Strategic Consistency of Exploration and Exploitation ...

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FBRXXX10.1177/0894486513504434Family Business ReviewMoss et al.

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Strategic Consistency of Exploration and Exploitation in Family Businesses

Family Business Review 2014, Vol. 27(1) 51–71 © The Author(s) 2013 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0894486513504434 fbr.sagepub.com

Todd W. Moss1, G. Tyge Payne2, and Curt B. Moore2

Abstract This study advances family business research by examining how the strategic consistency with which family businesses pursue exploration and exploitation initiatives affects performance. Using panel data of 94 family businesses operating in four high-tech industries over 12 years, we find that higher strategic consistency—continuity with past exploration and exploitation strategies stemming from managerial intentionality—yields higher levels of performance. This relationship is also moderated by environmental dynamism, munificence, and organizational size, which demonstrates the contingent and complex nature of the main relationship. Furthermore, in contrast to 113 nonfamily businesses, we find that the main relationship is stronger for family businesses. Keywords strategic consistency, exploration and exploitation, performance

Introduction Strategic consistency—continuity with past strategies stemming from managerial intentionality—is commonly argued to improve firm performance and chances of survival (Lamberg, Tikkanen, Nokelainen, & SuurInkeroinen, 2009). Support for consistency dates back to classic works such as Miles and Snow (1978), Porter (1980), Quinn (1980), Miller and Friesen (1982), and Hannan and Freeman (1984), and suggests that value resides within the development of routines, specialization, core capabilities, and stable stakeholder relationships. However, given the dynamic and highly competitive nature of most business environments of today, an orientation toward consistency may be less intuitive or espoused when compared to flexibility, adaptability, and speed. Highly adaptive firms would have the ability to alter the direction and speed of strategic activities in response to environmental and competitive changes and therefore gain competitive advantages and improved performance vis-à-vis their competition (Eisenhardt & Brown, 1998). The strategic orientation underlying the adaptation versus consistency deliberation is manifested in how resources are allocated toward activities and processes that promote exploration and/or exploitation (Siggelkow

& Levinthal, 2003; Voss, Sirdeshmukh, & Voss, 2008). Exploration refers to an organization’s activities focusing on search, risk taking, discovery, experimentation, and flexibility (March, 1991). Although typically more costly in the short term, exploration is vital to long-term performance because breakthroughs may take years to come to fruition (Bierly & Daly, 2007). Exploitation, on the other hand, refers to an organization’s activities aimed at refinement, production, execution, implementation, and efficiency (March, 1991). Exploitation typically generates shorter term gains as firms strive to improve quality and efficiency in their production efforts (Benner & Tushman, 2002). Scholars generally agree that exploration and exploitation (E/E) are independent dimensions of strategic importance that affect business performance (e.g., Gupta, Smith, & Shalley, 2006). Here, strategic consistency refers to both exploration and exploitation activities, even if used 1

Oregon State University, Corvallis, OR, USA Texas Tech University, Lubbock, TX, USA

2

Corresponding Author: G. Tyge Payne, Rawls College of Business, Texas Tech University, 15th & Flint Avenue Box 42101, Lubbock, TX 79409-2101, USA. Email: [email protected]

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simultaneously, but considers how the intensity of E/E decisions and actions, together and in relation one another, vary over time. For family businesses, the strategic approach or orientation to E/E is no less important than for other businesses. However, it is likely that the nature of family businesses, predicated by dual concerns for the family and the business when making strategic decisions (Anderson & Reeb, 2003; Chua, Chrisman, & Sharma, 1999), may influence the way family businesses approach E/E (Miller & Le Breton-Miller, 2006). Stated another way, family businesses tend to have different goals and objectives relative to nonfamily businesses (Yu, Lumpkin, Sorenson, & Brigham, 2011) such that organizational decisions and actions reflect an implicit assumption of constancy and longer time horizons (Le Breton-Miller & Miller, 2006; Lumpkin & Brigham, 2011). Indeed, Miller and Le Breton-Miller (2006) suggest that family businesses may gain competitive advantages in E/E because of the desire to make long-term investments in developing core capabilities, cultures, and external stakeholder relationships; such investments can be utilized to improve abilities in either exploration or exploitation. As explicitly noted by Miller and Le Breton-Miller (2006), the different and independent requirements of E/E generally preclude businesses from addressing both initiatives simultaneously. Thus, the continuity or consistency with which firms approach E/E becomes a central concern, especially when considered relative to performance. Hence, our key argument is that consistency over time, regardless of type, results in higher levels of organizational performance. Although little research has examined E/E in family businesses, scholars have previously suggested that not all family businesses are equally proficient at E/E activities nor approach such strategic activities similarly (Salvato & Melin, 2008; Sharma & Salvato, 2011). Building on this argument regarding the heterogeneity of family firms in terms of E/E, this study explores how family businesses’ strategic approaches to E/E influence performance longitudinally. Specifically, we seek to address three key research questions. First, is strategic consistency of E/E important to performance in family businesses? Second, does the relationship between strategic consistency of E/E and performance vary under different environmental and organizational contexts? And third, does the nature of these relationships differ for family and nonfamily businesses?

In addressing these questions, this study makes three primary contributions to the family business literature. First, despite recent work in the broader organization studies literature (Cao, Gedajlovic, & Zhang, 2009; Lavie, Stettner, & Tushman, 2010), the family business literature has not, to date, extensively examined E/E within family businesses. As a step forward, we offer up arguments as to why strategic consistency of E/E is germane to family businesses, particularly in terms of performance. As a second contribution, we explore how the consistency-to-performance relationship may be moderated by organizational and environmental factors. In the broader organization science literature, researchers have called for more studies that examine what moderators may influence E/E and its relationship to performance (e.g., Gupta et al., 2006). Here, we specifically examine organization size and the industry moderators of dynamism and munificence in relationship to E/E consistency in family businesses; we wish to determine how such factors may contingently influence the primary relationship. Finally, we compare family and nonfamily businesses to demonstrate how these relationships differ between these two broad organizational forms. One staple of family business research is to describe how and explain why family businesses may differ from nonfamily businesses, particularly in terms of performance (Dyer, 2003, 2006). Hence, we contribute to the literature through our longitudinal examination of family and nonfamily business differences in strategic consistency of E/E.

Theory and Hypothesis Development A strategic orientation regarding E/E refers to an organization’s broad strategic emphasis regarding exploration and/or exploitation that is intended to guide firm decisions and actions geared toward achieving competitive advantage and superior performance. Some scholars suggest that E/E dimensions compete with each other such that an increase in one necessitates a decrease in the other (e.g., March, 1991), whereas others adopt a complementary perspective where E/E may vary independently, depending on organizational specifics; a complementary and independent perspective is the more contemporary view presented in the E/E literature (e.g., He & Wong, 2004; Katila & Ahuja, 2002). Either way,


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Moss et al. most scholars agree that ambidexterity, the simultaneous pursuit of both dimensions of E/E, is a necessary condition for high performance and survival (He & Wong, 2004; Gupta et al., 2006). However, it remains unclear how E/E should be managed over time and if organizational (e.g., family involvement, structures) or environmental (e.g., industry) factors influence the E/E to performance relationship. The following arguments more specifically consider these issues, particularly as they apply to family businesses.

