Empirically eliciting complementarities in capabilities: integrating quasi-experimental and panel data methodologies

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1 Article Empirically eliciting complementarities in capabilities: integrating quasi-experimental and panel data methodologies Strategic Organization 8(2) The Author(s), 2010 Reprints and permissions: sagepub.co.uk/journalspermissions.nav DOI: / Joseph A. Clougherty University of Illinois at Urbana-Champaign, USA and CEPR-London, UK Thomas P. Moliterno University of South Carolina, USA Abstract Resource-based researchers are finding considerable support for the association between the creation and application of organizational capabilities and competitive advantage. However, measuring organizational capabilities presents empirical researchers with substantial difficulties. This study proposes quasi-experimental research design and panel data estimation techniques for future empirical work on capabilities: this approach takes advantage of more easily measured resources transferred in the strategic factor market to elicit what otherwise would be unknowable capabilities. This approach also allows researchers to uncover how, when building organizational capabilities through factor market resource acquisitions, firms differ significantly in their ability to leverage tangible and intangible complementary resources to enhance the operational performance of acquired resources. This empirical approach is illustrated through an investigation of the US international airline industry from 1983 to 1992, during which time the industry experienced a significant transfer in the rights to operate US international routes. The results of the analysis demonstrate how quasiexperimental design, when coupled with panel data estimation techniques, is a powerful econometric strategy for researchers interested in modeling heterogeneity in firm capabilities. Keywords capabilities, factor markets, panel data, quasi-experiment, research methods, resource-based view Introduction A central proposition of the resource-based view of the firm (hereafter RBV; Barney, 1991; Peteraf, 1993; Wernerfelt, 1984) is that firms bundle resources in a way that most effectively extracts their productive services (Penrose, 1959; Rumelt, 1974), and researchers have defined an organizational capability as a firm s capacity to deploy resources, usually in combination, using organizational processes, to effect a desired end, where that desired end is the enhanced productivity of the resources (Amit and Schoemaker, 1993: 35; see also Makadok, 2001). Newbert s (2007) survey of RBV empirical work found that while discrete resources enhanced performance or competitive

2 108 Strategic Organization 8(2) advantage in 37 percent of the studies reviewed, capabilities yielded a positive impact in 71 percent of the studies. Moreover, Newbert (2007) observes an increasing, and perhaps consequent, trend to examine the creation and application of organizational capabilities. In short, it is becoming clear that the possession of valuable, rare, inimitable, non-substitutable resources (Barney, 1991) is an insufficient condition for firm-level competitive advantage, and that competitive advantage derives instead, or as well, from capability creation and application. 1 While the organizational capability construct has gained traction with scholars exploring the sources of competitive advantage (Denrell et al., 2003; Makadok, 2001; Moran and Ghoshal, 1999), capabilities remain very difficult to measure (Armstrong and Shimizu, 2007; Barney, 1997; Ketchen et al., 2008; Newbert, 2007). Survey and interview techniques represent the dominant means via which the literature has attempted to capture capabilities, yet empirically minded scholars have been concerned about the potential biases and measurement error involved with basing capabilities on respondent opinion (Newbert, 2007). Accordingly, for research to advance our understanding of organizational capabilities, empirical studies need to be designed in ways that allow researchers to model them more precisely. This article makes progress in this regard by showing how fixedeffects regression analysis and quasi-experimental research design both of which are well-known to organizational scholars can yield important insights regarding organizational capabilities when integrated and applied as part of a single research design. Strategy researchers have predominantly employed three methodological techniques to capture heterogeneity in capabilities: (1) dummy variable estimation (Anand and Khanna, 2000; Guler, 2007; Henderson and Cockburn, 1994); (2) Chow tests of beta coefficients (Makadok, 1999); and (3) stochastic frontier modeling (Dutta et al., 2005). While these methodological approaches involve a number of different advantages and disadvantages, which we discuss in this article, one common critique of the three approaches is that in each case capability differences are estimated based on intra-industry and cross-firm comparisons, where firms control different, discrete resources. For example, Makadok (1999) examines the money market mutual fund industry, where each firm has a distinct, albeit comparable, portfolio of resources. However, Teece (1981) argues that making inferences on firm-level variation via intra-industry comparisons is subject to suspect validity due to the broadness of industry categories. Put simply, Teece questions whether other firms in an industry represent appropriate benchmarks to elicit heterogeneity for a particular firm. In addition to the intra-industry comparison critique, none of the previous methodological approaches attempts to uncover how organizations effectively integrate tradable resources. Yet, as we have already observed, the organizational capability construct describes the combination and joint leveraging of discrete resources (Amit and Schoemaker, 1993), some of which must necessarily be acquired by the firm in the strategic factor market (Barney, 1986; Dierickx and Cool, 1989; Makadok, 2001) in the process of adding to the organizational capability (Maritan, 2001). As a result, scholars require an empirical research design that specifically elicits the firm s capacity to create and leverage a bundle of resources by examining the complementarities among the resources (Adegbesan, 2009) that make up the capability. This article describes and illustrates how a quasi-experimental design approach, when combined with panel data estimation techniques, fills these methodological gaps. In particular, the method that we advocate in this article allows researchers to examine how the complementarity (Adegbesan, 2009) between tangible and intangible resources embedded in a firm-level capability leverages the performance of a resource acquired in the strategic factor market (Barney, 1986; Makadok, 2001) in order to supplement and/or build this capability (Maritan, 2001). That is, we offer researchers a means to measure how complementarities in organizational capabilities manifest as resourcelevel differences in performance. Moreover, our proposed method surmounts the intra-industry

