1. Making The Most Of What You Have

Strategic Management Journal Strat. Mgmt. J., 30: 457–485 (2009) Published online 30 December 2008 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.747 Received 13 September 2007; Final revision received 19 November 2008

MAKING THE MOST OF WHAT YOU HAVE: MANAGERIAL ABILITY AS A SOURCE OF RESOURCE VALUE CREATION TIM R. HOLCOMB,1 * R. MICHAEL HOLMES JR.,2 and BRIAN L. CONNELLY3 1

College of Business, Florida State University, Tallahassee, Florida, U.S.A. E.J. Ourso College of Business, Louisiana State University, Baton Rouge, Louisiana, U.S.A. 3 College of Business, Auburn University, Auburn, Alabama, U.S.A. 2

The current study investigates a central premise of the resource-based view of the firm—that managers are a potential source of value creation for the firm. Using data from professional sports teams, we test theory regarding the effects of managerial ability, human resource stocks, and managers’ actions on resource value creation. While results indicate managerial ability affects resource productivity, this effect is less pronounced with increases in the quality of firm resources. Further, we investigate the extent to which managerial actions that synchronize resource bundles account for the influence of managerial ability and resource context on a firm’s performance advantage. These results contribute to our understanding of resource management and provide empirical evidence for the importance of managerial ability in the resource-based view. Copyright  2008 John Wiley & Sons, Ltd.

profiles, identifying important resource characteristics that explain differences in firm performance (Peteraf, 1993; Peteraf and Barney, 2003). Despite intuitive appeal, this reasoning has proEfficient production with heterogeneous resources duced equivocal results (see Newbert, 2007 for a is a result not of having better resources review), leading some to criticize resource-based but in knowing more accurately the relative theory as overly focused on the characteristics productive performances of those resources of resources (e.g., Priem and Butler, 2001) and (emphasis included in the original, Alchian ‘remarkably na¨ive’ about how they are used (e.g., and Demsetz, 1972: 793). Barney and Arikan, 2001: 175). More recently, scholars have added that while owning or having access to valuable and rare resources is necessary Management research has a long history of using for competitive advantage, they must be effectively resource-based theory to explain differences in managed and synchronized to realize a competitive organizational outcomes (Barney, 1991; Barney, advantage (e.g., Hansen, Perry, and Reese, 2004; Wright, and Ketchen, 2001). To do so, scholars Kor and Mahoney, 2005; Mahoney, 1995). Howfocus attention on the heterogeneity of resource ever, despite recent efforts to integrate managerial processes into a theory of resource management Keywords: managerial ability; resource management; (e.g., structuring, bundling, and leveraging; see value creation; resource productivity; synchronization Sirmon, Hitt, and Ireland, 2007), scholars work*Correspondence to: Tim R. Holcomb, College of Business, ing in the resource-based tradition have not fully Florida State University, Tallahassee, FL 32306-1110, U.S.A. explored the actions firms take to create and sustain E-mail: [email protected]

INTRODUCTION

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an advantage or when those actions matter most. The current study addresses this void by focusing on managerial ability as a source of resource value creation. Building on extant literature, we describe a model wherein the extent of a performance advantage that a firm enjoys depends on the ability of managers to create value from resources the firm controls. Penrose (1959) provided initial insights on this relationship, noting that ‘the resources with which a particular firm is accustomed to working will shape the productive services its management is capable of rendering . . . but also that the experience of management will affect the productive services that all its other resources are capable of rendering’ (Penrose, 1959 : 5). Stressing the importance of managers to the resource-based view, Barney (1991) reasoned that a manager’s ability to understand and effectively use firm resources is itself a valuable resource that ‘has the potential for generating sustained competitive advantages’ for a firm (Barney, 1991 : 117). Although resource-based theory holds a place for managers, work in this area seldom examines managerial attributes that underlie resource value creation. From a firm’s perspective, managers create value by developing resource bundles that enable firms to undertake ‘novel and appropriate tasks, services, jobs, products, processes, or other contributions perceived to be of value’ and that produce greater utility or lower unit costs in use (Lepak, Smith, and Taylor, 2007: 183).1 Resource bundles represent unique combinations of resources that enable firms to take advantage of specific market opportunities when effectively deployed (Brandenburger and Stuart, 1996; Collis, 1994; Peteraf and Barney, 2003; Sirmon et al., 2007). While efforts to manage resources are important, greater value from individual bundles per se is not sufficient to create a performance advantage. Managers must be able to integrate or link them across ‘chunks’ (Gavetti, 1 We focus on managers as a potential creator of value for a firm. By value, we mean the difference between the benefit firms generate from resources they control in relation to their needs, where needs can be expressed in monetary terms as the cost the firm incurs or is prepared to incur for the job, task, product, or service rendered by a given resource or resource bundle (Lepak et al., 2007). Expressed in this way, greater value creation potential implies greater productivity from effective resource management (Barney, 1991; Peteraf, 1993). This view is largely consistent with the type of value that Bowman and Ambrosini (2000) label use value and is also consistent with an efficiency-oriented approach to value creation in the resourcebased view (Peteraf and Barney, 2003).

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2005: 600) or ‘islands’ (Postrel, 2002: 303) of distinct but interdependent activities. Indeed, firms realize a performance advantage when managers synchronize the resource management processes within and between interdependent bundles such that organizational performance is optimized. Our core premise is that managers differ in their ability to manage resources and to synchronize management processes in ways that enhance organizational performance. Managerial ability derives from experience and is tacit in nature, making it rare and difficult to imitate, suggesting that managerial ability is itself an important resource (Hitt et al., 2001; Kor, 2003). We rely on resource-based theory and research that examines the managerial ability construct to build arguments about when and how managers create a performance advantage for their firms. In doing so, our work makes several important contributions. First, we demonstrate that managerial ability can be a source of value creation when it allows superior bundling and deployment of resources. While scholars working from a resource-based perspective have established the importance of resource heterogeneity and recognize that resources have a latent potential to create value (Peteraf, 1993), we focus special attention on the contribution of managerial ability in extracting latent value from resources that firms control. Additionally, our theory and analyses explore when able managers matter most. In particular, we reason that the influence of managerial ability on resource value creation is greater with less valuable resources, presumably because able managers enable those resources to reach their potential through effective combination and use (Sirmon, Gove, and Hitt, 2008). Lastly, we ask how processes that produce valuecreating resource bundles work together so that firms might optimize performance. Specifically, while effectively bundling and deploying resources is important, to create a performance advantage, these processes must be synchronized. Previously, scholars have experienced difficulty untangling the nature of resource-based relationships, partly because of obstacles involved in operationalizing key constructs (Priem and Butler, 2001). To overcome these obstacles, we focus the study on a single industry by utilizing data from the National Football League (NFL) to investigate relationships between managerial ability, resource quality, resource value creation, and organizational performance. This approach is appropriate Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation for testing our theory for several reasons. First, sports teams are comparable across such dimensions as size, structure, and goals (Allen, Panian, and Lotz, 1979). They also share a common factor market and general environment. Such commonalities ease comparisons between teams and are important, as resource-based theory is ultimately about performance differences within industries (Peteraf and Barney, 2003; Mahoney, 1995). Additionally, while the quantity and individual task assignments (e.g., quarterback, running back, and so forth) of resources are similar across teams, resource combinations vary, creating differences in the output they produce. Consistency in measurement and reporting for these teams allows us to examine resource productivity and performance after isolating the effects of resource quality. This approach provides visibility to two of the most salient differences in sports teams: the abilities of their managers and the quality of resources they control. Lastly, the extent to which sports teams conform to a common set of normative rules, procedures, and policies minimizes the influence of institutional environments on performance outcomes, assuring consistency in the options available to rival organizations across time. Our choice of this setting follows several researchers who have used sports teams to test managerial (e.g., Bloom, 1999; Brown, 1982; Howard and Miller, 1993) and resource-based theories (e.g., Berman, Down, and Hill, 2002; Moliterno and Wiersema, 2007; Sirmon et al., 2008).2

THEORY AND HYPOTHESES Conceptualizing managerial ability Research in multiple disciplines has produced diverse theory regarding the origin of managerial ability. We define managerial ability as the knowledge, skills, and experience, which is often tacit, residing with and utilized by managers (Hitt et al., 2001; Kor, 2003).3 Although experience 2 See Wolfe et al. (2005) for a thorough review of empirical studies that draw on data from professional sports teams to test a wide range of research questions in management, organization theory, and economics. 3 This view of managerial ability as consisting of knowledge, skills, and experience embodied within an individual is largely consistent with prior descriptions of human capital (Becker, 1964; Schultz, 1961).

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can be a source of managerial ability and performance advantages, these relationships are complex. Theory distinguishes between different types of ability in a manner reflecting the degree of transferability and relevancy of ability to different firm and industry contexts. These types include firmspecific, industry-specific, and general components (Becker, 1964; Castanias and Helfat, 1991). General ability represents knowledge, skills, or experience that produce value for any firm that makes use of them. It has the greatest mobility and is less unique to a given context (Bailey and Helfat, 2003; Baum, Locke, and Smith, 2001). In contrast, firm-specific ability is least mobile and unique to a context, while industry-specific ability is somewhat transferable because of its relevance to firms within the industry (Castanias and Helfat, 1991; Sirmon et al., 2008). Because this study emphasizes the management of firm resources in a single industry, we focus on firm- and industry-specific sources of managerial ability that are most likely to produce a strategic advantage. From a strategic perspective, managerial ability derives from two main sources: domain expertise and resource expertise. Domain expertise refers to managers’ understanding of the industry context and the firm’s strategies, products, markets, task environments, and routines (Boeker, 1989; Kor, 2003; Spreitzer, McCall, and Mahoney, 1997). It captures the breadth of knowledge managers accumulate through formal education in a particular field and through ‘learning by doing.’ Although managers bring explicit knowledge derived through formal education into their firms, they build specific (tacit) knowledge about the firm and industry domain through their experiences and rely on this experience when making decisions about the appropriateness and sequence of actions (Collins et al., 2009; Fondas and Wiersema, 1997; Melone, 1994). Further, as managers accumulate domain expertise, they develop proficiencies and become more effective at aligning firm strategies with the industry context in ways that enhance organizational performance, because they understand better the opportunities to pursue and threats that require a response (Coff, 1999; Holcomb et al., 2009; Mahoney, 1995). Across different firm and industry domains, these skills differ. The more specific the ability embedded in managers, the more likely it is to be imperfectly transferrable to other firms and particularly difficult for rivals to imitate, Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