E/E Consistency and Performance in Family Businesses Family firms tend to apply longer time horizons in their strategic decision making than nonfamily firms. A longer term perspective, which reflects a concern for business continuity, is grounded in socioemotional factors (Gomez-Mejia et al., 2007) and is reflected in the behaviors of the firm, including placing more emphasis on research and development, building reputation, and improving or broadening market share (Miller & Le Breton-Miller, 2005). In each of these activities, the goal of the family business is to solidify the business for the future and, ultimately, pass the company on to the next generation (Ward, 1997). Hence, the family often closely identifies with the business (Arregle, Hitt, Sirmon, & Very, 2007), gains enhanced self-esteem from participating in the business (Westhead, Wright, & Ucbasaran, 2001), and time horizons are seldom limited to a single individual (Walsh & Seward, 1990). Also, family businesses tend to support the use of patient capital— investments geared toward long-term returns (Zellweger, 2007)—and tend to have longer tenured CEOs than nonfamily businesses (Tsai, Hung, Kuo, & Kuo, 2006). Together, such characteristics and behaviors suggest that family firms will more commonly have a long-term orientation (LTO) and can use that long-term temporal approach as a source of competitive advantage (Miller & Le Breton-Miller, 2005; Zellweger, 2007). Lumpkin and Brigham (2011) developed the LTO construct for family businesses arguing that LTO “values extended time horizons and assigns greater importance to the future” (p. 1151). LTO is composed of three dimensions: continuity, futurity, and perseverance. Continuity describes how an organization bridges the past, present, and future, leading to constancy and longevity. Futurity describes a belief in planning and the

assessment of future-oriented goals. Finally, perseverance describes how an organization values cumulative effort and demonstrates patience when investing for the future. This conceptualization of LTO views time holistically and recognizes how both past and present activities are important to the future (Lumpkin & Brigham, 2011). Such a perspective contrasts with a short-term orientation, which may support nimbleness and fast responses to changes in the environment, but may also promote decision makers to become overly focused on short-term financial gains at the expense of organizational survival (Laverty, 1996). Scholars tend to argue that most managers have short temporal orientations (Souder & Bromiley, 2012). Family firms can and do vary in terms of the approach to E/E, both compared to one another and across time. However, based on a culture of continuity, inclinations to consider the future, and support for perseverance (i.e., staying the course), we suggest that a more consistent approach to E/E—following a similar approach year to year in terms of balance and intensity—will produce better performance outcomes because it aligns more closely with the culture, identity, and goals of the family firm. In other words, a more consistent approach to strategy should fit better with the long-term approach associated with most family firms, be it emphasizing exploration, exploitation, or a balanced approach. For example, a firm may be considered strategically consistent if it placed 75% of its attention and resources into exploration and 25% into exploitation over multiple years, but it would also be considered highly consistent if it placed 100% of its efforts into exploitation year after year. Organizational learning perspectives, on which many E/E studies are based (e.g., March, 1991), suggest that E/E requires unique sets of resources, capabilities, processes, and routines such that these activities become reinforced, more efficient, and sought after over time (e.g., Lavie & Rosenkopf, 2006). However, the ability to develop new knowledge is dependent on the type and level of knowledge currently held (Cohen & Levinthal, 1990), which suggests that E/E activities may be evolutionary. Considering these effects of organizational learning, we recognize that there are distinct advantages of strategic consistency, as opposed to significant strategic change, that can lead to higher levels of performance. These advantages are largely based on the argument that there is value in the aligning organizational processes, systems, and structures to firm


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strategy and the incremental changes of the environment (Lamberg et al., 2009). Relative to discontinuous change in strategy regarding E/E, consistency has a number of advantages. First, we note the reaction of stakeholders, both internal and external, to change. For internal stakeholders, such as employees, there is likely to be some resistance to significant change. For firms heavily influenced or controlled by a family, the company is often bound to noneconomic preferences that are important to the family (Chua et al., 1999) and can have a major impact on how the firm approaches change in E/E (König, Kammerlander, & Enders, 2013). Also, although open resistance may not exist, an organizational culture that has developed over an extensive period of time to fit with a given strategic approach to E/E may be misaligned following major strategic change. Cultures typically change slowly because it is simultaneously a cognitive, behavioral, and emotional learning process (Schein, 1985); this may lead to substantial loss in efficiencies during the transition period. Furthermore, family cultures exert a powerful influence on the overall organizational culture, which can either facilitate or hinder the organization’s ability to change (Zahra, Hayton, Neubaum, Dibrell, & Craig, 2008). For external stakeholders, such as suppliers and buyers, there may be a period of confusion and role disputes depending on how and to what degree the strategic change influences those stakeholders. Fiss and Zajac (2006) state the nature of this problem well: “Since strategic change generally involves the reordering of priorities and the disruption of established relationships, such change tends to be controversial—both internally and externally” (pp. 1173-1174). Finally, implementation of strategy is often expensive since it often requires extensive change to multiple organizational processes and systems. Overall, we argue that for family firms, strategic consistency in E/E is beneficial to the performance of the business. We, therefore, state the following: Hypothesis 1: Strategic consistency in E/E will be positively related to firm performance of family businesses.

Organizational and Environmental Contingencies of Strategic Consistency Previous research suggests that the linkage between E/E strategic consistency and performance is likely affected

by contingencies. For example, DeSarbo, Di Benedetto, Song, and Sinha (2005) demonstrated that both organizational and environmental contingencies affect the relationship between consistent strategies—operationalized in terms of Miles and Snow’s (1978) typology— and firm performance. But as previously argued, flexibility and speed have emerged as important strategic issues in many contexts, and these characteristics may not coincide with a strict adherence to strategic consistency (Eisenhardt & Brown, 1998; Lamberg et al., 2009). Therefore, the relationship between E/E strategic consistency and performance may best be considered in relation to both structural and environmental factors (Lamberg et al., 2009). Here, we examine organizational size, environmental munificence, and environmental dynamism, which represent the most important contingency factors with regard to innovation (Damanpour, 1996). Organizational Size. Organization scholars have long observed that smaller firms will tend to have fewer resources to draw on than larger firms (Chen & Hambrick, 1995; Penrose, 1959). The availability of firm resources provides a buffer against the risk of internal or external shocks that may negatively affect performance (Bourgeois, 1981; Thompson, 1967). It makes intuitive sense that larger firms will have greater slack resources available to counter the deleterious effects of poor strategic decisions than smaller firms. However, large size is often associated with complex structures, inertia, and institutionalized norms, which may inhibit some larger firm’s ability to make changes or respond to opportunities (Hannan & Freeman, 1984; Tushman & Romanelli, 1985). Despite the risk of becoming too engrained and habitual, an important benefit of E/E strategic consistency is the ability to generate competencies through repetitive activities. For smaller firms that tend to engage in more focused E/E activities than larger firms, such competency development may be a key source of competitive advantage (Miller & Le Breton-Miller, 2006). For example, smaller biotech firms engage in focused exploration or exploitation alliances over time, while larger firms tend to vertically integrate (Rothaermel & Deeds, 2004); alliance building may allow for more speed and flexibility because they rely on other firms to shore up deficiencies in E/E and allows for continued focus in one area. Additionally, E/E activities rely on very different skill sets that could make frequent shifting