3 Clougherty and Moliterno 109 comparison challenge noted by Teece (1981) by making use of factor market transfers in specific resources, thus our comparisons are not based on different firms with different resources, but instead based on different firms operating the same particular resource. The methodological approach to capturing and measuring resource-level complementarities in firm-level capabilities that we advocate in this article builds from two core observations. First, the resources which make up a capability are more directly measurable than the capability itself (Newbert, 2007). Second, firms engage in capability building (Makadok, 2001) by acquiring the component resources in an organizational capability in the strategic factor market (Adegbesan, 2009; Barney, 1986; Makadok and Barney, 2001). Drawing on these observations, we propose the use of quasi-experimental research methodology (Campbell and Stanley, 1966; Cook and Campbell, 1979) coupled with econometric panel data estimation techniques (Greene, 2008; Wooldridge, 2002) to provide a means to elicit heterogeneity in firm capability creation in the presence of measurable and transferable resources. Specifically, by examining a focal resource that is traded between firms, we can measure the performance of that resource as a component of directly comparable organizational capabilities that is, two different resource bundles (Peteraf, 1993) in two different firms. This quasi-experimental design allows us to examine the degree to which a difference in performance for that focal resource ex ante and ex post factor market transfer is a function of complementarities between the focal resource and other tangible and intangible resources with which it is deployed. Using panel data estimation techniques to examine this performance at the focal resource-level (Coff, 1999) across the two firm-level manifestations of the same capability, we are able, then, to gauge the degree to which the focal resource s performance is effected by its relative complementarity (Adegbesan, 2009) with the other resources employed in the capability. Thus the methodological design described and demonstrated in this article contributes to a growing literature on measuring organizational capabilities. While scholars (Dutta et al., 2005; Makadok, 1999) have suggested other promising approaches to capability estimation, their techniques may lead researchers to make questionable intra-industry comparisons (Teece, 1981). While that shortcoming may partly be resolved with a matching-pairs procedure (Helfat and Teece, 1987), the approach described here is somewhat more specific. By training the empirical focus at the resource-level to consider firm-level differences in capabilities when employing the same focal resource, our design avoids the need for intra-industry comparisons and responds to calls to develop a better understanding of competitive advantage by focusing empirical attention on strategic factors and not firm performance levels (Coff, 1999). Also, since we are concerned here with how complementarities between resources within the capability manifest as resource-level performance, our approach also differs from the empirical capabilities literature in general (see Newbert, 2007), where heterogeneity in firm-specific capabilities is determined by modeling the association between the capability and a higher-level performance outcome. Two firms may have a common capability that yields differential competitive advantage, but this tells us little about how effectively each firm adds value to the component resources in the capability, and leverages complementarities between those resources, when making investments to add to the capability (Maritan, 2001). In other words, observed differences in capability performance outcomes may simply be a function of the fact that one firm acquired better component resources. Our design approach resolves this problem by examining how different firms deploy the exact same resource in the creation and application of important, and directly comparable, firm-level capabilities. To demonstrate the methodological approach described in this article, we employ international route-specific data for US airlines from 1983 to In these data, exact performance levels (i.e. load factors ) are associated with each resource (i.e. the international route). Central to our demonstration of the quasi-experimental design, the period of study is contextually important as a

