Entrepreneurial Orientation and Strategic Leadership: A Behavioral Approach 3030872998, 9783030872991

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Entrepreneurial Orientation and Strategic Leadership A Behavioral Approach

Brian S. Anderson

Entrepreneurial Orientation and Strategic Leadership

Brian S. Anderson

Entrepreneurial Orientation and Strategic Leadership A Behavioral Approach

Brian S. Anderson Henry W. Bloch School of Management University of Missouri–Kansas City Kansas City, MO, USA Ghent University Ghent, Belgium

ISBN 978-3-030-87299-1    ISBN 978-3-030-87300-4 (eBook) https://doi.org/10.1007/978-3-030-87300-4 © The Author(s) 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover pattern © Melisa Hasan This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 The EO Challenge 1 2 A Behavioral View of EO and Strategic Leadership17 3 Sustaining EO and the Role of the CEO29 4 Setting Expectations: Governance and Board Considerations39 5 Extending EO and the Role of the Top Management Team51 6 Leadership Considerations for EO in a Multi-­Business Firm61 7 Contextual and Industry Considerations in Strategic Leadership-EO Research71 8 Future Research Opportunities in the Strategic Leadership-EO Space83 Index99

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CHAPTER 1

The EO Challenge

Abstract  This chapter introduces the entrepreneurial orientation (EO) construct and touches briefly on critical debates in the literature regarding EO’s epistemological base and the ontological assumptions of various EO conceptualizations. I then turn to key assumptions of various EO conceptualizations in the context of leadership research, centering on temporal stability. An interesting paradox, when studying EO antecedents the critical question for researchers is to identify predictors that effect a construct that, by definition, does not vary much over time. I then discuss critical measurement issues in the EO literature, specifically focusing on antecedent-­to-EO models. This leads to a discussion of causal inference in this context, and challenges to establishing a causal claim in strategic leadership-­EO research. Keywords  Antecedents to Entrepreneurial Orientation • Temporal stability • Causal inference • Measurement theory

1.1   Introduction There is broad agreement that, all things being equal, entrepreneurial firms outperform conservative firms (Rauch et al., 2009). While we may debate how best to define, delineate, and measure entrepreneurial firms, the data is quite clear—broadly construed performance outcomes, such as performance composites, and specific outcomes such as sales growth rate, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. S. Anderson, Entrepreneurial Orientation and Strategic Leadership, https://doi.org/10.1007/978-3-030-87300-4_1

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positively correlate with a firm’s strategic penchant to engage in entrepreneurial activities (Rosenbusch et al., 2013). I stress correlate rather than cause, because the relevant literature just recently turned toward a focus on causal modeling and predictive theory construction (Anderson et al., 2020). As such, while we have thousands of entrepreneurial orientation studies, very few of them purport—and support—a causal claim. This book takes the preceding two points as given—behaving entrepreneurially matters, but we lack causal nomological understanding—and these points frame the research motivation for the book. If firms gain from entrepreneurial behavior, a reasonable question then is how firms can be more entrepreneurial. What are the factors, forces, and mechanisms through which firms may increase their entrepreneurial output, with the implicit assumption being that as the firm increases such output, it then increases its organizational performance? A fruitful set of research questions then center on the antecedents to firm-level entrepreneurial behavior. But to answer these questions, we must have a rigorous understanding of antecedents as causal predictors. Because correlational models are directionally agnostic, they are of limited use in developing—and building theory around—predictive models with entrepreneurial activities as the assumed outcome. To begin this conversation, we must first parameterize the notion of firm-level entrepreneurial activities. While multiple definitions and conceptualizations exist to capture a firm-level entrepreneurial posture (Covin & Lumpkin, 2011), this book focuses on the dominant perspective in the strategic entrepreneurship literature—entrepreneurial orientation (EO). First conceived over four decades ago, EO scholarship today continues to drive thought leadership and theory construction on the causes, consequences, and boundary conditions of firm-level entrepreneurial activity (Covin & Wales, 2019). For the purposes of this book, I concur with the broad empirical support for EO as a correlate to firm performance (Rauch et al., 2009). Therefore, EO is well suited as the vehicle through which we may explore potential predictors of EO under an assumed mediational chain of predictor → EO → performance. Further, recent scholarship and conceptual work strengthening the ontological and measurement assumptions underlying common EO conceptualizations enhanced the usefulness of EO in causal model specifications (Anderson et al., 2020). Collectively, then, EO is a suitable construct with which to explore antecedents to firm-­ level entrepreneurial activity through a causal lens.

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Because EO antecedent research is still in a nascent stage, this is an opportune moment to develop an integrated perspective on a set of potential predictors that offer fruitful and impactful research opportunities. I argue that a constellation of variables under the rubric of strategic leadership represent such an opportunity. Adopting a behavioral perspective, I argue that a behavioral view of strategic leadership—focusing on what a leader does and the types of decisions leaders make—aligns conceptually with the dominant EO perspective that what defines an entrepreneurial firm is the set of behaviors that place the firm in new product/market domains (Covin & Wales, 2019). To put it simply, there is opportunity to build predictive theory around EO by linking the decisions senior leaders make with the entrepreneurial strategic behaviors resulting from these decisions. In developing this behavioral perspective on the intersection of EO and strategic leadership, it is necessary to address certain epistemological assumptions in the EO literature that directly influence how scholars may pursue predictive theory construction and testing. I turn to these issues in the following sections.

1.2   Which EO Conceptualization Is “Best” There are three dominant EO conceptualizations in the literature, and among these, one preeminent in empirical EO research (Covin & Wales, 2019). The preeminent conceptualization was also the original and is the cumulative work of Miller (1983) and Covin and Slevin (1989, 1991). Under the Miller/Covin and Slevin conceptualization, EO “… is a strategic construct that reflects the extent to which firms are innovative, proactive, and risk taking in their behavior and management philosophies” (Anderson et al., 2009, p. 218). From this perspective, EO is the intersection of three underlying dimensions, and while each dimension has a distinct conceptual space, what gives EO meaning is the shared variance between these dimensions. As such, under the Miller/Covin and Slevin perspective we conceive of EO as a unidimensional or unitary construct, wherein the researcher sets aside the residual variance in the underlying dimensions and focuses on the common variance between these dimensions. Lumpkin and Dess (1996) offer the second EO conceptualization, which approaches EO from a multidimensional perspective. Under the Lumpkin and Dess (1996, p.  136) view, EO “refers to the processes,

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practices, and decision-making activities that lead to new entry,” and to the three original EO dimensions the authors add autonomy and competitive aggressiveness. In contrast to the Miller/Covin and Slevin conceptualization, the Lumpkin and Dess conceptualization argues that the manifestation of EO within a firm varies as a function of how that firm combines the underlying dimensions to achieve new entry. In a sense, under the Lumpkin and Dess conceptualization there is not an EO construct per se, because the five underlying dimensions, while defining EO’s conceptual space, do not come together in a hierarchical or combinatory fashion to a higher-order construct. As Covin and Wales (2019) noted, EO from the Lumpkin and Dess perspective is a firm’s entrepreneurship profile, with this profile being idiosyncratic to the firm itself. Anderson et  al. (2015) offer the third EO conceptualization, which, following the authors, is more a reconceptualization of the Miller/Covin and Slevin conceptualization than a novel approach to EO as in Lumpkin and Dess (1996). Adopting a measurement theory frame, Anderson et al. (2015) argue that the unidimensional approach offered by Miller/Covin and Slevin assumes that the shared variance of the three underlying dimensions implicitly assumes that antecedents to EO must then relate in the same way and with the same strength to each underlying dimension. This assumption becomes problematic when we consider that under the Miller/ Covin and Slevin conceptualization EO has distinctly behavioral elements—what the firm does—and attitudinal elements—senior executive beliefs and proclivities—and it is difficult to conceive of a given EO antecedent that predicts a behavior and an attitude in the same way and strength. Anderson et  al.’s (2015) solution was splitting EO into two dimensions, one capturing entrepreneurial behaviors, and one capturing managerial attitude toward risk. The total variance of the two dimensions then collectively defines the totality of EO’s conceptual space. Importantly, the underlying dimensions, while assumed to positively covary, also may have different antecedent relationships from this perspective. There are significant differences across each conceptualization; however, there is broad agreement on three key epistemological assumptions. First, EO is an attribute, or characteristic of a firm. As such, every business, and more specifically every business unit, has an EO, although the manifestations of EO vary by conceptualization. Second, EO is inherently behavioral in nature. For a firm to “be entrepreneurial” it must be engaging in certain activities that place the firm in new product/market domains, which implies that EO exists as a business-level (versus corporate-level)

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strategy. Third, there is an inherent sustainment in these entrepreneurial behaviors such that they occur with a degree of regularity. As Covin and Wales (2019) noted, as an organizational attribute, we expect that EO constitutes an organizing gestalt around sustained entrepreneurial behaviors, which delineates EO from other forms of corporate entrepreneurial behavior (e.g., corporate venturing) which may be discrete and sporadic. In practice, the commonalities across EO conceptualizations mean that while substantive differences exist and these differences have theoretical implications, cumulative knowledge generation around EO advances at the field level (Covin & Wales, 2019). The researcher’s choice of one EO conceptualization does not inherently limit the usefulness of a particular study to EO research with a different conceptualization. Rather, and to the benefit of EO research, scholars can contribute to our theoretical understanding of EO as an organizational attribute across each conceptualization; one conceptualization is not better or more useful than the others. As Covin and Wales (2012) noted, however, within a given study, the most important consideration for a researcher as it relates to EO conceptualizations is internal consistency—applying theory and prior literature in such a way as to align with the ontological and epistemological assumptions of the chosen conceptualization. Effectively, Covin and Wales’ (2012) counsel is to choose one EO conceptualization and ‘stick with it’ for any given study. As such, for this study I adopt the Anderson et al. (2015) EO conceptualization. As I will develop, the Anderson et al. (2015) conceptualization has certain advantages when looking specifically at a behavioral frame for strategic leadership theory and associated constructs. Specifically, I focus on the entrepreneurial behaviors dimension of the Anderson et al. (2015) EO conceptualization, while holding managerial attitude toward risk as a constant. There may be theoretically and empirically interesting strategic leadership constructs that predict changes in managerial attitude risk; however, these are not the focus of this book. My argument is that there are fruitful areas of research at the intersection of EO and strategic leadership theory when we consider how various strategic leadership constructs change how the firm engages in its various entrepreneurial behaviors.

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1.3   The Temporal Stability of EO There is an inherent paradox in EO research—to show a causal relationship, we must observe a change in both the predictor and in EO, but EO itself does not change much (Anderson et al., 2020). As previously discussed, scholars conceive of EO as a stable organizational trait, with the expectation that the firm engages in a sustained pattern of behaviors that may be either conservative (low EO) or entrepreneurial (high EO) but are nonetheless consistent (Covin & Lumpkin, 2011). The result is the assumption of chronic EO condition for each firm; while EO may vary within a narrow range, fundamentally the firm sets its level of EO agnostic to changing internal or external conditions. Notably, this is mostly a theoretical argument, because showing that firms have a chronic EO state requires substantial longitudinal observation, which is rare—and difficult to design—in empirical EO research. There are three key implications of EO’s temporal stability as it relates to considering various strategic leadership antecedents to EO.  The first implication is that we are most likely to see a change in a firm’s EO with immediacy following a change in the firm’s senior leadership (e.g., CEO, other top management team, significant Board turnover). To be clear, I am not arguing that we will see such a change. I am arguing that the likelihood of an EO change is highest after a leadership transition. As I will discuss further, the underlying reason is organizational inertia—after being in place for a period, leadership teams tend to settle into a predictable pattern of strategic decision-making (Miller & Friesen, 1982). The second implication of EO’s temporal stability is that we should a priori expect any effect size we see to be small and highly contextual. When we consider the narrow range in which a firm is likely to vary its EO, by definition, we are most likely to see antecedent variables that causally affect EO in small, but potentially meaningful, ways. It could also be the case that these effect sizes, being already close to zero, could change in strength and direction as a function of contextual factors and boundary conditions (Gelman & Weakliem, 2009). This complicates the ability to draw strong conclusions about just how a given predictor changes EO and limits the inference we may draw from any one strategic leadership­EO study. The third implication of EO’s temporal stability is that when we do see a change in the firm’s EO, there are likely to be multiple causal and contextual factors influencing the firm’s decision to change its EO condition.

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For example, consider a case of a firm experiencing a significant competitive disruption, which has negatively affected firm performance. The nature of the disruption and performance shortfall is such that the Board replaces the CEO, and the new CEO changes the firm’s EO in response. In this case, the new competitive threat, the performance decline, and the CEO change are all factors in the corresponding change to the firm’s EO. The challenge for EO researchers is to disentangle the causal ordering and account for confounding factors that happened at the same time as the change to the firm’s EO condition.

1.4   Measuring EO in the Context of Leadership Research There are three common approaches to measuring EO. Each has strengths and limitations in the context of studying strategic leadership antecedents to EO. Regardless of which approach researchers adopt, the most important consideration is to align measurement with the EO conceptualization used by the researcher. While researchers can use the measures across EO conceptualizations, how the researcher models the higher-order construct varies substantially (Covin & Wales, 2012). For example, a researcher may use the Covin and Slevin (1989) scale to model a unidimensional composite EO construct with a first-order reflective measurement model—consistent with the Miller/Covin and Slevin EO conceptualization. Alternatively, the researcher could use the same measures but specify EO as a first-order reflective, second-order formative measurement model consistent with the Anderson et  al. (2015) conceptualization. Theoretical considerations must drive the measurement decision. The first measurement approach, and the most common, is a survey-­ based design with psychometric indicators. The Covin and Slevin (1989) strategic posture scale is, by a large margin, the most popular such approach. The Covin and Slevin (1989) scale consists of nine items, with three each for the innovativeness, proactiveness, and risk-taking dimensions. As originally conceived, the nine indicators collapse—either with mean scale scoring or in a single-factor model—to capture a unidimensional or composite measure of EO. While it is common for researchers using the Covin and Slevin (1989) scale to remove one or more indicators for indicator reliability purposes in a study, the utility of the scale and its

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overall nomological validity have made it both useful and popular among EO researchers (Covin & Lumpkin, 2011). Among researchers employing the Miller/Covin and Slevin EO conceptualization, there have been numerous alternative psychometric EO scales (Covin & Wales, 2012), often changing the scale to meet the contextual considerations of the research question, such as changing the indicators to align with the non-profit industry or international firms (Morris et  al., 2011). However, an advantage of psychometric scales is that researchers often capture EO’s dimensional structure, and so may model EO as either a unidimensional composite or multidimensionally. Further, there are measurement extensions to incorporate the added EO dimensions under the Lumpkin and Dess (1996) EO conceptualization, and so it may be that the Covin and Slevin (1989) measurement instrument may combine with other instruments to model the Lumpkin and Dess (1996) multidimensional conceptualization. EO researchers often justify survey designs under the single best-­ informed respondent argument (Covin et  al., 2006). Researchers argue that in a firm, and particularly a small to medium-sized business, the CEO or managing principal is the individual developing and executing the firm’s strategy (Anderson & Eshima, 2013). As such, he or she is likely to be the most informed about the firm’s strategic condition and choices, and hence the best survey respondent. While proper, this approach may raise endogeneity concerns—specifically common method bias (Antonakis et  al., 2010)—in the context of leadership research. Best practice when using survey designs has researchers collecting data on the predictor and outcome variables from dissimilar sources (Antonakis et al., 2010). This may be challenging for certain research questions, particularly in the case of leadership traits or behaviors (e.g., the effect of CEO self-efficacy or narcissism on the firm’s EO). A second common EO measurement approach is content analysis of various text-based documents; among publicly traded firms, these documents may be the Board chair’s letter to shareholders or the firm’s annual report (i.e., SEC form 10-K), among other possibilities (McKenny et al., 2018). While content analysis can employ more sophisticated machine learning approaches to parse context, the most basic—and most common—approach counts specific words in these documents that associate with various EO dimensions (McKenny et  al., 2018). The underlying logic is that the more ‘EO words’ in each document, the higher the firm’s EO.

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There are two specific advantages to content analysis measurement in the context of EO-leadership research. The first advantage is the assumption that the document accurately reflects the perspective of senior leadership—that is, high content validity (McKenny et al., 2018). In the case of an annual report, the firm has a legal obligation to ensure that the text accurately reflects the firm’s operating condition. In the case of a chair’s (or CEO’s) letter to shareholders, while a communication professional may have written the letter, the senior executive likely at least approved the language. The second advantage for EO researchers is the ability to collect a larger sample. In the case of a firm’s annual reports, at least in the United States, every publicly traded firm must file a form 10-k, and so researchers have access to a larger pool of firms than is available through survey-based designs. That said, there is an important disadvantage to content analysis, and this disadvantage is such that I do not recommend content analysis for EO-strategic leadership research adopting a behavioral perspective. The challenge, as Kreiser et  al. (2020) note, is that content analysis is best suited to capture an attitudinal/dispositional perspective on EO, and not a behavioral perspective. Whether a chair’s letter, annual report, or other such document, the text often reflects forward-looking statements on the firm’s aspirations, or management’s interpretation of the firm’s operating context. As such, these documents best capture managements’ perspective on, or attitude toward, firm activities or what the firm intends to do in the future, as opposed to describing specifically what the firm did and what specific behaviors the firm employed (Kreiser et al., 2020). Using secondary financial indicators is the third EO measurement approach. The researcher collects publicly available financial data—spending on research and development, for example—and uses these variables as proxies for the underlying EO dimensions or elements of EO’s conceptual domain (Kreiser et  al., 2020; Miller & Le Breton-Miller, 2011). While common in the strategic management literature, this is a newer approach in the strategic entrepreneurship literature. There are two key disadvantages to using financial indicators as EO proxies. The first disadvantage is the content validity of the indicators. Across EO conceptualizations, the underlying dimensions are conceptually broad. Innovativeness, proactiveness, and risk-taking each has wide conceptual spaces, which makes it difficult to argue that a single proxy variable—particularly a financial indicator never intended to be an indicator for a latent construct—captures the entirety of the dimension’s

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conceptual domain. Researchers using this technique spend considerable effort theoretically justifying the appropriateness of each indicator, and I conjecture that the field has not universally accepted this measurement approach (Kreiser et al., 2020; Miller & Le Breton-Miller, 2011). The second disadvantage to using secondary financial indicators is the level of analysis problem. As discussed previously, scholars conceive of EO at the business-strategy level: the level of analysis that aligns with the firm’s product-market decisions. For small businesses with a single business model, the firm has one delineable EO.  For a large firm with multiple operating subsidiaries and multiple business models, the firm has multiple EOs (one for each business). Problematically, most financial indicators exist at the corporate level, with each operating subsidiary amalgamated into a single value. There is no clear solution to this problem, absent having access to subsidiary-level data. As such, a logical criticism of this measurement approach is that the indicators are tapping a corporate-strategy version of EO, which is conceptually distinct from EO as commonly conceived (Kreiser et al., 2020). Despite these disadvantages, there are three reasons for scholars to consider this management approach. The first reason, as Kreiser et al. (2020) argued, is that financial indicators excel at capturing what the firm did with its resources. For research models grounded in a firm-behavior lens, financial indicators are thus an ideal way to measure and then model such behaviors. The second reason favoring financial indicators is access to more firms in more industries. While the measures themselves may vary according to industry norm—the banking sector, versus real estate, versus healthcare and so forth—scholars can access new samples of firms that are otherwise impractical with survey designs and potentially content analytic designs. The final reason to consider financial indicators is global data availability. While accounting standards vary across countries, with the proliferation of available financial data from publicly traded firms listed on non-US exchanges, there is the intriguing prospect of pursuing international EO research with minimal cost and complexity when compared to survey designs.

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1.5   Causal Inference in EO and Leadership Research Anderson et al. (2020) give a detailed treatment of the challenges inherent to causal inference in EO research but focus on EO as a predictor. This book addresses the causal inference problem from a different direction— with EO as the outcome and leadership variables as predictors. However, insights developed by Anderson et al. (2020) apply to leadership antecedents to EO. The most salient of these is the infeasibility of random assignment of practicing senior leaders to an experimental condition. We cannot, for example, randomly assign practicing CEOs to a low self-efficacy condition or a high self-efficacy condition and measure how the firm’s EO changes over time. While laboratory experiments can bring insights in tightly controlled and well-specified situations, the generalizability of experimental designs will always face significant limitations (Anderson et al., 2020). Not being able to use experimental designs materially diminishes the ability for researchers to isolate causal mechanisms and will be a limiting factor in building causal understanding of the strategic leadership­EO space. Another factor limiting the ability to draw causal inference is selection effects on leadership positions (Clougherty et  al., 2016). Leaders make consequential firm-behavior decisions because they showed aptitude in prior decision-making. That is, being a senior executive is not something that happened by random assignment, but through purposeful action, and with the intention for the executive to make decisions that will improve firm performance. It follows then that a firm may have promoted an executive because he or she is likely to preserve the firm’s chronic EO state; for example, a high-EO firm may promote an executive who expresses commitment to supporting a high EO and that is what the executive does over time. In this case, the selection effect will upwardly bias model parameters of various leadership behaviors on EO and diminish causal inference (Certo et al., 2016). A third limiting factor in causal inference is confounding factors. Expressed as an omitted variable problem, in strategic leadership research there is a milieu of factors that obscure underlying causal mechanisms and potentially bias model parameters. At issue is the sheer number of variables that will influence firm EO: availability of resources (Covin & Slevin, 1991), industry structure (Kreiser et al., 2020), competitive threats (Wales et  al., 2013), decision-making style (Green et  al., 2008), and so forth.