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Moss et al. between strategies exceedingly difficult, especially for smaller firms that lack the resources of larger firms. Family control may be diluted as a business grows; this can be due to the need to delegate strategic decisionmaking authority to nonfamily members, decreases in the frequency of communication and interactions (Poza, 2007), less information to verify agent behaviors (Eisenhardt, 1989), or the introduction of more bureaucratic and formal mechanisms (Kimberly, 1976). The relationship between control and organizational size suggests that a family’s general LTO may be less influential as the organization gets larger. In other words, smaller firms will be more heavily influenced by the family and its desire for more consistent approaches to E/E, and this influence manifests as closer alignment between the strategic approach of the organization and its culture, processes, systems, and structures. Overall, we expect the size of the family business to negatively moderate the consistency-to-performance relationship such that it is stronger for smaller firms. Formally, we state the following: Hypothesis 2: Organization size will negatively moderate the relationship between strategic consistency in E/E and family firm performance such that consistency will be more important to smaller family firms. Environmental Munificence. Organizations are open systems and therefore interact with their external environment (Scott & Davis, 2007). They depend on the environment to provide resources to fulfill their missions and to operate their various systems (Pfeffer & Salancik, 1978; Thompson, 1967). Environments therefore vary with respect to the types and amount of resources available to a firm (Hannan & Freeman, 1977). Environmental munificence is defined as the scarcity or abundance of resources within an environment that influences the growth and survival of organizations (Castrogiovanni, 1991). In more munificent environments, there are abundant opportunities for growth and plentiful resources to support the pursuit of those opportunities (Dess & Beard, 1984). Since a key element of strategy is resource allocation, the availability of the resources from the environment is of particular interest to understanding strategic decisions of E/E in family firms. If munificence is high, family firms will likely remain consistent in their strategic

approaches to business since there will be few environmental pressures to change. In contrast, more hostile environments may lead firms to change strategic directions more frequently as family owners and managers explore various options needed to survive. Dyer and Mortensen (2005) report that although some family businesses refuse to adapt to hostile environmental pressures, those that did change, such as by switching from local to more international strategies, outperformed those that did not. Furthermore, family businesses, with their longer temporal orientations, will be more inclined to reinvest slack resources into the business as opposed to redistribute wealth to shareholders; this should perpetuate future returns. So, although we would expect that the strategic consistency-to-performance relationship would be moderated by munificence in all types of firms including family businesses, the moderation effect is especially strong and relevant for family businesses. Specifically, we suggest the following: Hypothesis 3: Environmental munificence will positively moderate the relationship between strategic consistency in E/E and family firm performance such that consistency will be more important to family firms operating in more munificent environments. Environmental Dynamism. Again drawing on the idea that an organization is an open system that interacts with its environment (Scott & Davis, 2007), scholars recognize that firms are affected by dynamism or the rate of change in the environment (Dess & Beard, 1984). Dynamic environments are often characterized by high rates of change both in market factors and in technology factors (Sharfman & Dean, 1991), suggesting that changes in customer preferences and technological advancements may affect the rate at which an environment changes, as well as its predictability. Previous studies focusing on E/E have utilized dynamism as a contingency factor and shown equivocal results (e.g., Bierly & Daly, 2007; Jansen, Van Den Bosch, & Volberda, 2006: Uotila, Maula, Keil, & Zahra, 2009). Scholars commonly suggest that in highly dynamic environments, more focus on exploration is needed because firms that attempt to incrementally exploit their current knowledge will fall behind in the development of new technologies and products. Conversely, firms operating in stable environments


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should focus more on incremental improvements to realize production efficiencies and cost reductions. Other scholars suggest that in environments marked by higher rates of change, balance between E/E becomes increasingly more important (Lin, Yang, & Demirkan, 2007). Following general arguments regarding the performance benefits of adapting to dynamic environments (e.g., Finkelstein & Hambrick, 1990; Goll & Rasheed, 1997), some scholars of E/E have noted that it may be beneficial for firms to shift their E/E emphasis to match their environment (Gupta et al., 2006; Raisch, Birkinshaw, Probst, & Tushman, 2009). However, being overly reactive with regard to strategy is commonly associated with lower levels of performance (Miles & Snow, 1978). Indeed, the literature on dynamic competency and core capability development argues for the importance of pursuing persistent, focused, and cumulative investments in capability building (Dieryckx & Cool, 1989; Teece, Pisano, & Shuen, 1997). Such capabilities, be they based on exploration or on exploitation, serve as sources of competitive advantage, which can be used to support the firm over time. For family firms following a longer time horizon, developing such capabilities will take precedent over more immediate initiatives and reactionary responses to dynamism. Furthermore, strategic decisions that require large amounts of change may be considered too risky or costly, and therefore avoided due to potential damages to the financial security of the family firm or its reputation. As such, more firms following a more consistent approach to E/E may be able to withstand turbulence such that firm performance is relatively higher, particularly over the longer time periods. Formally, we state the following: Hypothesis 4: Environmental dynamism will positively moderate the relationship between strategic consistency in E/E and family firm performance such that consistency will be more important to family firms operating in more dynamic environments.

Family Versus Nonfamily Differences Although the previous hypotheses were specifically developed with consideration to the idiosyncrasies of family businesses, more generalizable arguments could be made for these same relationships. There are arguments for the benefits of strategic consistency across both family and nonfamily firms, particularly

when considering contingencies. But although strategic consistency of E/E may be important to the performance of all types, the argument that family businesses tend to value long-term strategic approaches suggests that family firms may benefit more from them than nonfamily firms. Generally, we argue that the culture, identity, and values of the family firm will support a stronger strategic consistency-to-performance relationship over that of nonfamily firms. Hence, we also state the following: Hypothesis 5: The relationship between strategic consistency and performance is stronger for family firms than for nonfamily firms.

Method Sample To test the hypotheses, we gathered data from four hightech industries that would provide acceptable levels of variance in organizational size and environmental variables. High-tech industries were chosen because scholars have frequently shown that E/E are key strategic concerns and are vital to firm performance in these industries (e.g., Cao et al., 2009; Katila & Ahuja, 2002; Rothaermel, 2001). Yet assuming that all high-tech industries are marked by similar environmental characteristics based on anecdotal evidence may not be accurate. In fact, the hightech industries in our sample were characterized by markedly different levels of environmental munificence and dynamism. Thus, our sample of multiple high-tech industries allowed for testing the applicability of both the general hypotheses and those more specific hypotheses arguing for the moderating effects of size, munificence, and dynamism. Data for the sample covered the 13-year period from 1997 to 2009 in order to allow for adequate assessment of a firm’s strategic consistency over three 4-year panels of time (1997-2008), as well as allowing for a 1-year lag in performance. We used a purposive sample obtained through first gathering sales data from Compustat on all publiclytraded companies in the United States. Sales data were needed to generate environmental factors following Dess and Beard (1984), who also utilized industry sales data. After gathering sales data for companies from all industries available, we sorted the industries on the total number of observations from 1997 to 2009. Limiting the potential sample to the 50 largest industries in the database to ensure adequate sample size, we next regressed


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Moss et al. industry sales on time to generate the needed means, beta coefficients, and standard errors for each of the 50 industries to calculate industry munificence and dynamism as per Dess and Beard (1984). Examining the levels of munificence and dynamism for each industry revealed four high-tech industries that manifest extreme levels of high or low munificence and dynamism relative to the mean of the 50 industries: pharmaceuticals (standard industry classification [SIC] code = 2834; −0.44 standard deviations), Internet firms (SIC = 7370; 0.34 standard deviations), prepackaged software (SIC = 7372; 1.15 standard deviations), and computer systems integration (SIC = 7373; −1.01 standard deviations). Our sampling procedure resulted in 205 publicly-traded companies in these four industries that had complete data for the entire 13-year sample period. To differentiate family from nonfamily businesses, we adopted Chua et al.’s (1999) definition that a family firm is an organization governed and/or managed with the intention to shape and pursue the vision of the business held by a dominant coalition controlled by members of the same family or a small number of families in a manner that is potentially sustainable across generations of the family or families. (p. 25)

Based on the logic of Zachary, McKenny, Short, and Payne (2011) and Payne, Brigham, Broberg, Moss, and Short (2011), we designated a business as a family business if the founder, his or her family members, or both were either on the board of directors or in a senior management position. We excluded other methods of determining family business, such as fractional ownership (e.g., Anderson & Reeb, 2003), since ownership does not necessitate direct business involvement. We searched online sources such as Yahoo! Finance, BusinessWeek, and firm websites to determine family member involvement. Of the 205 businesses in our overall sample, 92 were determined to be family businesses and 113 were nonfamily businesses. We found both similarities and differences between the two samples. Nonfamily firms had similar levels of strategic consistency (family: M = 0.64, SD = 0.25; nonfamily: M = 0.72, SD = 0.16) and exploitation (family: M = 0.64, SD = 0.14; nonfamily: M = 0.61, SD = 0.25). The two samples were different in size (family: M = 2.52, SD = 0.82; nonfamily: M = 5.69, SD = 2.62), slack (family: M = 0.97, SD = 0.31; nonfamily:

M = 3.03, SD = 3.34), and exploration (family: M = 0.66, SD = 0.23; nonfamily: M = 0.80, SD = 0.53).