4 110 Strategic Organization 8(2) number of routes were traded between airlines in the strategic factor market. As a result, we can examine a focal resource s performance in the context of different organizational resource bundles with different tangible and intangible complementary resources. This, in turn, allows us to capture differences in firm-level capability creation and application. In the next section, we briefly outline the core theoretical proposition that our research design is uniquely able to test. We should note at the outset that our purpose in this article is focused on proposing and illustrating a methodological approach, and not theory-testing per se: the theoretical review and discussion that follow are offered as a means of providing a context to orient researchers who will apply the approach we advocate here. In particular, we draw upon theoretical scholarship that suggests that firms build their capabilities by bundling resources (Barney, 1986; Makadok, 2001; Peteraf, 1993), and that complementarities in this resource bundle (Adegbesan, 2009) synergistically leverage resource-level performance. This review leads us to articulate a proposition that derives from this literature, and which we use as a means to illustrate the methodological approach that follows. In the subsequent sections, we outline that research design in some detail, as well as compare and contrast it with other approaches to capability estimation. We then illustrate a specific application of the approach we advocate by using the airline data described above. The results we report show the efficacy of our design in exploring the theoretical proposition we outline at the outset. We conclude with a discussion of the implications of the methodological approach we propose. Theoretical context Different theoretical streams within the RBV posit different mechanisms by which resources drive firm-level competitive advantage (see Armstrong and Shimizu, 2007; Barney and Arikan, 2001; Dutta et al., 2005; Newbert, 2007). We focus here on one approach: namely, how the productive output of a resource s services (Penrose, 1959) are of greater value to a firm when effectively combined with other firm-level resources (Adegbesan, 2009; Denrell et al., 2003; Henderson and Cockburn, 1994; Holcomb et al., 2009; Kogut and Zander, 1992; Mahoney and Pandian, 1992; Makadok, 2001; Moran and Ghoshal, 1999; Peteraf, 1993; Sirmon et al., 2007; Somaya et al., 2007). Thus we follow Amit and Schoemaker (1993: 35) in conceptualizing capabilities as the ability to deploy resources, usually in combination, using organizational processes, to effect a desired end... [they are] intermediary goods generated by the firm to provide enhanced productivity of its resources (Grant, 1991; see also Adegbesan, 2009). Makadok (2001) observes that Amit and Schoemaker s (1993) definition of capabilities entails two important conceptual points. First, the organizational capability is firm-specific, whereas the resources that the firm uses to build that capability are acquired in the factor market. Second, a central focus of an organizational capability is to improve the performance contribution of the resources that comprise it. Of course, capabilities that effectively leverage performance gains in discrete resources do not materialize from the ether: they are intentionally constructed by the firm s managers (Maritan, 2001; Penrose, 1959; Sirmon and Hitt, 2009), require a supportive organizational structure to emerge (Barney, 1997) and relate in meaningful ways to the organizational context (Sirmon and Hitt, 2009; Sirmon et al., 2007). Organizational capabilities, therefore, are created by bundling resources acquired in the strategic factor market (Barney, 1986) with other tangible and intangible resources controlled by the organization. Dierickx and Cool (1989) employ a stock and flow metaphor to describe the underlying process: an organizational capability comprises the stock of a bundle of heterogeneous firm-level resources, and is depleted over time and augmented through a flow of additional factor market acquisitions. Simply put, capability building (Makadok, 2001) is a process combining

5 Clougherty and Moliterno 111 existing and new resources acquired through ongoing investment in the capability (Maritan, 2001). Adegbesan (2009), building upon work by Penrose (1959), Teece (1986) and Christmann (2000), makes an important recent contribution to this theoretical stream by arguing that the degree of complementarity in these resource combinations yields resource-level synergies. In other words, the process of effectively bundling resources to create, extend, or build an organizational capability entails acquiring and integrating resources that are more complementary with the firm s existing tangible and intangible resources. Taking these insights together, then, we observe that firms have heterogeneous bundles (Peteraf, 1993) of tangible and intangible resources (Barney, 1991) which they augment with factor market acquisitions (Barney, 1986; Dierickx and Cool, 1989), and this process of capability building (Makadok, 2001) is effective to the degree that it yields synergistic resource combinations (Adegbesan, 2009). As an illustration, consider an example from the empirical context we describe and examine later. A particular international airline route (e.g. New York Berlin) is a commodity resource (Denrell et al., 2003) that can be acquired and used similarly by different airlines: indeed, a strategic factor market exists for international route rights (GAO, 1992). Each airline in the industry necessarily possesses other tangible (e.g. a frequent flyer program, a domestic flight schedule, hub operations) and intangible (e.g. yield management expertise, operational logistics efficacy) resources ex ante acquisition of such a route in the factor market. By definition, these extant firm-specific resource portfolios are heterogeneous (Peteraf, 1993), and thus more, or less, complementary to the newly acquired route. An airline will then bundle the newly acquired international route with its extant resources in order to expand, and deploy, what we might label its flight logistics capability. If we are interested, then, in understanding the firm-specific flight logistics capability, we will want to capture how effectively the complementary tangible and intangible resources leverage the route-right resources acquired to add (Maritan, 2001) to that capability. Stated differently, we can expect variation across airlines in the performance associated with an international route ex post acquisition, and that variation is a function of the synergistic complementarity of the tangible and intangible complementary resources bundled with it (Adegbesan, 2009). In this example, the difference in route-level (i.e. resource-level) performance between two airlines operating the same international route before and after its factor market transfer provides a means to capture the resource complementarities embedded within different (i.e. cross-firm) manifestations of a comparable organizational capability. Drawing upon these arguments, we now are