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making it a potent source of superior performance (Hatch and Dyer, 2004). Resource expertise manifests through experience with resource management processes (Sirmon et al., 2007). Specifically, it represents the ability of managers to select and configure a firm’s resource portfolio, bundle resources into distinctive combinations, and deploy them to exploit opportunities in specific contexts. Although it seems reasonable to expect that managers only use their best resources, some conditions preclude them from doing so. For instance, because resources are not infinitely divisible, most firms are unable to simultaneously allocate physical assets, such as property and equipment, personnel, financial capital, and other resources, to produce different goods or services without reducing effectiveness. Such conditions make decisions about resource use more challenging. For human resources, constraints limiting effective use increase as proficiencies across different skill sets and abilities embedded within individuals vary (Coff, 1997; Lado and Wilson, 1994; Wright and Snell, 1998). Instead of maximizing an individual’s skill set or ability, managers must consider their marginal contribution, relying on knowledge about employees’ skills and abilities to combine them in a way that enhances the value-creating potential of a bundle (Alchian and Demsetz, 1972). Further, changes in the firm’s strategic priorities and business activities often necessitate tradeoffs between resource positions (Porter, 1996). Such trade-offs require managers to synchronize resource management processes across different bundles to achieve the necessary fit (Siggelkow, 2001; Thomke and Kuemmerle, 2002). It is this knowledge about human resource constraints and resource management processes upon which managers rely as they utilize resources across different settings (Helfat and Peteraf, 2003; Kor, 2003; Melone, 1994; Mishina, Pollock, and Porac, 2004). Managerial ability develops through the experiences managers gain over time (Borman et al., 1993; Cannella and Holcomb, 2005; Kor, 2003; McCall, Lombardo, and Morrison, 1988). Specifically, as managers accumulate experience ‘on the job,’ they try out and hone their knowledge and skills (Dechant, 1990; Garland, 1985), enabling them to gain proficiency in tasks that they regularly perform (Hatch and Dyer, 2004; Holcomb et al., 2009). Knowledge accumulated in this way is often tacit, making it difficult for rivals to expropriate Copyright  2008 John Wiley & Sons, Ltd.

and increasing its strategic importance to a firm (Hitt et al., 2001; Liebeskind, 1996; Szulanski, 1996). In support of this view, scholars link managerial ability directly to many performance outcomes. For example, in a study of the careers of 50 Methodist ministers, Smith, Carson, and Alexander (1984) found that churches led by able managers repeatedly experienced increased membership growth, greater charitable income, and higher property development levels. Similarly, using a sample of public U.S. firms, Hayes and Schaefer (1999) found that managerial ability had a positive effect on shareholder returns, while Holbrook et al. (2000) attributed greater innovation and firm growth to managers’ domain expertise in a sample of U.S. semiconductor firms. Other work links managerial ability to new product development (e.g., Ahuja, 2000; Powell, Koput, and Smith-Doerr, 1996; Subramaniam and Youndt, 2005), internationalization (e.g., Carpenter and Fredrickson, 2001; Carpenter, Sanders, and Gregersen, 2001; Hitt et al., 2006), competitive behavior (e.g., Ferrier, 2001; Hambrick, Cho, and Chen, 1996; Wiersema and Bantel, 1992), firm survival (e.g., Gimeno et al., 1997), dissolution of service-based firms (e.g., Pennings, Lee, and van Witteloostuijn, 1998), and outcomes such as profitability and growth (e.g., Carpenter et al., 2001; Geletkanycz and Hambrick, 1997; Hitt et al., 2001; Miller and Shamsie, 1999). Studies on sports teams also link managerial ability to performance. For instance, Sirmon and his colleagues (2008) found that able managers increase the likelihood of winning competitive contests and, more generally, affect outcomes of those contests by making choices that optimize resource use for a given competitive contest. Pfeffer and Davis-Blake (1986) found that previously successful managers had a positive effect on the percentage of games won during the regular season by teams in the National Basketball Association (NBA). Studying teams in Major League Baseball, Cannella and Rowe (1995) linked previous managerial performance (a manager’s average winning percentage over five seasons) with the percentage of games won during the season following succession. By explicitly locating managerial ability in resource-based explanations of value creation, we explicate a joint role for managers and the resources they manage in helping firms achieve Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation success. Given that resources vary in the degree to which they can realize their value creation potential unaided, and managers vary in their ability to extract value, we explore how these two phenomena work together. We also seek to extend resource-based theory by explaining how this interaction ultimately affects resource value creation. Resource productivity Scholars working in the resource-based tradition often ask what it is about resources that give them inherent potential for value creation. Less research, however, explores the extent to which firms exploit that potential. Nevertheless, most resource-based scholars agree that what a firm does with its resources is as important as which resources it possesses. The subtle, yet important, implication is that firms with superior managerial ability can realize a performance advantage (Adner and Helfat, 2003; Castanias and Helfat, 1991; Hansen et al., 2004). Firm and industry contexts impose constraints; thus, many value-creating resource combinations fail to occur because managers have neither the ability to recognize the opportunity nor the means to exploit it. This reasoning leads us to expect differences among managers in their ability to create value from the resources their firms possess. Illustrating this point, Penrose (1959) noted: The services yielded by resources are a function of the way in which they are used—exactly the same resource when used for different purposes or in different ways and in combination with different types or amounts of other resources provides a different service or set of services’ (Penrose, 1959: 25) One important indicator of the potential value that managers create from their resources is the level of resource productivity, which refers to the net benefits achieved from resource management. Differences across firms are attributable to resources having different levels of latent (i.e., unrealized) efficiency and to firms using those resources in different ways. Defined in this way, productivity increases when managerial actions produce greater utility with the same inputs or produce the same utility with fewer inputs (Bowman and Ambrosini, 2000; Lepak et al., 2007; Peteraf and Barney, 2003). Copyright  2008 John Wiley & Sons, Ltd.

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We have several reasons to believe that better managerial ability allows firms to exploit the untapped value of resources. First, superior knowledge of factor markets enable managers to be more effective than rivals at selecting valuable resources and negotiating their use on favorable terms (Makadok, 2001). Managers with more accurate expectations concerning their future value are better able to exploit market imperfections for developed or acquired resources (Amit and Schoemaker, 1993; Denrell, Fang, and Winter, 2003; Thomke and Kuemmerle, 2002). Second, managers with better knowledge of the firm and industry context are more likely to design strategies that create value by being more effective than rivals at bundling and deploying resources in new ways (Hansen et al., 2004; Lippman and Rumelt, 2003; Miller, 2003). Moreover, when managers reinforce and preserve this knowledge through experience, they deepen their skill sets and improve their value to the firm. Because managers differ in their abilities, the value they extract from resource combinations varies depending on managers’ understanding of relevant contingencies, including contextual factors that affect competitors’ resources as well as their own. In professional football, head coaches utilize their understanding of human resource (player) and competitive contexts to influence the outcomes of contests with rivals. Like their counterparts in traditional business organizations, head coaches face complexities and constraints that complicate management of human resources. Because players with the needed skills and abilities are limited, managerial actions involving their use largely determine the potential value resources create. However, unlike top managers of most business organizations, head coaches make both strategic and tactical decisions (Chandler, 1962) and are a central figure in player selection and evaluation. Additionally, in contrast to most business organizations in which personnel perform a variety of tasks as they progress through their career (Hatch and Dyer, 2004), football teams reflect a more specialized division of labor (Brown, 1982). Over football players’ careers and during the course of each game, they play a distinctive offensive or defensive role, very seldom more than one, and perform explicit tasks that depend on the role. For instance, on offense, linemen block for running backs and protect the quarterback on passing plays, but seldom accumulate yardage or score points; Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

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and quarterbacks produce yardage and score points passing, but seldom run the ball or block for others who do. Thus, resource management processes and their effects are critical in this setting. Broadly, head coaches perform two critical tasks involving management of team resources. First, they bundle players into cospecialized combinations (Tripsas, 1997). Each player performs a role determined by the task requirements of the bundle. Proficiencies vary between players, increasing the importance of managers’ knowledge about the potential marginal contribution of players for given roles in each bundle. Second, coaches deploy these bundles based on distinct task requirements for a given competitive context (e.g., offensive bundles for running and passing, defensive bundles to protect against the run and the pass). On a given play by the offense, only one or two players directly account for the yardage gained and points scored. Throughout a season, most players seldom score points, and only a few produce yardage. These conditions dictate that managers bundle resources with specialized skills and abilities into unique offensive or defensive combinations for specific contexts. Success is more likely when managers create superior bundles and deploy them effectively. Furthermore, resource management has an efficiency component. Because output differs with managerial actions that bundle and deploy resources in certain ways, effective actions can create value when they maximize the net benefits a bundle produces (Peteraf and Barney, 2003). In professional football, player salaries remain fixed within a given season, holding overall payroll costs at a consistent level. To succeed, head coaches attempt to enhance the value of a team’s offensive and defensive combinations in three ways. First, they acquire players with specific skills and abilities at predetermined salaries to fill defined roles on the team (e.g., quarterback, running back, etc.). Limits on the total number of players and the total payroll for teams in the league affect the managerial actions coaches take to structure their resource portfolio. Second, coaches select the starting lineup, which represents the initial group of players bundled and deployed on offense and defense. When bundled together, players’ skills and abilities combine to determine the productive output of each combination. For example, the marginal contribution of players’ skills and abilities bundled and deployed on defense combine to limit yardage gained and points scored by Copyright  2008 John Wiley & Sons, Ltd.