Variables Dependent Variables. Prior empirical tests of the E/E-toperformance relationship have yielded mixed results, suggesting that studies should utilize multiple measures of firm performance (Raisch & Birkinshaw, 2008; Simsek, Heavey, Veiga, & Souder, 2009). We used two measures of performance, Return on assets (ROA) and Return on equity (ROE); both measures were averaged across 3-year periods and lagged by 1 year relative to the E/E strategic consistency construct. We also utilized Tobin’s q as an additional performance measure to further validate our findings. We discuss the computation of Tobin’s q when reporting the results of our post hoc analyses in the Results section. Independent Variables. We used computerized content analysis of organizational narratives to compute our measures of E/E and E/E strategic consistency. Content analysis is a qualitative method that classifies or categorizes communications while also allowing for contextual inferences (Krippendorff, 2004; Weber, 1990). It is frequently used to examine issues of significance to management scholars that are difficult to otherwise study (Carley, 1997; Morris, 1994; Woodrum, 1984). Content analysis is often applied to organizational narratives such as annual reports, letters to shareholders, organizational mission statements, and IPO (initial public offering) prospectuses (e.g., Duriau, Reger, & Pfarrer, 2007; Moss, Short, Payne, & Lumpkin, 2011; Payne, Moore, Bell, & Zachary, 2013). Using content analysis to study organizational narratives has certain advantages that interview and survey methods do not share. It is applicable to both quantitative and qualitative research and to longitudinal research designs, and it is nonintrusive, thus freeing it from researcher demand bias (Duriau et al., 2007). Perhaps more important, content analysis enables ex post examination of past organizational narratives that prevents recall bias, an especially useful characteristic when studying a firm’s E/E strategy longitudinally, as this study does. Longitudinal studies are notoriously difficult to conduct through survey-based instruments and interviews due to factors such as respondent mortality. Content analysis of organizational narratives is thus one


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method that overcomes the limitations of surveys and interviews in longitudinal research. We used LIWC (Linguistics Inquiry and Word Count), a computer-assisted text analysis program, to examine the management discussion and analysis (MD&A) sections of 10(k) reports (e.g., Pennebaker, Francis, & Booth, 2001). Since every publicly-traded company must submit an annual report for shareholders, the MD&A sections are an important narrative for publicly-traded companies and represent a generalizable way of determining the emphasis a firm places on E/E across industries. LIWC has been previously used in content analysis in varied contexts such as negotiations (Olekalns & Smith, 2009), newspaper and magazine articles (Humphreys, 2010), and annual reports (Churyk, Chih-Chen, & Clinton, 2008). Content analysis through computers has many advantages over content analysis via human coders, such as (a) complete reliability, (b) explicit coding schemes for straightforward comparison of results, (c) coding scheme stability, (d) ease in revealing multiple occurrences of key concepts, and (e) simple manipulation of text that allows for results such as frequency counts and keyword-in-context listings (Morris, 1994). Computerassisted text analysis also allows for the analysis and comparison of large data sets much more economically and reliably than human coders do (Bligh, Kohles, & Meindl, 2004). Exploration and exploitation. Following the logic of Uotila et al. (2009), our goal was to use a measure of an organization’s emphasis on E/E that would accomplish three objectives: (a) the measure should cover a wide variety of corporate decisions and actions; (b) the measure should evaluate a large number of firms over multiple years; and (c) the measure should hold across a number of industries. To meet these objectives, we examined the MD&A sections of a firm’s 10(k) annual reports, which contain information regarding a company’s decisions and activities for a particular year. Annual reports and proxy statements are the most frequently used data sources in management research using content analysis and are used to glean the emphasis that a firm or its leadership places on a number of issues relevant to management scholars (Duriau et al., 2007; Short, Payne, Brigham, Lumpkin, & Broberg, 2009). To avoid confusion, and since the text of 10(k) reports frequently appear in the more colorful annual reports, in this article we use the terms 10(k) reports and annual

reports interchangeably. The 10(k) reports are published annually and contain sections such as the MD&A, financial statements, and independent auditors’ reports. Generally, MD&A sections are reserved for managers to discuss present strategies and results, as well as expectations for the future of the company. MD&A sections from 10(k) annual reports are also a common organizational narrative used to study top management disclosures regarding items such as the natural environment or business risk facing a firm (e.g., Brookhart, Beeler, & Culpepper, 2005). As one portion of a publicly-traded company’s annual report, MD&A sections detail top management’s explanations for their firm’s performance. We obtained the 10(k) annual reports from Morningstar Document Research. To measure E/E, we first used the LIWC software to generate word counts of the E/E language used in each firm-year for the MD&A sections of 10(k) reports. LIWC software standardizes these values and provides output in the form of word counts per 100 words in each narrative. Standardizing the word counts in this manner controls for the length of the particular narrative, since longer narratives have a greater likelihood of containing more E/E language. To ensure construct validity, we used March’s (1991) words as the starting point for the generation of custom dictionaries for E/E, following Uotila et al. (2009). We then included permutations of each word for each construct. For example, exploration included the word search, as well as searches, searched, and searching. A full list of the words used in the deductive content analysis is in Table 1. The word lists are mutually exclusive in that words in one dictionary are not repeated in the other dictionary, thus preventing confounding effects (e.g., Neuendorf, 2002). Uotila et al. (2009) also validated their computer-assisted text analysis classification scheme based on March’s (1991) conceptualization. Uotila et al. (2009) validated their word lists by comparing results from the computerized coding of E/E in news articles to those of human coders, providing further support for this coding scheme in examining MD&A sections. We therefore use a previously validated measure used in textual analysis of organizational E/E. Finally, to complement the deductive, theory-based approach to content analysis and to improve construct validity, we also developed an additional, custom list of inductive words that represent E/E strategies; the inductive approach used to develop this list follows the


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Moss et al. Table 1. List of Deductive and Inductive Words Used in Content Analysis to Operationalize Exploration and Exploitation. Variable Deductive Exploration (104 words)

Exploitation (101 words)

Inductive Exploration (39 words)

Exploitation (85 words)