able to articulate a core theoretical proposition that flows from this literature. Again, our purpose in this article is not to formally test hypotheses related to the theoretical context we have reviewed; rather, we highlight here the theoretical domain which the methodological approach we describe and illustrate in the remainder of this article is uniquely able to explore. Accordingly, we are particularly interested in that stream within the RBV of the firm that focuses on how firms create organizational capabilities (Amit and Schoemaker, 1993) by bundling resources acquired in the factor market (Barney, 1986; Dierickx and Cool, 1989; Makadok, 2001). We assume a factor market where sellers and buyers are in the same industry, require common operational capabilities and deploy focal resources transferred from one firm to another as component resources in those common capabilities. In this case, a transferred resource will only manifest a differential level of performance ex ante and ex post transfer if there exist inter-firm differences in the complementarity of the tangible and intangible resources with which it is bundled when creating the capability. Since firm-level resource bundles are defined to be heterogeneous (Peteraf, 1993), it follows that firms will be heterogeneous with respect to the degree to which the complementary resources they possess will be synergistic with the focal resource (Adegbesan, 2009). Formally:

6 112 Strategic Organization 8(2) Proposition When building organizational capabilities, firms in the same industry differ significantly in their ability to leverage tangible and intangible complementary resources to enhance the operational performance of acquired resources. Estimating complementarities in capabilities Measuring capabilities: existing approaches As already noted, measurement represents a major challenge for scholars considering the process of capability creation within organizations. Researchers must measure not only performance of the capability s component resources ex ante and ex post acquisition (Denrell et al., 2003; Makadok, 2001), but also the resource-specific value created in the bundling process. Thus, measuring the totality of the firm s resource management routine is challenging (Sirmon et al., 2007). A similar observation has recently been made by Armstrong and Shimizu (2007), who also suggest ways to advance empirical work in the resource-based tradition. Survey and interview-based studies represent the dominant means via which empiricists have elicited firm capabilities. Newbert s (2007) survey of the empirical literature found that researchers almost universally rely upon direct-report data to elicit organizational capabilities (see Hitt et al., 2001 for a notable exception). While primary data collection techniques yield considerable insights to management researchers, they also introduce the potential for respondent bias. Biases may be particularly evident when measuring intangible constructs such as organizational capabilities. For instance, in a large cross-national study of management practice, Bloom et al. (2007) show that managers find it difficult to objectively evaluate their own firm s qualities. Such self-assessment biases have also been demonstrated in other areas of managerial responsibility: for example, managers overwhelming find their own company s acquisition activity to be synergistic, despite the fact that acquisitions seem to be break-even propositions at best (Bruner, 2002). Not surprisingly, Newbert (2007: 137) concludes that measuring capabilities... often necessitates a greater need for primary data collection techniques, and often introduces a greater potential for slippage and respondent bias. Scholars have accordingly employed several analytical strategies dummy variable estimation (Anand and Khanna, 2000; Guler, 2007; Henderson and Cockburn, 1994), Chow tests of beta coefficients (Makadok, 1999) and stochastic frontier modeling (Dutta et al., 2005) to more accurately model heterogeneity in capabilities. We briefly review these approaches next. An early, and still common approach employed by empiricists to capture firm-level heterogeneity in capabilities is to introduce firm dummy variables in a regression specification. This approach has been used to elicit idiosyncratic firm effects in pharmaceutical research (Henderson and Cockburn, 1994), joint venture management (Anand and Khanna, 2000) and venture capitalist capabilities (Guler, 2007). While capturing firm-level capabilities with a dummy variable is efficient and elegant, the dummy variable estimates are extremely sensitive to the omitted variable bias, as any non-measured variables that correlate with firms and explain either within or between variation in the dependent variable will be captured by this approach. Thus, a number of validity threats exist with regard to interpreting these coefficient estimates as accurately capturing firm capabilities. Makadok (1999) offers another approach to capturing firm-specific capabilities, providing an insightful procedure to uncover firm-level capability differences that involves multiple steps. In this approach, capability differences principally derive from the beta coefficients for the effect of scale on both the demand and cost sides of families of funds in the mutual fund industry. Makadok (1999) then uses Chow tests to see if these beta coefficients are significantly different across firms. While Makadok s proposed approach is insightful, it involves multiple estimation steps that make