an opponent’s offense. Finally, coaches substitute and rebundle players with specific skills and abilities in response to actions by rivals. In doing so, they attempt to produce bundles that best leverage players’ skills and abilities for a particular context (e.g., a rushing play versus a passing play, blitzing the opponent’s quarterback, etc.). Thus, able managers who are more effective at bundling and deploying resources given a team’s strategic and competitive context can produce greater value. Together, these arguments suggest that managerial ability helps create value by enhancing resource productivity through superior use, leading to the following hypotheses: Hypothesis 1a: Managerial ability is positively associated with the level of resource productivity attained by a team’s offensive combination. Hypothesis 1b: Managerial ability is positively associated with the level of resource productivity attained by a team’s defensive combination. However, the extent to which managerial ability affects resource productivity depends on the value-creating potential, or quality, of the individual resources. Thus, the quality of resources included in each bundle may have a measureable effect on the value managers realize from different resource combinations. Next, we explore the role of resource quality in moderating the influence of managerial ability on resource value creation. Effects of managerial ability and resource quality on resource productivity Some resources have more inherent value-creating potential than others. We define resource quality as the value-creating potential of a resource, which contributes to the extent to which a firm can develop and implement strategies that improve performance (Barney, 1991). Although prior theory and empirical evidence links resource quality with measures of resource productivity (e.g., Amit and Schoemaker, 1993; Makadok, 2001; Youndt et al., 1996), our study departs from prior research by considering the effect of managerial ability on productivity as a function of resource quality. Research examining resource-based theory has not yet established compelling theory describing this interaction. Given prior work linking superior resources with resource productivity (e.g., Amit Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation and Schoemaker, 1993; Makadok, 2001) and our theory suggesting a similar relationship between managerial ability and resource productivity, one might reasonably expect a positive interaction; that is, higher levels of resource quality should strengthen the influence of managerial ability on resource productivity. Certain factors, however, suggest that this may not be the case. For instance, individual resources can produce lower marginal contributions when they perform tasks outside their specialization, such that managerial actions that redeploy them may significantly improve their individual contribution and the level of resource productivity achieved (Alchian and Demsetz, 1972). Further, some resources may be a strategically poor fit with others, which may have an effect on resource value creation (Siggelkow, 2001; Thomke and Kuemmerle, 2002). Under these conditions, superior managerial ability can be a source of insight into more effective combinations, enabling managers to create value by using resources more effectively. In support of this view, Morrow et al. (2007) found that valuable and difficult-to-implement actions that bundle and deploy existing resources in new ways could increase shareholder returns of firms in crisis. Specifically, these firms were able to create new goods and services valued but not anticipated by the market by recombining more effectively those resources that previously produced below-average returns. In turn, such actions led to recovery. Further illustrating this argument, Sirmon et al. (2008) demonstrated that as the quality of a firm’s resources decreases toward parity with rivals, managerial ability has a greater bearing on advantages achieved in contests between professional baseball teams. Thus, we reason that higher managerial ability will have an especially important influence on the productivity of resources with less demonstrated potential for value creation. As a practical example, Southwest Airlines Company utilizes an all-Boeing 737 fleet that carries fewer passengers and has a shorter maximum flight range on average than most of the company’s largest rivals (e.g., American Airlines, Delta Airlines, and United Airlines). Nevertheless, Southwest’s operating cost per seat mile is among the lowest in the industry and its profitability is among the highest. One way it accomplishes this feat is by bundling and deploying flight operations resources in a way that increases their value-creating potential. Specifically, Southwest bundles short-haul jets Copyright  2008 John Wiley & Sons, Ltd.

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from its all-Boeing fleet and efficient ticketing and check-in routines and personnel with ground personnel and resources that specialize in quickly preparing aircraft for departure. In doing so, Southwest is able to offer low-cost ‘no-frills’ service between midsized cities and smaller secondary airports in major cities, such as Hobby Airport (Houston), Love Field (Dallas), and Midway International Airport (Chicago). At the same time, we expect the positive relationship between managerial ability and resource productivity to weaken at higher levels of resource quality. When resource quality is high, there may be fewer opportunities for managers to add value by exploiting unidentified combinations. In this case, how managers combine and use resources may be less important because most combinations of high-quality resources will likely overwhelm the influence of managerial ability on value produced by the combination (Leibenstein, 1978). Such resources may remain valuable despite the presence of lower managerial ability. Further, superior resources may actually provide fewer degrees of freedom for managers to enhance productivity. In this situation, it is plausible managerial decisions could disrupt task performance, reducing the value derived from superior resources (Castanias and Helfat, 1991). Therefore, we propose that the positive relationship between managerial ability and resource productivity declines as resource quality increases. Formally:

Hypothesis 2a: Resource quality moderates the positive relationship between managerial ability and the level of resource productivity attained by a team’s offensive combination; this relationship is weaker as resource quality increases. Hypothesis 2b: Resource quality moderates the positive relationship between managerial ability and the level of resource productivity attained by a team’s defensive combination; this relationship is weaker as resource quality increases.

Synchronizing management processes to create a resource-based performance advantage Although we argue that managerial ability affects productive output, greater productivity from Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

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specific bundles is not sufficient to create a performance advantage.4 While managing resource bundles to increase their productivity is important, managers must synchronize management processes within and between the firm’s bundles (Sirmon et al., 2007). Synchronization involves the integration and balancing of interdependent bundles to ensure that activities reinforce and align with the firm’s strategic and competitive context (Siggelkow, 2001). A firm with many resource combinations that reinforce each other has a high degree of synchronization. Because of synchronization, colocated and interdependent bundles are more difficult for rivals to imitate and thus more valuable toward achieving a performance advantage than is the sum of individual resources or disjoint combinations (Porter, 1996; Thomke and Kuemmerle, 2002). Adjusting these combinations and using them in such a way as to match them with the firm’s strategic and competitive context is an important responsibility of managers (Amit and Schoemaker, 1993; Black and Boal, 1994; Teece, 2007). Building on extant literature, we argue that resource synchronization figures importantly in the effects of managers on performance. In previous research examining the relationship between managers and performance, scholars frequently examine managers’ influence on proximal actions or behaviors thought to influence those outcomes (e.g., Carpenter and Fredrickson, 2001; Eisenmann, 2002; Hambrick et al., 1996; Hayward and Hambrick, 1997; Miller and Shamsie, 2001; Smith et al., 1991). Indeed, researchers have put forward a variety of mechanisms by which managers affect performance, such as managing stakeholders (e.g., Donaldson and Preston, 1995), providing strategic direction (e.g., Papadakis and Barwise, 2002), leading with symbolic actions (e.g., Pfeffer, 1981), and manipulating dependencies (e.g., Carroll, 1993). Mediating influences, though clearly present, are rarely developed or empirically tested. We extend resource-based arguments in an effort to better understand managerial actions that may intervene in this relationship. 4 In this work, we apply a nuanced view of performance advantage to describe the performance of a firm relative to the average (mean) performance for all rivals in a given segment. Specifically, using data from sports teams, we measure performance as the percentage of the games a team wins during the regular season.

Copyright  2008 John Wiley & Sons, Ltd.

Central to our argument is our view that the influence of managerial ability on resource-based performance advantage reflects differences not only in the ability of managers to manage resources, but also in their ability to synchronize resource management processes across distinct business activities by trading off one resource position with another (Black and Boal, 1994; Galunic and Rodan, 1998; Helfat and Peteraf, 2003). Doing so is especially difficult when related business activities are incompatible (Gavetti, 2005; Porter, 1996; Postrel, 2002) or when external conditions constrain managerial choice (Holcomb and Hitt, 2007; Peteraf and Reed, 2007). Further, changes to a firm’s underlying activity system often require managers to make trade-offs in the way in which different bundles are linked. Returning to the prior example, Southwest Airlines produces value with an efficiency-oriented approach through its flight operations ‘bundle’ by operating efficient aircraft, keeping service add-ons to a minimum, and maintaining short turnaround times at the gate. However, Southwest creates a sustainable advantage not simply by optimizing its flight operations, but by synchronizing flight operations with other resource combinations (e.g., flight and crew management, customer service, aircraft maintenance, marketing, and so forth) to produce greater utility for customers (Black and Boal, 1994; Porter, 1996; Sirmon et al., 2007). Such linkages produce interdependencies requiring greater managerial coordination and coupling, making them more difficult for rivals to imitate (Lippman and Rumelt, 1982; Rivkin, 2000). Raff’s (2000) examination of the interdependencies between distinctive combinations in U.S. book retailing demonstrates the extent to which synchronization produces an advantage. Borders leverages a category management capability that makes a wider assortment of books available in each store and a replenishment capability that utilizes category profiles and actual sales information to better manage inventory levels at each store. These capabilities make Borders’ sophisticated information technology capability that tracks inventory and produces sales forecasts more valuable. Together, these combinations reinforce each other to produce distinct performance advantages for Borders. Specifically, the firm’s information technology capability enables the replenishment capability, which increases the benefits created Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation by the category management capability. Similarly, in a manufacturing setting, Milgrom and Roberts (1990) found resource combinations that produced wider product assortment and flexible manufacturing reinforced one another: the wider the assortment, the more valuable is flexible manufacturing; conversely, the more flexible the manufacturing system, the greater the value realized from increased product assortments. Additional empirical and field research examining the airline industry (e.g., Peteraf and Reed, 2007), the petroleum industry (e.g., Helfat, 1997), financial services (e.g., Chung, Singh, and Lee, 2000; Siggelkow, 2001), the typesetter industry (e.g., Tripsas, 1997), and pharmaceuticals (e.g., Thomke and Kuemmerle, 2002) provide further evidence for the benefits of synchronization. Not all resource combinations need to be synchronized with each other. If the contribution of one combination to a performance advantage does not rely on the presence of another, the combinations are independent (Thomke and Kuemmerle, 2002). In contrast, two or more combinations are interdependent if they interact with and/or reinforce each other. Combinations reinforce each other when the contribution of one to a firm’s performance increases in the presence of another (Black and Boal, 1994); that is, the degree of synchronization between combinations is evidenced by the extent to which they complement each other (Milgrom and Roberts, 1990). This conceptualization closely parallels Black and Boal’s (1994) cogency relationships and is consistent with the concept of asset orchestration (Teece, 2007), strategic fit (e.g., Porter, 1996; Siggelkow, 2001), equifinality in open systems theory (e.g., Doty, Glick, and Huber, 1993), and resource configuration (e.g., Miller, 1986). We reason that managerial ability figures importantly in decisions that affect the synchronization of interdependent resource combinations. Combined with earlier arguments, we suggest that differences in the level of synchronization that managers derive can account for differences in performance among rivals. The foundation for this hypothesized relationship is as follows. First, as noted, managerial ability determines, in part, the extent to which managerial decisions create value by using resources in ways that alter efficiency levels (Adner and Helfat, 2003; Castanias and Helfat, 2001; Kor, 2003). Because managers’ ability differs among firms that control Copyright  2008 John Wiley & Sons, Ltd.