Words used in content analysis Discover, Discoverability, Discoverable, Discoverably, Discovered, Discoverer, Discoverers, Discoveries, Discovering, Discoverist, Discoverists, Discoverment, Discoverments, Discovers, Discovery, Experiment, Experimental, Experimentalism, Experimentalist, Experimentalists, Experimentalize, Experimentally, Experimentarian, Experimentarians, Experimentation, Experimentations, Experimentative, Experimentator, Experimented, Experimenter, Experimenters, Experimenting, Experimentist, Experimentists, Experimentor, Experimentors, Experiments, Explorability, Explorable, Explorable, Explorate, Explorates, Exploration, Explorationist, Explorationists, Explorations, Explorative, Exploratively, Explorator, Explorators, Exploratory, Explore, Explored, Explorement, Explorer, Explorers, explores, Exploring, Exploringly, Flexibility, Flexible, Flexibleness, Flexibly, Innovate, Innovated, Innovates, Innovating, Innovation, Innovational, Innovationist, Innovationists, Innovations, Innovative, Innovatively, Innovativeness, Innovator, Innovators, Innovatory, Play, Played, Player, Players, Playful, Playing, Playingly, Playlike, Plays, Research, Risk, Risked, Risker, Riskers, Riskful, Riskier, Riskiest, Riskily, Riskiness, Risks, Risky, Search, Searchable, Searchableness, Searched, Searcher, Searchers, Searches, Searching, Searchingly, Variation, Variational, Variationally, Variations, Variative, Variatively Choice, Choicer, Choices, Choicest, Efficience, Efficiencies, Efficiency, Efficient, Efficiently, Executable, Executant, Executant, Executants, Execute, Executed, Executer, Executers, Executes, Executing, Execution, Execution, Executional, Executioner, Executioners, Executions, Executions, Executively, Executiveness, Executor, Executorial, Executors, Executorship, Executory, Exploit, Exploitability, Exploitable, Exploitation, Exploitational, Exploitationally, Exploitations, Exploitative, Exploitatively, Exploitatory, Exploited, Exploiter, Exploiters, Exploiting, Exploitive, Exploitively, Exploits, Exploiture, Implement, Implementable, Implemental, Implementation, Implemented, Implementer, Implementers, Implementing, Implementor, Implementors, Implements, Production, Productional, Productions, Productivity, Refine, Refined, Refinedly, Refinedness, Refinement, Refiner, Refineries, Refiners, Refinery, Refines, Refining, Select, Selectability, Selectable, Selected, Selectedly, Selecting, Selection, Selectional, Selectionalism, Selectionist, Selectionists, Selections, Selective, Selectively, Selectiveness, Selectivities, Selectivity, Selectly, Selectness, Selector, Selector, Selectors, Selectors, Selects Adapt, Adapting, Adaptive, Adaptors, Create, Created, Creates, Creating, Creation, Creative, Creator, Develop, Developed, Developer, Developers, Developing, Development, Developmental, Develops, Inventions, Laboratories, Laboratory, Labs, Patent, Patented, Patents, Pioneer, Pioneered, Prospect, Prospecting, Prospective, Prospectively, Prospects, Research, Researcher, Researchers, Researching, Scientist, Scientists Accountant, Accountants, Administering, Administration, Administrative, Advertise, Advertised, Advertisement, Advertisements, Advertiser, Advertisers, Advertising, Assemble, Assembled, Assembler, Assemblers, Assemblies, Assembly, Audited, Auditing, Auditors, Audits, Automate, Automated, Automatic, Automatically, Automating, Automation, Commercialization, Commercialize, Commercialized, Commercializing, Commercials, Commoditized, Commoditizing, Commodity, Conventional, Deploy, Deployable, Deployed, Deploying, Deployment, Deployments, Distributor, Distributors, Increment, Incremental, Incrementally, Increments, Launch, Launched, Launches, Maintain, Maintained, Maintaining, Maintains, Manufacture, Manufactured, Manufacturer, Manufacturers, Manufacturing, Marketed, Marketer, Marketers, Marketing, Optimization, Optimize, Optimizer, Optimizing, Optimum, Procured, Procurement, Promotion, Promotional, Promotions, Replicated, Replication, Replicators, Routine, Routinely, Salesforce, Salespeople, Salespersons, Standardized, Throughput


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procedures of prior studies and demonstrated by Short, Broberg, Cogliser, and Brigham (2010). Following this procedure, one author initially generated a list of over 14,000 unique words that were repeated at least three times in any MD&A section. This author then scanned this list and selected 170 words that were consistent with March’s (1991) conceptualizations of E/E and supported the construct validity of the list. The next step advocated by Short et al. (2010) was to assess the degree of interrater reliability. We utilized three expert judges who, provided with March’s (1991) definitions of E/E, then scanned this reduced list of 170 words and provided their feedback on the validity of the inductive list. After receiving feedback from the judges, the custom dictionaries retained those words that were agreed on by at least two of the three judges, resulting in a final inductive word list of 169 words with an interrater reliability of 99%. This level of interrater reliability is quite high given the guidelines for acceptable reliabilities of 75% to 80% (Ellis, 1994). Thus, the inductive procedure resulted in an additional list of words for E/E with high degrees of construct validity. As with the deductive word lists, we took care to ensure that any words on the list of inductive words were unique to each list and that words were not duplicated on the deductive word lists to prevent confounding. We thus removed 45 more words, for a final total of 124 inductively derived words (Table 1). Examples of words selected by the judges that were consistent with March’s (1991) description of exploitation included refinement, production, implementation, efficiency, automation, distributor, and manufacturer. Automation, for example, is used to reduce variability in output of manufacturing activities, which is analogous with exploitation rather than experimentation associated with exploration. Exploration, on the other hand, was described using words like flexibility, experimentation, variation, discovery, and innovation, creation, pioneer, and research. Pioneering activities, for example, connote blazing new trails into uncharted territory—an exercise in exploring rather than exploiting. Given the procedure we followed to inductively derive additional word lists for E/E coupled with the interrater reliability of the three judges, we are confident that the inductive words for exploitation refer to only exploiting activities and would not be mistakenly referring to exploration activities, and vice versa. Finally, we summed the LIWC output scores for the deductively- and inductively

derived words to create final scores for E/E. By including both deductively- and inductively derived words in the content analysis, we hope to leave no doubt as to the construct validity of the two independent E/E measures. Strategic consistency. The independence of E/E—the values are autonomous and can vary over time—is an important condition discussed in the extant literature (Gupta et al., 2006). Therefore, to measure strategic consistency, we measured the Euclidean distance between a firm’s strategy at three sequential points in time (four 3-year blocks of time) using both the two component values derived from the word count data. The Euclidean distance is the most frequently used measure to gauge differences in organizational characteristics and is appropriate when the dimensions in question are orthogonal and not highly correlated (Kim & Finkelstein, 2009). The E/E word counts in this sample of family businesses have a correlation approaching significance, but not highly so (α = .19, p < .10), providing support for using the Euclidean distance. To account for changes in a firm’s strategy over time and the independence of the two E/E dimensions, we used Lamberg et al.’s (2009) measure of strategic consistency (SC). Formally, SC is measured as follows: SC =

1 , 1 + | a | ∗d

where a is the angle between trajectory of Yearn − 1 to Yearn, and Yearn to Yearn + 1, measured in radians (0 < a < π). Additionally, d is the Euclidean distance travelled in the exploration-exploitation space from Yearn to Yearn + 1. This measures consistency in two ways as noted by Lamberg et al. (2009): (a) If a strategic trajectory is the same from Yearn − 1 to Yearn + 1, then a = 0 and SC = 1, or (a) if d = 0, suggesting that the exact same levels of E/E have occurred from Yearn to Yearn + 1, then SC =1 as well. A value of SC = 1 therefore represents high consistency whereas a value approaching 0 represents low consistency. Size, munificence, and dynamism moderators. Firm size was determined as the log of the number of employees, averaged over each 3-year time period. Following Dess and Beard (1984) and others (Misangyi et al., 2006; Nadkarni and Narayanan, 2007), we calculated


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Moss et al. Table 2. Excerpts of Exploration and Exploitation Language from Management Discussion and Analysis Sections of Annual Reports. Dimension Exploration (deductive) Exploration (inductive) Exploitation (deductive) Exploitation (inductive)

Excerpt “We are a specialty healthcare company focused on the discovery, development, and commercialization of proprietary pharmaceuticals.” Opko Health, 2007 “IBM is working with clients and governments around the world to explore these opportunities and implement new ideas.” IBM, 2008 “During 2003, the Company entered into an operating lease for certain computer equipment used in the Company’s development lab.” Ants Software, 2005 “The objective of this technology is to create a battery with a virtually unlimited shelf life prior to activation.” Trimol Group, 2001 “We are following an aggressive growth strategy by rapidly exploiting our technology to create 3D chat, entertainment, information and e-commerce sites for our company and for third parties.” Worlds.com, 1999 “These changes . . . are essential to the consistent execution of our business model and sustainability of our international growth.” Reliv International, 2004 “These expenses will continue to be pressured by the cost of the hard launch in Japan scheduled for late 2001 . . .” Usana Health Sciences, 2000 “The Company is subject to a number of risks including its ability to scale-up its manufacturing capabilities and secure an adequate supply of raw materials, its ability to successfully market, distribute and sell its products . . .” Vivus, 1997

environmental munificence as the growth in total sales within an industry calculated as the regression slope coefficient divided by mean sales. Likewise, the measure for environmental dynamism was calculated as the instability of total sales defined as the standard error of the regression slope coefficient for an industry divided by the industry mean. Following Aiken and West (1991), we mean centered the independent variables before calculating the interaction terms.

variables. Industry (i.e., munificence and dynamism) was controlled using Dess and Beard’s (1984) measures explained earlier in this section. Finally, we controlled for E/E as individual constructs, averaged over the 3-year blocks, to control for their effect on the hypothesized relationships that utilized our strategic consistency measure.