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Critically, these factors are also likely to correlate with various strategic leader behaviors and decisions. To illustrate, consider a simple research question positing that having a higher ratio of independent directors on the Board increases firm EO. We could imagine a situation where an exogenous threat to the firm led to bringing on more independent directors and correlates with a change to the firm’s EO condition. If the model omits the exogenous threat, there will be a biased parameter estimate for the director ratio—EO relationship. The above limiting factors, combined with constrained data collection ability from senior executives and Boards of Directors, limits the research designs and statistical tools available to researchers to address causal inference challenges. Instrument variable designs would require detailed knowledge of the causal processes leading to senior leadership decisions (i.e., antecedents to the antecedents in the model), which compounds well-known challenges to successfully employing instruments (Angrist & Pischke, 2008). Regression discontinuity and natural experiments may be a possibility but would require insight on an exogenous shock that would have affected firms in such a way as to cause a change in senior leadership behavior at scale (Imai et al., 2013). Certainly possible, but not as probable for designing and conducting multiple studies in the space. Synthetic controls (Abadie et  al., 2010), Bayesian penalized priors (Anderson, 2021), and the impact threshold of a confounding variable (Busenbark et al., 2021) approach offer possibilities, and may be proper depending on the research question and data generation process. I propose, however, an alternate approach to model identification and causal inference, which relies on both research design and workflow considerations. The nature of strategic leadership-EO research questions means that no one design and set of measures are ideally suited. As such, I recommend the triangulation method (Turner et al., 2017). As Turner et  al. (2017) describe, the triangulation approach uses multiple studies with distinctive designs and different measures all asking the same question and reported in the same manuscript. The strengths of one design mitigate the weaknesses of another. More concretely, however, using multiple designs minimizes the likelihood that a given omitted variable would be affecting all three designs in an equivalent way and with similar strength, thus biasing all three studies. A researcher could not claim a causal model, but the model parameters and nomological conclusions are more consistent with the true underlying relationship between the model variables.

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With this approach, researchers should use proper statistical tools for each design, and new methods, including Bayesian workflows (Schad et al., 2021) could be particularly useful. The most important consideration for researchers to improve causal inference is to think deeply and carefully about the measures used in each study. As Loken and Gelman (2017) noted, high-quality measurement underpins the efficacy of a model. Noisy and unreliable measures will yield equally noisy and unreliable nomological conclusions. This applies to EO measures and strategic leadership measures.

References Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505. Anderson, B.  S. (2021). Endogeneity in strategic entrepreneurship research. In V. K. Gupta, G. V. Shirokova, A. Karna, & A. B. Goktan (Eds.), Handbook of strategic entrepreneurship (pp. 1–30). Edward Elgar. Anderson, B. S., Covin, J. G., & Slevin, D. P. (2009). Understanding the relationship between entrepreneurial orientation and strategic learning capability: An empirical investigation. Strategic Entrepreneurship Journal, 3(3), 218–240. Anderson, B. S., & Eshima, Y. (2013). The influence of firm age and intangible resources on the relationship between entrepreneurial orientation and firm growth among Japanese SMEs. Journal of Business Venturing, 28(3), 413–429. Anderson, B.  S., Kreiser, P.  M., Kuratko, D.  F., Hornsby, J.  S., & Eshima, Y. (2015). Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. Anderson, B. S., Schueler, J., Baum, M., Wales, W. J., & Gupta, V. K. (2020). The chicken or the egg? Causal inference in entrepreneurial orientation–performance research. Entrepreneurship Theory and Practice, 1–28. https://doi. org/10.1177/1042258720976368 Angrist, & Pischke. (2008). Mostly harmless econometrics: An Empiricist’s companion. Princeton University Press. Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120. Busenbark, J. R., Yoon, H. (. E.)., Gamache, D. L., & Withers, M. C. (2021). Omitted variable bias: Examining management research with the impact threshold of a confounding variable. Journal of Management, 1–33. https:// doi.org/10.1177/01492063211006458

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Certo, S. T., Busenbark, J. R., Woo, H.-S., & Semadeni, M. (2016). Sample selection bias and Heckman models in strategic management research. Strategic Management Journal, 37(13), 2639–2657. Clougherty, J.  A., Duso, T., & Muck, J. (2016). Correcting for self-selection based endogeneity in management research: Review, recommendations and simulations. Organizational Research Methods, 19(2), 286–347. Covin, J. G., Green, K. M., & Slevin, D. P. (2006). Strategic process effects on the entrepreneurial orientation–sales growth rate relationship. Entrepreneurship Theory and Practice, 30(1), 57–81. Covin, J. G., & Lumpkin, G. T. (2011). Entrepreneurial orientation theory and research: Reflections on a needed construct. Entrepreneurship Theory and Practice, 35(5), 855–872. Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87. Covin, J. G., & Slevin, D. P. (1991). A conceptual model of entrepreneurship as firm behavior. Entrepreneurship Theory and Practice, 16(1), 7–25. Covin, J. G., & Wales, W. J. (2012). The measurement of entrepreneurial orientation. Entrepreneurship Theory and Practice, 36(4), 677–702. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Gelman, A., & Weakliem, D. (2009). Of beauty, sex and power: Too little attention has been paid to the statistical challenges in estimating small effects. American Scientist, 97(4), 310–316. Green, K., Covin, J. G., & Slevin, D. (2008). Exploring the relationship between strategic reactiveness and entrepreneurial orientation: The role of structure-­ style fit. Journal of Business Venturing, 23(3), 356–383. Imai, K., Tingley, D., & Yamamoto, T. (2013). Experimental designs for identifying causal mechanisms. Journal of the Royal Statistical Society., 176(1), 5–51. Kreiser, P.  M., Anderson, B.  S., Kuratko, D.  F., & Marino, L.  D. (2020). Entrepreneurial orientation and environmental hostility: A threat rigidity perspective. Entrepreneurship Theory and Practice, 44(6), 1174–1198. Loken, E., & Gelman, A. (2017). Measurement error and the replication crisis. Science, 355(6325), 584–585. Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21(1), 135–172. McKenny, A. F., Aguinis, H., Short, J. C., & Anglin, A. H. (2018). What doesn’t get measured does exist: Improving the accuracy of computer-aided text analysis. Journal of Management, 44(7), 2909–2933. Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791.

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Miller, D., & Friesen, P. H. (1982). Innovation in conservative and entrepreneurial firms: Two models of strategic momentum. Strategic Management Journal, 3(1), 1–25. Miller, D., & Le Breton-Miller, I. (2011). Governance, social identity, and entrepreneurial orientation in closely held public companies. Entrepreneurship Theory and Practice, 35(5), 1051–1076. Morris, M. H., Webb, J. W., & Franklin, R. J. (2011). Understanding the manifestation of entrepreneurial orientation in the nonprofit context. Entrepreneurship Theory and Practice, 35(5), 947–971. Rauch, A., Wiklund, J., Lumpkin, G.  T., & Frese, M. (2009). Entrepreneurial orientation and business performance: An assessment of past research and suggestions for the future. Entrepreneurship Theory and Practice, 33(3), 761–787. Rosenbusch, N., Rauch, A., & Bausch, A. (2013). The mediating role of entrepreneurial orientation in the task environment–performance relationship: A meta-­ analysis. Journal of Management, 39(3), 633–659. Schad, D. J., Betancourt, M., & Vasishth, S. (2021). Toward a principled Bayesian workflow in cognitive science. Psychological Methods, 26(1), 103–126. Turner, S. F., Cardinal, L. B., & Burton, R. M. (2017). Research design for mixed methods: A triangulation-based framework and roadmap. Organizational Research Methods, 20(2), 243–267. Wales, W.  J., Parida, V., & Patel, P.  C. (2013). Too much of a good thing? Absorptive capacity, firm performance, and the moderating role of entrepreneurial orientation. Strategic Management Journal, 34(5), 622–633.

CHAPTER 2

A Behavioral View of EO and Strategic Leadership

Abstract  This chapter discusses a behavioral perspective on EO, and contrasts the ontological assumptions made under a behavioral perspective with the attitudinal perspective also found in the EO literature. While arguing for the primacy of a behavioral perspective, the chapter briefly discusses the role of organizational culture in the EO literature, and how these discussions may be useful in an EO-leadership context. The chapter then outlines a behavioral perspective on strategic leadership and contrasts this perspective with the ex officio view common in strategic leadership research. I then introduce resource allocation decisions as the organizing frame around a behavioral perspective on EO and strategic leadership, premised on the argument that what leaders commit resources toward, and how those resource allocation decisions influence entrepreneurial behaviors, is central to understanding how leaders influence the firm’s level of EO.  The chapter concludes with an introduction of a research model of EO and strategic leadership. Keywords  Behavioral theory • Resource allocation • Attitudinal perspective • Organizational culture

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2.1   Attitude, Behavior, or Both? There is a perennial debate—and one without resolution—in the EO literature regarding whether we may best construe EO as fundamentally behavioral in nature, fundamentally attitudinal, or a combination thereof (Covin & Wales, 2012). As a latent construct, EO can be, quite literally, anything the research chooses it to be, subject to norms of validity and theoretical consistency (Podsakoff et al., 2016). In this book, I adopt the perspective that EO is behavioral in nature, and that in terms of modeling strategic leadership antecedents to EO, the more fruitful theoretical questions lie in a behavioral frame. However, it is important to understand the attitude/behavior debate because it illuminates how we consider managerial risk-taking in the EO context. The behavioral EO perspective argues that what gives rise to a firm garnering the entrepreneurial label are the observable entrepreneurial acts that place the firm in new product/market domains (Covin & Lumpkin, 2011). There are three key words in the preceding sentence, the first of which is observable. From a behavioral perspective, entrepreneurial behaviors must be external-facing and customer-centric. In practice, this means that firms that invest heavily in, for example, supply chain technology, while innovative, are not necessarily entrepreneurial unless they create customer value (Miller, 1983). The second key word is act. A firm must do entrepreneurial things to be entrepreneurial—plans, discussions, and intentions are not sufficient (Covin & Lumpkin, 2011). The final keyword is new. Innovation is a necessary element to EO—the firm must be pursuing the new, and it must be doing innovative things within their product/ market domain. That is, the innovation must be customer centric. The attitudinal EO perspective is not conceptually opposite to the behavioral perspective. Rather, the attitudinal perspective is often a complement to, or necessary element of, the behavioral perspective (Covin & Wales, 2012). The attitudinal or, equivalently, dispositional perspective argues that we must understand the motivations, thought processes, and decision-making approaches among senior leadership to understand how the EO manifests within a firm (Covin & Lumpkin, 2011). This is particularly the case with risk and managerial attitudes toward risk. Pursuing new, external-facing activities necessarily involves a measure of strategic risk by committing resources toward an uncertain outcome. Risk is inherent to innovation, and as such, inherent to EO (Miller, 1983). The attitudinal perspective argues that a complete view of EO necessitates incorporating

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the disposition of senior leaders toward risk as part of EO’s conceptual domain (Covin & Wales, 2019). Conceptually, it seems logical to incorporate managerial attitude toward risk within EO’s conceptual domain, and indeed, this is the dominant perspective in the literature. The challenge, and this is particularly salient when considering strategic leadership antecedents to EO, is in modeling. As Anderson et  al. (2015) noted, a first-order reflective construct—the measurement approach used most with the Miller/Covin and Slevin EO conceptualization—necessarily assumes that a predictor causally relates, in the same manner and strength, to each underlying indicator or conceptual dimension. This assumption is critical to the validity of first-order reflective measurement—each indicator is equally valid and hence perfectly interchangeable (MacKenzie et al., 2011). The preceding assumption, however, is problematic when we consider both behavioral and attitudinal elements to EO’s conceptual domain. Consistent with a reflective measurement model, the researcher is assuming that a given antecedent would theoretically predict EO as a behavior, and EO as an antecedent/disposition (Anderson et al., 2015). When we consider the range of possible strategic leadership variables, this assumption may prove problematic. For example, we might imagine that the presence of co-CEOs may influence the firm’s entrepreneurial behaviors. But it is difficult to imagine the co-CEO structure influencing these individual’s attitudes toward risk in the exact same way (particularly when considering there would be two people with two different risk attitudes). The solution, as discussed by Anderson et al. (2015), is to split EO into its behavioral and attitudinal elements. While these dimensions could combine into a higher-order and formatively measured construct, they do not necessarily have to be when modeling antecedent relationships (Anderson et al., 2015). What must exist, however, is accounting for managerial attitude toward risk in the model specification by including this variable as a covariate. This is the approach I take in this book and will detail further with the introduction of a strategic leadership-EO research model.

2.2   Organizational Culture Considerations While I adopt a behavioral EO perspective, it is worthwhile to note the role of organizational culture in the EO conversation as it relates to strategic leadership antecedents. There is an assumption that high-EO firms

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will have what scholars describe as an entrepreneurial organizational culture (Anderson et al., 2009; Covin & Slevin, 1991). While conceptually difficult to define precisely, an entrepreneurial organizational culture is one in which, as a collective, the organization allows and encourages employees to purse innovative projects, to take risks, and to be proactive in addressing competitive threats (Ireland et al., 2009). Given this overlap, we can find references in the literature to EO also encompassing organizational culture within its conceptual domain (Anderson et al., 2009). In this book, I argue that organizational culture is a distinct construct or set of constructs from EO. However, there are elements of an ‘entrepreneurial’ organizational culture that are likely to strongly covary with EO. For example, a rewards system that encourages managerial risk-taking is likely to positively associate with EO (Kuratko et  al., 2005). This is potentially problematic for two reasons. The first is related to the presence of confounding variables. While difficult to measure, there is face validity to the argument that a “strong corporate culture” or a related concept of tacit and implicit organizing conditions will correlate with leadership decisions made by the firm and the firm’s EO (Ireland et al., 2009). For example, the so-called 20%-Rule at Alphabet, Inc., Google’s parent company, specifies that employees should devote 20% of their working time toward projects that have no clear or immediate payoff (Bock, 2015). This organizing policy, without formal structure, has clear implications for how managers evaluate the productivity of their employees and how they organize their team’s time. Similarly, the intention of the program is for employees to dream up major innovations that the firm can commercialize. As such, this organizational variable is a meaningful compound between certain leadership variables and the firm’s EO—excluding this variable from a model will bias parameter estimates. The second reason that culture variables are potentially problematic is selection effects, specifically among leader selection. Firms perceived to have a more entrepreneurial culture may attract senior executives that enjoy and thrive in these types of cultures. These individuals then pursue more entrepreneurial behaviors, and the net result is an upward bias in various leader characteristics and EO. Unfortunately, there is no clear or simple solution to this problem because any remedy will depend on the research model at hand and the data collection process. However, strategic leadership-EO researchers must be aware of the potential endogeneity challenges from organizational culture variables and develop a suitable mitigation approach.

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2.3   Strategic Leadership Through a Behavioral Frame I argue that the most promising perspective from which to view the effect of strategic leadership on EO is through a behavioral lens. I am not referring here to the behavioral perspective on corporate governance offered by Westphal and Zajac (2013), which adopts a social psychology lens focusing on the interpersonal relationships between leaders. Rather, the behavioral lens I am suggesting is theoretically grounding a research model from the perspective of how and why a given leadership variable is likely to influence the firm’s behavior toward entrepreneurial activity. There must be action and activity taking center stage. To be a causal predictor of EO from this perspective necessarily implies that a change in the leadership variable results in a change in EO, which in turn implies the expectation of change in the leadership variable. This notion of change has important implications for how scholars motivate research questions and the range of leadership variables most likely to influence EO. For example, personality traits are likely to be more relevant in the immediacy of a leadership turnover, and then less over time given EO’s temporal stability. These traits, which would include demographic variables, education, gender, and personality, are stable or otherwise temporally fixed, and while they may correlate with EO, they are not likely to be causal factors in firm-level EO. This would include psychological traits such as narcissism, psychopathy, and the big five personality traits, among others. That said, however, it is likely that these variables are contextual factors or boundary conditions that merit consideration and potentially inclusion in a research model. A behavioral perspective on strategic leadership focuses then on the actions leaders take and the behaviors leaders engage in that are likely to influence how the firm approaches and then pursues entrepreneurial behavior. For example, Cao et al. (2015) found that CEO bonding ties, the relationships a CEO builds within the firm, and CEO bridging ties, the relationships cultivated outside of the firm, correlate with EO.  The argument advanced by Cao et al. (2015) centers on various learning effects that influence the decision to engage in entrepreneurial behavior. These variables convey a sense of action. A leader may develop new relationships, or may let relationships lapse, that over time influence his or her knowledge, and hence potentially the entrepreneurial activities the firm pursues.

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A behavioral perspective also favors certain theoretical lenses over others commonly found in the strategic leadership literature. The attention-­ based view, for example, may be particularly salient because the firm may organize in such a way that leaders may be more attuned to changes in the environment that favor entrepreneurial action (Shepherd et  al., 2017). Similarly, because pursuing EO entails spending considerable resources in valuable and unique ways, the resource-based view may useful (Wales et  al., 2021). Conversely, the upper echelons perspective, which argues that demographic variables and prior experience influence leader behavior (Engelen et al., 2015), may be less relevant because, as mentioned previously, most of these variables do not change over time. However, I argue that the most useful theoretical frame when motivating strategic leadership-EO research models from a behavioral perspective will be resource dependency theory (Pfeffer & Salancik, 1978). While resource dependency occupies a principal place in organizational theorizing, the perspective is not common in EO or strategic entrepreneurship research. This oversight is unfortunate because, as I will develop further, the central argument of resource dependency theory—that a leader must acquire control over critical resources to shift power dynamics in the organization’s favor (Pfeffer & Salancik, 1978)—is an overarching framework to connect specific, behaviorally oriented strategic leadership variables to entrepreneurial behavior outcomes. The crux of my argument is that leaders pursue entrepreneurial behaviors as a mechanism to lower environmental uncertainty by building defensible product-market leadership positions, and that various strategic leadership variables enable or constrain the capability, motivation, and implementation of these behaviors.

2.4   The Role of Resource Allocation Decisions Central to resource dependency theory as underlying a behavioral perspective on strategic leadership-EO research is resource control. Resource dependency theory focuses externally—the firm operates as an open system, with its behavior constrained by externalities and interdependencies in its environment (Casciaro & Piskorski, 2005). These externalities influence managerial behavior, and organizational leaders look to use and control both internal and external resources to deal with unfavorable externalities and ideally shape their external environment (Hillman et al., 2009). As such, applying resource dependency theory in this context requires that leaders be able to acquire critical resources and to make

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meaningful resource allocation decisions. Because EO is a resource-­ consuming posture, access to resources and the ability to direct resources is a prerequisite to entrepreneurial behavior (Covin & Slevin, 1991). The CEO or senior-most executive and the Board of Directors are key actors from a resource acquisition and control perspective (Hillman & Dalziel, 2003). To the extent that other members of an organization’s top management team may direct resources or may materially influence resource acquisition and allocation, these individuals may also be meaningful in developing a research model. However, outside of the CEO and the Board, the set of executives in an organization most central to resource allocation decisions that affect EO are those who lead strategic business units or other operating subsidiaries that have independent profit and loss authority and delineable business models. These individuals run businesses with a delineable EO—that is, closer to business-level strategy and the direct product/market interface. There is a rich stream in the corporate entrepreneurship literature discussing the centrality of executives at lower levels in the organization but who lead substantive businesses (Kuratko et  al., 2005). Once again, resource acquisition and control play a significant role in the success of these individuals. A business unit executive has two spheres where he or she must acquire and gain control over resources—the external competitive environment, and the internal environment in competition with other business units (Casciaro & Piskorski, 2005). As Pfeffer and Salancik (1978) noted, executives adept at acquiring resources in both spheres tend to become organizational power centers, with increasing resource allocation authority over time. However, grounding the strategic leadership-EO conversation in a resource dependency frame necessarily shifts defining strategic leadership from an ex officio perspective toward a hierarchically agnostic perspective. The ex officio perspective argues that because an individual occupies a given position, he or she is a strategic leader (Rowe, 2001). Conversely, I am arguing that what makes an individual a strategic leader in this context is the role he or she plays in acquiring and allocating resources. A CEO, Board, and business unit head certainly, but conceptually, strategic leadership may manifest anywhere in an organization where an individual is able to gain control over resources and direct those toward—or away from— entrepreneurial behaviors. Individuals exercise strategic leadership in this context when their behaviors enable or constrain EO as a function of resource allocation decisions.