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the same or comparable resources and face similar competitive conditions, the value that managers create from those resources varies depending on how they combine and use their resources. Although resource productivity per se is not sufficient to ensure performance benefits, we maintain that it is an important first step. Second, managerial ability figures importantly in decisions to synchronize processes within and between value-creating resource bundles (Sirmon et al., 2007). In professional football, maximizing productivity with one bundle might be offset by reductions in the productivity of the other, because constraints on players and payroll create zero-sum resource allocation across the bundles. Therefore, team performance outcomes depend not only on the level of productivity achieved by the team’s offensive or defensive combination, but also on a coach’s ability to synchronize processes involving the structuring, bundling, and deploying of resources within and between these two combinations. Such synchronization involves both strategic and tactical decisions. For example, teams with highly productive defenses may pursue less risky, ball control-oriented offensive resource combinations that enable the team to control the ball for a greater percentage of each contest. Doing so requires that coaches define task requirements and manage the team’s resource portfolio in such a way as to ensure efficiency goals are met. By influencing the length and pace of a game, coaches can limit opponents’ ability to produce points. Thus, decisions across bundles are complex and interdependent, and managerial ability may be an important source of superior synchronization. While managerial actions that combine and use resources are important (Peteraf and Bergen, 2003; Sirmon et al., 2007), creating a performance advantage depends significantly on synchronization efforts that balance interdependencies among resource bundles within the strategic and competitive context. Accordingly, we predict the following: Hypothesis 3: Managerial ability has a positive influence on organizational performance through its mediating effect on resource synchronization. Further, following previous arguments, we expect this relationship becomes increasingly important with less valuable resource portfolios. In response, we extend our prior hypothesis to Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

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suggest that the effect of managers’ ability to synchronize interdependent resource combinations on organizational performance depends significantly on a firm’s resource context. Specifically, we expect the positive mediation between managerial ability and resource synchronization to weaken at higher levels of resource quality. Thus, we predict: Hypothesis 4: Resource quality moderates the positive relationship between managerial ability and organizational performance through resource synchronization; this relationship is weaker as resource quality increases.

METHODS Sample Professional football is a highly competitive sport wherein teams consist of the same number of players, and head coaches combine and utilize these players to perform similar interdependent tasks. Football head coaches generally have the primary responsibility for actions involving the management of a team’s players, performing duties similar to chief executive officers in traditional business organizations. Specifically, our sample consists of data for sports teams that competed in the NFL from the 1980 season through the 2000 season. Archival data on coaches, players, and team performance are available for multiple years. These characteristics allow consistent measurement of constructs and comparison across organizations, making professional football teams an appropriate context for empirical tests of resource-based theory. We compiled data on the performance of each NFL team, individual player demographic and performance data, and biographical data for head coaches from Total Football II: The Official Encyclopedia of the National Football League (Carroll et al., 1999) and from The Pro Football Encyclopedia: The Complete and Definitive Record of Professional Football (Maher and Gill, 1997). We supplemented these sources with data obtained from the NFL, The Pro Football Hall of Fame, and major newspapers. Twenty-eight teams competed between 1980 and 1993. Prior to the 1994 season, the NFL added two teams to increase the number to thirty. Therefore, our sample contains 602 individual team-year observations across the years (seasons) included in our study. Copyright  2008 John Wiley & Sons, Ltd.

Dependent and intervening variables Organizational performance In measuring performance, we operationalized organizational performance as team winning percentage within each season. Specifically, we used the ratio of games won to total games played during the regular season. This particular measure has several methodological advantages over alternative measures of performance. First, the measure has a mean value of 0.500 (or 50%) for all teams in a given year and is easily comparable across teams and periods. Further, a team’s winning percentage determines whether it will secure a spot in the playoffs and have the opportunity to win a league championship. Team winning percentage is not only a visible and intuitive metric of performance in this context, but is consistent with absolute measures of organizational performance in prior studies using sports teams (Berman et al., 2002; Moliterno and Wiersema, 2007; Wolfe et al., 2005).5 Although an empirically interesting aspect of examining the performance of sports teams in this way is that competitive contests represent a zero-sum game in which one team’s win is another team’s loss (Jenkins, Pasternak, and West, 2005), the percentage of games a team wins is best understood as an absolute measure of performance, in which the mean value for all teams in a given season is embedded within the measure of a team’s winning percentage in each season. Resource productivity We assessed the productive output of resource bundles using measures of resource productivity for the two primary resource combinations used in professional football: offense and defense. For each combination, we specified resource productivity as the net productive output per dollar spent on player salaries. This measure indicates the extent to which the resources within each bundle are efficiently producing output. First, we used multiple indicators to assess fully the net productive resource output for each 5 In supplementary analyses, we also used the total number of wins for each team during each season as the measure of organizational performance. For each team-year observation in the dataset, we summed the total number of wins by the team during the regular season and the post-season, added one, and took the natural logarithm of this number. Using this measure of organizational performance did not substantively change our results.

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Managerial Ability as a Source of Resource Value Creation team’s offensive resource combination and defensive resource combination (Boyd, Gove, and Hitt, 2005). For each indicator, team data was collected for each game played during the regular season. We constructed team-level indices for rushing yards gained, passing yards gained, rushing yards allowed, passing yards allowed, points scored from offensive touchdowns, and points allowed from touchdowns scored by an opponent’s offense.6 Additionally, we standardized scores for each team-level indicator. Second, we produced a team-level measure of net productive output for the offensive and defensive resource combinations. Specifically, for the team’s offensive resource combination, we summed the standardized scores for total rushing yards gained, total passing yards gained, and total points scored by the offense for games played during the regular season. The net productive output for a team’s defensive resource combination consisted of the total yards (rushing and passing) and total points (rushing and passing) allowed during the same period. Team management and players, the NFL, and journalists who follow professional sports teams commonly utilize these measures to gauge the relative performance of teams’ offensive and defensive units. Next, we determined the total payroll dollars spent on players’ salaries for each resource combination during the season. While the total number of players on each NFL team and the number that participate on a given play have remained stable across seasons, logic governing human resource mobility and total league-wide payroll costs has evolved. The Collective Bargaining Agreement (CBA) of 1993 between the players’ union and franchise owners in the NFL ushered in the era of ‘free agency’ while instituting a salary cap that limited the percentage of total revenue that any one team could pay to players (Leeds and von Allmen, 2002). Beginning with the 1994 season, the CBA specified a ‘hard’ cap that no team can exceed and a ‘hard’ floor that reflects the minimum payroll level above which each team must spend. Under terms of the CBA, the NFL adjusts the cap and the floor annually based on the league’s total revenues. 6 In professional football, a touchdown is worth six points to the scoring team. While the offense is primarily responsible for teams’ touchdown production, teams can also produce touchdowns by returning a kickoff or punt for a touchdown or by a defense producing a turnover. In our measure of net productive output for the offense, we only included the points for which a team’s offensive resource combination was directly responsible.

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Previously, free agency and league-wide salaries followed rules that allowed the NFL to compensate teams for the loss of free agents (Quirk and Fort, 1997). During this period, player salaries were subject to market forces that set pay and contract terms based on a range of different individual- and teamlevel factors. Under both approaches, players negotiate the financial and contractual terms of their employment directly with teams. In turn, teams determine salaries for individual players that reflect overall payroll conditions for the team and the need for specialized skills and abilities to accommodate specific roles/tasks in each bundle. For each player, we summed base salary earned, incentive bonus payments received (e.g., for individual performance), and an annual allocation of any signing bonus the player received at the beginning of a contract. We applied a minimum-use criterion for determination of the players to include when calculating the dollars spent for players’ salaries attributed to each resource combination. The requirement for inclusion of a player’s salary in total payroll computations for either combination was three-fold: the player was eligible for participation in a team’s regular season games, the player had a designated task assignment on the offense (defense), and the player participated in one or more plays for the combination in at least one game during the season. This criterion excluded salaries for players with specialized roles outside the offense and defense (e.g., placekicker, punter), members of a team’s practice squad,7 and players considered ineligible for competition by the league (e.g., physically-unable-to-perform, under suspension for violation of league policies). As a final step, we calculated the ratio of net productive resource output to payroll dollars applying the minimum use criteria for each combination. Specifically, for a team’s offensive (defensive) combination, we calculated the ratio of the net productive output for the offense (defense) to payroll dollars for players assigned to the offensive (defensive) bundle to derive our measure of resource productivity. Importantly, our approach overcomes limitations with previous measures of productivity noted by other scholars (e.g., Datta, Guthrie, and Wright, 7 In accordance with the terms of the 1993 CBA, NFL teams are allowed to employ players that practice with the team and receive compensation and benefits, but did not appear on the team’s active roster. By rule, practice squads may not exceed five (5) players per team.

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2005; Koch and McGrath, 1996). First, by considering payroll dollars, this measure incorporates potential differences in resource costs that may accompany increased productive output for different resource combinations. Thus, consistent with our conceptualization, resource combinations are more productive if they generate greater output with a given level of inputs or generate the same level of output with fewer inputs. Second, each resource combination can directly influence key elements of productive resource output (e.g., yards gained, points scored, yards allowed, and points allowed).

for determination of the players to include when calculating the dollars spent for players’ salaries attributed to each resource combination was that the player was eligible for participation in a team’s regular season games and had a designated task assignment (role) on offense or defense. Lastly, we calculated the ratio of net productive resource output to the total payroll dollars spent on player salaries that met our minimum-use criteria. This step reflects our conceptualization of resource synchronization as net outputs of the two resource combinations relative to the net costs incurred for them.

Resource synchronization

Independent and moderating variables

As noted in the development of our hypotheses, resource synchronization represents the extent to which managerial bundling and deployment processes reinforce and align a firm with its strategic and competitive context. Given limits in the total number of players and total payroll considerations, decisions involving salaries for players with specialized roles attributed to one resource combination (i.e., the offense) can limit decision options available to managers for players with specialized roles on the other (i.e., the defense). As a result, head coaches face trade-offs, requiring them to synchronize the two combinations during a season. In tests for mediation, we operationalized resource synchronization as the ratio of net combined productive resource output for the team to the total payroll dollars spent on all player salaries with specialized offensive or defensive roles on the team. In a broad sense, this construct measures the degree to which managerial actions effectively integrate and balance the two combinations given constraints in the number and total cost of human resources to the team. We first reversed the sign of the resource combination score for the defensive combination. We then summed the standardized net productive output scores during the regular season for the offensive combination and defensive combination, respectively. Larger positive scores are associated with higher levels of net productive output. Next, we calculated the total payroll dollars spent on players’ salaries with specialized roles on the team’s offense and defense during the season, using the same salary components described previously. In this case, our minimum-use criterion

Managerial ability

Copyright  2008 John Wiley & Sons, Ltd.