Control Variables. Following previous research using multiple industries, we included the direct effects of the aforementioned firm size and environment (i.e., munificence and dynamism) variables, as well as slack, exploitation, and exploration (Cao et al., 2009; He & Wong, 2004; Iyer & Miller, 2008; Jansen, Tempelaar, van den Bosch, & Volberda, 2009; Uotila et al., 2009). As mentioned previously, firm size is measured as the log of total number of employees. Firm age was considered but was eliminated because of the high correlation with size; age was initially determined from founding dates given on company websites. Slack is measured as a firm’s current ratio and is used to control for the possibility that greater liquid resources available to a firm may affect its E/E activities. Size and slack were averaged over the 3-year blocks of data, similar to the independent

When data are collected on the same set of subjects over a period of time, observations on the same subject and observations within the same time period are likely to create correlated and non-constant variance in error terms (Johnson & Wichern, 2002); this violates the ordinary least squares assumptions of independent and constant variances of error terms (Beck & Katz, 1995). As a result, we employed a linear mixed model using SAS PROC MIXED. Linear mixed models expand the general linear model to control for data that exhibit correlated and nonconstant variability in error terms (Krull & MacKinnon, 2001). The mixed linear model we used in our analyses is a version of a multilevel linear model (Goldstein, 1987) or hierarchical linear model (Bryk & Raudenbush, 1992). We incorporated two effects to control for the ordinary least squares violations described

Analysis and Results


62


Table 3. Descriptive Statistics and Correlations of Key Variables. M 1 Return on assets −0.35 2 Return on equity −0.27 3 Exploration and 0.64 exploitation strategic consistency 4 Size 2.52 5 Slack 0.97 6 Exploit 0.64 7 Explore 0.66 8 Munificence 0.09 9 Dynamism 0.03 10 Founder active 0.96

SD

1

2

1.46 4.99 0.25

.62*** .39***

.16**

0.82 0.31 0.14 0.23 0.05 0.01 0.19

.46*** .09 .24* .05 .02 .03 .25***

.28*** .06† −.21*** −.27*** .02 .04 −.07†

3

4

5

6

7

8

9

.35** −.20 .43*** .19 .07 .16** .04

−.15 −.05 .03 .05 .51*** .02† .04 −.20* −.12 −.17 .11** −.18*** −.20*** −.46*** .12** −.07 .06† −.09* .09* .14** .25***

Note. N = 92. † p < .10. *p < .05. **p < .01. ***p < .001.

above. First, we incorporated a random linear effect to control for random variation in our dependent variables associated with each firm in a 3-year period, which is represented in our statistical models as the “linear” variable. We found that 6.8% of the variance in ROA (τ00 = 0.018, p < .05) and 6.1% of the variance in ROE (τ00 = 0.013, p < .05) was explained by these 3-year time periods. Second, we employed an autoregressive design capable of controlling for the nonindependence of error terms across time, in which we found the AR1 (autoregressive 1) error covariance structure had the best fit with the data when compared to alternative error covariance structures (e.g., anti-dependence, ARMA [autoregressive-moving-average], compound symmetry, etc.) Finally, we also used a hierarchical approach of our mixed models to test our hypotheses. Hierarchical regression is appropriate when analyzing models with multiplicative terms or when analyzing variables that are highly correlated more generally (Cohen & Cohen, 1983). Following the process used in Wiklund and Shepherd (2005), we added the next higher order interaction term to each step of the hierarchical analysis, while also assessing incremental model fit represented by changes to the −2 log likelihood (LL) statistic. An interaction between independent variables exists if the interaction term is significant and increases model fit when compared to models with the independent variables alone (Cohen & Cohen, 1983).

Results Table 3 contains descriptive statistics and correlations for our sample of family firms, averaged over the entire

sample time frame. Generally, the correlations among the independent and control variables ranged from −.32 to .65. To ensure that there were no multicollinearity issues, we mean centered the main effects variables used in the interaction terms and applied multicollinearity diagnosis. Variance inflation factors of the independent and control variables were all less than 2.0, well below critical values (Hair, Black, Babin, & Anderson, 2010). Table 2 indicates that E/E strategic consistency is a unique construct from E/E. Additionally, larger family firms were more consistent, and had higher performance, than smaller firms. Firms with higher slack emphasized more exploration, which makes intuitive sense because greater liquidity shields firms from negative effects of riskier exploration activities. Tables 4 and 5 present the results of longitudinal mixed-model analysis using ROA and ROE as the dependent variables, respectively. Model 1 in each table represents the effect of the control variables on ROA (Table 4) and ROE (Table 5) performance measures in our sample of family firms. Model 2 in each table shows the effect of E/E strategic consistency on firm performance when added to the control variables. Table 4 indicates that E/E strategic consistency has a significant relationship with ROA (β = 2.032, p < .05) and significantly improved the model fit (Δ in −2LL = 7.1, Δ in degrees of freedom [df] = 1, p < .05). Table 5 indicates that E/E strategic consistency has a significant relationship with ROE (β = 1.555, p < .05) and significantly improved the model fit (Δ in −2LL = 5.0, Δ in df = 1, p < .05). Consequently, we find support for Hypothesis 1, such that family firms that are consistent in their


63

Moss et al. Table 4. Regressions of Exploration and Exploitation Strategic Consistency and Moderators on Return on Assets.

Linear Slack Founder active Exploit Explore Size Munificence Dynamism Exploration and exploitation strategic consistency Consistency × Size Consistency × Munificence Consistency × Dynamism −2 Log likelihood −2 Log likelihood change Akaike information criterion

Model 1

Model 2

Model 3

Model 4

−0.644** 4.485*** 0.970† 4.485*** −4.877** −58.228*** −2.693 −5.111*

−0.732*** 4.439*** 0.951† −5.181** −2.765* 1.117*** −6.390 −73.390** 2.032*

−0.807*** 4.339*** 1.020* −6.164*** −3.494** 2.106*** −7.466 −92.296*** 5.728**

−0.378** 1.766*** 0.278 −0.782 −1.856*** 0.061 −5.238 −30.680** −0.726

Model 5 −0.656** 4.517*** 0.882† −4.057** −2.024† 1.202*** −4.867 −147.750** −1.937

−1.700*

12.960*

155.600* 9865.1

9858.0 7.1* 9864.7

9843.2 14.8*

9843.7 14.3*

9839.2

9575.5

9844.8 13.2* 9840.8

†

p < .10. *p < .05. **p < .01. ***p < .001.

Table 5. Regressions of Exploration and Exploitation Strategic Consistency and Moderators on Return on Equity.