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2.5   A Research Model of Strategic Leadership and EO To summarize, I posit that a behavioral perspective on strategic leadership—focusing on what leaders do and the actions they take—is a useful lens to understand how leadership constructs may connect with firm-level EO.  The behavioral focus necessarily implies action or activity in focal leadership constructs and variables; stable traits and time-invariant variables, while potentially correlates, are not likely to be causal factors in predicting firm-level EO.  Further, I argue that the central explanatory mechanism for why leadership behaviors causally influence EO is the desire for strategic leaders to acquire and control resources that, deployed through the firm’s EO, address challenging externalities and ideally shape the firm’s operating environment. Lastly, while the CEO and the Board occupy central positions in this behavioral view, strategic leaders who have access to or otherwise control critical resources can exist anywhere in an organization, and hence the strategic leadership-EO connection may manifest at multiple levels and at multiple points with an organization. The preceding implies an overall research model with significant complexity. As such, I will not delineate all constructs and variables under the rubric of a behavioral perspective on strategic leadership that may be useful for examination. Rather, as shown in Table  2.1, I categorize these variables and offer a set of reasons why these variables may be useful starting points in building our understanding of strategic leadership’s influence on EO.

Table 2.1  Strategic learning variable categories and possible connections Category

Potential connection and focus areas

Social psychological

Behaviorally oriented social psychological variables would include specific behaviors undertaken by leaders to build and use social relationships to acquire, control, and use resources. For example, Cao et al. (2015) found that CEOs who build social capital within the firm and with key external constituencies create better awareness of potentially valuables resources. In another example, leaders at lower hierarchical levels may build social relationships to influence resource allocation decisions or to create influence over key resources (Kuratko et al., 2005) (continued)

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Table 2.1 (continued) Category

Potential connection and focus areas

Psychological

Relevant psychological variables are those that are malleable over time and may be part of a temporal causal chain that involves a leader experiencing an event at time t that changes a psychological condition at time t + 1 that influences resource acquisition and control behaviors. For example, leaders who experience significant performance loss may experience a decline in general self-efficacy, which in turn may influence the willingness to acquire and leverage novel resources (Sherer et al., 1982). Notably, in the EO literature research often posits psychological variables as correlates influencing managerial attitude toward risk (Engelen et al., 2013). As such, researchers may want to carefully consider how these variables may apply through a behavioral lens Cognitive processes, particularly related to learning and knowledge generation, are existing and well-accepted correlates to EO (Anderson et al., 2009). From one perspective, knowledge is a critical resource, and knowledge of the usefulness of potentially valuable resources and how to deploy resources is particularly salient to pursuing new entrepreneurial behaviors (Anderson et al., 2009). In a related stream, mechanisms that direct and channel managerial attention toward or away from resource acquisition and control may influence the conditions under which leaders pursue behaviors that influence EO (Titus & Anderson, 2018) Variables related to organizational structure and governance may play direct causal roles in EO and may be critical boundary conditions for the relationship between other strategic leadership variables and EO. For example, compensation structures may favor the use—or disuse—of resources, which may constrain the range of EO options a leader may pursue (Ireland et al., 2009). Similarly, the extent to which leaders work in an organic and less hierarchical structure supplies flexibility in directly applying resources (Anderson et al., 2009), but also may increase social capital building opportunities across an organization, which strengthens the positive effect of leader social capital on EO

Cognitive processes

Structure and governance

References Anderson, B. S., Covin, J. G., & Slevin, D. P. (2009). Understanding the relationship between entrepreneurial orientation and strategic learning capability: An empirical investigation. Strategic Entrepreneurship Journal, 3(3), 218–240. Anderson, B.  S., Kreiser, P.  M., Kuratko, D.  F., Hornsby, J.  S., & Eshima, Y. (2015). Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. Bock, L. (2015). Work rules!: Insights from inside Google that will transform how you live and Lead. Hachette Book Group.

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Cao, Q., Simsek, Z., & Jansen, J. (2015). CEO social capital and entrepreneurial orientation of the firm: Bonding and bridging effects. Journal of Management, 41(7), 1957–1981. Casciaro, T., & Piskorski, M.  J. (2005). Power imbalance, mutual dependence, and constraint absorption: A closer look at resource dependence theory. Administrative Science Quarterly, 50(2), 167–199. Covin, J. G., & Lumpkin, G. T. (2011). Entrepreneurial orientation theory and research: Reflections on a needed construct. Entrepreneurship Theory and Practice, 35(5), 855–872. Covin, J. G., & Slevin, D. P. (1991). A conceptual model of entrepreneurship as firm behavior. Entrepreneurship Theory and Practice, 16(1), 7–25. Covin, J. G., & Wales, W. J. (2012). The measurement of entrepreneurial orientation. Entrepreneurship Theory and Practice, 36(4), 677–702. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Engelen, A., Neumann, C., & Schmidt, S. (2013). Should entrepreneurially oriented firms have narcissistic CEOs? Journal of Management, 42(3), 698–721. Engelen, A., Neumann, C., & Schwens, C. (2015). “Of course I can”: The effect of CEO overconfidence on entrepreneurially oriented firms. Entrepreneurship Theory and Practice, 39(5), 1137–1160. Hillman, A. J., & Dalziel, T. (2003). Boards of directors and firm performance: Integrating agency and resource dependence perspectives. Academy of Management Review, 28(3), 383–396. Hillman, A.  J., Withers, M.  C., & Collins, B.  J. (2009). Resource dependence theory: A review. Journal of Management, 35(6), 1404–1427. Ireland, R. D., Covin, J. G., & Kuratko, D. F. (2009). Conceptualizing corporate entrepreneurship strategy. Entrepreneurship Theory and Practice, 33(1), 19–46. Kuratko, D. F., Ireland, R. D., Covin, J. G., & Hornsby, J. S. (2005). A model of middle-level managers’ entrepreneurial behavior. Entrepreneurship Theory and Practice, 29(6), 699–716. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334. Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791. Pfeffer, J., & Salancik, G.  R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2016). Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organizational Research Methods, 19(2), 159–203.

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Rowe, W. G. (2001). Creating wealth in organizations: The role of strategic leadership. The Academy of Management Executive, 15(1), 81–94. Shepherd, D. A., McMullen, J. S., & Ocasio, W. (2017). Is that an opportunity? An attention model of top managers’ opportunity beliefs for strategic action. Strategic Management Journal, 38(3), 626–644. Sherer, M., Maddux, J.  E., Mercandante, B., Prentice-Dunn, S., Jacobs, B., & Rogers, R.  W. (1982). The self-efficacy scale: Construction and validation. Psychological Reports, 51(2), 663–671. Titus, V. K., & Anderson, B. S. (2018). Firm structure and environment as contingencies to the corporate venture capital–parent firm value relationship. Entrepreneurship Theory and Practice, 42(3), 498–522. Wales, W. J., Kraus, S., Filser, M., Stöckmann, C., & Covin, J. G. (2021). The status quo of research on entrepreneurial orientation: Conversational landmarks and theoretical scaffolding. Journal of Business Research, 128, 564–577. Westphal, J. D., & Zajac, E. J. (2013). A behavioral theory of corporate governance: Explicating the mechanisms of socially situated and socially constituted agency. The Academy of Management Annals, 7(1), 607–661.

CHAPTER 3

Sustaining EO and the Role of the CEO

Abstract  This chapter builds from the perspective in the EO literature that the CEO or senior-most strategic decision-maker in the firm is central to that firm’s EO.  Central in the sense that this individual decides the firm’s strategic posture and makes resource allocation decisions that either constrain or enable EO; the firm’s EO reflects the CEO’s decisions. This chapter builds on the possibility of individual-level behavioral variables that may influence EO, for example learning engagement, cognitive flexibility, and social capital. At this point, I briefly touch on the concept of an individual-level EO, and level of analysis issues when investigating leadership-­EO questions. I then address expected EO differences among CEOs who founded the firm from those who are non-founders, and in the founder discussion, I focus on imprinting effects and the extent to which legacy may factor into EO persistence. The chapter concludes by exploring an alternative condition, in which the CEO has less direct impact over EO, with the firm’s posture emerging organically, independent of the CEO. Keywords  Imprinting effect • Individual-level EO • Founder CEOs • CEO personality traits

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3.1   The Centrality of the CEO The CEO, or senior-most managing principal, plays a significant role in EO research. Because EO exists at the business strategy level, scholars assume that the executive charged with developing and executing business unit strategy will be the one most responsible for setting the unit’s EO (Covin & Wales, 2012). This logic extends to the empirical setting, where EO researchers seek survey responses from the senior unit executive as the “single most informed respondent” (Anderson & Eshima, 2013). In short, the CEO is best able to discuss the unit’s entrepreneurial behaviors because he or she implemented—and played a role in conceiving—these behaviors. As we consider EO through a behavioral and resource dependency lens, the CEO’s relationship with the Board deserves special attention. The CEO is accountable to the Board for resource allocation decisions (Pfeffer & Salancik, 1978). As such, how the Board views resource allocation and how the CEO and Board work together to identify critical resources and develop strategies that use EO to gain power in the market is a fruitful area for research. While we examine the application of resource dependency theory and how CEOs use Board resources to influence innovation (Desender et al., 2013), this is an underdeveloped area of EO scholarship. CEOs should be most attuned to the power structures and dependencies in the external environment that require mitigation (Casciaro & Piskorski, 2005). As such, CEOs take steps to acquire critical resources that should enable EO, and also in how to leverage EO as a resource itself to gain market power. This is a novel perspective in the EO literature—EO as both a resource-consuming posture and as a resource itself to improve the firm’s competitive position. CEOs are positionally able—and motivated—to acquire resources and to direct resources to shape the external environment to be more favorable to the firm. Pursuing innovative, proactive behaviors that establish market leadership positions is a highly effective way in which CEOs may establish market power. As such, it is reasonable to expect a broad range of possible behavioral variables at the CEO level that may explain firm-level EO, which I discuss below.

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3.2   CEO-Specific Behavioral Variables Because of the centrality of the CEO to management research broadly, there is no shortage of potential variables that researchers could select and model. These variables center on cognitive, social psychological, and psychological aspects of the CEO, and this will be my focus here. Further, these variables should also incorporate change and temporal variation. While, for example, researchers have identified stable traits of CEOs—narcissism, for example (Engelen et  al., 2013)—as correlates to EO, these variables are not likely to be causally related. That is, for a change in a predictor to cause a change in the outcome, both variables must incur a state change (Anderson et  al., 2020). Psychological, sociological, and environmental variables that do not change over the study period are less useful when viewing the strategic leadership—EO relationship through a behavioral lens. There are three overarching meta-theoretical frames that best capture individual-level variables at the CEO level that influence EO—learning and knowledge creation (Anderson et al., 2009); attention and attention shifting (Shepherd et  al., 2017); and network creation and leveraging (Cao et al., 2015). These frames may intersect with each other—for example, a CEO who builds social relationships that cause an attention shift toward a new opportunity. The key point is that the meta-theoretical frames appear as complements to the underlying resource acquisition and control behavior, which is common in the resource dependency literature (Wry et al., 2013). These meta-theoretical frames also provide conceptual structure to connect a variable at the individual level to a variable at the organizational level. Consider, for example, cognitive flexibility—the ability to adjust thinking and behaviors to changing environmental conditions (Kiss et  al., 2020). A cognitively flexible CEO, as Kiss et al. (2020, p. 2202) describe, “possess[es] the ability to switch between different modes of thinking, find workable solutions to seemingly conflicting problems, and combine and recombine knowledge gleaned from different sources in new ways ….” Cognitive flexibility thus improves the ability to take in and process complex information—that is, to learn. The learning advantage stemming from cognitive flexibility, theoretically, should allow these CEOs to envision new resource combinations and opportunities for power acquisition that increase firm-level EO (Eshima & Anderson, 2017).

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In another example, consider the CEO’s relationship to the Board of Directors. Garg and Eisenhardt (2017, p.  1837) describe an effective CEO as one who “both capture[s] useful advice from directors (resources) and yet maintain[s] control over the strategy-making process (power)”. From Garg and Eisenhardt’s (2017) perspective, effective CEOs use the Board as a resource for learning and knowledge creation, while also letting the Board help to direct and frame critical issues in the environment that call for CEO attention. The combinative effect of these benefits—learning and attention shifting—should enable CEOs to pursue entrepreneurial behaviors with a higher degree of strategic fit with the organization. Consider another example in which the CEO takes part actively in trade associations and engages in industry-level political action (Hillman et al., 2009). One benefit to these behaviors is to build relationships with key stakeholders in the CEO’s industry, including potential competitors. These social relationships may expose opportunities to leverage entrepreneurial behaviors to gain control over key resources in the industry or mitigate a threat to the firm’s competitive position, such as using a within-­ industry joint venture to exploit an innovative technology (Ireland et al., 2003).

3.3   Level of Analysis Considerations Connecting a phenomenon that occurs within and as a function of individual action to the behavior of a large firm—particularly a firm that may generate tens of billions of sales per year—requires careful consideration. There is certainly a rich history of such connection, with upper echelons theory research being the most prominent stream (Simsek et al., 2010). Underlying this research is the assumption that the thoughts, feelings, desires, motivations, and actions of CEOs and other senior leaders influence firm behavior through the decisions these individuals make. The decision is the critical connection here. For example, consider the argument by Lewis et al. (2014) that CEOs with MBA degrees are more likely to publicly disclose pertinent environmental information than CEOs with law degrees. The degree itself is not the causal mechanism—the argument is that the training underlying the two degrees influenced the decision made by the CEO to then make a disclosure (Lewis et al., 2014). Senior leader individual-level variables causing organizational action implies the presence of theoretically necessary mediator relationships. Researchers often theorize about such intervening mechanisms, but

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unfortunately, they rarely directly model these mechanisms (Gonzalez & MacKinnon, 2021). The result is the belief, though empirically under-­ identified, that there must be mid-range mechanisms through which individual-­level variables and constructs effect organizational-level outcomes. This is important for scholars looking to pursue strategic leadership connections to EO, for two reasons. First, omitted mediators may induce endogeneity—the observed statistical relationship between the individual-level predictor and the firm-level outcome may be spurious because of the omitted causal mechanism (Antonakis et al., 2010). The second reason is that EO may only be distally related to the individual-­ level variable, and as such, scholars should expect that any such effects will be weak and close to zero (Gelman & Weakliem, 2009). A multilevel perspective may be valuable then for EO researchers to consider in the context of strategic leadership-EO research. As Wales et al. (2020) note, a multilevel perspective has the advantage of making explicit how individual-level variables, particularly among senior leaders, influence organizational attributes such as culture and structure, which then influence firm-level behavior. A key insight by Wales et al. (2020) is to encourage EO researchers to consider more proximate relationships to EO that may exist at the boundaries of different hierarchical manifestations of EO and associated levels of analysis. This has important implications for leadership-­EO research, because it is likely that critical intervening mechanisms connecting individual-level variables to firm-level EO may exist at levels of analysis in between the individual and the firm—at the level of key organizational groups, networks, or social structures that act to translate executive decisions into firm behavior (Wales et al., 2011). An intriguing—but underdeveloped—perspective in the EO literature is the argument that an individual-level EO may be a factor in why CEOs pursue higher levels of EO for their firm (Covin & Wales, 2019). I mention this here because EO researchers theorize the notion of CEOs being inherently entrepreneurial, or innovative, or risk-takers, but with mixed empirical support (Covin & Wales, 2019). My argument is that if there is a suitable way in which to conceptualize and measure an individual-level EO or related construct, implicit in these constructs is the assumption that individual-level EO would be a temporally stable trait of an individual (as EO is at the firm level). As such, a person’s EO is not likely to change much over time, and hence not likely to be a good prospect to build a causal predictive theory in the rubric of strategic leadership-EO research.

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3.4   Founder CEOs and Setting the Chronic EO State There are distinct implications for strategic leadership-EO research when considering the role of founder-CEOs. Paradoxically, there is not a rich research stream on founders and their role in setting the arc of the firm’s EO. This is a noteworthy oversight, because under the assumption that a firm’s strategic posture—whether entrepreneurial or conservative—is chronic, it is reasonable to assume that the founder and founding team had a significant role in setting this chronic state during the firm’s start­up period. Falling broadly under the rubric of founder imprinting, this literature stream assumes that the founder made the first decisions about the firm, particularly related to operating norms, culture, and strategic decision-­ making style (Ellis et al., 2017). Further, this literature often argues—with empirical support—that these first decisions persist well after the founder exits the firm (Ellis et al., 2017). Indeed, such imprinting effects can last for decades after the founder’s departure (Chandler, 1962). Founder imprinting effects may therefore offer clues as to the temporal stability of EO and why firms persist in a chronic EO state. They may also offer insights as to when firms may change their chronic state, such as when the founder completely steps away from the firm (e.g., does not keep a Board seat) or unexpectedly passes away, and the difficulties the successor and non-founder-CEO faces in changing the firm’s EO state. Because imprinting effects may persist for a sustained period, it is possible that one or more generations of non-founder-CEOs may be necessary for a firm to change its chronic EO state. Interestingly, Microsoft may be a contemporary example of this phenomenon—the firm mostly kept its strategic posture during the transition from founder Bill Gates to successor (although also founder) CEO Steve Ballmer, although it appears to have changed under current CEO Satya Nadella. Connecting back to the resource dependency literature, another lens through which EO researchers may view the founder/non-founder transition is through the firm’s succession planning. One of the original five strategic responses outlined by Pfeffer and Salancik (1978), succession planning is the firm’s purposeful attempt to select and develop future executives to keep its control over key resources and reduce environmental dependencies. It may be, for example, that underlying whether CEO

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turnover results in a shift from one chronic EO state to another is the Board’s desire to use succession planning to increase or keep organizational power.

3.5   CEOs with Less Impact on EO To conclude this chapter, it is worth exploring circumstances in which CEOs may have less influence on the firm’s EO. Less influence is a critical distinction, because ceteris paribus, CEOs should keep primary executive control over the firm’s strategy and resource allocation decisions (Hillman et al., 2009). However, there may be personal characteristics of the CEO, or industry, and organizational structure contextual factors that influence whether a CEO is personally involved in setting the firm’s EO. For example, it is possible that a founder who has been CEO for an extended period, and therefore likely created a strong imprinting effect, leads an organization that simply behaves in a particular way without direct intervention of the CEO.  The long-tenured CEO has ingrained processes, routines, and ways of thinking about the environment and competition within the firm that he or she may not feel require intervention when making decisions on changing the firm’s EO. Another example is the internal versus external focus of the CEO. Smith and White (1987) raise the possibility that whether the CEO primarily focuses on within-firm issues (particularly in the case of internally promoted CEOs) or tends to have a more externally focused perspective will reflect in the dominant strategy adopted by the firm. As such, because strategic leaders use EO in such a way as to diminish environmental uncertainty and gain competitive power, CEOs that are more externally oriented will by more likely to be involved with setting the firm’s EO and associated resource allocations. Conversely, an internally focused CEO may be less likely to directly influence EO because he or she does not see its usefulness as a strategic tool. To be clear, I am not arguing that a firm with an internally focused CEO will have lower EO; rather, internally focused CEOs are less likely to be directly involved with setting or changing the firm’s EO. CEOs of large, global, multi-business firms merit discussion. The scale and scope of these organizations necessarily require CEOs to focus on corporate strategy—what businesses should we be in—versus directly involved in the product/market decisions that define the firm’s EO (Covin & Wales, 2019). That said, there is ample literature outlining how CEOs

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of even the largest firms will involve themselves in resource allocation decisions that directly influence EO either across business units or within a specific business unit. For example, an acquisition for specific intellectual property that may benefit only one aspect of the firm, but nonetheless incurs substantial cost at the corporate level (e.g., Facebook’s acquisition of Oculus VR for $2 billion). In thinking about CEO influence on EO in these firms, it may therefore be the case that the CEO pays attention to one constituent unit EO or a handful of them and is directly involved in influencing these units’ EOs, but that attention does not extend to all business units. Lastly, while its popularity as an organizational form has waned in recent decades, conglomerate structures are less likely to encourage CEOs to be directly involved in business-level strategy, including the constituent unit’s EO (Dess & Robinson Jr., 1984). In a conglomerate structure, firm size is not necessarily the driving consideration for the decline in CEO attention to unit EO, but the firm’s purposeful decision to view senior executive roles as resource allocators rather than direct product-market allocators (Dess & Robinson Jr., 1984). As such, we would expect that the CEOs or senior executives in charge of the constituent businesses will be the most involved in setting their unit’s EO.

References Anderson, B. S., Covin, J. G., & Slevin, D. P. (2009). Understanding the relationship between entrepreneurial orientation and strategic learning capability: An empirical investigation. Strategic Entrepreneurship Journal, 3(3), 218–240. Anderson, B. S., & Eshima, Y. (2013). The influence of firm age and intangible resources on the relationship between entrepreneurial orientation and firm growth among Japanese SMEs. Journal of Business Venturing, 28(3), 413–429. Anderson, B. S., Schueler, J., Baum, M., Wales, W. J., & Gupta, V. K. (2020). The chicken or the egg? Causal inference in entrepreneurial orientation–performance research. Entrepreneurship Theory and Practice, 1–28. https://doi. org/10.1177/1042258720976368 Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120. Cao, Q., Simsek, Z., & Jansen, J. (2015). CEO social capital and entrepreneurial orientation of the firm: Bonding and bridging effects. Journal of Management, 41(7), 1957–1981.