7 Clougherty and Moliterno 113 it challenging for future researchers to adopt. Yet, the strength of the Makadok (1999) approach resides in eliciting both demand- and cost-side effects of firm-level capabilities. Finally, Dutta et al. (2005) recently employed a stochastic frontier method to estimate firm differences in the ability to translate R&D expenditures in the semiconductor and computer industries into citation-weighted patent counts. This technique represents a notable and important step in increasing the empirical rigor of capability estimation, and may prove particularly useful for researchers examining the relationship between a firm s existing bundle of resources and achieved performance. In other words and mapping Dutta et al. s (2005) empirical technique onto Newbert s (2007) review stochastic frontier estimation may be fruitful for scholars employing a resource heterogeneity approach, where the fundamental research question is concerned with the association between heterogeneity in firm capabilities and higher-level (i.e. firm) performance. However, this methodology is less well suited for empirical research where the objective is to examine organizational variation in bundling resources and building capabilities (Makadok, 2001; Moran and Ghoshal, 1999), thereby achieving improvements in resource-level performance. The three empirical approaches outlined above entail both advantages and disadvantages in terms of empirically eliciting cross-firm heterogeneity in capabilities. In addition to the individual drawbacks noted above, the three empirical strategies are subject to a common critique in that inferences regarding capability differences are all based on cross-firm comparisons. For instance, when considering firm-level differences in the ability to translate R&D expenditures into citation counts in the semiconductor industry, Dutta et al. (2005) effectively treat Texas Instruments (US$8 billion in sales and 36,000 employees) and Cypress Semiconductor Inc. (US$401 million in sales and 5000 employees) 2 equally. Teece (1981) points out that eliciting firm-level variation from such intra-industry comparisons is subject to suspect validity due to the industry categories being too broad to effectively control for confounding effects. To be exact, Teece (1981: 180) states that there is mounting evidence that commonly recognized industry categories are too broad to be relied upon to control for confounding effects... firms lumped together may differ markedly on important criteria such as the composition and breadth of product lines, the markets in which they compete, and their strategic posture. As this brief review indicates, the empirical researcher s toolkit is sparse when it comes to techniques for modeling organizational capabilities. And notwithstanding the RBV s focus on the transfer of resources via a strategic factor market (Barney, 1986, 1988; Makadok and Barney, 2001), we are aware of no methodology to examine how effectively organizations integrate tradable resources. This is a particular shortcoming in the literature since such integration represents a non-trivial source of rent generation and appropriation (Adegbesan, 2009; Barney, 1989; Denrell et al., 2003; Makadok, 2001; Moliterno and Wiersema, 2007), and examples of this type of transfer are plentiful: firms frequently transfer store locations (i.e. the retail industry), production facilities (i.e. the manufacturing industry) and product lines (i.e. the consumer product industry). Consider, for example, Johnson and Johnson s 2006 US$16.5 billion purchase of Pfizer s consumer healthcare unit, which transferred well-known product lines (i.e. resources) like Listerine, Rolaids, Zantac, Lubriderm and Rogaine. Thus, in addition to adding to the empirical options available to researchers for capturing capabilities, and overcoming some of the limitations of the existing empirical options, the research design we describe and illustrate in the following sections is specifically targeted at measuring capabilities when the researcher s objective is to understand how firms leverage a particular resource acquired in the factor market by bundling it with other complementary firm-specific resources (e.g. Somaya et al., 2007).

8 114 Strategic Organization 8(2) Measuring capabilities with quasi-experimental design Overview. It is helpful to first describe our proposed methodological solution to the issues raised in the preceding paragraphs in broad conceptual terms. As noted in the earlier discussion of the theoretical context, we are interested in how (when creating or extending an organizational capability) firms differentially enhance a focal resource (transferred through the factor market) by leveraging the tangible and intangible complementary resources with which the focal resource is bundled. Consider, then, a particular, focal, commodity resource (Denrell et al., 2003) transferred from Firm X to Firm Y which both firms deploy as part of a particular capability (Moran and Ghoshal, 1999). Measuring performance of the focal resource when controlled by Firm X and Firm Y, we can capture empirically how much of the resourcelevel performance is a function of its complementarity with the tangible and intangible resources with which it is bundled in two different firm-specific manifestations of the capability (i.e. Firm X s and Firm Y s). If, as a function of these complementarities, the transferred resource is more productive when controlled by one firm or other, then we have effectively elicited the difference in Firm X s and Firm Y s capabilities as conceptualized by Amit and Schoemaker (1993) and described earlier. We highlight two important aspects embedded in the foregoing conceptual description of our proposed approach. First, the lack of random assignment and controlled manipulation in the comparison suggests that the proposed methodological approach can best be categorized as a quasiexperiment (Campbell and Stanley, 1966; Cook and Campbell, 1979). In design terms, our approach borrows from the rotation and replacement designs for quasi-experimentation (see Roos, 1973): the individual firm attributes (capabilities) are estimated while holding constant the situational attributes (control over the focal resource). Second, there exists a clear time-series structure to this quasi-experiment: one firm controls and deploys the resource in a given period, the resource is then transferred through the factor market, and another firm subsequently controls and deploys the resource in a later time period. Accordingly, this time series allows researchers to employ modern panel data econometric techniques to analyze more precisely the structural relationships within the quasi-experiment. We briefly describe each of these aspects of our proposed research design and then describe the data necessary to conduct this kind of analysis. Quasi-experiments. The literature on quasi-experimentation methodology is large and extensive as it crosses