orientation—cost leadership vs. differentiation). We predict that

2 We would like to emphasize here, that we are not advocating that exploration alliances are detrimental for performance, thus in essence, ?rms should not be engaging in this type of alliance. First, we are only able to explore the effect on short-term performance. Exploration alliance, literally, are argued to have more distant effect in the longer term, which we are unable to capture. Second, our focus has been on the overall alliance portfolio of a ?rm, and not on the type and effect of an individual alliance. Our ?ndings suggest that ?rms with exploration-heavy alliance portfolio are less likely to obtain greater short-term ?nancial performance than those with exploitation-heavy alliance portfolio.

Y. Yamakawa et al. / Research Policy 40 (2011) 287–296

293

Panel A: Exploration alliance ratio and firm age
High

Panel B: Exploration alliance ratio and cost strategy
High

Younger firms

High on cost strategy

Firm performance

Older firms

Firm performance

Low on cost strategy
Low Exploitation Exploration Exploitation Exploration

Low

Alliance portfolio

Alliance portfolio

Panel C: Exploration alliance ratio and differentiation
High High

Panel D: Exploration alliance ratio and industry growth
Low industry growth

Firm performance

Low on differentiation strategy

Firm performance

High industry growth

High on differentiation strategy
Low Exploitation Exploration Low Exploitation Exploration

Alliance portfolio
Fig. 2. Interaction effects.

Alliance portfolio

?rms whose alliance-formation choices ?t their strategy will have enhanced performance. Speci?cally, ?rms that achieve ?t by matching either exploitation alliances with a cost leadership strategy (H3(a)Hypothesis 3a) or exploration alliances with a differentiation strategy (H3(a)Hypothesis 3b) will see enhanced performance. The coef?cient for the interaction between cost leadership and exploration alliance ratio is negative and signi?cant (p < .01), supporting H3(a)Hypothesis 3a. Similarly, H3(a)Hypothesis 3b is supported by the positive and signi?cant relationship (p < .01) for the interaction between differentiation strategy and exploration alliance ratio. Indeed, the results highlight the importance of strategic ?t. We plotted these interaction graphs in Fig. 2B and C. Hypothesis 4 examines the effect of environmental conditions such as industry contexts. Speci?cally, we explore the in?uence of industry growth on the relationship between ?rms’ alliance portfolio and ?nancial outcome. We argue that in a high-growth industry ?rms bene?t more by forming more exploration alliances, while in a low-growth industry ?rms bene?t more by forming more exploitation alliances. Hypothesis 4 is supported as the interaction term exhibits a signi?cant (p < .01) and positive result. The interaction effect is depicted in Fig. 2D. In order to examine the robustness of our ?ndings, we experimented with several additional measures of ?rm performance. For example, we tested industry adjusted ROA. While not as strong as using ROA due to reduced variance after adjustments at industry levels, the patterns were generally consistent. Furthermore, we

tested “productivity” (the net income per employee) as well as “Tobin’s Q” for additional measures of ?rm performance. The results are consistent, and are qualitatively similar to our main ?ndings, thus providing further support to our ?ndings. 5. Discussion This study has adopted an extended resource-based view and examined the performance consequences of a ?rm’s alliance portfolio in terms of exploration and exploitation (March, 1991) while considering its ?t with ?rm characteristics, strategic orientation, and industry condition. Our ?ndings show that it is not only important to consider alliances as expansions of ?rms’ boundaries but also fruitful to understand the relationships among ?rms, their alliances, the external environment, and their impacts on ?rm performance. We ?nd that ?rms forming more exploitation alliances (as opposed to exploration alliances) tend to have higher performance in the near term. This suggests that exploitation alliances may bring more direct and immediate bene?ts to the parent ?rm when compared with exploration alliances, which supports March’s (1991) original contention that returns to exploitation are “positive, proximate, and predictable.” Given the time frame and the nature of our study to investigate the performance implications, additional research expanding the time horizon and examining the impact of the choice of exploration vs. exploitation alliances on intermediate and long term performance needs to be undertaken.