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Casciaro, T., & Piskorski, M.  J. (2005). Power imbalance, mutual dependence, and constraint absorption: A closer look at resource dependence theory. Administrative Science Quarterly, 50(2), 167–199. Chandler, A.  D. (1962). Strategy and structure: Chapters in the history of the American industrial Enterprise. MIT Press. Covin, J. G., & Wales, W. J. (2012). The measurement of entrepreneurial orientation. Entrepreneurship Theory and Practice, 36(4), 677–702. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Desender, K. A., Aguilera, R. V., Crespi, R., & GarcÍa-cestona, M. (2013). When does ownership matter? Board characteristics and behavior. Strategic Management Journal, 34(7), 823–842. Dess, G.  G., & Robinson, R.  B., Jr. (1984). Measuring organizational performance in the absence of objective measures: The case of the privately-held firm and conglomerate business unit. Strategic Management Journal, 5(3), 265–273. Ellis, S., Aharonson, B. S., Drori, I., & Shapira, Z. (2017). Imprinting through inheritance: A multi-genealogical study of entrepreneurial proclivity. Academy of Management Journal, 60(2), 500–522. Engelen, A., Neumann, C., & Schmidt, S. (2013). Should entrepreneurially oriented firms have narcissistic CEOs? Journal of Management, 42(3), 698–721. Eshima, Y., & Anderson, B.  S. (2017). Firm growth, adaptive capability, and entrepreneurial orientation. Strategic Management Journal, 38(3), 770–779. Garg, S., & Eisenhardt, K. M. (2017). Unpacking the CEO–board relationship: How strategy making happens in entrepreneurial firms. Academy of Management Journal, 60(5), 1828–1858. Gelman, A., & Weakliem, D. (2009). Of beauty, sex and power: Too little attention has been paid to the statistical challenges in estimating small effects. American Scientist, 97(4), 310–316. Gonzalez, O., & MacKinnon, D.  P. (2021). The measurement of the mediator and its influence on statistical mediation conclusions. Psychological Methods, 26(1), 1–17. Hillman, A.  J., Withers, M.  C., & Collins, B.  J. (2009). Resource dependence theory: A review. Journal of Management, 35(6), 1404–1427. Ireland, R. D., Hitt, M. A., & Sirmon, D. G. (2003). A model of strategic entrepreneurship: The construct and its dimensions. Journal of Management, 29(6), 963. Kiss, A. N., Libaers, D., Barr, P. S., Wang, T., & Zachary, M. A. (2020). CEO cognitive flexibility, information search, and organizational ambidexterity. Strategic Management Journal, 41(12), 2200–2233.

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Lewis, B. W., Walls, J. L., & Dowell, G. W. S. (2014). Difference in degrees: CEO characteristics and firm environmental disclosure. Strategic Management Journal, 35(5), 712–722. Pfeffer, J., & Salancik, G.  R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row. Shepherd, D. A., McMullen, J. S., & Ocasio, W. (2017). Is that an opportunity? An attention model of top managers’ opportunity beliefs for strategic action. Strategic Management Journal, 38(3), 626–644. Simsek, Z., Heavey, C., & Veiga, J. (j.) F. (2010). The impact of CEO core self-­ evaluation on the firm’s entrepreneurial orientation. Strategic Management Journal, 31(1), 110–119. Smith, M., & White, M. C. (1987). Strategy, CEO specialization, and succession. Administrative Science Quarterly, 32(2), 263. Wales, W., Monsen, E., & McKelvie, A. (2011). The organizational pervasiveness of entrepreneurial orientation. Entrepreneurship Theory and Practice, 35(5), 895–923. Wales, W. J., Covin, J. G., & Monsen, E. (2020). Entrepreneurial orientation: The necessity of a multilevel conceptualization. Strategic Entrepreneurship Journal, 14(4), 639–660. Wry, T., Cobb, J. A., & Aldrich, H. E. (2013). More than a metaphor: Assessing the historical legacy of resource dependence and its contemporary promise as a theory of environmental complexity. The Academy of Management Annals, 7(1), 441–488.

CHAPTER 4

Setting Expectations: Governance and Board Considerations

Abstract  This chapter explores the role of the Board and corporate governance, which is an under-researched area in the EO literature. The chapter adopts the perspective that the Board is central to setting the overall strategic parameters for the firm’s EO; the Board decides the chronic EO state. The chapter then briefly draws from a resource dependency frame to describe how Board composition and structure may influence EO.  The chapter discusses merger and acquisition decisions and how they may influence the firm’s organic EO, and poses the question of whether a firm may import EO through acquisition. The chapter also addresses the Board’s role in managing capital risk as it relates to entrepreneurial behaviors and allocating capital toward projects with uncertain outcomes. I conclude the chapter with a discussion of the possible enabling effect of various executive compensation structures on the firm’s EO. Keywords  Board governance • Mergers and acquisitions • Capital risk • Executive compensation

4.1   Setting EO’s Parameters There is a central paradox in EO research—to show a causal relationship, a change in a predictor must come before a change in EO, but EO itself does not change much (Anderson et  al., 2020). The majority of EO © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. S. Anderson, Entrepreneurial Orientation and Strategic Leadership, https://doi.org/10.1007/978-3-030-87300-4_4

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researchers assume that a firm must engage in a sustained pattern of entrepreneurial behavior such that EO manifests as a temporally stable organizational trait (Covin & Slevin, 1991). That is, we do not consider a firm to be entrepreneurial unless we see the firm making regular discrete entrepreneurial acts that, over time, demonstrate EO as a defining attribute of the firm itself. As Covin and Lumpkin (2011, p. 858) noted: As such, while we subscribe to the premise that ‘behavior is the central and essential element in the entrepreneurial process’ (Covin & Slevin, 1991, p.  8), the occasional exhibition of firm-level entrepreneurial behavior is insufficient to infer the existence of an EO. A firm must exhibit entrepreneurial behaviors on an ongoing or sustained basis such that that pattern of behavior is generally recognized as a defining attribute of the firm.

In an analogous way to a chronic promotion and prevention focus drawn from regulatory focus theory (Tumasjan & Braun, 2012), firms have a chronic EO state, ranging from more entrepreneurial to more conservative. Further—while we lack empirical research in this area because of the significant challenges to collecting this type of longitudinal data— there is a low probability that firms would fundamentally change their EO state. Rather, it is highly probable that a firm will operate for its entire existence at one end of the EO scale. That said, while EO is temporally stable, this does not necessarily imply zero variance in EO. Rather, EO varies within a band or range of values that, while still reflective overall of the firm’s chronic EO state, allows for variance over time and as a function of meaningful predictor variables that cause EO to change within the band. The band allows for adjustments and modifications in firm-level entrepreneurial behavior, and is reflective, as I argue in this book, of strategic leadership variables influencing EO, but also other organizational and environmental factors that contribute to EO’s variance (Rosenbusch et al., 2013). While it is a new perspective in the EO literature, I propose that the Board of Directors’ primary role regarding EO is through setting and maintaining this band. Notably, there are few discussions of the role the Board of Directors plays in the EO literature. To conjecture, one possibility may be that empirical EO research often centers on small to medium sized businesses where the Board role may differ in composition and influence from a large, publicly traded firm (Desender et al., 2013). When we consider the substantial literature on Boards in the strategic management literature,

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most work centers on large firms, and typically from an agency theory or upper echelons perspective where legal oversight responsibilities substantively guide the Board’s role (Hillman & Dalziel, 2003). However, there is an ongoing conversation about the Board’s role in shaping firm strategy, and this role applies irrespective of organizational size (Carpenter & Westphal, 2001). This is the conversation I am joining, predicated on CEOs working with Boards to decide capital allocation parameters that shape future entrepreneurial behaviors. Viewing the Board-EO connection through the lens of the Board setting the range of EO’s variance implies a behavioral orientation—what the Board does and the decisions the Board makes, as opposed to traits, such as percentage of insider Board members (Desender et al., 2013), or structure, such as CEO duality (Krause et al., 2014), as more significant predictors of EO. As such, decisions such as capital allocation, capital structure, debt burden, and significant mergers and acquisitions are likely to bear on EO’s variance because these variables influence the level of capital available to pursue entrepreneurial behaviors (Judge Jr & Zeithaml, 1992). That said, there are elements of Board composition—Board interlocks, for example—that may be useful to consider. It may also be the case, however, that a causal relationship between Board behaviors and EO varies by firm size. In small firms, it is reasonable to assume that Board involvement in product/market decisions takes on greater saliency. In the nascent venture literature, using Board resources, knowledge, and networks plays a significant role in developing and bringing to market new products that build market leadership positions (Garg & Eisenhardt, 2017). As firms grow, however, Board discussions and decisions change to questions of capital allocation, compensation, and eventually corporate strategy decisions—which businesses the firm should be in—that move farther from business-level strategy (Judge Jr & Zeithaml, 1992). As such, particularly among large firms, the more likely connection between Board decisions and EO is through intervening mechanisms— mediators—that translate Board action to business-level strategy. The role of the CEO and top management team being the most likely here, with firm executives making use of new capital resources or merger and acquisition decisions to change—or support—firm or business unit EO in response to changing environmental conditions. This logic grounds Board behavior in the strategic leadership-EO conversation consistent with the resource dependency perspective, wherein a Board is itself a resource for

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exploitation, or its role centers on making resource allocation decisions that shape executive action (Hillman & Dalziel, 2003). This is the perspective I focus on for the rest of this chapter, and the perspective I argue offers EO researchers fruitful research questions for future study.

4.2   Board Decisions and EO It is helpful to unpack further the role of the Board and Board decisions through the lens of resource dependency theory. Pfeffer and Salancik (1978) outline four benefits Boards give organizations: (a) advice and counsel; (b) channels of information flow; (c) preferential access to resources; and (d) legitimacy. All four benefits relate to a Board’s two primary functions: (1) exercising managerial control; and (2) resource provisioning (Hillman & Dalziel, 2003). While agency theory dominates empirical work on the role and value of Boards, as Hillman et al. (2009, p. 1408) note, “empirical evidence to date suggests that [resource dependency theory] is a more successful lens for understanding boards.” I argue that in terms of explaining how a Board influences firm EO, the resource dependency perspective offers a useful starting point. From the resource dependency perspective, Boards play a significant role in addressing environmental dependencies and in acquiring critical resources that improve the firm’s competitive position (Pfeffer & Salancik, 1978). Regarding addressing external dependencies, prior work suggests that increasing the size of a Board, and increasing the number of outside directors, correlates with the level of environmental complexity (Desender et al., 2013). The logic is that the greater the environmental dependencies, the more useful a larger, diverse Board in helping the firm address these dependencies. Separately, there is empirical evidence suggesting that firms in complex environments gain from an entrepreneurial strategic posture (Rosenbusch et al., 2013). As such, it may be the case in complex environments that we may see a causal relationship between the decision to increase Board size and diversity, to increasing firm EO, to improved financial performance. Such a possibility aligns with Pfeffer’s (1972, p. 226) observation that … “board size and composition are not random or independent factors, but are, rather, rational organizational responses to the conditions of the external environment.” Regarding resource acquisition, there are three primary ways in which Boards influence the amount and availability of resources acquired from the external environment. First, firms may appoint directors to the Board

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from key external constituencies, such as a key supplier or technology partner (Wry et  al., 2013). The logic being to create a deeper sense of dependence between the two firms and allow the focal firm greater access to a key resource. This may be useful to enabling or supporting firm EO, particularly in the case where the firm’s pursuit of a new entrepreneurial opportunity hinges on access to a key external resource. The second way in which Boards help with resource acquisition is through improved access to capital markets. For example, Stearns and Mizruchi (1993) find that firms access to certain financial instruments varied as a function of directors’ experience with those instruments. Such instruments and access— and the constraints they may impose (e.g., interest expense)—will influence the ability of the firm to pursue certain types of entrepreneurial opportunities. The final way Boards influence resource acquisition is through environmental shaping. In this case, the Board may pursue action to lock-up critical resources for the firm or take steps to minimize the ability of competitors to acquire critical resources. This approach may take the form of political advocacy, as suggested by Pfeffer and Salancik (1978), and implemented, consistent with Lester et al.’s (2008) observation that firms will selectively recruit former political officials to Board membership to use the experience and social capital of these directors to help shape the firm’s regulatory environment. The ability of a Board to engage in environmental shaping is likely to influence the extent to which a firm may use EO as a power mechanism to enhance the firm’s competitive position. That is, the stronger the shaping ability, the higher we might expect firm EO. The Lester et al. (2008) study mentioned previously highlights a complementary Board role to resource acquisition, and that is the Board as a resource itself. Fitting into the advice and counsel benefit discussed by Pfeffer and Salancik (1978), Board interlocks, the social capital of individual directors, and the extent to which individual directors engage in knowledge transfer and the creation of new relationships between firm leadership and external constituencies are key enabling mechanisms for firm EO. Indeed, that external Board ties play a critical role in exposing new strategic opportunities—exploitable through firm EO—is well-­ established in the strategic management literature (Carpenter & Westphal, 2001; Judge Jr & Zeithaml, 1992). Lastly, the resource dependency literature posits a dynamic perspective on the role and usefulness of Boards in addressing external dependencies. Because of the intentionality behind Board size and composition, to

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maximize Board usefulness, firms should adjust the Board depending on changing environmental exigencies, resource needs, and strategic priorities (Lang & Lockhart, 1990; Pearce & Zahra, 1992). This raises the possibility that a change in Board size or composition may precede a change in firm EO, or in the range in which EO manifests. Testing this hypothesis would involve specifying a mediation chain from a change in environmental exigency to a change in Board composition to a change in firm EO.

4.3   Mergers and Acquisitions In the strategic/corporate entrepreneurship literature, merger and acquisition (M&A) behavior falls outside of EO’s conceptual domain, as do equity and non-equity joint ventures. The reason, as Covin and Wales (2019) discuss, is that the entrepreneurial behaviors that constitute EO involve the sustained pattern of decisions that place the firm in new product/market domains under the rubric of new entry. By contrast, decisions such as M&A, joint ventures, alliances, and related activities, while potentially entrepreneurial, are discrete activities that do not rise to the level of a distinguishable trait of a firm. While firm engagement in corporate entrepreneurial behaviors correlates positively with firm-level EO, we can safely categorize these corporate entrepreneurial behaviors as independent, and hence, potential antecedents and consequences to EO (Ireland et al., 2009). In the resource dependency literature, Board involvement in M&A activity is a valuable mechanism through which Boards address external dependencies and power imbalances (Hillman et al., 2009). For example, Boards may use M&A to reduce or eliminate a competitive threat, to obtain control over key resources and prevent or diminish competitors’ access to those resources, and lastly to diversify operations away from a source of resource risk where control is unavailable or impractical (e.g., a commodity resource) (Hillman et al., 2009). Such explanations for M&A behavior may have an indirect effect—that is, preceding a mediator as the causal mechanism—on firm EO. For example, a firm may pursue an acquisition of a potential competitor to acquire critical intellectual property that it can then exploit in new product/market domains. For example, Facebook acquired both Instagram and Oculus VR early in these firms’ existence because they owned valuable intellectual property that could, if undeterred, have represented meaningful competitive threats to Facebook’s existing business. Facebook would go on to

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exploit this intellectual property by building new business units based on these acquired resources. A similar argument explains joint venture activity, in which a Board may pursue an equity joint venture with a potential competitor to exploit a new opportunity while diversifying risk. For example, the creation of the HULU streaming service was an equity joint venture between NBC Universal and The Walt Disney Company—neither of which, at the time, were willing to invest in developing a streaming service independently. In addition to Boards using acquisitions to enable future entrepreneurial behavior, there is also the possibility of Boards using acquisitions—and more likely mergers in this case—to fundamentally change or alter the firm’s chronic EO state. For example, then-CEO Bob Iger predicated The Walt Disney Company’s acquisition of Pixar, in part, on the necessity to infuse Walt Disney Animation with a new sense of innovativeness, creativity, and technological change (Iger, 2019). Mr. Iger argued that Walt Disney Animation operated in what we might classify as a low EO band— pursuing tried and true product/market extensions that, while profitable, did not meet his expectations for contributing to top-line growth (Iger, 2019). In buying Pixar—what we might consider to be a high-EO firm— it caused an upward shift in Walt Disney Animation’s EO. Lastly, and to allow for a measure of conjecture, there is an interesting possibility related to whether a firm can “import” its EO. In the preceding Pixar example, the shift in EO from the acquisition primarily related to one business unit’s (Walt Disney Animation) EO. The question of importing EO I refer to here relates to changing an entire firm’s EO condition. Closely related conceptually to the Covin and Miles (1999) notion of strategic renewal or domain redefinition, the research question would be the Board pursuing a transformational merger that—whether directly intended or as a by-product—fundamentally shifts the firm’s EO.  For example, in one of the largest mergers of all time, we could argue that the combination of AOL and Time-Warner in 2000 changed, at a fundamental level, the approach of the new firm to entrepreneurial behaviors and opportunity. Such possibilities are, however, difficult to study empirically, given their rareness.

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4.4   Managing Capital Risk In their EO reconceptualization, Anderson et  al. (2015) argued that because EO as originally conceived by Miller (1983) and Covin and Slevin (1989) mixed dispositional and behavioral dimensions under the rubric of EO, and that such specification created interpretational confounding concerns, it is proper to split the dispositional and behavioral dimensions into distinct conceptual spaces. This is the perspective I adopt in this book, and I have ignored the role that dispositions, and particularly managerial attitudes toward risk, may play in the strategic leadership-EO conversation. Part of the justification for this approach is the recognition by Anderson et  al. (2015) that there is an inherent, and behavioral, element of risk-­ taking to innovative and proactive entrepreneurial activity. Thus, the entrepreneurial behaviors dimension already includes the most important aspect of risk-taking’s conceptual domain from a behavioral standpoint. The preceding point is important when considering the role of the Board in setting and managing the firm’s capital risk. In this context, I am referring to capital risk as the possibility that firm investments in new entrepreneurial behaviors will fail to meet performance targets and, at worse, could result in a total investment loss. As I have discussed previously, EO is a resource consuming strategic posture that depends on both the availability of capital for investment and on the willingness of senior leadership to pursue investments with uncertain outcomes (Covin & Slevin, 1991). Importantly, not all capital investments, particularly in large firms, will rise to Board-level discussion. The level at which an investment triggers Board review will be idiosyncratic to each firm and would require substantive immersion by the researcher in the firm to unpack how the Board would approach reviewing and making an investment decision. However, it is reasonable to assume that should the Board be involved in such a decision, the opportunity and investment is likely to be of important strategic utility and/or require substantial capital resources. As such, the Board’s role in making capital allocation decisions and in monitoring the performance of these investments will bear on the firm’s EO and, potentially, contribute to the firm’s EO range. In most cases, firms adopt either internal rates of return or other hurdle rates to evaluate the potential of an investment opportunity. Culturally, these targets become either tacit or explicit metrics through which managers assess both the potential of the opportunity as well as the potential for the Board

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to fund the opportunity (Hillman & Dalziel, 2003). These metrics then become a mechanism through which the Board supports the firm’s EO range, but also a mechanism through which the Board may shift the firm’s range in response to changing environmental exigencies. It is worthwhile to note, however, that the source of capital may be a factor in a capital allocation decision, particularly if the firm relies on its weighted cost of capital in evaluating an investment opportunity. In the EO literature, researchers often speak of debt financing and the role of leverage in the firm’s capital stack as indicative of the firm’s EO (Miller, 1983). The logic is that a willingness to take on debt speaks to the level of risk senior leadership is willing to accept to fund entrepreneurial initiatives (Kreiser et al., 2020). While excessive leverage may correlate with a firm’s level of EO, as Miller (1983) noted, simply taking on debt does not, by itself, define a firm’s strategic posture. As such, while the source of funds and access to certain financial instruments may play a role in setting the range of a firm’s EO, the more crucial decision as it relates to the Board is the use of funds and allocation decisions. That said, it is important to note that there is an inherent endogeneity to the Board’s capital allocation decisions and the firm’s pursuit of entrepreneurial opportunities. The Board is making the decision to allocate capital because it believes that the opportunity is worth pursuing, and there is a reasonable belief in a positive return above the firm’s cost of capital. This shifts the conversation away from EO as a real options strategy, where the firm is making multiple ‘bets’ with the hope that one or more of these options will generate positive returns (Covin & Wales, 2019). There is a presumed intentionality to the Board’s capital allocation decisions as it relates to entrepreneurial behaviors used as an exploitation mechanism, rather than a tool to explore product/market spaces for undiscovered opportunities.