We used two different measures of managerial ability in this study. First, we calculated the weighted career winning percentage for each manager for each team-year observation to assess the extent to which a manager is consistently successful or unsuccessful over time using Dirks’s (2000) formula for managerial ability: Career winning percentage × (1 − (1/total number of years as a head coach)). We computed this measure at the end of the previous season. The first part of the equation calculates a career winning percentage, which represents the proportion of regular season games a manager’s teams have won to the total number of games played over his or her career as a head coach. We then adjust this measure to account for the manager’s total number of years as a head coach. This particular measure recognizes that a high winning percentage accumulated over a large number of seasons may provide a stronger indication of managerial ability than a high winning percentage accumulated over fewer seasons. Additionally, we formed a composite measure of managerial ability by analyzing dimensions that proxy the accumulation of managerial knowledge and skills. We gathered data for each manager (head coach) for each year beginning with the 1980–1981 season through the 2000–2001 season for the following items: total number of seasons (years) as a head coach, total number of regular season games coached as a head coach, total number of regular season wins as a head coach, total number of coach of the year awards as a head coach, total number of division titles as a head coach, total number of league championships as a head coach, and total number of Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation players named all-pro (see below for a description of this term) as a head coach. We evaluated the appropriateness of these dimensions by addressing two questions: ‘how well do the items load on a single factor?’ and ‘how internally consistent are the items?’ We assessed the first criterion with a principal components factor analysis, as this technique extracts variance from a larger set of indicators to create a single measure (Pedhazur and Schmelkin, 1991). All eight items had absolute factor loadings exceeding 0.40 (Tabachnick and Fidell, 2001). Following the Kaiser criterion and the scree plot, we extracted one factor with an eigenvalue greater than one. The resulting factor demonstrated internal consistency (α = 0.741) and explained 81.2 percent of the total item variance. We constructed a scale for managerial ability by summing the standardized values of each item making up the scale.

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in the status of MVPs, all-pro players, and allconference players based on the number of players who receive such honors annually. Fewer players reach MVP status than reach all-pro or allconference status, and fewer reach all-pro status than all-conference status. Thus, MVP is the highest status honor, followed by all-pro, followed by all-conference. To avoid double-counting players within each status, we included players selected in more than one category in the category representing the highest status category. Finally, we weighted scores for each category to reflect differences in status. Specifically, we multiplied the standardized scores for each status category by its inverse ratio using values of 1.0, 0.66, and 0.33 for MVP, all-pro, and all-conference levels, respectively.8 Control Variables

Resource quality Among sports teams, resource quality is an important predictor of performance (Cannella and Rowe, 1995; Dirks, 2000; Sirmon et al., 2008). The NFL has a well-established procedure for identifying the highest quality players each year. At the end of each season, coaches and players, together with journalists from major metropolitan newspapers, select a league-wide offensive most valuable player (MVP) and defensive MVP, an all-pro team representing the best player at each position from across the league, and an all-conference team representing the best player at each position from each of the league’s two conferences. For each year in the study, we determined the number of MVPs and representatives (if any) named to all-pro and all-conference teams by the NFL at the conclusion of the previous year. Using a team’s roster of players at the beginning of the current season, we computed resource quality measures that represent the quality of individual resources assigned to a team’s offensive resource combination and defensive resource combination, respectively. Specifically, we divided the number of MVPs, all-pro, and all-conference players on each team by the total number of players named to each status (e.g., league MVP, all-pro, all-conference) to account for variation from year to year in the total number of players selected (see Dirks [2000] for further discussion of this procedure). This procedure recognizes differences Copyright  2008 John Wiley & Sons, Ltd.

We selected several variables to control for conditions that could legitimately account for variance in organizational performance during a season. Competitive rivalry can limit or directly influence performance outcomes by affecting the intensity of competition (Chen, Su, and Tsai, 2007; Ferrier, 2001; Ramaswamy, 2001; Young, Smith, and Grimm, 1996). In this study, we consider the level of equality across teams within each division, because these organizations are direct competitors. Specifically, we measure the competitive rivalry in each division for each season by calculating the standard deviation of the number of games won across teams within a division (Canella and Rowe, 1995). Higher (lower) values indicate more (less) inequality and lower (higher) competitive rivalry. League-wide player strikes in the NFL occurred in 1982 and 1987, disrupting organizational activities for a portion of both seasons. We coded strike year flag as a dummy variable (0/1), with one indicating the year in which the NFL experienced a league-wide strike. 8 MVPs are superior in status to all-pro selections. All-pro selections are superior in status to all-conference selections. For example, assume the selection of two MVPs, 24 players on the all-pro team, and 48 players on the all-conference team in a given year. If a particular organization had one player elected as an MVP, and had four players elected to the allpro team and six players to the all-conference team,
its resource
 quality score would be computed as follows: 1.0 × 1 2 +



 4 − 1 6 − 4 0.66 × + 0.33 × = 0.54. 24 48

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The NFL instituted the league-wide salary cap in 1994 following the terms of the 1993 CBA. Although this cap places a limit on the maximum cumulative salary that each team can pay in player salaries, teams maintain discretion over total payroll dollars based on salaries and incentive bonuses they award to each player. We coded the salary cap flag as a dummy variable (0/1), with one indicating those seasons in which the NFL operated under the salary cap. League-wide expansions took place prior to the 1995 and 1999 seasons when the NFL added new teams to the league. Such expansions influence resource availability because the total number of players increases when new teams join the league. Therefore, we coded league reorganization flag as a dummy variable (0/1) to indicate the year in which league reorganization occurred, with one indicating the year in which the NFL completed a reorganization. Previous research links manager succession with organizational performance (Allen et al., 1979; Cannella and Rowe, 1995). Therefore, we included a manager succession flag dummy variable (0/1), with one indicating that a succession occurred between the prior and current seasons. This flag identifies succession events that occurred between seasons. Each year, during the off-season, the NFL holds a draft wherein teams select players, usually from college teams. Team winning percentages from the previous year generally determine the selection order; teams with lower percentages pick earlier. This process gives weaker teams an opportunity to select higher quality resources. Therefore, we control for average draft position of each organization during the draft that precedes the current season. Because the difference between two adjacent draft positions early in a draft can represent a more significant difference in the potential quality of the players than does the same difference in adjacent positions later in the draft (Berman et al., 2002), we took the natural logarithm of the draft positions before calculating the draft position total for each team. We then summed the draft positions for each team and divided by the number of players drafted by the team. In our empirical context, players’ ages may affect the quality of a team’s human resources. We control for the average age of the players in the current season by calculating the difference in the year a season was completed and a player’s birth Copyright  2008 John Wiley & Sons, Ltd.

year for each player. We then calculated an average for each team-year observation. To test our mediation hypotheses using organizational performance as the dependent variable, we included previous organizational performance to control for regression to the mean. Regression to the mean can be a confounding factor in research examining variation in performance over time (Allison, 1990; Edwards, 1994). Thus, we computed the winning percentage of the team in the regular season as the ratio of the total number of games won to total games played in the regular season for the three years immediately prior to the current season. The three-year lagged average minimizes the effect of anomalies (both high and low) on performance during the season (Sirmon et al, 2008). Finally, we included year indicators (dummy variables) for each year in the sample to account for potential period effects for all models (Bergh, 1995; Greene, 2003), and to control for contemporaneous correlation in our design (Certo and Semadeni, 2006). Model specification and estimation We employed cross-sectional time series regressions to analyze our hypotheses. Our observation unit in the models is a team-year. Because we organized observations for teams into a pooled cross-sectional time series dataset, there is potential for both nonindependence and cross-sectional heteroskedasticity. Thus, ordinary least squares regression could produce correlated error terms, understated standard errors, and inflated t-statistics. Scholars commonly use fixed- or random-effects models to mitigate this problem (Certo and Semadeni, 2006). The choice between fixed- and random-effects models depends on underlying statistical assumptions. In contrast to fixed-effects models, which use only within-unit information to calculate estimates, random-effects models use both between- and within-unit information to calculate estimates (Wooldridge, 2002). In other words, random-effects models assume the panellevel disturbance changes over time. Compared with fixed-effects estimators, which remain stable over time for each unit (e.g., firm), random-effects estimators allow the unit effect to vary over time. We used the Hausman (1978) specification test to evaluate the use of random-effect estimators. For our models, the Hausman tests yielded statistically Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation nonsignificant results, suggesting the independent variables were uncorrelated with the fixed effects. As a result, we used random-effects time series regressions with robust estimators that provide controls for autocorrelation (Bergh, 1995). The random-effects model is given by the following equation: yit = β0 + β1 xit1 + β2 xit2 + · · · + βk xitk + αi + uit where yit is the dependent variable, βk is the vector of coefficients, xit is the vector of predictors, αi represents the random effects, and uit is the error term. For each t, E (uit |Xi , αi ) = 0 and

E (uit |Xi ) = β0

Using Stata (ver. 9) and setting the team as the cross-sectional variable, we estimated our models with the xtreg function. We included the re (random effects) option (xtreg depvar [indepvars] if [, re RE options] in Stata) and estimated conservative robust standard errors for all model coefficients (Wooldridge, 2002). This procedure estimates cross-sectional time series regression models using estimators that produce a matrix-weighted average of the between and within results. Further, our findings were robust to use of cross-sectional time series regression with fixed-effects estimators to correct for heteroskedasticity and generalized least square (GLS) estimators with controls for autocorrelation (xtregar in Stata). Results were substantially similar; using both approaches, all signs were the same, and the levels of significance were similar. Applying this alternative analysis (i.e., fixed-effects analysis) to panel data can produce inconsistent standard errors when the number of panels exceeds the number of observations per panel (Hsiao, 2002). Certo and Semadeni (2006) describe this problem and potential remedies. As we noted, following their recommendations for panel datasets with relatively fewer periods, we include time dummy variables to control for contemporaneous correlation in our design resulting in reasonably stable standard errors across models.