Linear Slack Founder active Exploit Explore Size Munificence Dynamism Exploration and exploitation strategic consistency Consistency × Size Consistency × Munificence Consistency × Dynamism −2 Log likelihood −2 Log likelihood change Akaike information criterion

Model 1

Model 2

Model 3

−1.038* −0.043 0.040 −5.427 6.153* −0.100 1.371 −1.994

−1.030* −0.044 0.042 −6.414 7.115* −0.066 1.250 −2.094 1.555*

1.367* −0.301 0.127 −0.256 −3.152** 1.406** −8.276* −27.306 6.773***

Model 4

Model 5

1.703* −0.594† −0.370 −0.549 −1.040* −0.027 −1.025 −15.038 0.541

4.826** −0.240 0.073 0.470 −2.455** −0.113 −6.126 −144.930† −2.701

2.514*** −2.527 9579.5

9574.5 5.0* 9578.5

9559.3 15.2** 9563.3

9563.3 11.2* 9554.3

145.040* 9560.0 14.5** 9564.0

†

p < .10. *p < .05. **p < .01. ***p < .001.

emphasis of their particular E/E strategies outperform firms that are less consistent.

Hypothesis 2 was likewise supported, as shown in Model 3 of Tables 4 and 5. Table 3 indicates that firm


64


size negatively moderates the relationship between E/E strategic consistency and ROA (β = −1.700, p < .05) and significantly improved the model fit (Δ in −2LL = 14.8, Δ in df = 2, p < .05). Table 5 indicates that firm size negatively moderates the relationship between E/E strategic consistency and ROE (β = −2.514, p < .001) and significantly improved the model fit (Δ in −2LL = 15.2, Δ in df = 2, p < .01). Consequently, we find support for Hypothesis 2, such that being consistent with their E/E strategy has a stronger, positive impact on firm performance for smaller firms. Hypothesis 3 was tested in Model 4 of Tables 4 and 5. Table 3 indicates that environmental munificence positively moderates the relationship between E/E strategic consistency and ROA (β = 12.960, p < .05) and significantly improved the model fit (Δ in −2LL = 14.3, Δ in df = 2, p < .05). However, Model 4 in Table 5 fails to indicate a significant moderating effect of environmental munificence on the E/E strategic consistency-to-ROE relationship. Consequently, we find partial support for Hypothesis 3, which suggested that consistency in E/E strategy is more important for firms in environments with higher munificence. We found support for Hypothesis 4, as shown in Model 5 of Tables 4 and 5. Table 4 indicates environmental dynamism positively moderates the relationship between E/E strategic consistency and ROA (β = 155.600, p < .05) and significantly improved the model fit (Δ in −2LL = 13.2, Δ in df = 2, p < .05). Table 5 indicates that environmental dynamism positively moderates the relationship between E/E strategic consistency and ROE (β = 145.040, p < .05) and significantly improved the model fit (Δ in −2LL = 14.5, Δ in df = 2, p < .01). Consequently, we find support for Hypothesis 4, such that consistency in E/E strategy is more important for firms in dynamic environments.

Post Hoc Analyses To further validate our theory and results, we modeled our hypothesized relationships using an alternative dependent variable to ROA and ROE. In these analyses, we utilized Tobin’s q, which represents a market-based measure. For Tobin’s q, we used Chung and Pruitt’s (1994) operationalization because the required data were available from Compustat and because of its high predictive accuracy (e.g., Cho & Pucik, 2005; Tanriverdi & Venkatraman, 2005). This equation is given as follows:

Tobin’s q =

MVE + PS + DEBT , TA

where MVE is the product of a firm’s share price and the number of common stock shares outstanding; PS is the liquidating value of the firm’s outstanding preferred stock; DEBT is the value of the firm’s short-term liabilities net of its short-term assets, plus the book value of the firm’s longterm debt; and TA is the book value of the total assets of the firm. Tobin’s q represents a long-term, future-oriented measure of performance (Hoskisson, Hitt, Wan, & Yiu, 1999; Keats & Hitt, 1988). For the family firms in our sample, 1-, 3-, 4-, or 6-year time spans did not produce significant variance between the time spans, nor did any of these time spans explain a significant amount of variance in Tobin’s q. Our data suggest that the LTO of family businesses is especially lengthy in terms of market-based performance and valuations, in which their time horizon is in excess of 6 years. These findings appear to be consistent with the theory we develop on strategic consistency that is derived, in part, from research on LTO in family business. Consequently, we utilized the average value of Tobin’s q across the 12-year time span (lagged 1 year from the independent variables). We report our findings using Tobin’s q as dependent variable in Table 6. The results of our analysis indicates a significant, positive relationship between E/E strategic consistency and Tobin’s q (β = 0.44, p < .001), which supports Hypothesis 1. We also find size (β = −0.59, p < .001), environmental munificence (β = 0.22, p < .05), and environmental dynamism (β = 0.44, p < .01) all moderate the relationship between E/E strategic consistency and Tobin’s q as hypothesized in Hypothesis 2, Hypothesis 3, and Hypothesis 4, respectively. In sum, these findings reported in Table 6 confirm our previous findings and provide further support for all of our hypotheses. Hypothesis 5 suggests that E/E strategic consistency will be more important for family businesses than for nonfamily businesses. Table 7 presents the results of the analysis using a binomial moderating variable that represents family versus nonfamily businesses. The results from Model 3 demonstrate a stronger relationship between E/E strategic consistency and ROA (β = 3.258, p < .05), as well as the relationship between E/E strategic consistency and ROE (β = 2.423, p < .05). Furthermore, including the interaction term increased model fit for ROA (Δ in −2LL = 11.1, Δ in df = 2, p < .05) and ROE (Δ in −2LL = 17.4, Δ in df = 2, p < .05). In sum, we find support for Hypothesis 5.


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Moss et al. Table 6. Regressions of Exploration and Exploitation Strategic Consistency and Moderators on Tobin’s q. Variable

Model 1

Model 2

Model 3

Slack Founder active Exploit Explore Size Munificence Dynamism Exploration and exploitation strategic consistency Consistency × Size Consistency × Munificence Consistency × Dynamism R2 ΔR2 F

−0.17 0.01 0.20* −0.15 0.33*** −0.16 −0.14

−0.07 0.05 0.05 −0.26* 0.21* −0.18* −0.17* 0.44***

−0.16† 0.03 −0.08 −0.33*** 0.20** −0.17* −0.19* 0.08

0.35 0.10*** 7.97***

−0.59*** 0.22* 0.44** 0.61 0.16*** 15.52***

25.00 5.67***

†

p < .10. *p < .05. **p < .01. ***p < .001.

Table 7. Moderated Mixed-Model Analysis of Multiple Measures of Firm Performance Between Family (N = 92) and Nonfamily (N = 113) Businesses on E/E Strategic Consistency. DV = ROA Linear Slack Founder Active Exploit Explore Size Munificence Dynamism E/E strategic consistency Family firm Consistency × Family firm −2LL −2LL Change AIC

Model 1

Model 2

0.046 4.876*** 0.023 1.403* −2.023* 0.988** −5.501 18.623

−0.029 4.044*** 0.040 1.234* −2.082* 1.005*** −6.390* 7.133 1.552*

9591.2

9583.6 7.6* 9653.8

9611.2

DV = ROE Model 3

Model 1

Model 2

Model 3

−0.531* 4.709*** 0.422 −2.807 −1.173 1.293*** −3.440 −22.792 2.205* 1.232* 3.258* 9572.5 11.1* 9552.7

0.643*** 4.484*** 0.940* −4.877*** −2.692** 1.206*** −5.111 −48.227**

−0.656** 4.517*** 0.882† −4.057** −2.024† 1.202*** −4.867 −120.260** −1.937

9865.1

9858.7 6.4* 9876.8

4.304** 1.441 −5.886** −3.334** 1.117** −6.449* −138.560** 2.743** 1.112 2.423* 9882.5 17.4* 9900.5

9883.2

Note. E/E = exploration and exploitation; DV = dependent variable; ROA = return on assets; ROE = return on equity; LL = log likelihood; AIC = Akaike information criterion. † p < .10. *p < .05. **p < .01. ***p < .001.