4.5   Executive Compensation Structure In the governance literature, executive compensation plays a significant role in shaping executive behavior (Martin et al., 2013). Agency theory, resource dependency theory, attention theory, and other theoretical perspectives argue, to varying degrees, that Boards can use compensation to incentivize certain behaviors and managerial logic (Graffin et al., 2020). I agree, but also argue that the underlying logic is not the interesting opportunity for scholars interested in strategic leadership-EO research. Rather, I

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argue that the interesting questions are how the Board uses compensation to shape the firm’s EO range over time, and the extent to which the Board is successful at such intentional use of compensation. There are two ways in which the Board’s approach to compensation should shape a firm’s EO range. The first way, already in wide use, is to align compensation to certain financial performance metrics (Martin et al., 2013). The choice of metric or metrics, however, is a determining factor for incentivizing entrepreneurial behavior. For example, profitability-­ based targets may constrain entrepreneurial behaviors because senior leaders may be less likely to make substantial resource commitments from operating cash flows. Similarly, stock market-based metrics may also constrain entrepreneurial behaviors if such metrics tend to encourage shorter-­ term strategic actions that may not align with the time and resource commitment required of sustained investment in new entrepreneurial opportunities. In contrast, and while an empirical question, longer-term metrics centered on sales growth or market leadership—common outcome measures in EO research—may be more useful for the Board to incentivize entrepreneurial behavior. The second way in which a Board can use compensation is by mitigating risk aversion. In the corporate entrepreneurship literature, Kuratko et al. (2005) speak to the tendency for managers to avoid the risk associated with entrepreneurial investments because of the consequences—perceived or actual—of entrepreneurial failure. While confounded with considerations related to professional development, opportunities for promotion, and the prospect of job loss (Ireland et al., 2009; Kuratko et al., 2005), compensation may be a valuable tool in either mitigating economic down-side loss or in providing the opportunity for outsized gain for pursuing a new entrepreneurial initiative. For example, equity-based awards for conceiving and leading new entrepreneurial opportunities that add to an executive’s existing compensation structure may prove useful. While often overlooked in the EO literature, I argue that Boards play a significant role, particularly in larger firms, in setting the parameters for firm EO.  Adopting the perspective that Board intentionality toward resource allocation decisions that bear on the firm’s ability—and desire— to pursue entrepreneurial behaviors should yield valuable insights to advance the strategic leadership-EO conversation.

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References Anderson, B. S., Kreiser, P. M., Kuratko, D. F., Hornsby, J. S., & Eshima, Y. (2015). Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. Anderson, B. S., Schueler, J., Baum, M., Wales, W. J., & Gupta, V. K. (2020). The chicken or the egg? Causal inference in entrepreneurial orientation–performance research. Entrepreneurship Theory and Practice, 1–28. https://doi. org/10.1177/1042258720976368 Carpenter, M.  A., & Westphal, J.  D. (2001). The strategic context of external network ties: Examining the impact of director appointments on board involvement in strategic decision making. Academy of Management Journal, 44(4), 639–660. Covin, J. G., & Lumpkin, G. T. (2011). Entrepreneurial orientation theory and research: Reflections on a needed construct. Entrepreneurship Theory and Practice, 35(5), 855–872. Covin, J. G., & Miles, M. P. (1999). Corporate entrepreneurship and the pursuit of competitive advantage. Entrepreneurship: Theory and Practice, 23(3), 47–47. Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87. Covin, J. G., & Slevin, D. P. (1991). A conceptual model of entrepreneurship as firm behavior. Entrepreneurship Theory and Practice, 16(1), 7–25. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Desender, K. A., Aguilera, R. V., Crespi, R., & GarcÍa-cestona, M. (2013). When does ownership matter? Board characteristics and behavior. Strategic Management Journal, 34(7), 823–842. Garg, S., & Eisenhardt, K. M. (2017). Unpacking the CEO–board relationship: How strategy making happens in entrepreneurial firms. Academy of Management Journal, 60(5), 1828–1858. Graffin, S. D., Hubbard, T. D., Christensen, D. M., & Lee, E. Y. (2020). The influence of CEO risk tolerance on initial pay packages. Strategic Management Journal, 41(4), 788–811. Hillman, A. J., & Dalziel, T. (2003). Boards of directors and firm performance: Integrating agency and resource dependence perspectives. Academy of Management Review, 28(3), 383–396. Hillman, A.  J., Withers, M.  C., & Collins, B.  J. (2009). Resource dependence theory: A review. Journal of Management, 35(6), 1404–1427. Iger, R. (2019). The ride of a lifetime: Lessons learned from 15 years as CEO of the Walt Disney Company. Random House Publishing Group. Ireland, R. D., Covin, J. G., & Kuratko, D. F. (2009). Conceptualizing corporate entrepreneurship strategy. Entrepreneurship Theory and Practice, 33(1), 19–46.

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Judge, W.  Q., Jr., & Zeithaml, C.  P. (1992). Institutional and strategic choice perspectives on board involvement in the strategic decision process. Academy of Management Journal, 35(4), 766–794. Krause, R., Semadeni, M., & Cannella, A. A. (2014). CEO duality: A review and research agenda. Journal of Management, 40(1), 256–286. Kreiser, P.  M., Anderson, B.  S., Kuratko, D.  F., & Marino, L.  D. (2020). Entrepreneurial orientation and environmental hostility: A threat rigidity perspective. Entrepreneurship Theory and Practice, 44(6), 1174–1198. Kuratko, D. F., Ireland, R. D., Covin, J. G., & Hornsby, J. S. (2005). A model of middle-level managers’ entrepreneurial behavior. Entrepreneurship Theory and Practice, 29(6), 699–716. Lang, J. R., & Lockhart, D. E. (1990). Increased environmental uncertainty and changes in board linkage patterns. Academy of Management Journal, 33(1), 106–128. Lester, R. H., Hillman, A., Zardkoohi, A., & Cannella, A. A. (2008). Former government officials as outside directors: The role of human and social capital. Academy of Management Journal, 51(5), 999–1013. Martin, G. P., Gomez-Mejia, L. R., & Wiseman, R. M. (2013). Behavioral stock options as mixed gambles: Revisiting the behavioral agency model. Academy of Management Journal, 56(2), 451–472. Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791. Pearce, J. A., & Zahra, S. A. (1992). Board composition from a strategic contingency perspective. Journal of Management Studies, 29(4), 411–438. Pfeffer, J. (1972). Size and composition of corporate boards of directors: The organization and its environment. Administrative Science Quarterly, 17(2), 218–228. Pfeffer, J., & Salancik, G.  R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row. Rosenbusch, N., Rauch, A., & Bausch, A. (2013). The mediating role of entrepreneurial orientation in the task environment–performance relationship: A meta-­ analysis. Journal of Management, 39(3), 633–659. Stearns, L.  B., & Mizruchi, M.  S. (1993). Board composition and corporate financing: The impact of financial institution representation on borrowing. Academy of Management Journal, 36(3), 603–618. Tumasjan, A., & Braun, R. (2012). In the eye of the beholder: How regulatory focus and self-efficacy interact in influencing opportunity recognition. Journal of Business Venturing, 27(6), 622–636. Wry, T., Cobb, J. A., & Aldrich, H. E. (2013). More than a metaphor: Assessing the historical legacy of resource dependence and its contemporary promise as a theory of environmental complexity. The Academy of Management Annals, 7(1), 441–488.

CHAPTER 5

Extending EO and the Role of the Top Management Team

Abstract  This chapter centers on the top management team, which is an under-explored context in EO research. I begin the chapter by tackling the question if the firm wants to maximize its EO, should it universally have entrepreneurially minded senior executives; that is, to be an entrepreneurial firm, its senior leaders should themselves be inclined toward entrepreneurship. I also briefly consider the perspective sometimes raised in the EO literature that a firm’s performance yield from EO may be a function of counterbalance among the executive team—a CEO with a high tolerance for entrepreneurial risk may benefit from a chief operating officer with a modest risk tolerance. I then explore differences across senior executive roles, and how those roles may enable or constrain the firm’s EO. I then investigate the role of performance evaluations, and how the firm’s appraisal structure may enable or constrain EO. Lastly, I consider the role of middle management in the firm’s EO, specifically through the lens of strategic execution. Keywords  Entrepreneurial minded • Entrepreneurial dominant logic • Performance evaluation • Middle management

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5.1   Does Every Manager Need to Be Entrepreneurial? As Covin and Wales (2019) have noted, there is a persistent conversation in the literature about individual-level EO; the notion that innovativeness, proactiveness, and risk-taking extend from the firm level to a person. To date, while extensions exist in this direction, I will not argue here for this approach in the context of strategic leadership-EO research. That said, the general notion of an entrepreneurial disposition, or a favorable perspective on entrepreneurship and new venture creation among leaders is useful to consider in this context. For example, McGrath and MacMillan (2000) speak to an entrepreneurial mindset, referring broadly to how senior leaders view risk, opportunity, and uncertainty as being associated with an organizational commitment toward entrepreneurial activities. Mitchell et  al. (2002, p. 97) develop the notion of entrepreneurial cognitions, defined as “the knowledge structures that people use to make assessments, judgments, or decisions involving opportunity evaluation, venture creation, and growth.” Ireland et al. (2009) incorporate both perspectives when proposing that entrepreneurial cognitions within an organization’s leadership will cause the emergence of an entrepreneurial strategic vision that correlates to firm-wide entrepreneurial behavior. Building from the preceding, Covin and Lumpkin (2011, p. 861) argue that an entrepreneurial dominant logic, which would encompass an organizational pervasiveness of managers and senior leaders with shared entrepreneurial cognitions, is likely to be causally adjacent to EO, in that The concept of entrepreneurial dominant logic well captures the collective mindset exhibited by entrepreneurial firms and is consistent with the notion that sustained patterns of entrepreneurial behavior, as is needed to infer the existence of an EO, are the result of top management beliefs, attitudes, and philosophies regarding the value and advisability of entrepreneurial actions.

Taken holistically, a reasonable argument—although we require empirical work to test this argument—is that while not every senior manager must ‘be entrepreneurial’ in the sense that he or she shows certain entrepreneurial behaviors, among high EO firms, there is likely to be a shared sense, or disposition, among decision-makers that views entrepreneurial behaviors favorably. To borrow from McGrath and MacMillan (2000), an

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entrepreneurial mindset permeates the firm, and is a distinguishing characteristic of how managers approach entrepreneurial opportunity. Lastly, it is worth mentioning the possibility, raised by Wales et  al. (2020), of EO manifesting at the top management team level. From this perspective, EO exists as an organizing gestalt, reflecting the shared beliefs among senior executives toward entrepreneurial opportunity, which then permeates the organization. The preceding aligns well with the dispositional perspective in the EO literature, in what Covin and Slevin argued (1991, p.  7) “reflect the top managers’ overall strategic philosophy on effective management practice.” As Wales et  al. (2020) also note, however, viewing EO at the top management team level as an operating philosophy may require conceptualizing EO beyond its traditional innovativeness, proactiveness, and risk-taking dimensions. Nonetheless, further conceptual, and empirical work focusing specifically on the top management team would be useful to the EO literature.

5.2   Alignment Versus Counterbalance Within the EO literature, and when adopting the perspective that EO manifests as an organizing gestalt or philosophy toward entrepreneurial behavior, researchers assume little within-manager variance in this philosophy (Lumpkin & Dess, 1996). That is, the notion of a shared perspective toward EO assumes that the management team “buys in” to this philosophy resulting in philosophical homogeneity; there is general alignment among senior executives in their operating philosophy (Covin & Lumpkin, 2011). Again, there is scarce empirical work in this area, and as such, these perspectives are still conceptual. However, borrowing from the strategic management literature, a competing perspective suggests that there may be usefulness in considering how differing perspectives among senior executives may improve organizational outcomes. The alternative to alignment is counterbalance—firms offset executive weaknesses in certain areas with other executives who show strengths in these areas (Hambrick & Cannella, 2004). For example, firms led by founder-CEOs who excel at driving innovation and capturing new entrepreneurial opportunities but may struggle with scaling the business and building efficient processes may hire a counterbalance senior executive who excels at operations. In this way, the firm benefits from executive competence in multiple areas, and taken holistically, the firm uses the counterbalance approach to maximize firm performance (Marcel, 2009).

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Adopting a counterbalance perspective may be useful under the rubric of organizational ambidexterity. Often argued in the literature—with empirical support—that among large organizations maximizing firm performance requires a temporally-bounded mix of exploratory and exploitative activities (Uotila et  al., 2009). This ambidexterity could take the form of different operating modes among the firm’s various business units, that is, some business units behave entrepreneurially and explore, while other units approach their business conservatively to exploit and extract maximum profits (Fernhaber & Patel, 2012). Ambidexterity could also take the form of the firm oscillating between more exploratory and more exploitative strategies as opportunities and operating conditions change (Boumgarden et al., 2012). In this book, I adopt the frame that most firms operate within an EO band, or range, and that such shifts between an exploratory and exploitative strategic focus, as described previously, are not likely to be widespread. However, when we view EO within a multi-business firm from a portfolio perspective, it is likely that we would see business units with distinct levels of EO. In this way, the firm maximizes performance through aligning the level of EO for each business unit with the nature of the unit’s operating context, and hence, the corporate level strategy reflects organizational ambidexterity. If the firm pursues an ambidexterity strategy, it follows that the firm needs unit managers, and potentially senior executives, with different perspectives toward entrepreneurial opportunity. As Busenbark et al. (2016) argue, such an approach aligns with a configural perspective toward executive leadership, wherein the operating philosophy of the unit executive should align with the strategic requirements demanded of the unit’s environment; a dynamic, complex environment with multiple emerging entrepreneurial opportunities requires an entrepreneurially minded senior executive. Again, while we require empirical work to test this possibility, I conjecture that firms interested in maximizing performance through organizational ambidexterity benefit most from philosophical heterogeneity among executives in terms of entrepreneurial behaviors.

5.3   Differences Across Executive Roles As we consider whether philosophical homogeneity or heterogeneity among the top management team and unit executives maximizes overall EO, it is useful to note that the organizational benefits from whether an

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executive is entrepreneurial minded depends on the executive’s role and responsibilities within the organization (Marcel, 2009). It may be, for example, that entrepreneurially minded executives with line or operating responsibilities—that is, those with profit and loss responsibility for a unit—are most important to the organization when maximizing EO. Such a perspective aligns well with the existing EO literature on the role of these executives in setting the firm’s entrepreneurial behavior (Covin & Slevin, 1991). However, I offer a nuanced perspective, drawing from the book’s organizing theme around a behavioral perspective grounded in resource dependency when motivating strategic leadership-EO research. I concur with the importance—and primacy—of executives with line or operating responsibilities when evaluating that executive’s contribution to firm EO. But support executives—those tasked with functional area responsibilities who support line executives—also merit attention because of the role they play in resource acquisition and allocation decisions. Support executives, such as the chief financial officer, chief information officer, chief human resource officer, and so forth, often control substantial organizational resources. In large firms, these executives may take part in setting firm policy, serve on the firm’s Board or other corporate Boards, and typically report directly to the CEO (Ma & Seidl, 2018). Most importantly, however, these executives influence procurement, and often constrain—or enable—the desire of a line executive to pursue a particular strategy. Because of the centrality of their procurement role, a line executive wanting a particular resource or capability must get the support (or approval) of the relevant support executive (Ma & Seidl, 2018). As such, support executives can wield substantial organizational power, and their perspective toward entrepreneurial behaviors, and willingness to support those behaviors, is an important—but overlooked—perspective in the EO conversation. In the context then of strategic leadership-EO research, when looking at the contribution of a particular executive or executive role on the firm’s EO, I argue that researchers should look beyond the title and focus on the resources that the executive controls and can influence. Such control directly influences the organization’s ability to pursue entrepreneurial opportunities at the corporate level, and as such, is likely to be a significant predictor of entrepreneurial behavior.

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5.4   Performance Evaluation and Incentives As with most senior executives, incentive-based compensation plays a role in the extent to which other executives and leaders throughout the organization pursue entrepreneurial behaviors. Here though, I argue that Boards have an added mechanism through which to encourage managers toward entrepreneurial behavior, and that is through the performance evaluation and feedback process. Importantly, as Bennett and Levinthal (2017) have noted, to maximize organizational impact performance, evaluation should align with compensation incentives—if you tell an executive to pursue innovation in her evaluation, you should reward her later innovation activity with incentive-based compensation. But how the organization evaluates managerial performance is an overlooked area within EO research, despite its importance in shaping leader behavior. Ideally, performance evaluations reflect organization priorities and values (Yuan & Woodman, 2010). They also set the operating parameters through which senior leaders influence and control the behavior of subordinate leaders (Bennett & Levinthal, 2017). As such, performance evaluations align well with the behavioral perspective advanced in this book. High EO firms should have a performance evaluation system that if not necessarily encouraging entrepreneurial behavior among managers, would not punish such behavior either. In this way, the performance evaluation system minimizes downside risk for leaders who take entrepreneurial initiative (Yuan & Woodman, 2010). If these leaders are entrepreneurial minded, and if the organization gives leaders a degree of operating autonomy (Lumpkin & Dess, 1996), performance evaluations that do not punish risk-taking and initiative should help encourage entrepreneurial behavior. It follows then that changing the performance evaluation system may be the single most effective mechanism for a Board to shift the firm’s EO band. Further, it is possible that changes in performance evaluation have the unintended consequence of changing the firm’s EO band, which may be the underexplored causal mechanism explaining why the arc of a firm’s EO theoretically resembles the lifecycle of the firm itself (Wales et  al., 2021). Consider that as firms grow more complex, Boards institute greater control over leader behavior though performance evaluation (Tuggle et al., 2010). If those evaluations punish risk-taking behavior, over time, the firm’s EO band should shift to be more conservative. Should the Board wish to change the firm’s band to become more entrepreneurial, a

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starting point would be to change the performance evaluation system. This is an intriguing possibility for strategic leadership-EO researchers to consider—testing this hypothesis would require substantive access to an organization to evaluate but could have significant theoretical implications for the field.

5.5   A Middle Management Perspective Lastly, it is worthwhile to discuss the role of middle management in the strategic leadership-EO conversation. In the corporate entrepreneurship literature, there is a significant body of work conceptualizing and testing the role that middle managers play in shaping a firm’s entrepreneurial activity. As Kuratko et al. (2005, p. 701) assert, entrepreneurial behaviors at the middle management level may be “most critical” to the firm’s ability to execute its corporate entrepreneurship strategy. The argument rests on the significant role that middle managers play in strategic implementation. As Kuratko et al. (2005, p. 701) describe The role of middle-level managers focuses on effectively communicating information between the firm’s two internal managerial stakeholders (top-level managers and operating-level managers). To fulfill this role, middle-level managers interactively synthesize information, disseminate that information to both top- and operating-level managers and then as appropriate, champion projects that are intended to create newness (e.g., a product, service, or business unit).

I share a similar perspective as Kuratko et  al. (2005) and agree that researchers should account for the role of middle management in driving firm EO. This perspective also aligns with Wales et al.’s (2020) argument for a multilevel perspective on EO, and that managers at this level possess distinct entrepreneurial competencies that can either enhance or stymie entrepreneurial initiatives. Notably, these initiatives could be those developed and imposed by senior leadership, in which case middle managers function more in the execution and translation role as discussed by Kuratko et al. (2005). Alternatively, middle managers may conceive and develop their own initiatives, and engage in issue selling and resource acquisition behaviors, and function more in the project champion role (Kuratko et al., 2005).

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Nonetheless, from the behavioral perspective advanced in this book, it may be that while middle managers play a distinct role in shaping and contributing to the firm’s EO, there may not be theoretical differences in the vein of the Kuratko et al. (2005) model. This is not to say that the Kuratko et al. (2005) model does not apply. However, when we consider that strategic leadership may manifest at all levels of an organization, and that organizational controls such as performance evaluations and incentive-­ based compensation also influence leader behavior across all organizational levels, that a manager occupies a particular mid-level may not be as pertinent as whether the manager is able to acquire and control critical resources.

References Bennett, V.  M., & Levinthal, D.  A. (2017). Firm lifecycles: Linking employee incentives and firm growth dynamics. Strategic Management Journal, 38(10), 2005–2018. Boumgarden, P., Nickerson, J., & Zenger, T.  R. (2012). Sailing into the wind: Exploring the relationships among ambidexterity, vacillation, and organizational performance. Strategic Management Journal, 33(6), 587–610. Busenbark, J. R., Krause, R., Boivie, S., & Graffin, S. D. (2016). Toward a configurational perspective on the CEO: A review and synthesis of the management literature. Journal of Management, 42(1), 234–268. Covin, J. G., & Lumpkin, G. T. (2011). Entrepreneurial orientation theory and research: Reflections on a needed construct. Entrepreneurship Theory and Practice, 35(5), 855–872. Covin, J. G., & Slevin, D. P. (1991). A conceptual model of entrepreneurship as firm behavior. Entrepreneurship Theory and Practice, 16(1), 7–25. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Fernhaber, S.  A., & Patel, P.  C. (2012). How do young firms manage product portfolio complexity? The role of absorptive capacity and ambidexterity. Strategic Management Journal, 33(13), 1516–1539. Hambrick, D. C., & Cannella, A. A. (2004). CEOs who have COOs: Contingency analysis of an unexplored structural form. Strategic Management Journal, 25(10), 959–979. Ireland, R. D., Covin, J. G., & Kuratko, D. F. (2009). Conceptualizing corporate entrepreneurship strategy. Entrepreneurship Theory and Practice, 33(1), 19–46.