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variable must be significantly associated with the dependent variable when the mediator is not in the model, and the mediator must be significantly associated with the dependent variable when the independent variable is not in the model. In partial mediation, the independent variable remains significantly associated with the dependent variable when the mediator is included in the model. In full mediation, the independent variable becomes nonsignificant when the mediator is added. We also tested for mediation using the product of coefficients in a path model to derive the standard error of the indirect (mediated) effect (Sobel, 1982). Using the matrix formulae derived by Sobel (1982), we apply the product of coefficients strategy to obtain point estimates and first-order standard errors for the indirect effect ‘path’ in an equation simultaneously modeling the paths created by the direct effect of X (predictor) on Y (outcome), as well as the indirect effect of X on Y through M (mediator); these standard errors permit significance testing using critical ratios for measuring specific indirect effects (i.e., the path coefficient of each individual mediation path). Specifically, we examined the indirect effect of managerial ability and the managerial ability/resource quality interaction on performance via resource synchronization. This approach uses the critical ratio to test for significance, as follows:

z = ab



a 2 σb2 + b2 σa2 + σa2 σb2

Test for mediation

where a is the coefficient corresponding to the effect of independent variable on the mediator; b is the coefficient corresponding to the effect of the mediator on the dependent variable partialling out the effect of the independent variable; ab, the product of the a and b paths, represents the indirect effect of the independent variable on the dependent variable through the mediator; and σa2 and σb2 are the variances of the coefficients for paths a and b, respectively.9 We compare this ratio to a standard normal distribution to establish statistical significance (Preacher and Hayes, 2004).

We test for mediation using two approaches. First, we employ the traditional approach suggested by James and Brett (1984; see also Baron and Kenny, 1986). Following this approach, the independent

9 The square roots of σa2 and σb2 are the standard errors of the estimates.

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RESULTS Table 1 presents descriptive statistics and intercorrelations for all variables except for the year indicators (summary statistics for these dummy variables are available from the authors). Measures of resource productivity, managerial ability, resource quality, and synchronization show significant positive correlations with organizational performance during the current period. The univariate and multivariate normality scores for all variables were examined and found to be within acceptable levels. Furthermore, to evaluate multivariate multicolinearity, we calculated variance inflation factors (VIF) for the variables used in each regression model (not shown). All VIF scores were less than two, meeting neither the critical value of 10 suggested by Neter, Wasserman, and Kutner (1989) nor the more stringent value of two suggested by Cohen et al. (2003). Thus, multicolinearilty does not appear to influence our results. Tables 2 and 3 report the results of the tests of our first two hypotheses. Table 2 contains results using weighted career winning percentage as the measure of managerial ability. Table 3 presents results from regression models that test the same relationships using our composite measure of managerial ability. Hypothesis 1 predicted that managerial ability would have a positive effect on the level of resource productivity attained by a team’s offensive combination (Hypothesis 1a) and defensive combination (Hypothesis 1b). Results indicate that the relationship between managerial ability and resource productivity for offensive and defensive combinations is positive and significant (using head coaches’ weighted career winning percentage: b = 2.172; p < 0.001 and b = 1.085; p < 0.01; using the composite measurement scale: b = 0.073; p < 0.001 and b = 0.048; p < 0.01). Thus, we find support for Hypothesis 1. With Hypothesis 2, we predicted the positive effect of managerial ability on the level of resource productivity attained by a team’s offensive combination (Hypothesis 2a) and defensive combination (Hypothesis 2b) would be weaker as the quality of resources bundled in each combination increased. Results in Table 2 (Model 3) and Table 3 (Model 3) provide reasonable support for this hypothesis using both measures of managerial ability. Specifically, the coefficient for the managerial ability/resource quality interaction term is negative and Copyright  2008 John Wiley & Sons, Ltd.

significantly predicts the level of resource productivity for team offenses using both measures of managerial ability (using weighted career winning percentage: b = −2.480; p < 0.01; using the composite measurement scale: b = −0.073; p < 0.001). This result indicates that the effect of managerial ability is weaker at higher levels of human resource quality for the offense. For the defense, the managerial ability/resource quality interaction term is negative and significantly predicts the level of resource productivity using the composite measurement scale for managerial ability (b = −0.195; p < 0.05), but yielded statistically nonsignificant results using the weighted career winning percentage. We plotted the relationship between managerial ability (using the composite measurement scale) and levels of resource productivity for the offense and defense at the mean-level of resource quality and at plus- and minus-one standard deviation from the mean for the offensive resource combination and for the defensive resource combination (see Figures 1 and 2, respectively; Aiken and West, 1991). Results suggest that the mitigating influence of resource quality on the managerial ability-resource productivity relationship differs between the bundles. Because we characterized a team’s resource contexts as having a moderating effect on the relationship between managerial ability and resource value creation, we conducted additional analyses to ascertain the significance of the interaction between managerial ability and resource quality attributed to a team’s offensive and defensive combinations. To do this, we created a measure of high resource quality when resource quality is greater than the mean and zero otherwise, and a measure of low resource quality when resource quality fell below the mean. We created measures of high managerial ability and low managerial ability following the same approach. Next, we produced four interaction terms: high managerial ability/high resource quality, high managerial ability/low resource quality, low managerial ability/high resource quality, and low managerial ability/low resource quality. Finally, we tested these interactions using randomeffects regression with robust estimators to provide controls for autocorrelation. At low resource quality, results indicate that both high and low levels of managerial ability (using head coaches’ weighted career winning percentage) are negatively and significantly related to resource productivity for the offensive Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Mean

Copyright  2008 John Wiley & Sons, Ltd. 0.130∗∗ 0.133∗∗ 0.306∗∗∗ −0.054 0.058 0.005 −0.025 −0.144∗∗∗ 0.153∗∗∗ −0.007

0.218∗∗∗ 0.152∗∗∗ 0.364 −0.028 −0.001 0.001 −0.089∗ −0.173∗∗∗ 0.176∗∗∗ −0.077†

0.138 0.131 0.146

1.011 0.291 0.497 0.070 0.401 0.302 11.725

0.088∗ −0.431∗∗∗ 0.060 −0.065† −0.104∗∗ 0.090∗ −0.018

0.254∗∗∗ 0.117∗∗ 0.308∗∗∗

∗∗∗

a

n = 602 for all variables. p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05; †p < 0.10 b Results for year dummy variables are available upon request.

0.317∗∗∗

0.344∗∗∗

0.186

0.246∗∗∗

0.139∗∗∗

0.260∗∗∗

2.648

−0.357∗∗∗

0.481∗∗∗

0.425∗∗∗

1.829

3

0.388∗∗∗

2

0.634∗∗∗ 0.521∗∗∗

1

0.187 0.160 1.878

s.d.

Descriptive statistics and correlationsa,b

1. Organizational performance 0.500 2. Resource synchronization −0.025 3. Resource productivity 0.227 (offense) 4. Resource productivity −0.245 (defense) 5. Managerial ability 0.059 (composite measure) 6. Managerial ability 0.373 (weighted career winning percentage) 7. Resource quality (offense) 0.064 8. Resource quality (defense) 0.066 9. Previous organizational 0.497 performance 10. Rivalry intensity 2.962 11. Strike year flag 0.093 12. Salary cap flag 0.442 13. League reorganization flag 0.005 14. Manager succession flag 0.201 15. Average draft position 4.691 16. Average age 27.335

Table 1.

−0.115∗∗ 0.478∗∗∗ −0.067 −0.064 −0.076∗ 0.134∗∗ −0.032

0.026 0.096∗ 0.159∗∗∗

0.126∗∗

0.103∗∗

4

0.033 0.039 −0.084∗ −0.067† −0.273∗∗∗ 0.168∗∗∗ −0.022

0.068 0.072† 0.313∗∗∗

0.718∗∗∗

5

0.004 0.038 −0.025 −0.144∗∗∗ −0.569∗∗∗ 0.177∗∗∗ −0.034

0.153∗∗∗ 0.149∗∗∗ 0.431∗∗∗

6

−0.001 0.005 −0.013 −0.034 −0.081† 0.108∗∗ −0.015

0.072 0.353∗∗∗

7

0.025 0.006 −0.013 −0.036 −0.072† 0.142∗∗∗ −0.016

0.292∗∗∗

8

0.002 0.008 −0.024 −0.248∗∗∗ −0.186∗∗∗ 0.337 0.011

9

−0.217∗∗∗ 0.057 0.011 −0.002 −0.069 0.084∗

10

−0.306∗∗∗ −0.024 −0.051 0.159∗∗∗ −0.027

11

0.078† 0.072† −0.510∗∗∗ 0.090∗

12

0.143∗∗∗ −0.101∗ −0.007

13

−0.118∗∗ 0.007

14

−0.021

15

Managerial Ability as a Source of Resource Value Creation 473

Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Copyright  2008 John Wiley & Sons, Ltd.

∗∗∗

a

n = 602. Values are unstandardized regression coefficients. p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05; †p < 0.10 b Results for year dummy variables are available upon request.

Year dummy variablesb Wald χ 2

Managerial ability × Resource quality (defense)

Managerial ability × Resource quality (offense)

Managerial ability (using weighted career winning percentage)

Resource quality (defense)

Resource quality (offense)

Average age

Average draft position

Manager succession flag

League reorganization flag

Salary cap flag

Strike year flag

Competitive rivalry

Intercept

included 534.30∗∗∗

−4.551∗∗∗ (1.157) 0.005 (0.060) −4.545∗∗∗ (0.368) 0.960∗∗ (0.353) −1.282† (0.803) 0.077 (0.167) 0.713∗∗ (0.228) −0.004 (0.005) 2.611∗∗∗ (0.402)

−4.891∗∗∗ (1.194) 0.012 (0.062) −4.554∗∗∗ (0.382) 0.995∗∗ (0.366) −1.587† (0.828) −0.469∗∗∗ (0.145) 0.967∗∗∗ (0.232) −0.005 (0.005) 2.970∗∗∗ (0.411)

included 602.32∗∗∗

2.172∗∗∗ (0.371)

Model 2

Model 1

included 617.95∗∗∗

2.160∗∗∗ (0.370) −2.480∗∗ (1.234)

−4.335∗∗∗ (1.161) 0.005 (0.059) −4.546∗∗∗ (0.367) 0.909∗ (0.354) −1.216 (0.802) 0.061 (0.167) 0.668∗∗ (0.229) −0.004 (0.005) 2.876∗∗∗ (0.429)

Model 3

Resource productivity (offense)

included 567.52∗∗∗

0.760∗ (0.383)

−2.423∗ (1.153) 0.013 (0.059) −0.363 (0.354) −0.503 (0.348) −0.854 (0.795) −0.097 (0.138) 0.545∗∗ (0.224) −0.004 (0.005)

Model 1

included 582.55∗∗∗

0.655∗ (0.331) 1.085∗∗ (0.369)

−2.352∗∗ (1.147) 0.012 (0.059) −0.405 (0.352) −0.514 (0.346) −0.694 (0.792) 0.170 (0.164) 0.436∗ (0.226) −0.003 (0.005)

Model 2

−1.281 (1.889) included 584.32∗∗∗

0.776† (0.462) 1.057∗∗ (0.371)

−2.306∗∗ (1.147) 0.012 (0.059) −0.406 (0.352) −0.516 (0.345) −0.669 (0.793) 0.171 (0.164) 0.429∗ (0.226) −0.003 (0.005)

Model 3

Resource productivity (defense)

Table 2. Panel data analysis: effect of managerial ability (using weighted career winning percentage) and managerial ability/resource quality interaction on resource productivitya

474 T. R. Holcomb, R. M. Holmes Jr., and B. L. Connelly

Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Copyright  2008 John Wiley & Sons, Ltd.