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The results of our analyses also reveal the importance of organizational, strategic, and environmental ?t in relation to a ?rm’s alliance portfolio and its performance consequences. We ?nd that ?rms whose alliance-formation choices ?t their age will experience high performance. In other words, as we predicted, younger ?rms bene?t more from a higher ratio of exploitation alliance while older ?rms bene?t more from a higher ratio of exploration alliances in their alliance portfolio. Interestingly, while we ?nd that a higher exploration alliance ratio in a ?rm’s alliance portfolio does not necessarily lead to better immediate ?nancial performance, it leads to better performance even in the short-term when combined with a differentiation strategy. Similarly, we ?nd that cost leadership strategy further strengthens the link between an exploitation alliance ratio and ?rm performance. In other words, ?rms whose alliance-formation choices ?t their competitive strategy (e.g., higher ratio of exploration alliances and differentiation strategy, higher ratio of exploitation alliances and cost leadership strategy) will experience enhanced performance. This may re?ect a positive reaction by shareholders to the announcement of alliances that ‘?t’ with the expressed strategy of the ?rm and their expectations regarding the actions of the ?rm. Furthermore, our results also con?rm that ?rms whose allianceformation choices ?t the industry context (e.g., level of growth) will earn greater bene?ts in terms of immediate ?nancial performance. While ?rms may differ in their internal resources and strategic orientation, they operate under the same industry, and face similar challenges that the environment brings. From this standpoint, our research receives strong support for the role of the environment. In particular, we ?nd that industry growth is an important factor in understanding ?rms’ performance consequences of their alliance-formation choices. In sum, our study calls researchers’ attention to the interplay among the concepts of strategic choice (exploration vs. exploitation), internal and external ?t, and ?rm performance—in other words, the importance of boundary conditions through ?t. We ?nd that the relationship between a ?rm’s alliance portfolio in terms of exploration vs. exploitation and its ?nancial performance is contingent upon certain conditions of ?t—namely, organizational characteristics (e.g., ?rm age), strategic orientation (e.g., cost leadership, differentiation), and environmental context (e.g., industry growth). Under certain conditions, exploration- vs. exploitationheavy alliance portfolio may well drive ?rms to achieve better performance. These results caution us against the universal adoption of the balance assumption, and point out that there are boundary conditions under which a different mix of exploration and exploitation may be more bene?cial (Mc Namara and Baden-Fuller, 2007). Building on recent works (e.g., Ruiz-Ortega and Garcia-Villaverde, 2008; Vorhies et al., 2009), our study extends our understanding of how the relationship between resources and ?rm performance is contingent on internal and external ?t conditions—namely, the ?t between alliance portfolio and ?rm characteristics, strategic orientation, and industry contexts. 5.1. Contributions Theoretically, our study contributes to the resource-based view and the exploration/exploitation literature in the following ways. First, while there is little understanding of the mechanism by which the process of how strategies are deployed, and ultimately, rents are created (Barney, 2001), we show in our study how ?rms’ strategic choices between exploration and exploitation alliance formation can drive ?rm performance. Second, we conceptually clarify the relationships between alliance-formation choices, ?rms’ competitive strategy, and performance. We not only provide the performance implications of

?rms’ alliance portfolio (in terms of exploration and exploitation) but also consider the important role of organizational ?t (?rm characteristics—age), strategic ?t (strategic orientation—cost leadership, differentiation), and environmental ?t (industry contexts—industry growth). Our ?ndings highlight the importance of ?t that under certain conditions when resources are combined effectively with the appropriate orientation of deployment and strategic use, as well as under the appropriate environmental circumstances, it will lead to superior performance. The resourcebased view has been criticized for its lack of considering the boundary conditions (Priem and Butler, 2001). Prior studies in general have been limited in terms of under-investigating the role of internal and external constraints. Building on the recent stream of research that has succeeded to complement the resource-based view by meshing it with other perspectives such as that of competitive strategy and relational perspective (e.g., Ruiz-Ortega and Garcia-Villaverde, 2008; Vorhies et al., 2009), we have further shown the importance of expanding ?rms’ boundaries to include their inter-?rm relationships as well as their immediate environmental conditions when investigating ?rm performance. This is an important step towards a more holistic understanding of ?rms as well as their behaviors that drive performance. In terms of the exploration–exploitation paradigm, we have added nuance to the balance assumption by providing systematic evidences that there is no absolute criterion for the balance between exploration and exploitation. Rather, such a balance may need to vary with different ?rm characteristics, strategic orientation, and industry environments. In other words, an absolute balance criterion, if adopted indiscriminately, may be harmful to ?rms under certain conditions (Lin et al., 2007). From a different angle, our study also contributes to the alliance literature in general. We have shown that the recent research streams, although accepting the explorative and exploitative motives in alliance formation, do not explore extensively the performance implications of such motives. Put differently, the literature lacks proper speci?cation of how exploration vs. exploitation in a ?rm’s alliance portfolio impact ?rm performance. Moreover, the literature on strategic alliances also tends to overlook the interaction between ?rm characteristics, internal resource conditions, the market factors, as well as the con?guration of these resources such as the strategic orientation in explaining the performance consequences of exploration vs. exploitation motivations in alliance formation. Overall, our results support the assertions that ?rms are bounded by their stages of development, their strategic orientations, and the industry environment. Methodologically, this study has several contributions as well. We have de?ned the measure of exploration and exploitation to consider both content and experience. We have also created composite measures of cost leadership and differentiation strategies by integrating important dimensions from existing studies (Berman et al., 1999; Hambrick, 1983; Kotha and Nair, 1995). Further, we have examined ?ve manufacturing industries, unlike previous research that generally focused only on one industry (Park et al., 2002; Rothaermel, 2001a). As a result, we have provided heterogeneity within our sample and generated results in a multi-industry framework. As a noteworthy point, our study suggests that the exploration/exploitation in alliances may differ from the selfexploratory/exploitative activities in nature. For example, although it is reasonable to argue that exploitation should be based on previous exploration within a ?rm’s own boundary, it may not be true under the context of alliances. Even without prior exploration, ?rms may still form exploitation alliances, not only due to their resource constraints, but also as a means to utilize partners’ resources and legitimize their own existence.