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Kuratko, D. F., Ireland, R. D., Covin, J. G., & Hornsby, J. S. (2005). A model of middle-level managers’ entrepreneurial behavior. Entrepreneurship Theory and Practice, 29(6), 699–716. Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21(1), 135–172. Ma, S., & Seidl, D. (2018). New CEOs and their collaborators: Divergence and convergence between the strategic leadership constellation and the top management team. Strategic Management Journal, 39(3), 606–638. Marcel, J.  J. (2009). Why top management team characteristics matter when employing a chief operating officer: A strategic contingency perspective. Strategic Management Journal, 30(6), 647–658. McGrath, R.  G., & MacMillan, I.  C. (2000). The entrepreneurial mindset: Strategies for continuously creating opportunity in an age of uncertainty. Harvard Business Press. Mitchell, R. K., Busenitz, L., Lant, T., McDougall, P. P., Morse, E. A., & Smith, J.  B. (2002). Toward a theory of entrepreneurial cognition: Rethinking the people side of entrepreneurship research. Entrepreneurship Theory and Practice, 27(2), 93–104. Tuggle, C. S., Sirmon, D. G., Reutzel, C. R., & Bierman, L. (2010). Commanding board of director attention: Investigating how organizational performance and CEO duality affect board members’ attention to monitoring. Strategic Management Journal, 31, 946–968. Uotila, J., Maula, M., Keil, T., & Zahra, S. A. (2009). Exploration, exploitation, and financial performance: Analysis of S&P 500 corporations. Strategic Management Journal, 30(2), 221–231. Wales, W. J., Covin, J. G., & Monsen, E. (2020). Entrepreneurial orientation: The necessity of a multilevel conceptualization. Strategic Entrepreneurship Journal, 14(4), 639–660. Wales, W. J., Kraus, S., Filser, M., Stöckmann, C., & Covin, J. G. (2021). The status quo of research on entrepreneurial orientation: Conversational landmarks and theoretical scaffolding. Journal of Business Research, 128, 564–577. Yuan, F., & Woodman, R. (2010). Innovative behavior in the workplace: The role of performance and image outcome expectations. Academy of Management Journal, 53(2), 323–342.

CHAPTER 6

Leadership Considerations for EO in a Multi-­Business Firm

Abstract  This chapter addresses an oft-mentioned issue in EO research: EO at the corporate strategy level. Scholars conceive of EO existing at the business strategy level, delineated by business model. By extension, this implies that for firms with multiple business models, there would be an equivalent number of ‘EOs.’ I explore this issue, and introduce the concept of a portfolio approach, wherein senior leadership aligns a unit’s EO to its product-market opportunity in the context of the firm’s corporate strategy. I then briefly discuss whether the firm’s overall EO is an additive function of the unit-level EO or viewed as a reflection of the common variance in EO across units. I then turn to the resource competition question inherent to multi-business firms, and how this competition effects unitand corporate-level EO. Lastly, I briefly review the role of organizational structure in the EO literature. Keywords  Corporate strategy • EO portfolio • Resource competition • Additive EO

6.1   Corporate Strategy and EO In smaller firms, there is little distinction between business-level strategy and corporate-level strategy. These firms commonly have a single delineable business model, defined by an overarching value proposition that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. S. Anderson, Entrepreneurial Orientation and Strategic Leadership, https://doi.org/10.1007/978-3-030-87300-4_6

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connects the firm’s products and services, supported by a revenue model that aligns with this value proposition (Covin & Slevin, 1989). While potentially diverse in geographic reach, for these firms the product/market decisions at the heart of business-level strategy define the firm’s business (Covin & Slevin, 1989). To put it simply, there is one business and one business model, and hence one EO for the firm. A firm that chooses to diversify its business by either creating a new business model or acquiring an unrelated business now has a distinction between business unit strategy and the firm’s overall corporate strategy. Drawing from the strategic management literature, corporate strategy concerns decisions as to which businesses the firm will be in, as opposed to how the firm competes in those businesses (Menz & Scheef, 2014). While activities such as mergers and acquisitions, equity joint ventures, and related behaviors often fall under the rubric of EO, the distinguishing characteristic of a corporate strategy decision is whether the decision changes the mix of underlying businesses under the corporate umbrella (Covin & Miles, 1999). There would seem, then, little utility to a corporate strategy perspective on EO.  Across all three major EO conceptualizations, each defines EO under the rubric of business-level strategy, the product/market decisions that dictate how the firm competes in the businesses it chooses to be in (Covin & Wales, 2019). Kreiser et al. (2020), however, note that it may be useful for EO scholars to consider how EO may manifest at the corporate level, arguing that the resource allocation decisions made by senior executives that affect multiple constituent businesses reflect in the overall firm’s behavioral orientation toward entrepreneurship. While there is merit to the Kreiser et al. (2020) argument, and while there may be a delineable corporate-level EO, for this book I adopt the perspective of EO at the business unit level, which reflects the set of innovative and proactive behaviors that define how the unit uses EO to improve performance and capture a market leadership position (Covin & Slevin, 1991). In practice, this means that I assume that firms with multiple business units have a delineable EO for each unit. I further assume that while unit EO may covary across units, consistent with the Kreiser et al. (2020) argument, I argue that they do not have to covary. This perspective creates new opportunities for theory construction in strategic leadership-EO research, as I outline below.

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6.2   A Portfolio Approach As we then consider a multi-business firm with multiple delineable business models, from the corporate strategy perspective, there is a legitimate question of the role of the CEO and corporate leadership in influencing unit EO. CEOs are central in resource acquisition and allocation (Pfeffer & Salancik, 1978). As such, CEO resource decisions will, by definition, constrain or enable unit EO. But this centrality does not necessarily imply that the CEO will be directly influencing the unit’s strategy. Further, it is also possible that CEOs direct attention differentially across business units, depending on the importance and interests of the CEO (Ocasio, 2011). As such, within a given firm, the CEO may be directly involved in crafting unit EO or may not be directly involved. The question of CEO involvement in unit EO raises the question of how, and to what end, CEOs may wish to directly influence unit EO for the overall benefit of the firm. Returning to the organizational ambidexterity argument advanced previously in this book, there may be corporate benefits to CEOs and corporate leadership manipulating unit EO to maximize overall corporate performance. Underlying this possibility is the assumption that the firm’s constituent businesses have different environmental constraints and by extension different market opportunities (Rawley, 2010). There is a further assumption that each unit differentially contributes to the corporate parent, and as such there is an inherent diversification of revenue or profitability that requires corporate oversight (Zhou, 2011). What follows, then, is a portfolio-based approach to corporate oversight of unit EO. From the corporate perspective, senior executives have a view of the parent’s overall environmental constraints, opportunities, and resource dependencies (Menz & Scheef, 2014). Further, corporate leaders work under the performance expectations—and compensation structure—set by the Board as it applies to the firm’s entire operations (Graffin et al., 2020). As such, these individuals have the knowledge, the incentive, and the resource control to help shape unit EO.  Given that EO is an important driver of sales growth rate, and given that EO is a resource-­ consuming posture, and given the possibility that firms use EO to gain control over critical resources and exogenous opportunities, it follows that multi-business firms gain by having corporate leadership directly involved in managing unit EO.

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A portfolio-based approach to managing unit EO necessitates the design and implementation of organizational controls and monitoring systems to ensure units adhere to prescribed and agreed upon EO levels (Bennett & Levinthal, 2017). For example, setting differential performance targets and resource allocation guidelines are two mechanisms through which corporate leaders may ensure unit compliance. That said, there may be a curvilinear effect to consider in terms of organizational controls—while controls are likely to be beneficial in corporate oversight and maximizing corporate performance as a function of manipulating unit EO, excessive control is likely to impede EO across all units, and in doing so, negatively influence corporate performance.

6.3   Formative and Reflective Perspectives Building from the portfolio-based approach, we can revisit the question of how we might define corporate EO in this context. From one perspective, we might define corporate EO as the additive sum of the unit-level EO. That is, corporate EO takes its meaning from the total variance in EO at the unit level, in an analogous way to a formatively construed higher-­ order latent construct (Anderson et  al., 2015). Because each unit must have an EO, corporate EO is a function—potentially weighted—of the extent to which each unit’s EO contributes to the total variance at the corporate level. As with a formatively measured construct (MacKenzie et al., 2011), this approach assumes that while unit EO may covary across units, they do not have to covary. Further, from this perspective, units are functionally independent, in the sense that unit EO reflects the environmental and competitive context of that unit, and not any other unit’s context. Directionally, we can think of unit EO causing the manifestation of corporate EO, and any change in unit EO will result in a change to corporate EO. An alternative to the formative approach is to view corporate EO through a reflective measurement lens. From this perspective, we define corporate EO as a function of the shared variance of unit EO.  That is, corporate EO takes its meaning from the extent to which unit-level EO shares commonalities between units (Anderson et al., 2015); in its most simplistic form corporate EO would be the average of the constituent units’ EO. From a reflective measurement perspective (MacKenzie et al., 2011), this approach assumes that there exist commonalities across units such that they are functionally dependent—each unit’s EO shares variance

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with other unit EOs, stemming, as it were, from a presumed underlying common core. Directionally, we can think of unit EO reflecting corporate EO, in that changing corporate EO reflects in changes to unit EO. I will not conjecture as to which conceptualization is proper. As Podsakoff et al. (2016) note, constructs are not inherently formative or reflective, and the choice of measurement model must align with the conceptual development of the construct. In both cases, to model corporate EO, a researcher would need to measure unit EO, which may be difficult in large firms. Further, it may be challenging to develop such a sample with enough corporate parents to have sufficient statistical power to identify a model. One possibility, however, may be using business segment data from US listed publicly traded firms, which these firms must provide in their annual report as required under federal securities law (Titus & Anderson, 2018). There is more work necessary, but should researchers devise a reliable measurement model based on unit EO, such an approach could open significant new research opportunities in the strategic leadership-­EO conversation.

6.4   Resource Competition Regardless of formatively or reflectively construing corporate EO from a portfolio-based perspective, there are practical considerations that will affect senior leaders’ ability to influence unit EO. As subsidiaries, units also operate under shared constraints that reflect the priorities, resources, and competitive position of the parent. As such, both the corporate level and the unit level create operating contexts that constrain or enable strategic choice, and result in functional dependencies (Zhu & Westphal, 2021). The most significant shared constraint is access to organizational resources. Among even the financially strongest corporations, resources are finite. Public firms enjoy access to public equity and debt markets that can create significant opportunities for resource acquisition, but that opportunity only exists to the extent to which individuals and other financial institutions are willing to supply capital (Stearns & Mizruchi, 1993). Human capital, physical resources, and necessary production materials all have similar constraints. In a multi-business firm, there is then an inherent competition for scarce resources among units (Rawley, 2010). How corporate and unit leadership resolves competitive demands bears directly on unit EO, given the resource requirements to pursue new entrepreneurial opportunities.

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From the corporate perspective, one approach to resolving resource constraints is the unit budgeting model. To preserve a degree of autonomy, corporate parents may give units independent profit-and-loss responsibility, with each unit expected to meet a contribution margin target back to the corporate parent (Zhou, 2011). In this way, each unit “eats what it kills” in the sense that so long as the unit meets its contribution margin, it has the autonomy to set prices, manage its cost structure, make strategic investments, and hold sufficient reserves (Rawley, 2010). That said, while the unit enjoys operational degrees of freedom, this approach can increase costs because units may not share resources or costs between each other, and potentially may limit the unit from tapping the corporate parent to acquire resources at scale. From the corporate parent perspective, such an approach requires fewer coordinating mechanisms, although this comes at the cost of less oversight and control. From the unit perspective and depending on the firm’s budget approach, addressing resource constraints may involve coalition building and social capital (Cao et al., 2015). From a within-firm resource dependency perspective, units with control over critical resources occupy a power position, and other unit CEOs may use social influence as a mechanism to gain access to these resources, or to inhibit competing units from obtaining these resources (Rawley, 2010). Political maneuvering is therefore common in multi-business firms as unit CEOs jockey for scarce resources that enable unit EO. From the corporate parent perspective, this internal competition may yield stronger unit performance, as it rewards those units successful at obtaining resources (Zhou, 2011). A drawback, however, is that units do not have an incentive to work collaboratively, which may increase costs and diminish cross-unit opportunity exploitation (Zhou, 2011).

6.5   The Role of Organizational Structure In the EO literature, there is substantive empirical support for structural organicity as an EO covariate. Briefly, organic structures have flat organizational hierarchies, easy vertical and horizontal communication, high work autonomy, and light managerial control (Anderson et al., 2009). At the opposite end of the spectrum, mechanistic structures have substantial organizational hierarchies, rigid communication rules, and strict managerial control over what employees work on and how they go about that work (Anderson et al., 2009). As we consider organizational structure in

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the context of strategic leadership-EO research, structural organicity will be a relevant and theoretically important consideration. At its root, structural organicity reflects the degree to which leaders can pursue new initiatives and entrepreneurial opportunities with autonomy. In the EO literature, autonomy is a common theme, and underpins the conversation about how EO permeates across an organization and allows for leaders at multiple levels to advance EO within the sphere of their control (Lumpkin & Dess, 1996). Time management, operating discretion, and organizational support for taking on risk factor in leader autonomy (Covin & Wales, 2019). While the Lumpkin and Dess (1996) EO conceptualization includes autonomy within EO’s conceptual domain, another argument is that autonomy is a critical antecedent to entrepreneurial behavior (Covin & Wales, 2019). That is, the presence of autonomy is a causal factor in increasing EO, and in a multi-business firm, we would expect that CEOs of high EO units enjoy wide operating autonomy. Autonomy, however, is not absolute. It is worth noting that while the true conglomerate corporate firm still exists, as an organizing gestalt, few multi-business firms use this structure today. By true conglomerate, I am referring to corporate parents that keep very loose control over their subsidiary businesses (Dess & Robinson Jr., 1984). Most modern multi-­ business firms exist in a matrix, or hybrid, structure wherein a corporate parent supplies common or shared services that each unit uses (Nell & Ambos, 2013). These common services—human resources, information technology, marketing, and so forth—create sources of common variance that increases unit EO covariance to the extent that such services relate to EO. A notable extension, then, in the strategic leadership-EO conversation would be the extent to which unit CEOs are able to use these common resources to advance unit EO, and the extent to which corporate EO varies as a function of unit resource exploitation. That is, corporate parents who build strong common services that units tap to advance their EO should see enhanced EO at the corporate strategy level. Conversely, corporate parents who mandate use of poorly performing common services are likely to see depression of EO among all units, and hence lower corporate EO. Maximum EO in the multi-business firm may then result from a combination of high operating autonomy among unit CEOs with highly capable and appropriately resourced shared services that function as a force multiplier for unit EO.

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References Anderson, B. S., Covin, J. G., & Slevin, D. P. (2009). Understanding the relationship between entrepreneurial orientation and strategic learning capability: An empirical investigation. Strategic Entrepreneurship Journal, 3(3), 218–240. Anderson, B.  S., Kreiser, P.  M., Kuratko, D.  F., Hornsby, J.  S., & Eshima, Y. (2015). Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. Bennett, V.  M., & Levinthal, D.  A. (2017). Firm lifecycles: Linking employee incentives and firm growth dynamics. Strategic Management Journal, 38(10), 2005–2018. Cao, Q., Simsek, Z., & Jansen, J. (2015). CEO social capital and entrepreneurial orientation of the firm: Bonding and bridging effects. Journal of Management, 41(7), 1957–1981. Covin, J. G., & Miles, M. P. (1999). Corporate entrepreneurship and the pursuit of competitive advantage. Entrepreneurship: Theory and Practice, 23(3), 47–47. Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87. Covin, J. G., & Slevin, D. P. (1991). A conceptual model of entrepreneurship as firm behavior. Entrepreneurship Theory and Practice, 16(1), 7–25. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Dess, G.  G., & Robinson, R.  B., Jr. (1984). Measuring organizational performance in the absence of objective measures: The case of the privately-held firm and conglomerate business unit. Strategic Management Journal, 5(3), 265–273. Graffin, S. D., Hubbard, T. D., Christensen, D. M., & Lee, E. Y. (2020). The influence of CEO risk tolerance on initial pay packages. Strategic Management Journal, 41(4), 788–811. Kreiser, P.  M., Anderson, B.  S., Kuratko, D.  F., & Marino, L.  D. (2020). Entrepreneurial orientation and environmental hostility: A threat rigidity perspective. Entrepreneurship Theory and Practice, 44(6), 1174–1198. Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21(1), 135–172. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334. Menz, M., & Scheef, C. (2014). Chief strategy officers: Contingency analysis of their presence in top management teams. Strategic Management Journal, 35(3), 461–471.

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Nell, P. C., & Ambos, B. (2013). Parenting advantage in the MNC: An embeddedness perspective on the value added by headquarters. Strategic Management Journal, 34(9), 1086–1103. Ocasio, W. (2011). Attention to attention. Organization Science, 22(5), 1286–1296. Pfeffer, J., & Salancik, G.  R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2016). Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organizational Research Methods, 19(2), 159–203. Rawley, E. (2010). Diversification, coordination costs, and organizational rigidity: Evidence from microdata. Strategic Management Journal, 31, 873–891. Stearns, L.  B., & Mizruchi, M.  S. (1993). Board composition and corporate financing: The impact of financial institution representation on borrowing. Academy of Management Journal, 36(3), 603–618. Titus, V. K., & Anderson, B. S. (2018). Firm structure and environment as contingencies to the corporate venture capital–parent firm value relationship. Entrepreneurship Theory and Practice, 42(3), 498–522. Zhou, Y.  M. (2011). Synergy, coordination costs, and diversification choices. Strategic Management Journal, 32(6), 624–639. Zhu, D. H., & Westphal, J. D. (2021). Structural power, corporate strategy, and performance. Strategic Management Journal, 42(3), 624–651.

CHAPTER 7

Contextual and Industry Considerations in Strategic Leadership-EO Research

Abstract  I begin this chapter with a brief review of the task environment—environmental hostility, dynamism, and complexity—as a moderating factor in the EO-performance literature, and direct-effect relationships of the task environment to EO. But I quickly turn my attention to contextual factors that go beyond the task environment, and that represent fruitful avenues of inquiry in the strategic leadership—EO context. I focus first on capital velocity expectations, and how capital efficiency may influence leadership decisions and resource allocation decisions regarding EO. I then explore how the speed of digital transformation in an industry creates a series of moderating conditions on various strategic leadership predictors and EO outcomes. I next turn to a brief discussion of differences between public and private firms, specifically in the context of disclosure requirements and performance expectations among publicly traded firms. I conclude with how various boutique EO conceptualizations—international EO, non-profit EO, and so forth—relate to the research model presented in the book. Keywords  Task environment • Digital transformation • Capital velocity • International EO

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7.1   Going Beyond the Task Environment From the earliest EO research, scholars have considered the role of the firm’s task environment in both directly influencing EO, and as contextual factors influencing the relationship between EO and firm performance (Covin & Slevin, 1989; Miller, 1983). Predominantly drawing from Dess and Beard’s (1984) conceptualization of the task environment along three dimensions—munificence, dynamism, and complexity—meta-analyses consistently report dozens of studies incorporating the firm’s task environment in EO research, which includes studies using these variables as theoretically meaningful covariates (Rosenbusch et al., 2013). To briefly review, environmental munificence—which is the conceptual opposite of environmental hostility—reflects the extent to which the firm’s operating environment supports organic growth and ample resource availability (Rosenbusch et  al., 2013). Munificent environments exist when market demand increases at an equal or faster rate than sales growth among constituent firms. In short, the market ‘pie’ is getting larger as a function of demand, which supports broad firm growth (Dess & Beard, 1984). In contrast, in hostile environments, firm growth is a function of building market leadership positions while competing aggressively for market share (Rosenbusch et al., 2013). Environmental dynamism, conceptually opposite from environmental stability, is a function of two factors in the environment—the predictability in the rate of change, and the predictability of the magnitude of change (Dess & Beard, 1984). An example is helpful here. Consider the microprocessor industry, which on the surface would seem to be very dynamic. Microprocessors are, however, a very stable industry, because Moore’s Law—which specifies that the number of transistors able to be on a given silicon chip, and hence processing power, doubles at regular intervals— governs innovation in the industry. As such, the microprocessor industry is quite stable, with the pace and magnitude of change well understood. Significant uncertainty, by contrast, is a distinguishing characteristic of dynamic environments. Lastly, complexity refers to the heterogeneity of the firm’s operating environment and resource needs (Dess & Beard, 1984). While often conceptualized in terms of the firm’s technology needs, in its earlier development complexity referred to the broad range of inputs and outputs necessary to enable the firm to compete in its industry (Dess & Beard, 1984). For example, a maker of app-based video games has a narrower set

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of inputs and outputs than a commercial aircraft manufacturer with a global supply chain and different regulatory regimes for its customers. As Dess and Beard (1984 p. 56) note, … “managers facing a more complex (i.e., heterogeneous) environment will perceive greater uncertainty and have greater information-processing requirements than managers facing a simple environment.” While empirically these relationships may not be as well-established as theorists suggest, most EO scholars assume that the task environment moderates the relationship between EO and firm performance such that the EO-performance relationship strengthens for firms in dynamic, hostile, and complex environments (Rosenbusch et al., 2013). Paradoxically, these environmental conditions tend to negatively relate to, or otherwise depress, EO directly. As Kreiser et al. (2020) note, in the environments where it is most beneficial for the firm to behave entrepreneurially, these same environments tend to cause retrenchment from these behaviors. As Covin and Lumpkin (2011) note, the prevalence of task environment research in the EO conversation is such that it is difficult to motivate high-potential research questions in this area. That does not necessarily imply that we have as rich an understanding of these factors as causes and boundary conditions as we might wish, but rather, as the authors comment, “… we believe that much of the value added of these lines of research has now been realized” (Covin & Lumpkin, 2011, p. 865). That said, there may be useful contributions in the strategic leadership-EO context by examining task environment considerations related to how senior leader behaviors in different operating contexts influence firm EO. Environmental complexity is an intriguing potential area for EO researchers to consider, given that researchers often overlook complexity in the EO literature. As Dess and Beard (1984) note, complex environments require greater diversity of resources from the external environment and require complex internal systems and capabilities to monitor and manage those resources. When we consider a potential causal chain, it may be that complexity leads to greater cognitive flexibility on the part of senior leadership, because complex environments expose leaders to a wider array of resource needs and sources. In this case, it may be that complexity increases cognitive flexibility that then increases firm EO.  But, because complex environments are also highly uncertain, it may also be the case that a firm uses EO as a mechanism to acquire critical resources to reduce resource dependencies and hence lower complexity. As such, there may be

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a key temporal frame to evaluate how complexity influences EO, and how EO influences complexity over time.