the boundaries of many different disciplines. Cook and Campbell (1979: 57) point out that quasi-experimental methods are often called for since it is rare in our society to have valuable resources distributed on a random basis. While this approach to research design received quite a bit of attention after the seminal contributions by Campbell and Stanley (1966) and Cook and Campbell (1979), scholarly work in this area has waned over the last two decades. Indeed, Hollenbeck (2002) notes that quasi-experiments are rapidly becoming a lost science and art in organizational scholarship, and Grant and Wall (2009) argue that quasi-experiments are an underutilized tool for achieving internal and external validity while examining the effects of naturally occurring changes in macroscopic variables. In some respects, then, we respond to these calls for management scholarship to re-embrace quasi-experimentation. Quasi-experiments more readily allow temporally extended evaluations than do field or lab experiments, as they can examine managerially induced changes that are more likely to last than interventions introduced by researchers. For instance, Romanelli and Tushman (1986) note that longitudinal quasi-experiments are valuable for examining the relative impact of managers and environments on organizational activity. Further, quasi-experiments bridge the

9 Clougherty and Moliterno 115 t 1 t 2 t 3 t 4 t 5 t 6 t 7 t 8 t 9 t 10 (1) O A O A O A X O B O B O B O B O B O B O B (2) O A O A O A O A O A O A O A X O B O B O B (3) O C O C O C O C O C O C O C O C O C O C Figure 1. Quasi-experimental design for capability measurement scholar practitioner divide more easily by forcing researchers on examining the variables that managers shift, as opposed to the variables that researchers shift in lab and field experiments. Quasi-experimentation is particularly well-suited to addressing the research domain described by the theoretical proposition we have outlined. Consider Figure 1, which illustrates a quasiexperimental time-series design using Cook and Campbell s (1979) standard notation. For the purposes of illustration, let us imagine Figure 1 describes a time series of 10 resource-level observations (t 1 t 10 ) in an industry with three firms A, B and C, and let X indicate the transfer of the resource from one firm to another. In Panel 1, Firm A transfers a resource to Firm B between the t 3 and t 4 observations. The difference between Panel 1 and 2 indicates that this design has features akin to Cook and Campbell s (1979) Interrupted Time Series with Switching Replications, as different samples receive the treatment (resource transfer) at different times. While the switching replications design procedure as opposed to other forms of interrupted time-series designs (see Cook and Campbell, 1979: Ch. 5) is not technically required to employ our research approach, it does strengthen both internal and external validity as the effect is demonstrated with different populations in different settings and at different moments in history. For this reason, Cook and Campbell (1979: 225) note that the switching replications feature is clearly powerful for ruling out most validity threats. Panel 3 indicates that over the sampling frame Firm C is involved in no factor market transactions. It is important to include firms that do not engage in resource transfers in the sample: a no-treatment control group is strongly advised as it mitigates the threat of history and allows for stronger causal inferences (Cook and Campbell, 1979). A quasi-experimental design such as we have described here allows researchers to consider the performance of a given resource (i.e. the resource transferred between firms as the treatment X) when deployed by two different firms in the same industry as part of two comparable organizational capabilities. Differences in resource-level performance before and after this transfer will effectively elicit the degree to which the focal resource s performance is effected by the resource bundle with which it is deployed at the time of measurement, and, thus, the capability differences between the two firms. Furthermore, eliciting such an exact comparison of cross-firm capabilities (based not on an intra-industry variation but on variation within a particular resource) follows through on Teece s (1981) encouragement to be far more specific and exact in analyzing cross-firm variation, as well as Coff s (1999: 130) recommendation that rent... be identified at the factor level rather than the firm level. To do so effectively, however, requires layering modern panel data econometric techniques onto this well-established quasi-experimental design. We discuss the essential techniques next. Estimation technique and measuring complementary resources. Joining modern panel data techniques (Greene, 2008; Wooldridge, 2002) with quasi-experimental research design yields strong inferences

10 116 Strategic Organization 8(2) that are less subject to validity threats. In particular, we employ a fixed-effects estimation technique: where the fixed-effect is set at the resource level. This estimation technique generates a withinestimator, which is pivotal to our proposed research design for eliciting capability differences between firms. While there are many econometric properties that favor the fixed-effects procedure as a more cautious and conservative panel data technique (i.e. as compared to the random-effects procedure), there is one particular property of fixed-effects that is particularly relevant to our purpose here: the coefficient estimates for the various explanatory variables from a within-groups estimator strictly reflect the impact of within-panel variation. In other words, the coefficient estimates for the explanatory variables do not garner any explanatory power from differences between resources (i.e. between-group variation); rather, all explanatory power in these estimations is generated by period over period differences in the focal resource (i.e. within-group variation). 