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5.2. Managerial implications Our study offers valuable insights for managerial implications. For example, our ?ndings attest to the need for exploration orientation in larger and more established corporations. In the process of building organizational memories and routines, exploitation activities are found to crowd out exploration activities. As ?rms get older, the crowding effect tends to become stronger. Given the implications of organizational simplicity (Miller, 1993), competency traps (Levitt and March, 1988), and core-rigidity (Leonard-Barton, 1995), managers may be more inclined to the repetition and ?ne-tuning of existing resources and capabilities. Most importantly, managers in large established ?rms often give disproportional attention to exploitation activities so as to reap economic bene?ts from its large pool of resources. However, our ?ndings suggest otherwise. We ?nd that the older the ?rms, the greater returns their explorationoriented alliance portfolio will bring about. Firms that engage in exploration alliance do break away from their existing boundaries to obtain new resources (e.g., knowledge base). This in turn allows them to increase their ability to create valuable, impactful new innovation (S?rensen and Stuart, 2000). When it comes to strategic orientation, our ?ndings suggest that managers of ?rms that focus on differentiation should embrace more exploration alliances while those of ?rms that focus on cost leadership strategy should embrace more exploitation alliances. Finally, the need for environmental ?t suggests that when the industry is in high-growth, managers should be more prone to engaging in exploration alliances, whereas when the industry is in low-growth, they should be more prone to engaging in exploitation alliances to reap the short-term ?nancial bene?ts from strategic alliances. 5.3. Limitations and future research Our research is not without limitations, which in turn, should offer opportunities for future research. First of all, our study has relied more on ?rms that have complete information. This may have excluded some smaller and younger ?rms that have not proven their business models prior to IPO, which may impact the relationship between exploration/exploitation alliance orientation and performance. Furthermore, while the time period of our study was intended to represent a relatively stable period among the sample industries in order to evoke less alternative explanations, future research could explore more recent years including the impact of ?nancial crisis or the dotcom bubble on the relationship under inquiry. Secondly, future research endeavors will bene?t from a greater sample size as well as from greater variety of sectors. While the study has made a conscious attempt to integrate previously separated research into one study, it will be worthwhile for future research to expand to broader industry contexts and further investigate the links between alliance-formation choices and the subsequent performance implications. Finally, the performance consequences of alliances can be decided by each ?rm’s speci?c investment into the alliances, and future study linking ?rm-speci?c or alliance-speci?c resources to consequences of exploration vs. exploitation alliance formation might give us additional explanatory power in explaining ?rm performance. 6. Conclusion Our study has highlighted the importance of using an extended resource-based view to examine the relationship between a ?rm’s alliance-formation choices and its performance. Our ?ndings suggest that performance implications of a ?rm’s alliance portfolio depend on organizational, strategic, and environmen-

tal ?t. Speci?cally, it is critical to consider the level of ?t among alliance formation (exploration/exploitation), ?rm age, ?rm strategy (cost leadership/differentiation), and industry condition (industry growth) so as to gain a better understanding of ?rm performance.

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