7.2   Capital Velocity Expectations For the purposes of this discussion, I am referring to capital velocity to encompass both asset velocity and capital asset velocity. Asset velocity refers to the totality of the firm’s assets, which would include cash, receivables, inventory, and related sources of assets, with capital assets referring to the firm’s physical infrastructure, and velocity referring to the efficiency with which a firm is able to use these assets to generate revenue. Slack resources are a closely related concept (George, 2005). In the strategic management literature, organizational slack broadly refers to assets held by the firm beyond those needed to run the business (George, 2005). Further, we can distinguish between unencumbered slack resources, such as cash, that leaders may easily redeploy, and encumbered resources within the firm’s cost structure, such as wages above market value, which are not easy to redeploy (George, 2005). We find both empirical and conceptual investigations of the relationship between slack resources and EO in the literature; scholars assume that the presence of slack increases EO, because of EO’s resource-consuming nature (Anderson, 2010). What makes capital velocity a notable extension of the slack resource conversation is that capital velocity relates specifically to the firm’s ability to use their resources to generate revenue. That is, there is motion inherent to velocity, and as such, capital velocity aligns well with the behavioral perspective advanced in this book. Slack resources, in contrast, represent an available pool of resources, but are static, in the sense that there is no a priori requirement to use slack resources for any specific purpose (George, 2005). The higher the firm’s capital velocity, the more efficient the firm is at using all its available resources—slack included—in generating revenue. I posit that capital velocity will be a theoretically meaningful contextual factor influencing various possible strategic leadership-EO research models. Broadly, the higher the firm’s capital velocity, the higher the firm’s asset conversion efficiency, and I would argue, the faster the firm is able to deploy resources to capture new entrepreneurial opportunities. Capital velocity in this context is a proxy for execution ability, and greater execution ability should in most cases be a positive contextual factor. For example, consider a research model hypothesizing that sales growth-based compensation incentives for senior executives should

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positively increase firm EO. High capital velocity should strengthen the main effect relationship, by allowing the firm to use its resources more efficiently toward high-potential entrepreneurial opportunities. In another example, consider a research model hypothesizing that CEO social capital positively increases EO, predicated on the argument that social capital increases exposure to more opportunities and potential resource combinations that capture market value, and that social capital improves the ability of the CEO to implement new strategic actions (Cao et al., 2015). Because a high capital velocity firm is already skilled at execution and runs with a high degree of efficiency, CEOs of these firms should see a multiplicative effect of their social capital on driving higher levels of EO.

7.3   Digital Transformation Shifting to a macro-perspective, it is difficult to imagine a more universal disruptive exogenous force than rapid digital transformation. At the intersection of technology, businesses processes, human resources, marketing, and related domains, in the practitioner literature, digital transformation of business over the coming decades will create both significant disruption and significant opportunity (Khanagha et al., 2020). That said, there are recent theoretical developments that speak to how management researchers may incorporate these developments into delineable research models; however, the space is novel (Khanagha et al., 2020). As it pertains to strategic leadership-EO research, I argue that the binary perspective of whether digital transformation occurs is not particularly useful. There is ample anecdotal understanding that digital transformation is occurring, it is impactful, and there are few, if any, industries unaffected. Instead, I argue that the salient contextual factors are: (a) the within-industry speed at which this transformation occurs; and (b) the extent to which this transformation renders existing business models at a competitive disadvantage. There are industry contexts in which we might imagine the preceding two factors manifesting orthogonally, or simultaneously. For example, we might argue for the slow pace of digital transformation in the higher education industry. The adoption of online teaching technologies and pedagogies, while certainly experiencing periodic bursts (e.g., the introduction of the Massively Online Open Course [MOOC] in the early 2010s and the COVID-19 pandemic in 2020), is now decades old. However, should technology-enabled education delivery models pioneered by firms that

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exist outside of traditional university structure become widely accepted and valued by the market, it would make university business models obsolete and under significant competitive pressure. While theoretical work is still nascent, there are interesting implications of the speed of digital transformation through the strategic leadership-EO lens, particularly as it relates to a resource dependency perspective. For example, resource obsolescence and dealing with obsolescence is one such factor (Hillman et al., 2009). As a firm faces rapid digital transformation, it is likely that current strategically valuable resources may lessen in salience, and new resource needs emerge. A well-known phenomenon in the organizational theory literature (Pfeffer & Salancik, 1978), what potentially makes resource obsolescence in the digital transformation space theoretically different is the speed at which individual resources may become obsolete and the extent to which a wide range of resources may rapidly deteriorate in value. News and media businesses are interesting examples of how rapid technological change have both altered their customer-­facing value proposition and the stability of their business models in a very brief period, when compared to the overall longevity of these industries. That said, while resource obsolescence might place downward pressure on firm competitiveness, the speed of digital transformation may also expose, at a much faster rate, new entrepreneurial opportunities for exploitation (Khanagha et al., 2020). For firms in a high-EO range, being in an industry undergoing rapid transformation may exponentially benefit in terms of value capture, because these firms are already well-equipped and comfortable with innovation and with making proactive strategic moves that improve the firm’s leadership position. Going further, an interesting paradox emerges as it relates to the speed of digital transformation. In a similar manner to environmental munificence and complexity, rapid digital transformation is likely to cause substantial uncertainty about the usefulness of existing resources and existing business models (Khanagha et  al., 2020). These environments are also likely to have an exceptionally low signal-to-noise ratio, such that it is difficult for leaders in this context to identify the salient trends and key insights to better inform decision-making (Ocasio, 2011). At the extreme possibility is cognitive overload, wherein uncertainty causes senior leaders to retrench and concentrate on variables that are well-known, but with declining utility (Shepherd et al., 2017). As such, one research model is rapid digital transformation causing cognitive overload which results in

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declining EO. The paradox, however, is that as with hostile environments, rapid digital transformation leads to more conservative behavior at the same time when firms gain the most from increasing their EO.

7.4   Differences Between Public and Private Firms While idiosyncratic to each firm, ownership structure is likely to play a meaningful contextual role in the relationship between various strategic leadership variables and EO. That said, there are research design considerations when contrasting public and private firms. Specifically, firm size and firm complexity are part of this conversation. Smaller firms tend to be privately held, particularly in the United States, and smaller firms with only one delineable business unit have one EO to manage and for leaders to influence. Obtaining a reasonable contrasting sample of smaller, less complex publicly traded firms will be difficult (but possible). Conversely, it is easy to collect financial and related data for large, publicly traded firms, but exceptionally difficult to collect financial data from large, privately held firms. As such, disclosure and data availability will constrain robust empirical testing of a potential contrast. That said, there are theoretically motivated differences as a function of ownership type that may influence strategic leadership-EO models. Drawing from Fitza and Tihanyi (2017), I will focus on three specifically—access to financial markets, ownership goals, and employee incentives. Private firm leadership made the endogenous choice to forego raising capital in the public markets. This choice preserves managerial control and, potentially, blocks ownership of a founder or founding team. The consequence of that choice, however, is constrained ability to raise capital (Fitza & Tihanyi, 2017). Because EO is a resource-consuming posture, limited capital access may lower available or potential slack resources, which may then negatively moderate potential strategic leadership-EO relationships. Regarding managerial goals, as Cennamo et  al. (2012) note, is that specific goals and goal longevity differ across ownership forms. For example, owners of private firms may favor preservation of control and ensuring generational transfer of ownership (Cennamo et al., 2012). As such, private firms may have a higher risk-return threshold that would structurally lower desire to pursue projects with highly uncertain outcomes. The result being that private firm ownership defined by managerial goal setting may negatively moderate a given strategic leadership-EO model.

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Lastly, depending on the specific ownership structure (i.e., an employee-­ owned firm), private firms may have limited ability to use equity-based incentives for executive compensation. Certainly, private firms may use employee bonuses and other financial incentive-based compensation. However, employees in private firms tend to have lower upside gain to these incentives than comparable employees in publicly traded firms. Unfortunately, collecting data on private firm incentive compensation will be challenging to do at scale. Nonetheless, if financial incentives are theoretically meaningful predictors or boundary conditions within the strategic leadership-EO conversation, limitations on these incentives in private firms may diminish the ability of private firms to aggressively pursue new entrepreneurial opportunities.

7.5   Boutique EO Conceptualizations Lastly, while senior EO scholars often discourage the practice, there are ongoing conservations in the literature about stretching EO’s conceptual domain into new contextual areas (Covin & Wales, 2019). Briefly, domain stretching involves using the core of a well-developed construct’s conceptual space and then extending that space into new conceptual areas or narrowing the space to focus on a specialty area that the broader construct does not specifically address (George & Marino, 2011). For example, self-­ efficacy is a well-established construct in the psychology literature (Chen et al., 2001); entrepreneurial self-efficacy (Shahriar, 2018) is an example of conceptually stretching the core of the self-efficacy construct to focus directly on the entrepreneurship context. Importantly, there is no objective or correct definition of a construct or a construct’s conceptual domain. Latent constructs are theoretical devices invented by researchers to give meaning to conceptual and not directed observable phenomenon (Podsakoff et al., 2016). There is then nothing inherently wrong with stretching a construct’s conceptual domain to encompass new areas or specialty domains. The concern with doing so, however, is whether the researcher shows sufficient validity for the new specialty area or expanded space independent of the primary construct’s (George & Marino, 2011). For example, a concerning approach would be to simply take a well validated scale for a construct, change the indicator prompts to suit the specialty area, and use the measurement approach to justify the stretched conceptual domain. The problem with this approach is that the resulting

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construct will lack the rigorous theoretical development that motivates the original construct, and hypothesis tests drawn from the looser conceptual definition will lead to an interpretational confounding problem—the statistical tests being divorced from the theoretical research model (George & Marino, 2011). The better approach when the researcher desires to expand a construct’s conceptual domain into a new context or specialty area is to apply the same theoretical rigor to the new construct as that which characterizes the existing construct. While the new will borrow conceptually from the existing, the new is still a new construct, and the researcher should develop and motivate the construct as such. In the EO literature, there are two exemplars in this regard. Covin and Miller (2014) introduce the concept of international EO, and through rigorous conceptual development, discuss the similarities and differences between EO and international EO, measurement issues for researchers to consider, and outline a high-potential research stream focused specifically on the international EO construct. Similarly, Morris et al. (2011) approach the question of how EO manifests within the non-profit context and offer a conceptualization of EO in this context that aligns with the structural and organizational considerations of non-profit organizations. The preceding two boutique EO conceptualizations are notable because both contexts—international and non-profit—also occupy significant attention in the strategic leadership literature. It may be useful for researchers interested in strategic leadership-EO research in these domains to look to the respective EO conceptualization rather than the entrepreneurial behavior-­ centric EO approach adopted in this book. In summarizing, there are significant opportunities to investigate contextual influences within strategic leadership-EO research. Opportunities exist to apply well-established contextual factors in new models, and opportunities exist to apply new and or overlooked factors in existing models. Identifying and testing theoretical boundary conditions are important mechanisms through which we build predictive theory, and scholars have no shortage of interesting and useful conditions to consider.

References Anderson, B.  S. (2010). Resource knowledge, organizational slack, and entrepreneurial orientation (PhD) (J.  G. Covin, Ed.). Indiana University. https:// search.proquest.com/openview/c4d81e1c21e2b5db2ba396fa85c35170/1 ?pq-­origsite=gscholar&cbl=18750

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Cao, Q., Simsek, Z., & Jansen, J. (2015). CEO social capital and entrepreneurial orientation of the firm: Bonding and bridging effects. Journal of Management, 41(7), 1957–1981. Cennamo, C., Berrone, P., Cruz, C., & Gomez-Mejia, L. R. (2012). Socioemotional wealth and proactive stakeholder engagement: Why family–controlled firms care more about their stakeholders. Entrepreneurship Theory and Practice, 36(6), 1153–1173. Chen, G., Gully, S.  M., & Eden, D. (2001). Validation of a new general self-­ efficacy scale. Organizational Research Methods, 4(1), 62–83. Covin, J. G., & Lumpkin, G. T. (2011). Entrepreneurial orientation theory and research: Reflections on a needed construct. Entrepreneurship Theory and Practice, 35(5), 855–872. Covin, J.  G., & Miller, D. (2014). International entrepreneurial orientation: Conceptual considerations, research themes, measurement issues, and future research directions. Entrepreneurship Theory and Practice, 38(1), 11–44. Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87. Covin, J. G., & Wales, W. J. (2019). Crafting high-impact entrepreneurial orientation research: Some suggested guidelines. Entrepreneurship Theory and Practice, 43(1), 3–18. Dess, G. S., & Beard, D. (1984). Dimensions of organizational task environments. Administrative Science Quarterly, 29(1), 52–73. Fitza, M., & Tihanyi, L. (2017). How much does ownership form matter? Strategic Management Journal, 38(13), 2726–2743. George, B. A., & Marino, L. (2011). The epistemology of entrepreneurial orientation: Conceptual formation, modeling, and operationalization. Entrepreneurship Theory and Practice, 35(5), 989–1024. George, G. (2005). Slack resources and the performance of privately held firms. Academy of Management Journal, 48(4), 661–676. Hillman, A.  J., Withers, M.  C., & Collins, B.  J. (2009). Resource dependence theory: A review. Journal of Management, 35(6), 1404–1427. Khanagha, S., Ansari, S. (. S.)., Paroutis, S., & Oviedo, L. (2020). Mutualism and the dynamics of new platform creation: A study of Cisco and fog computing. Strategic Management Journal, Special Issue, 1–31. https://doi. org/10.1002/smj.3147 Kreiser, P.  M., Anderson, B.  S., Kuratko, D.  F., & Marino, L.  D. (2020). Entrepreneurial orientation and environmental hostility: A threat rigidity perspective. Entrepreneurship Theory and Practice, 44(6), 1174–1198. Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791.

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Morris, M. H., Webb, J. W., & Franklin, R. J. (2011). Understanding the manifestation of entrepreneurial orientation in the nonprofit context. Entrepreneurship Theory and Practice, 35(5), 947–971. Ocasio, W. (2011). Attention to attention. Organization Science, 22(5), 1286–1296. Pfeffer, J., & Salancik, G.  R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2016). Recommendations for creating better concept definitions in the organizational, behavioral, and social sciences. Organizational Research Methods, 19(2), 159–203. Rosenbusch, N., Rauch, A., & Bausch, A. (2013). The mediating role of entrepreneurial orientation in the task environment–performance relationship: A meta-­ analysis. Journal of Management, 39(3), 633–659. Shahriar, A.  Z. M. (2018). Gender differences in entrepreneurial propensity: Evidence from matrilineal and patriarchal societies. Journal of Business Venturing, 33(6), 762–779. Shepherd, D. A., McMullen, J. S., & Ocasio, W. (2017). Is that an opportunity? An attention model of top managers’ opportunity beliefs for strategic action. Strategic Management Journal, 38(3), 626–644.

CHAPTER 8

Future Research Opportunities in the Strategic Leadership-EO Space

Abstract  I conclude the book with a discussion of how EO-strategic leadership research theoretically contributes to the EO conversation specifically and the strategic entrepreneurship conversation broadly. I briefly review potential high-impact strategic leadership-EO research questions, then discuss the theoretical foci necessary to shift from descriptive contributions to building predictive theory. I then discuss how a Bayesian approach to modeling empirical strategic leadership-EO research may aid in building predictive theory. I conclude by suggesting guidance for EO researchers to make use of this book in developing new research questions. Keywords  Resource dependency theory • Prescriptive theory • Research design • Predictive modeling

8.1   Crafting an EO Contribution With EO research well-established, a legitimate concern—particularly for new researchers interested in the field—is whether there are still fruitful EO research questions for study. This may be particularly the case for junior scholars considering specializing in EO research while developing a tenurable research profile. My argument is an unequivocal yes, particularly when we consider that little EO research embraces the challenge of causal inference and building predictive theory. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. S. Anderson, Entrepreneurial Orientation and Strategic Leadership, https://doi.org/10.1007/978-3-030-87300-4_8

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One way to view the state of EO research is that we have a substantive body of work describing how EO manifests, and in how EO covaries with a considerable number of meaningful organizational variables (Rauch et  al., 2009). Contributing to this stream, while certainly still possible, requires careful positioning of the paper to draw from the relevant literature and to motivate the usefulness of another descriptive EO study. But, contributing to the predictive theory stream is, to use an analogy, a wide-­ open vista of possibilities. Scholars in this EO stream have fewer barriers to entry beyond—and these are not trivial—the challenges inherent to showing causal inference when investigating a firm-level phenomenon. The preceding is not to say that EO researchers embracing predictive theory construction have an easier path to motivating a theoretical contribution. Descriptive EO research informs predictive EO research, and there are significant bodies of EO research that, while descriptive in nature, are not likely to be useful to convert to a predictive lens. For example, a study positing that EO causes an increase in sales growth rate, with a rigorous empirical design to maximize causal inference, certainly moves into the predictive theory realm. However, that this relationship is so well known in the descriptive theory based EO literature is not likely to rise to the level of a theoretical contribution expected from top entrepreneurship research journals. Joining the EO conversation from the predictive theory perspective requires a researcher to use existing theoretical scaffolding to motivate meaningful possibilities for high impact research questions. Wales et al.’s (2021) work will be particularly useful for EO scholars in this regard. Drawing from a bibliometric analysis of over 800 empirical and conceptual EO studies over the past 50 years, the authors show key theoretical paradigms and landmark EO studies that ground significant theoretical EO conversations. For example, a configurational perspective—the notion that various alignments of strategy, structure, and environmental choices maximize firm performance—factors heavily in the EO space. Indeed, as Miller (2011) notes, the theoretical focus of the landmark Miller (1983) paper was not EO (nor was the phrase entrepreneurial orientation mentioned in that paper) but was to illustrate the application of a configurational approach to viewing entrepreneurial activity within firms. The overarching consideration for strategic leadership-EO researchers is to draw from prior EO research, and make use of these insights, but not necessarily feel constrained by this substantive body of existing work. It is also likely that while there is existing theoretical scaffolding in the EO

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literature, as Wales et al. (2021) note, there are still opportunities to integrate multiple theoretical frames into the EO conversation. For example, while the resource-based view (RBV) is a frequent lens through which to explain the relationship between EO and firm performance (Wales et al., 2021), and RBV complements, in many ways, resource dependency theory (Hillman et al., 2009), RBV by itself is not likely to have sufficient explanatory power to build predictive theory around strategic leadership­EO relationships (Wales et al., 2021). To illustrate further, consider the complementary role that socially constituted agency theory may play in connecting individual-level decision-­ making processes under the rubric of firm-level resource dependency decisions (Westphal & Zajac, 2013, p. 624): In contrast, the notion of socially constituted agency emphasizes that what an individual senses, considers, and acts upon all stem from a process of interpretation that is fundamentally social and fundamentally based on the multiplex roles and identities of that individual, which themselves are based on prior socialization and other personal experiences or characteristics.

From this perspective, for example, while the desire to accumulate market power and lower environmental uncertainty motivates a CEO to pursue entrepreneurial behaviors, socially constituted agency theory describes why a CEO may build relationships with certain external stakeholders to better inform resource allocation decisions (Westphal & Zajac, 2013). The preceding just one example, the key point being that in shifting toward a predictive theory construction lens, EO researchers in the strategic leadership space will need to draw on multiple theoretical perspectives to explicate causal relationships. Lastly, while discussed previously in this book, it bears repeating that modeling strategic leadership-EO relationship requires researchers to pay close attention to measurement issues and measurement error. As Cohen et al. (2003) note, measurement error in the dependent variable increases the amount of unexplained variance in a regression equation. This is problematic because there is an a priori expectation of small effect sizes for leadership predictors—that is, researchers should assume the proportion of explained variance will be low for a given model. Excessive measurement error in the dependent variable increases the noise in the model and makes it less likely to see statistically meaningful parameter estimates. Using measurement models for EO designed to lower measurement

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error—the strategic entrepreneurial behaviors (SEB) scale developed by Anderson et  al. (2019) as one example—or estimation approaches that account for measurement error (e.g., structural equation modeling) are useful options.