3 To better understand why this estimation technique must be layered onto quasi-experimental design to examine the theoretical research domain we have outlined, we need to pause and consider the explanatory variables that must necessarily be included in our proposed research design. Specifically, researchers will need to separate and identify the tangible and intangible complementary resources in the organizational capability that deploys the focal resource. Intangible complementary resources can be measured with a dichotomous change in ownership treatment variable that is set to 1 when the quasi-experimental treatment occurs (i.e. when the acquiring firm controls and deploys the focal resource ex post strategic factor market transfer) and 0 otherwise. A fixedeffects regression of the focal resource s performance on this variable yields coefficient estimates that capture any significant differences in focal resource performance that is a result of intangible firm-specific resources with which the focal resource is bundled in the firm-specific capability. For the change in ownership treatment variable to capture these intangible complementary resources, however, it is also crucial to explicitly capture levels of tangible complementary resource(s) deployed in the firm-specific capability. For this, the researcher needs to measure any contextually relevant observable complementary resource(s) that both the divesting and acquiring firms bundle with the focal resource as part of the organizational capability. By explicitly modeling the contextually relevant tangible complementary resource(s), the change in ownership variable captures all the other intangible complementary resources. In short, the fixed-effects regression coefficients for observable complementary resource(s) and the change in ownership treatment variables effectively capture the complementarities in the firm-specific manifestation of the organizational capability. Put very concretely, the coefficient estimates for the explanatory variables in a fixed-effects estimation do not garner any explanatory power from between-group variation. So, in our context, the importance of American Airline s complementary hub system for the Dallas Mexico City market does not derive from variation in comparison to the Los Angeles Mexico City operation of Alaska Airlines, where that airline may have a smaller complementary hub and lower load factors on the international route. Instead, we are holding constant any specific effects with regard to that airlineoperated international route (our fixed-effect), and strictly capturing the impact of within-panel variation for the explanatory variables on within-panel variation for the dependent variable. Furthermore, the treatment variables concerning ownership changes accordingly capture any additional withinpanel variation associated with the acquiring airlines, as compared to the relevant selling airlines. More generally, recall the quasi-experimental design illustrated in Figure 1. Here, the researcher is interested in capability differences between Firms A and B: specifically, are there differences in the performance associated with the resource transferred at X when deployed as part of one firm s capabilities rather than the other s? Using our approach, the researcher would estimate a fixed-effect model regressing resource-level operational performance on both the contextually relevant observable complementary resource(s) and the change in ownership treatment variable described earlier. Since the within-estimators modeled by fixed-effects represent within-panel variation in

11 Clougherty and Moliterno 117 the dependent and independent variables, the coefficient estimates for the change in ownership treatment variables strictly capture the difference in operational performance for the focal resource that is due to intangible complementary resources. Furthermore, the coefficient estimate(s) for the observable complementary resource(s) captures the effect on the focal resource s performance that is due to firm-specific levels of tangible complementary resources. In sum, the coefficient estimates for our proposed explanatory variables, when considered together, will be an empirical manifestation of firm-level capability differences in general, and differences in firm-specific resource complementarities (tangible and intangible) in particular. The quasi-experimental estimation of capabilities: an empirical example The US international airline industry We demonstrate our proposed methodological design for eliciting organizational capabilities using data drawn from the US international airline industry. This context presents a good setting to employ and illustrate this design for a number of reasons. First, the International Civil Aviation Organization (ICAO) reports yearly statistics on the international city-pair operations for world airlines. These international city-pair segments (e.g. American Airlines from Miami to Buenos Aires) represent airline-specific resources, as the right to serve routes is granted by the US government after bilateral negotiations with the host-country government (de Murias, 1989). Moreover, the reported statistics can be compiled to create performance measures at the resource (i.e. international route) level. Specifically, load factors measure the percentage of seats occupied by revenue passengers for an airline s operation in a particular international city-pair segment. Load factors are considered to be a critical operational performance measure for the airline industry (Baum and Korn, 1996; Korn and Baum, 1999; Lazzarini, 2007; Porter, 1996), as they are the basis for comparative advantage and a necessary ingredient for superior profitability. Consistent with the research focus of the theoretical domain which we described earlier, the transfer of international routes from one airline to another allows for the critical direct comparison of different firms operating the same resource before and after its transfer in the factor market. The period witnessed a significant transfer in the rights to operate US international routes from the traditional international airlines (PanAm, TWA, Eastern) to the traditional domestic airlines (American, United, Delta): see Table 1 for the relevant asset sales. For example, a particular international city-pair route (e.g. New York Berlin) was transferred via the strategic factor market between two carriers (e.g. from PanAm to Delta) with the acquiring firm then