Discussion and Areas for Future Research Our results demonstrate that strategic consistency— continuity with past strategies stemming from managerial

intentionality—is an especially important factor for family business performance, but more so for smaller firms or those operating in highly munificent and/or dynamic environments. Furthermore, we found that the relationship between E/E strategic consistency and performance is


66


stronger for family firms than for nonfamily firms. In general, these findings support both within- and betweengroup differences of family firms (Miller & Le Breton-Miller, 2005). In other words, family firms do differ significantly from nonfamily firms in terms of the strategic consistency-to-performance relationship, but there is also heterogeneity among family firms. Building on our initial arguments and the results of our study, we offer the following discussion points and associated areas for future research. The relationship between strategic consistency and firm performance is largely argued to exist due to the congruence between the structures, processes, and cultures of family firms and more consistent approaches to E/E. Although our findings are useful in terms of understanding how family firms approach E/E strategy and subsequent performance, the assumed linkage and congruence between a family firm’s LTO and strategic consistency should be more extensively considered. Indeed, the nature of this relationship between LTO and consistency remains somewhat obscure, as does the linkage between LTO and performance. Lumpkin and Brigham (2011) argue that LTO is a temporal orientation of the dominant coalition—a higher order heuristic—and is composed of the three dimensions of futurity, continuity, and perseverance. It seems logical that these three dimensions serve as independent drivers of decision making and organizational behaviors, which eventually result in strategic actions and then outcomes. Although this progressive linkage from LTO to outcomes seems reasonable, there are limited studies that empirically link LTO to performance, directly or through mediations such as strategic consistency might represent. Furthermore, previous research has not conclusively identified if the dimensions of LTO operate independently or interdependently. Therefore, although our study argues for a linkage between LTO and strategic consistency and empirically demonstrates a tie from strategic consistency to outcomes, the exact role of LTO (e.g., as an antecedent of strategic consistency) in guiding a firm’s approach to strategic E/E activity is unclear and should be more thoroughly investigated. The application of LTO to strategic activity likely involves specific mechanisms that promote E/E consistency. Miller and Le Breton-Miller (2006) discuss how both agency-based and stewardship-based forces can provide advantages that facilitate E/E; these factors can also be utilized to discuss the mechanisms that link LTO

to E/E consistency. Agency advantages are derived from power, alignment of incentives, and knowledge about the business, which allows for close monitoring and the ability of the family to control strategic decisions. Stewardship advantages, on the other hand, come through strong commitments of the family, careful screening of employees, and through the development of relationships with outside stakeholders; these mechanisms also provide for more control, involvement, and, therefore, consistency of strategic activity that aligns with the family’s interests. Given that our study asserts a positive relationship between strategic consistency and firm performance in family firms, we suggest future research should more specifically consider how the agency- and stewardship-based mechanisms affect the nature of strategic consistency (or change) in terms of E/E. Furthermore, one could consider how LTO, and its dimensions, may be measured and utilized to better understand these relationships. As supported in our study, temporal considerations are a fundamental part of strategic decision making (Perez-Nordtvedt, Payne, Short, & Kedia, 2008; Venkatraman, 1989). Of late, scholars have expressed concern about temporal myopia (Levinthal & March, 1993; Miller, 2002), which is when a firm’s decision makers are overly focused on the short term such that technologies are underdeveloped, research and development funds are cut, and essential training programs are eliminated (Walsh & Seward, 1990; Zaheer, Albert, & Zaheer, 1999). Generally, temporally myopic organizations overvalue more immediate results to the detriment of the long-term outcomes (Laverty, 1996). Although myopia seems to be a common concern, and a problematic one according to our findings, an interesting question remains as to whether some firms, particularly family firms, may get hyperopic—overly concerned with longer time horizons. Fundamentally, it appears that a balanced, multitemporal perspective, as advocated by Le Breton-Miller and Miller (2011), may serve as a source of competitive advantage for family firms and should be more fully conceptually developed and empirically tested in the future. Although our findings further legitimate and enhance our understanding about the temporal concerns of family businesses, limitations of our study do exist. First, our measure for the E/E strategic consistency is based on the language utilized by managers in annual reports. So, although cognitive strategy formulation arguments


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Moss et al. support the use of such a measure, it is possible that the language used in the annual reports does not actually reflect the actions and behaviors of the organizations. Therefore, we cannot incontrovertibly link language used in the 10(k) reports with the organization’s actual orientation or its subsequent behavior in E/E. Although organizational-level orientations have been previously tied to narrative discourse (e.g., Payne, et al., 2011), it is possible that top managers use such organizational narratives to control impressions; the language, therefore, may be inconsistent with a firm’s beliefs, cultures, actions, or even other organizational narratives (e.g., Clatworthy & Jones, 2006; Fiol, 1995). Another limitation is based on the sample. In addition to a relatively small sample of family firms, we examined only hightech industries; this limits generalizability to other industries. In particular, we caution researchers about the word lists developed and utilized in this study. It may be that the language used by the managers of our sampled firms may not apply to other industries. These limitations, however, point to additional future research directions for family business scholars and, more generally, the literature on temporality in organizations. First, although this study supports the idea that an LTO—as a dominant logic—is a primary driver influencing the E/E strategic orientation in family businesses, we cannot know exactly how much influence is being exerted and if there are other, and possibly competing, logics present. As described by Bettis and Prahalad (1995), a dominant logic is pervasive in organizations and predisposes organizations to certain kinds of strategic problems because the dominant logic acts as a filter to limit the information that decision makers consider. This pervasiveness and predisposition, in turn, affect various aspects of the organization, including values and expectations, competitive strategy, reinforced behavior, and performance. Indeed, a dominant logic acts to shape the way a firm formulates strategies, molds expectations, and rewards behavior over time (Obloj, Obloj, & Pratt, 2010). We need to know how dominant logics are developed, change, and persist, especially when there are multiple dominant logics competing. Are there levels of dominant logics? Do they exist at different levels of the organizations or within different factions, such as with family members and nonfamily members? How does one dominant logic “win out” over a competing one? Answering such questions would help shed light on the how the family influences the firm strategic orientations and how those orientations are translated into

specific strategic actions. It would also shed light on the extent to which a LTO influences strategic decision making in nonfamily businesses. Another area in need of future research is related to the moderating effect of size on the consistency-toperformance relationship. Previous research suggests that some firms might achieve a state of E/E balance through the formation of alliances with companies that possess complementary strategies (Gupta et al., 2006). Such alternative ways of achieving balance (e.g., Rothaermel & Deeds, 2004), rather than shifting from one strategy to another, may allow a company—particularly smaller organizations—to be both ambidextrous and strategically consistent. Hence, there seems to be ample opportunity to examine how alliances play into the strategic approach used by both family and nonfamily businesses. Arguably, through alliances, family firms may be able to specialize in one area of exploration or exploitation, remain flexible, and still maintain family control. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Author Biographies Todd W. Moss (PhD, Texas Tech University) is currently an assistant professor of Entrepreneurship and Sustainability at the Whitman School of Management, Syracuse University. He enjoys research at the intersections of entrepreneurship, social responsibility, and innovation. G. Tyge Payne is an associate professor of Strategic Management and Jerry S. Rawls Professor of Management at the Area of Management, Rawls College of Business, Texas Tech University, Lubbock, Texas, USA. Curt B. Moore is an assistant professor of Entrepreneurship, Strategy, and International Business at the Area of Management, Rawls College of Business, Texas Tech University, Lubbock, Texas, USA.


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