∗∗∗

a

n = 602. Values are unstandardized regression coefficients. p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05; †p < 0.10 b Results for year dummy variables are available upon request.

Year dummy variablesb Wald χ 2

Managerial ability × Resource quality (defense)

Managerial ability × Resource quality (offense)

Managerial ability (using composite measurement scale)

Resource quality (defense)

Resource quality (offense)

Average age

Average draft position

Manager succession flag

League reorganization flag

Salary cap flag

Strike year flag

included 534.30∗∗∗

−3.519∗∗ (1.173) 0.001 (0.061) −0.516 (0.363) 0.156 (0.363) −1.541 (0.819) −0.346∗∗ (0.148) 0.864∗∗∗ (0.231) −0.005 (0.005) 2.884∗∗∗ (0.408)

−4.891∗∗∗ (1.194) 0.012 (0.062) −4.554∗∗∗ (0.382) 0.995∗∗ (0.366) −1.587† (0.828) −0.469∗∗∗ (0.145) 0.967∗∗∗ (0.232) −0.005 (0.005) 2.970∗∗∗ (0.411)

included 554.47∗∗∗

0.073∗∗∗ (0.023)

Model 2

Model 1

included 585.02∗∗∗

0.072∗∗ (0.023) −0.073∗∗∗ (0.020)

−3.611∗∗ (1.177) 0.003 (0.061) −5.319∗∗∗ (0.369) 0.271 (0.360) −1.535 (0.819) −0.346∗∗ (0.147) 0.857∗∗∗ (0.231) −0.005 (0.005) 2.882∗∗∗ (0.412)

Model 3

Resource productivity (offense)

included 567.52∗∗∗

0.760∗ (0.383)

−2.423∗ (1.153) 0.013 (0.059) −0.363 (0.354) −0.503 (0.348) −0.854 (0.795) −0.097 (0.138) 0.545∗∗ (0.224) −0.004 (0.005)

Model 1

included 648.19∗∗∗

0.789∗∗ (0.407) 0.048∗∗ (0.021)

−3.882∗∗∗ (1.108) −0.015 (0.056) 5.790∗∗∗ (0.332) 0.446 (0.331) −0.599 (0.817) −0.025 (0.136) 0.684∗∗ (0.218) −0.004 (0.006)

Model 2

−0.195∗ (0.985) included 668.43∗∗∗

0.790∗ (0.405) 0.047∗ (0.021)

−3.904∗∗∗ (1.106) −0.013 (0.056) 0.359 (0.329) 0.452 (0.330) −0.577 (0.814) −0.024 (0.135) 0.684∗∗ (0.218) −0.004 (0.006)

Model 3

Resource productivity (defense)

Panel data analysis: effect of managerial ability (using measurement scale) and managerial ability/resource quality interaction on resource productivitya

Competitive rivalry

Intercept

Table 3.

Managerial Ability as a Source of Resource Value Creation 475

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476

T. R. Holcomb, R. M. Holmes Jr., and B. L. Connelly where: Y = resource productivity (offensive combination) X = managerial ability Z = resource quality

+Y

+Z –Y –X –Z +X

Figure 1. Interaction of managerial ability and resource quality on the level of resource productivity for offensive combinations

+Y where: Y = resource productivity (defensive combination) X = managerial ability Z = resource quality

+Z –Y –X +X

–Z

Figure 2. Interaction of managerial ability and resource quality on the level of resource productivity for defensive combinations

combination (with high managerial ability: b = −66.472; p < 0.001; using the composite measurement scale: b = −12.632; p < 0.001). Using Stata’s test command to test linear hypotheses after estimation, we verified the interaction terms differed statistically from each other. The difference between the coefficients for the high managerial ability/low resource quality interaction term and the high managerial ability/high resource quality interaction term was statistically significant (χ 2 = 5.62, p < 0.01). The difference between the Copyright  2008 John Wiley & Sons, Ltd.

coefficients for the high managerial ability/high resource quality interaction term and the low managerial ability/high resource quality interaction term was also statistically significant χ 2 = 16.73, p < 0.001, respectively). Hypothesis 3 predicted that the indirect effect of managerial ability on organizational performance through resource synchronization is positive. That is, superior managerial ability leads to more effective resource synchronization, which leads to better performance. Again, the resource context is expected to affect this relationship, weakening its strength at higher levels of resource quality. We represent this relationship with Hypothesis 4. We calculated the coefficients used to test the underlying direct and indirect effects based on four steps recommended by Baron and Kenny (1986). Table 4 reports the test of this hypothesis. In Step 1, we regressed organizational performance (the dependent variable) on the control variables, including a control for resource quality (Model 1). We then added our composite measure of managerial ability and the interaction term (Model 2).10 The coefficient for managerial ability and the interaction term are significant (managerial ability: b = 0.009; p < 0.001; managerial ability/resource quality: b = −0.017; p < 0.10). In Step 2, we examined direct and moderated effects on resource synchronization (Model 3). Managerial ability and the association of the managerial ability/resource quality term with resource synchronization are also significant (managerial ability: b = 0.086; p < 0.01; managerial ability/resource quality: b = −0.236; p < 0.01). In Step 3, we regressed performance on resource synchronization without managerial ability and the interaction term (Model 4). Resource synchronization, as expected, is positively and significantly associated with performance (b = 69.064; p < 0.001). Finally, we added managerial ability and the managerial ability/ resource quality term to the model. As shown in Model 5, the relationship between resource synchronization and performance is positive and significant (b = 68.648; p < 0.001). However, the coefficients for managerial ability and the interaction term are no longer significant, suggesting mediation. Results of the Sobel test using the product of coefficients formula confirm the mediating 10 In supplementary analyses, we used the measure of managerial ability based on Dirks’ (2000) weighted career winning percentage formula. Using this alternative measure did not substantively change our results.

Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Copyright  2008 John Wiley & Sons, Ltd.

∗∗∗

a

n = 602. Values are unstandardized regression coefficients. p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05; †p < 0.10 b Results for year dummy variables are available upon request.

Year dummy variables Wald χ 2

Managerial ability × Resource quality (team)

Managerial ability (using composite measurement scale)

Resource synchronization

Resource quality (team)

Average age

Average draft position

Manager succession flag

League reorganization flag

Salary cap flag

Strike year flag

Competitive rivalry

included 114.46∗∗∗

0.009∗∗∗ (0.003) −0.017† (0.010) included 125.69∗∗∗

0.129 (0.152) 0.327∗∗∗ (0.061) −0.005 (0.008) −0.012 (0.047) 0.034 (0.046) −0.008 (0.107) −0.038∗ (0.019) 0.053† (0.031) −0.001∗ (0.001) 0.103∗∗ (0.047)

Organizational performance

Organizational performance 0.091 (0.152) 0.377∗∗∗ (0.059) −0.004 (0.007) −0.006 (0.043) 0.028 (0.043) −0.007 (0.100) −0.052∗∗ (0.019) 0.055† (0.032) −0.001∗ (0.000) 0.091∗∗ (0.042)

Model 2

Model 1

included 449.33∗∗∗

0.310∗∗ (0.121) 0.174∗∗ (0.051) −0.003 (0.006) −0.052 (0.036) 0.015 (0.036) −0.111 (0.096) −0.027† (0.015) 0.030 (0.025) −0.001∗ (0.000) 0.070∗∗ (0.037) 69.064∗∗∗ (4.077)

−5.195∗∗ (1.695) 4.326∗∗∗ (0.679) 0.001 (0.087) 0.015 (0.519) 0.321 (0.520) −0.621 (1.197) −0.325 (0.211) 0.648 (0.352) −0.008 (0.007) 1.815∗∗∗ (0.529) 0.086∗∗ (0.035) −0.236∗∗ (0.117) included 152.52∗∗∗

Organizational performance

Model 4

Resource synchronization

Model 3

0.310∗∗ (0.128) 0.044 (0.056) 0.002 (0.007) 0.009 (0.038) 0.016 (0.038) −0.106 (0.100) −0.028† (0.016) 0.032 (0.026) −0.001∗ (0.001) 0.065† (0.041) 68.648∗∗∗ (4.141) 0.003 (0.002) −0.019 (0.016) included 451.24∗∗∗

Organizational performance

Model 5

Panel data analysis: indirect effect of managerial ability and the managerial ability/resource quality interaction on organizational performancea

Previous organizational performance (prior three-year average)

Intercept

Table 4.

Managerial Ability as a Source of Resource Value Creation 477

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T. R. Holcomb, R. M. Holmes Jr., and B. L. Connelly

effect.11 Specifically, the relationship from managerial ability to performance through resource synchronization is positive and significant (z = 3.350; p < 0.001). The relationship from managerial ability/resource to performance through resource synchronization is negative and significant (z = −2.011; p < 0.05). Thus, we find support for Hypotheses 3 and 4.

DISCUSSION This study demonstrates that managers are an important source of value creation, providing insight into the argument that managers and resources jointly determine firm success (Castanias and Helfat, 2001). Our results reveal three main findings. First, managers differ with respect to their ability to manage resources, and these differences help explain why some firms create more value from their resources than others do. Scholars have criticized resource-based research for neglecting the influence of managers on resources (Priem and Butler, 2001; Sirmon et al., 2007). Not including managers in a study’s empirical design essentially assumes homogeneity in managerial ability across firms. We show this is not a realistic assumption. Second, the influence of managerial ability is contingent on the quality of a firm’s resources. Managers with superior ability have a stronger effect on resource productivity when the quality of individual resources is lower. However, the statistical significance of this effect differs between bundles, suggesting this relationship may vary as a function of the tasks each bundle performs. Third, managerial ability plays an important role in determining how firms synchronize their resources to create a performance advantage. Specifically, we find the effect of managerial ability on performance through resource synchronization is positive. This relationship becomes increasingly 11 A key, and often implicit, assumption in standard tests for mediation is that estimated equations in the set are uncorrelated. In supplementary analyses, we applied a two-stage least squares (2SLS) approach, which is an instrumental variable estimation technique, in tests for mediation to account for the possibility that error terms from our tests could violate these assumptions (see Shaver, 2005, for further description of this method). Specifically, we used Stata’s ivreg procedure with robust estimators that provide controls for autocorrelation. The results did not substantively change; coefficients for variables of interest to this work were significant and in the expected direction. We are thankful to an anonymous reviewer for this suggestion.