8.2   High-Impact Research Questions After committing to furthering our predictive understanding of strategic leadership-EO research, a key question is where EO scholars begin. There has already been notable work in the leadership-EO space from which scholars may build. For example, Cao et al.’s (2015) study suggest that CEO bonding and bridging ties influence firm-level EO. Similarly, Engelen et al. (2015) found that the moderating effect of CEO narcissism on the EO-firm performance relationship varied as a function of market concentration and environmental dynamism. Further, Engelen et  al. (2015) found CEO overconfidence exhibited a predominantly negative relationship to EO. While these and other studies give foundational insights, they are contributors to our descriptive understanding of EO. In the rest of this section, I outline four high-potential research questions—by no means an exhaustive list—that are fruitful areas to pursue and that adopt a behavioral perspective on the strategic leadership-EO relationship and that attempt to shift toward predictive theory in the EO conversation. Change in Board Interlocks Over Time A board interlock forms when “… a person is on the board of directors of two or more corporations, providing a link or interlock between them” (Fich & White, 2005, p.  175). In the strategic management literature, agency theory, the upper echelons perspective, and resource dependency theory all posit varying relationships between how interlocks may influence organizational performance and strategy formation (Zona et  al., 2018). I propose revisiting the interlock question through the resource dependency theory lens, which shifts the interlock question to how firms may proactively use interlocks to address power imbalances and to shape the external environment to be more favorable to the firm (Hillman et al., 2009). With EO as the strategic outcome in question, a fruitful question is the extent to which firms proactively capture new entrepreneurial opportunities—exploited through the firm’s EO—by proactively using interlocks to

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either gain access to resources or to proactively gain a market power position. Importantly, such a question must exist over time and there must be proper temporal sequencing. That is, preceding a change in the firm’s EO, we should see firms making changes to its board that either shift existing interlocks or create new interlocks that set up the firm’s ability to use its EO to gain a favorable market position. CEO Succession and Change in EO Because EO exists in a chronic state, we should expect the potential for a change in EO to correspond to a change in CEO. By itself, this is an interesting research question, and, with suitable longitudinal data, may yield valuable insights into the temporal stability of EO and the range in which EO manifests for a given firm over time. That said, an extension of the preceding would be to evaluate how firms may manipulate CEO succession practices for the purpose of supporting or changing the firm’s EO state. An often-overlooked prediction by resource dependency theory, as Pfeffer and Salancik (1978) note, firms may use succession planning both to address internal power dynamics and to better position the firm to address changing environmental exigencies. Succession may also have important implications for firm innovation. For example, Zhu et al. (2020) find support for their argument that newly promoted insider CEOs with substantial experience on their current firm’s board pursue less strategic change in promotion than insider CEOs with substantial experience on external boards. An interesting question, then, is to capture how boards engage in succession planning for the purpose of changing—or supporting—the firm’s strategic posture at CEO turnover. Multilevel EO Conceptualization There is now substantive theoretical work in the EO literature on the manifestation of EO in the multi-business firm, and the value of a multilevel conceptualization of EO to account for the possibility of EO occurring, and influencing firm performance, at the individual, team, unit, and corporate level (Wales et al., 2020). I believe that such a conceptualization, if operationalized into proper measurement instruments and supporting research designs, will yield valuable, and potentially predictive, insights for EO scholars. It may very well be the case that a multilevel EO

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perspective allows strategic leadership researchers the opportunity to connect leadership variables at multiple hierarchical levels with EO at the equivalent level, with collective implications for EO at a firm or corporate level. Building from the preceding, an intriguing research question related to multi-business firms is the relationship between the CEO at the corporate strategy level and the senior-most executives leading constituent businesses. Adopting the multilevel EO perspective advanced by Wales et al. (2020), the manifestation of EO differs as a function of hierarchical level— the EO manipulated by the corporate CEO is different than the EO manipulated by the unit CEO. Unpacking how, for example, a corporate CEO uses internal social capital and power structures to influence unit-­ level CEO behavior—and vice-versa—would demonstrably advance our understanding of how large corporations manage and advance EO across a wide range of businesses and geographies. Changes in Executive Compensation and EO Over Time At its most basic, Boards use compensation to reward—or punish—senior leadership performance, often measured by stock price-based metrics (Martin et al., 2013). Drawing from an agency theory logic, tying executive compensation to stock performance aligns the financial interest of the CEO to the financial interest of the stockholders, thus lessening the agency problem (O’Connell & O’Sullivan, 2014). Importantly, Boards use compensation retrospectively to acknowledge prior performance, and prospective to incentivize executives to pursue future behaviors (Graffin et  al., 2020). The latter is the more interesting dynamic for EO scholars. As argued previously in this book, one role of the Board is setting the parameters, or range, of EO around its chronic state. An interesting question is how the Board proactively uses executive compensation to support this range, or potentially to change this range. For example, a firm with a chronically low EO and with a tight EO range may discourage significant EO investments—and hence strategic risk—by emphasizing value growth in the firm’s stock price. Similarly, the board of a firm with a high EO and with a wider EO may set significant, long-term goals for stock price appreciation predicated on incentivizing the CEO to pursue entrepreneurial initiatives that may fundamentally change the firm’s basis for competition over time.

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Because of the breadth of the strategic leadership-EO space, there are dozens more high potential research questions in addition to the four outlined previously. The commonalities across the four, as with other research questions, is three-fold. First, there must be action in the predictor—the variable itself must vary to see a future change in EO. Second, time is a factor—there must be temporal sequencing between the change in the predictor and EO. The length of this sequence varies according to research question and context, but the sequence must be present. Third, there must be intentionality to the change—a board, CEO, or other leader must be engaging in the behavior for the purpose of changing the firm’s EO. This last point makes the change in predictor endogenous to EO, as is the case with most strategic decisions (Rocha et al., 2019). Nonetheless, intentionality, thus leading to a non-spurious connection between the predictor and EO, is a critical element to building predictive theory in this area.

8.3   Predictive Theory Construction Arguing for a shift to a predictive theoretical lens calls for a brief review of the defining elements of descriptive and predictive theory. Descriptive theory centers primarily on associational, or correlational, relationships that often inform the beginning of predictive theory. In the EO literature, for example, Covin and Slevin (1991) argue that EO is a resource-­ consuming strategic posture. The presence of resources was thus a necessary element for the firm to engage in sustained entrepreneurial behavior. The authors do not imply, nor should they, that the presence of resources causes EO.  Rather, they argue that the possession of sufficient surplus resources is an attribute found in high-EO firms—a correlation with substantial empirical support (Rauch et al., 2009). Predictive theory, in contrast, moves past statements of correlation to enable researchers to make confident predictions that after seeing a change in variable x, we will see a corresponding change in variable y. In the words of Gelman and Imbens (2013), predictive theory allows for forward causal inference—a deep understanding of the result from an activity or intervention such that the relationship resembles a law-like connection between two or more variables. In a management context, for illustration, descriptive theory would allow an executive to understand why a particular sales campaign did not produce the intended results. In contrast, predictive theory would allow the executive to predict the success of a campaign as a

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function of a set of explanatory variables with, ideally, a high degree of precision. There is obvious value to such predictive theory, but the nature of the social sciences and the absence of objective scientific fact makes predictive theory elusive (Gelman & Imbens, 2013). That said, there are key elements for predictive theory construction that strategic leadership-EO researchers should keep in mind when evaluating potential research questions. While there are ample theory construction frameworks scholars may choose from, I will borrow Bacharach’s (1989) framework for critically evaluating the predictive power of an organizational theory. While Bacharach’s (1989) framework has significantly more elements than what I present below, the following three are, I would argue, critical to strategic leadership-EO theory construction. Variable (Construct) Specificity As Bacharach (1989) note—also mentioned in more recent work (Bettis, 2012)—most organizational theories are simply too broad to be empirically testable, let alone seen through a predictive lens. The issue is variable specificity, such that a researcher can minimize measurement error, content validity, contamination, and deficiency among the constructs of interest to realistically make cause and effect statements of size and direction. Consider, for example, the statement that CEOs with greater learning ability perceive a higher number of new business opportunities in the external environment. Learning ability in what way? What does a one-unit increase in learning look like? How do we measure business opportunities? The critical consideration is that predictive theory centers around a statement of causal magnitude or probabilistic likelihood (Christensen & Raynor, 2003). That x positively relates to y—a common hypothesis in the management literature—is descriptive. The goal is to use precision in the variables such that the researcher can quantify the expected change in the outcome to a reasonable degree. For example, a descriptive statement would be that executive stock option compensation positive relates to higher EO.  A predictive theory statement, in contrast, would allow a researcher to predict with much greater specificity something like adding incentive compensation of greater than 20% of an executive’s base compensation in long-term (at least a three-year vesting period) stock options increases the likelihood by at least 25% of the executive pursuing a significant (greater than 20% of available capital) entrepreneurial opportunity within the option vesting period.

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Identifying the Causal Mechanism The second key element in predictive theory construction is careful explication of the causal mechanism connecting the two variables together (Christensen & Raynor, 2003). Showing and isolating the specific causal mechanism is, however, a product of significant empirical work that rules out competing theoretical explanations. This is a further challenge in developing predictive theory—there must be a substantial body of descriptive work that informs the development of a robust, and reliable, predictive theory. There is a salient consideration here regarding unmodeled mediator variables—by definition, the mechanism through which a predictor effects the outcome (Gonzalez & MacKinnon, 2021). Consider, for example, the statement by Cao et al. (2015, p. 1961) on the hypothesis that CEO bonding social capital increases firm EO: “As such, a CEO with more bonding social capital has greater social influence and control over the flows of knowledge and resources and becomes more aware of and effective in pursuing innovative combinations within the firm ….” In the preceding, we might construct a causal chain of the following: bonding social capital → social influence → control of information flows → awareness of and effective innovative combinations → EO, with the → notation read as “leads to.” What the paper modeled, however, was the relationship between variables capturing bonding social capital and variables capturing EO. The Cao et al. (2015) argument structure is not unique in the EO—or broader management—literature. That there are multiple potential mediators connecting the effect of one variable to another reflects reality, particularly in the social sciences (Gonzalez & MacKinnon, 2021). But complex causal chains make it difficult to articulate the specific causal mechanism connecting two variables clearly and precisely. Furthermore, causal mediation modeling is itself complicated and difficult to execute with precision (Imai et al., 2010). The solution, therefore, may be for strategic leadershipEO researchers to carefully consider the underlying complexity of a proposed causal chain. It may be that causal adjacency must take priority over model completeness when developing initial predictive theory in this space. Identifying Relevant Boundary Conditions Lastly, as Christensen and Raynor (2003) note, critical to building predictive management theory is understanding the boundary conditions of the theory—locations, times, and other contextual factors where the theory’s predictions no longer hold. Similarly, understanding those contextual

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factors that moderate—change the nature or strength of the theory’s prediction—is equally important (Vancouver & Carlson, 2015). Incorporating moderating variables and boundary conditions into the theory’s predictions illuminates contingencies that help translate theory into practice and give nuance to better apply what is a statement of general relationships to firm-specific conditions. Consider, for example, the Cao et al. (2015) study referenced previously. In addition to positing that CEO bonding and bridging ties would influence EO, the author further argued for, and found empirical support for, environmental instability as a meaningful contextual factor. Importantly, as with developing predictive theory from a descriptive theory basis, finding relevant boundary conditions and moderators is something that emerges over time through repeated tests of the theory’s predictions in new circumstances and contexts (Christensen & Raynor, 2003). In practice, the preceding insight has two key implications for researchers in this area. First, the theory will have limited theoretical specificity at the onset as a function of the extent to which the researcher is aware of potential boundary conditions and moderators. Second, and relatedly, there will be an inherent uncertainty in the predictions made by the theory as a function of unspecified contextual factors, and this uncertainty will persist until a sufficient corpus of knowledge builds around the theory’s predictions. As I will discuss next, the inherent uncertainly favors a specific empirical approach for predictive theory building.

8.4   Predictive Theory through a Bayesian Lens A thorough discussion of the benefits of Bayesian modeling over frequentist methods lies well beyond the scope of this book (Etz et  al., 2018; Schad et al., 2021). However, I am an advocate for a Bayesian approach to modeling relationships in the strategic entrepreneurship literature (Cottle & Anderson, 2020). In terms of strategic leadership-EO research, particularly geared toward building predicting theory, I would argue that Bayesian modeling offers three key advantages, as I explain below. Quantifying Uncertainty By default, Bayesian inference expresses model parameters in terms of probabilities, with a corresponding credibility interval around those probabilities (Kruschke & Liddell, 2018). In Bayesian inference, the credibility interval expresses the corresponding probabilities of the range of

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parameter values produced by the model. In this way, ironically, the credibility interval functions in the way that some researchers interpret— although incorrectly—the confidence interval produced in frequentist statistics (Morey et al., 2016). The credibility interval is itself a distribution, and, in the case of the Gaussian specification, follows a familiar normal shape with the peak of the distribution referring to the median parameter estimate with the highest probability given the data and the specified model (Kruschke & Liddell, 2018). The credibility interval allows a researcher to quantify the amount of uncertainty produced by a model. Unlike a frequentist model in which the researcher estimates a point value assumed to be asymptotically true in the population, the credibility interval allows the researcher to acknowledge a range of effect sizes produced by the model, but that the data is consistent with effect sizes that, while possible, may not be probable (Kruschke & Liddell, 2018). This feature of Bayesian modeling is useful for research in which small effect sizes close to zero are the expectation (Gelman & Weakliem, 2009). The researcher allows for models with estimates that may include zero or models with data and assumptions consistent with positive and negative effect sizes. Leveraging Strong Priors to Adjust for Unmodeled Parameters We can think of Bayesian inference as taking the form of Posterior ∝ Prior * Likelihood, where we read that the posterior distribution is in proportion to the prior distribution multiplied by the likelihood function. The posterior is the outcome of our model, expressed as a probability distribution as mentioned previously. The likelihood function is our model specification, conditioned on our data. The prior is the distinguishing feature of Bayesian inference and one of its key strengths. Through the prior, we encode what we know—or what we feel confident assuming—about an underlying relationship into our model from the outset (Kruschke & Liddell, 2018). As we consider the preceding proportionality equation, the stronger the prior relative to the likelihood function (and hence the data), and the stronger the influence of the prior on the posterior distribution. Often, a Bayesian modeler will make use of weakly informed priors, using the prior for regularization and stabilization of the model rather than to encode prior knowledge (Kruschke & Liddell, 2018). However, the prior does have an intriguing use in terms of addressing unmodeled confounders. If the researcher identifies key unmodeled confounders, such as selection effects or known, but unmodeled, covariates, and has confidence in the direction

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and extent to which such confounders may influence model parameters, the researcher can use a strong prior to adjust the model for these possibilities (Anderson, 2021). Note that this technique does not advance causal inference in terms of a counterfactual condition or related inference approach. However, in terms of recovering a more consistent—and ideally plausible— estimate, the strong prior approach offers researchers more tools to improve the predictive power of a model, while accounting for potential confounders that may be impossible to collect and to model directly. Robust Hierarchical Modeling The final advantage of Bayesian inference for strategic leadership-EO researchers is its strength in multilevel/hierarchical modeling. Hierarchical models include nested data structures such as repeated observations of the same entity over time or observations of multiple entities nested within higher-order entities (e.g., employees nested under a manager). Under certain conditions, multilevel models allow a researcher to both account for potential dependence in the model as a function of the hierarchical structure (e.g., managers may systematically rate their employees in a particular way), and to account for within- and between-entity variance that allows for richer understanding of the model and the data (Feller & Gelman, 2015). In the case of leadership research, shrinkage in a multilevel model gives a more robust estimate of an underlying parameter, which may be important in these settings. The partial pooling in a multilevel model pulls—or shrinks—extreme observations closer to the within-entity mean structure, and in this way supplies a robust mechanism to address potential outliers in the data while keeping the observation (Gelman, 2006). For example, it may be the case that a firm shows notable change in EO as a function of a particular leadership variable, but this case is rare. A multilevel model allows the researcher to account for this possibility without sacrificing much in terms of parameter efficiency, while also allowing the researcher to better explore this underlying observation.

8.5   Conclusion The purpose of this book is to argue that there is opportunity to build predictive theory around EO by linking the decisions senior leaders make with the entrepreneurial strategic behaviors resulting from these decisions. Given the continued importance of EO research to the strategic

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entrepreneurship field, advancing theory around EO helps our field realize its potential to elucidate how, when, why, and where firms may use entrepreneurship to advance their competitive position, and by extension, organizational performance. In this book, I put forward a behavioral perspective arguing that building predictive theory in the strategic leadership-EO space requires a focus on the actions and behaviors that leaders engage in and the resulting strategic behaviors resulting from those leader behaviors. This implies a necessary dynamism and activity to the theoretical lenses developed to describe underlying causal processes. This is a shift from adopting a trait-based perspective, for example, a ratio of insider directions or CEO education that, while possible to change within a study window, is likely to be temporally stable. As such, researchers should consider those leadership variables wherein change is inherent to the construct. For example, building social capital, using board interlocks to develop new knowledge, and changing executive compensation to incentivize entrepreneurial behaviors all are dynamic, changing constructs that may causally precede a change in firm EO. That said, researchers should not be under the illusion that research in this stream will be easy, that insights will develop quickly, or that the necessary designs allow for rapid data collection. The desire to build predictive theory will require substantial research across a wide variety of questions and with varying research designs that triangulate meaningful predictions and associated boundary conditions. It is, therefore, an exciting time to be an EO researcher, and junior scholars embarking on a research career can take heart in knowing that a construct with origins dating back five decades has ample runway ahead.

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Index

A Antecedents, 2–4, 6, 7, 11, 12, 19 Attention, 63 Attention shifting, 31, 32 Attitudinal perspective, 18 B Bayesian modeling, 92, 93 Behavioral orientation, 41 Behavioral perspective, 18, 21, 22, 24 Board interlocks, 41, 43, 86, 95 Boundary conditions, 91, 92, 95 Business-level, 4 Business-level strategy, 61, 62 Business strategy, 30 Business units, 30, 36, 62, 63 C Capital risk, 46 Capital stack, 47 Capital velocity, 74, 75 Causal, 2, 6, 7, 11–13 Causal adjacency, 91

Causal claim, 2 Causal inference, 83, 84, 89, 94 Causal mechanism, 32, 33, 44, 91 Causal predictor, 21 Causal relationship, 39, 41, 42 Chronic EO, 40, 45 Cognitive flexibility, 31 Compensation, 41, 47, 48, 88, 90, 95 Complexity, 72–74, 76, 77 Conceptual domain, 9, 10 Configurational perspective, 84 Content analysis, 8, 9 Content validity, 90 Contextual factors, 21 Corporate-level strategy, 61 Corporate parent, 63, 66, 67 Credibility interval, 92, 93 Culture, 19, 20 D Digital transformation, 75–77 Diversify, 62 Dynamism, 72

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. S. Anderson, Entrepreneurial Orientation and Strategic Leadership, https://doi.org/10.1007/978-3-030-87300-4

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INDEX

E Endogeneity, 8, 47 Entrepreneurial disposition, 52 Entrepreneurial dominant logic, 52 Entrepreneurial mindset, 52, 53 Environmental dependencies, 42 Environmental shaping, 43 Environmental uncertainty, 22 Executive compensation, 78 F Financial indicators, 9, 10 Formatively measured construct, 64 Founder imprinting, 34 I Incentive-based compensation, 56, 58 Individual-level EO, 52 Interpretational confounding, 46 J Joint venture, 32, 44, 45 L Latent construct, 18 M Measure, 33 Measurement, 2, 4, 7–10, 13, 19, 78, 79, 85–87, 90 Mediator, 32, 33, 44 Merger and acquisition (M&A), 44 Middle management, 57 Multi-business firm, 54

Multilevel, 87, 88, 94 Multilevel perspective, 33 Munificence, 72, 76 N New entry, 4 O Organizational ambidexterity, 54 Organizational power, 23 P Performance evaluation, 56–57 Portfolio-based approach, 63, 64 Predictive theory, 2, 3, 83–86, 89–92, 95 R Reflective measurement, 19, 64 Resource acquisition, 42, 43 Resource allocation, 62, 64 Resource allocation decisions, 30, 35, 36 Resource-based view (RBV), 85 Resource constraints, 66 Resource-consuming, 30 Resource dependencies, 22, 23, 30, 31, 34, 41–44, 47, 55, 73, 85–87 Resource obsolescence, 76 S Sales growth, 1, 48, 72, 74 Scale, 7, 8, 12 Selection effects, 20

 INDEX 

Self-efficacy, 25 Signal-to-noise ratio, 76 Slack resources, 74 Social capital, 25 Structural organicity, 66, 67 Succession planning, 87

T Task environment, 72, 73 Temporally stable, 40 Temporal sequencing, 87, 89 Temporal stability, 6, 34 Triangulation, 12

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