operating that route as part of its flight schedule. Importantly, while the traditional US international airlines faced financial problems that led to the timing of asset transfers during this period, the international routes themselves were generally profitable, making them attractive resources on the strategic factor market. This setting therefore allows us to model how firms in the same industry (e.g. PanAm and Delta) differ in their ability to leverage tangible and intangible complementary resources to enhance the performance of the transferred route-right (i.e. the focal resource). In accordance with the requirements already described, these data are organized into panels on the basis of a particular US airline-operated international city-pair route; thus, observations are at the operating-airline/route/year level of analysis. The unbalanced panel data consist of 12 airlines over the period with 1188 specific airline routes (i.e. panels) residing within a total of 4004 observations. Table 2 provides summary statistics on the variables by airline. To provide further clarity on the nature of the panel data set, consider Figure 2, which builds upon the general quasi-experimental design structure depicted in Figure 1, but is specific to our

12 118 Strategic Organization 8(2) Table 1. International route sales between major US airlines Buyer Seller Year Routes Price a American Eastern 1990 US Latin America $471 American TWA 1991 US London $445 Delta PanAm 1991 US Europe $526 United PanAm 1986 Trans-Pacific $716 United PanAm 1990 US London $400 United PanAm 1992 US Latin America $148 a In millions. Source: US GAO (1992). Table 2. Descriptive statistics Airline N Variable means Route- Domestic- Domestic- Total- Routeload-factor hub-size load-factor passengers competitors (millions) American ,279, Alaska , Continental ,191, Delta ,531, Eastern ,584, Frontier ,048, Northwest ,559, PanAm ,498, Republic ,622, TWA ,779, United ,590, Western ,429, Total ,645, t 1 t 2 t 3 t 4 t 5 t 6 t 7 t 8 t 9 t 10 (1) O I O I O I X O D O D O D O D O D O D O D (2) O I O I O I O I O I O I O I X O D O D O D (3) O D O D O D O D O D O D O D O D O D O D (4) O U O U O U O U O U O U O U O U O U O U Figure 2. Quasi-experimental design for airline capability measurement

13 Clougherty and Moliterno 119 data and context. Here O represents a yearly observation in the panel, X represents the treatment (the factor market transfer of operational rights between airlines), I represents the traditional US international airlines, D represents the traditional US domestic airlines and U represents the other US airlines that have international operations. Panel types 3 and 4 indicate that the research design involves both equivalent and non-equivalent no-treatment control groups. In particular, the equivalent no-treatment control groups are panels where the three traditional US domestic airlines received the right to operate international routes through non-acquisition means (type 3). Furthermore, the non-equivalent no-treatment control groups are panels composed of other US airlines (e.g. Northwest and Alaska Airlines) that had rights to operate international routes but did not acquire additional international route-rights (type 4). Dependent and explanatory variables Our dependent variable is the route-level operational performance of the airline employing the rights to operate a specific international city-pair in a given year (e.g. Delta s performance on the New York Berlin route in 1992). Compiling data from ICAO s Traffic by Flight Stage series, we created annual load factor measures (the percentage of seats occupied by revenue passengers) for an airline s operation in a particular international city-pair segment (hereafter referred to as route-load-factor). Importantly, this variable is measured for every year a particular route appears in the data: that is, we measure this operational performance before and after the resource s factor market transfer between two airlines. As noted earlier, our design requires two primary explanatory variables. The first of these is a measure of the contextually relevant tangible complementary resource(s) deployed by the firm as part of the capability for which the transferred resource is being acquired. In the airline industry, hubs are the obvious candidate for the status of key resource (Ghemawat and Pisano, 2001: 118), as hubs help airlines reap network economies. Indeed, a driving force behind route transfers was the significant network economies that large domestic airlines could bring to bear on international routes (Clougherty, 2002, 2006). Accordingly, we measure the size of the domestic hub which an airline can bring to bear in international competition, as this is clearly a vitally important tangible complementary resource in the flight logistics. The US Department of Transportation s T-3 Airport Activity Statistics provides data on the number of annual passengers carried by an airline at a specific airport: with higher passenger numbers reflecting the airport s status as a hub for the airline. Passenger numbers at an airport (hereafter referred to as domestic-hub-size) capture the degree to which an airline can take advantage of hub-and-spoke effects out of the relevant US city. For instance, the number of Delta s annual passengers at JFK airport acts as the domestic-hub-size measure for Delta s JFK-based international routes. The second main explanatory variable described in our proposed design is the change of ownership variable that captures the intangible complementary resources deployed by the firm as part of the capability for which the transferred resource is being acquired. Thus, and as described earlier, we create three treatment variables AA-acquisition UA-acquisition and DL-acquisition set to 1 in the years when, respectively, American, United and Delta operated an international route previously operated by a traditional US international carrier (PanAm, TWA and Eastern), and 0 otherwise. In order to yield more robust causal inferences, we control for two additional airline-based drivers of international operational performance. An airline s overall domestic-load-factor (annual percentage of all domestic seats filled by revenue passengers) and an airline s total