Copyright  2008 John Wiley & Sons, Ltd.

important with less valuable resource portfolios. In other words, an underlying premise of strategic management theory is that managers influence performance, and we empirically demonstrate that an important means through which managers do so is by more effectively bundling, deploying, and synchronizing resources the firm controls. This study makes an important contribution to the resource-based perspective. Although resources may provide a performance advantage, realizing this advantage depends on the way in which managers bundle, deploy, and synchronize resources (Lichtenstein and Brush, 2001; Newbert, 2007; Sirmon et al., 2007). Prior research from the resource-based perspective envisions a firm’s resource endowment as an antecedent to performance advantage. We add that management and synchronization processes figure importantly in firms’ ability to realize a performance advantage. Further, as results indicate, some managers are better able to effectively manage and synchronize firm resources than others. From an empirical standpoint, our study supports recent extensions of resource-based logic by providing evidence that managers and resources jointly determine advantages gained over time. Although previous research establishes the general link between managers and different performance outcomes (e.g., Carpenter and Frederickson, 2001; Eisenmann, 2002; Hambrick et al., 1996; Hayward and Hambrick, 1997; Miller and Shamsie, 2001; Smith et al., 1991), our extension to resource-based theory is that managers’ abilities to create greater resource value underlie this link. We find empirical support for this view, showing that resource synchronization mediates the influence of managerial ability on organizational performance. This finding has important implications for resource-based theory, highlighting some of the boundary conditions that potentially influence productive resource output and organizational performance. This study is one of the first to hypothesize explicitly and find such a mediating relationship. We also find an important interaction effect between managerial ability and resource quality. In particular, managerial ability becomes increasingly important in firms at lower levels of resource quality. This relationship is less evident in firms with superior resource endowments, which suggests high-quality resources may already be performing at ‘peak’ levels. Resource-based theory suggests that performance varies as a function Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation of the value, rarity, imitability, and substitutability of resources firms’ control (Barney, 1991). Our finding extends this view by suggesting the influence of able managers on resource value creation depends on the quality of the resources. Finally, this finding addresses the concerns of scholars admonishing resource-based theorists for overlooking factors that condition the value that firms create from their resources (Priem and Butler, 2001). Our study also contributes to the literature on managerial ability. A considerable amount of research in applied psychology and management literatures addresses the ability construct (Hunter and Hunter, 1984; Phillips and Gully, 1997; Ree, Earles, and Teachout, 1994). These scholars have established a reasonably clear connection between individual ability and both individual and group performance (Earley and Lituchy, 1991; Spreitzer et al., 1997; Thomas and Mathieu, 1994). However, from a strategic perspective, managerial ability is relatively unexplored and rarely operationalized. Conceptually, we establish the critical role of managerial ability in the resource-based view. Further, we put forward a more specific definition of managerial ability, synthesize relevant research, and examine the influence of managerial ability in a context that is amenable to capturing the construct. We also develop a measure of managerial ability as an individual-level construct that managers carry with them between firms. Our study also has several implications for managers. Perhaps the most obvious implication is that managers do matter. The resource-based perspective clearly establishes the importance of resources in determining performance differences between firms, but our study adds that resources are not sufficient by themselves. Firms must pay close attention both to their resource endowment and to the ability of their managers to extract value from those resources. Managers themselves would also do well to recognize that improving their ability to extract value from resources is a skill that can transcend organizational boundaries. Taking conscious steps to increase their own managerial ability, therefore, may make them more successful as they enter new organizational contexts. The foremost implication for managers may be that managerial ability is most important in firms with lower quality resources. This idea suggests that firms may be justified in pursuing new Copyright  2008 John Wiley & Sons, Ltd.

479

and more able managers during times of crisis, such as when performance declines (Robbins and Pearce, 1992; Barker and Duhaime, 1997), especially when declines deplete the value of a firm’s resource endowment. Crisis firms often possess inadequate or underperforming resources (Hambrick and D’Aveni, 1988). In these circumstances, new and more able managers can potentially develop bundling and deployment strategies that more effectively match resources to the competitive context. These actions can improve the value of resources the firm controls, allowing it to reverse its fortunes (Morrow et al., 2007). Limitations The findings related to the relationship between managerial ability, resource quality, and resource productivity point to one of the limitations of the current work. The data suggest that managerial actions involving the bundling, deploying, and synchronization of firm resources intervene in the relationship between managerial ability and performance. However, we did not examine individual resource management processes, such as the actions taken to structure the resource portfolio. Professional football teams, like organizations in other industries, differ with respect to their policies about the structure of their resource portfolio and their philosophies governing actions they pursue between seasons (Allen et al., 1979). Part of the explanation behind our findings may be that able managers are more effective at structuring their resources, rather than effectively bundling and deploying them. Future research that accounts for the effects of different resource management processes (e.g., Holcomb, Holmes, and Hitt, 2006; Morrow et al., 2007; Sirmon et al., 2007) may contribute to our understanding of the processes that underlie our findings. Our measure of organizational performance is not without its limitations. Although one team’s win is another team’s loss, winning percentage as a measure of organizational performance does not quantify the degree of advantage directly achieved by an organization relative to rivals. Indeed, because competitive contests are a zerosum game, this measure embeds the mean value of 0.500 (or 50%) for all teams in the league within a team’s winning percentage for the season, making it different from conventional measures of relative performance. Further, this work also stops Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

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short of examining the sustainability of an organization’s performance advantage over time. Future research might consider the extent to which managerial ability not only contributes to differences in performance between organizations more directly, but also influences the persistence and sustainability of a performance advantage over time. Such questions are important in light of research suggesting that competitive dominance and sustainability can exist independently, and sports leagues may be one of many valuable and nontraditional contexts in which to study such phenomena (e.g., Powell, 2003). Given the peculiarities of professional sports teams, it is also reasonable to speculate about the generalizability of these findings to firms in other industries. Head coaches have options available to influence performance outcomes in ways not directly analogous to the options available to managers in traditional business organizations. For example, whereas senior executives often wait long periods before the results of specific resource management actions are known, coaches often receive immediate feedback on the outcome of decisions they make and can adjust bundling and deployment actions more quickly. Nonetheless, professional football teams do resemble traditional organizations in that football teams utilize a division of labor similar to the M-form structure (see Chandler, 1962). As in traditional organizations, success depends critically on human capital and the ways in which it is used (Hitt et al., 2001; Hatch and Dyer, 2004; Kor and Leblebici, 2005). Further, this study does not examine performance variations in resource productivity and synchronization attributable to different institutional influences or environmental contingencies. In this work, we address the immediate effects of managerial ability on resource productivity and organizational performance in an empirical setting that partially controls for such external conditions. Nonetheless, although we believe that there are similarities that may make these results applicable to other types of organizations, including corporations and governmental agencies, we would encourage replication in other industry and institutional contexts. Finally, this study does not examine these relationships in view of other managerial attributes, such as personality and values (e.g., Pfeffer, 1977; Hambrick and Mason, 1984; Meindl and Ehrlich, Copyright  2008 John Wiley & Sons, Ltd.

1987; Cannella and Holcomb, 2005). A more complete analysis of managerial ability could incorporate specific managerial styles and related cognitive factors. Future research A number of additional areas for research seem evident. An interesting and potentially important question is whether and how a firm’s environmental context affects resource productivity. Because of high environmental uncertainty and varying degrees of environmental munificence, realizing a performance advantage over time is difficult (Lichtenstein and Brush, 2001). As a result, firms often seek to establish a series of temporary advantages (Holcomb et al., 2006). Uncertainty created by instability in the environment produces information deficits, making cause-and-effect relationships difficult to identify and interpret. For example, Carpenter and Frederickson (2001) found that environmental uncertainty moderated the relationship between the characteristics of top management teams and the strategic posture of their firms, such that the effect was stronger at moderate levels of uncertainty. Extending such analysis to the framework presented here may reveal additional contingencies surrounding the effects of managerial ability, resource quality, and resource productivity on the competitive advantage a firm achieves. Another interesting question is whether and to what extent managerial discretion (i.e., latitude of managerial action; Hambrick and Finkelstein, 1987) strengthens or weakens the effect of managerial ability on resource value creation and performance. Although the answer seems simple with respect to productive resource output, research suggests that managerial discretion may have different indirect effects on performance outcomes depending on other internal and external factors (e.g., power and environmental uncertainty; see Haleblian and Finkelstein, 1993). Since discretion necessarily endows managers with more latitude to pursue significant strategic change, is it possible for managers with greater discretion to extract more value from higher quality resources? Conversely, might such discretion be dysfunctional, especially in the presence of agency problems (e.g., Makadok, 2003)? Similarly, to what extent does managerial discretion and managerial ability interact with specific resource management actions Strat. Mgmt. J., 30: 457–485 (2009) DOI: 10.1002/smj

Managerial Ability as a Source of Resource Value Creation to affect resource-based performance advantages? We believe it is important for researchers to attempt to isolate these effects in future research.

CONCLUSION In conclusion, we believe this research represents significant progress toward untangling the complexities surrounding relationships between managerial ability, human resources, and resource value creation. While results indicate managerial ability and actions affect resource productivity, their influence is moderated by the quality of the organization’s human resources. Importantly, we extend research examining managerial actions in the resource-based view by demonstrating the importance of synchronization on resourcebased performance advantages. Specifically, while managerial actions that increase the productive output of resource bundles are important, managers must also effectively synchronize management processes within and between these bundles to realize enhanced performance. Our examination of these relationships has significant implications for managerial practice and future research on resource management and the resource-based view.

ACKNOWLEDGEMENTS We gratefully acknowledge valuable comments from David Sirmon, Trevis Certo, Bert Cannella, Glenn Rowe, Gerald Ferris, Gilad Chen, Richard Woodman, James Combs, Annette Ranft, and Bruce Lamont. Moreover, we are thankful for the guidance that Editor Will Mitchell provided in conjunction with the feedback from two anonymous reviewers. We presented an earlier version of this manuscript at the 2005 Academy of Management Annual Meeting.

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