Torben Juul Andersen, Jerker Denrell and Richard Bettis-1. 2007. Strategic Responsiveness and Bowman’s Risk-Return Paradox, Strategic Management Journal, 28(4)

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  Strategic Management Journal Strat. Mgmt. J., 28: 407–429 (2007) Published online 5 February 2007 in Wiley InterScience ( DOI: 10.1002/smj.596 Received 12 December 2003; Final revision received 7 August 2006 STRATEGIC RESPONSIVENESS AND BOWMAN’S RISK–RETURN PARADOX TORBEN J. ANDERSEN,1 JERKER DENRELL2 and RICHARD A. BETTIS3 * Copenhagen Business School, Frederiksberg, Denmark Graduate School of Business, Stanford University, Stanford, California, U.S.A. 3 Kenan-Flagle
  Strategic Management Journal Strat. Mgmt. J. , 28 : 407–429 (2007)Published online 5 February 2007 in Wiley InterScience ( DOI: 10.1002/smj.596  Received 12 December 2003 ; Final revision received 7 August 2006  STRATEGIC RESPONSIVENESS AND BOWMAN’SRISK–RETURN PARADOX TORBEN J. ANDERSEN, 1 JERKER DENRELL 2 and RICHARD A. BETTIS 3 * 1 Copenhagen Business School, Frederiksberg, Denmark  2 Graduate School of Business, Stanford University, Stanford, California, U.S.A. 3 Kenan-Flagler Business School, University of North Carolina, Chapel Hill,North Carolina, U.S.A. One of the most enduring puzzles in the strategy literature is the negative association betweenrisk and return known as the Bowman paradox. This paper formalizes a model of strategicconduct based on the concept of strategic fit and the heterogeneity of firm strategic capabilities.This model is shown mathematically to yield the negative association of the Bowman paradox.Furthermore, the model makes several other testable predictions. To examine these predictions,simulated data from the model are compared with a large empirical study of 45 industries during1991–2000. The predictions of the model are consistent with the empirical data. Copyright  2007 John Wiley & Sons, Ltd. The negative association between cross-sectional,accounting-based, firm performance and the vari-ance of performance known as the ‘Bowman para-dox’ (Bowman, 1980, 1982, 1984) has inspireda rich stream of research and continues to fasci-nate strategy scholars (e.g., Bettis, 1982; Baird andThomas, 1985; Fiegenbaum and Thomas, 1986,1988, 2004; Miller and Bromiley, 1990; Bromi-ley, 1991; Miller and Chen, 2003, 2004; Ruefli,Collins, and LaCugna, 1999). This negative asso-ciation was unexpected, since higher returns aregenerally thought to require higher risks. Further-more, it was counter to the financial market-basedresults embodied in the capital asset pricing model(CAPM). Even though more than 25 years havepassed since Bowman (1980) observed the nega-tive correlation, there is no general agreement onthe source of this phenomenon. The interest in andsignificance of this issue is obvious, since strategy Keywords: environmental analysis; inverse risk–returnrelationships; risk management *Correspondence to: Richard A. Bettis, Kenan-Flagler BusinessSchool, University of North Carolina, McColl Building, ChapelHill, NC 27599, U.S.A. E-mail: r proposes to have important things to say about bothreturns and the risks associated with those returns.Explanations for the source of the Bowman para-dox include various contingencies, strategy con-duct and statistical artifacts. The most commonexplanation today is probably the effect of perfor-mance relative to a reference point and its impacton managerial risk taking as discussed by prospecttheory (Kahneman and Tversky, 1979, 1984; Tver-sky and Kahneman, 1986) and the behavioral the-ory of the firm (Cyert and March, 1963; Marchand Shapira, 1987, 1992).Here we pursue Bowman’s (1980) srcinal incli-nation that effective management makes a differ-ence and can positively influence both the meanand variance of performance (e.g., Bettis 1982;Baird and Thomas, 1985). A major contribu-tion of the paper is that we show how a rela-tively simple model, closely associated with com-mon conceptualizations of effective strategy, canexplain the Bowman paradox. We adopt a three-pronged research approach combining mathemati-cal derivations, model simulations, and empiricalstudies. Copyright  2007 John Wiley & Sons, Ltd.  408 T. J. Andersen, J. Denrell, and R. A. Bettis In what follows, we first provide a generaloverview of the different rationales that have beendeveloped to explain Bowman’s paradox. We thendevelop a model of strategic fit in the presence of heterogeneous firm capabilities, and show how wecan mathematically adduce the Bowman paradoxfrom this model. Next we simulate the model toexamine its predictions. We then perform a com-prehensive empirical analysis across 45 differentindustries from 1991 to 2000 to assess model pre-dictions. The results of this empirical analysis con-form with the simulation results to a considerabledegree. Finally, we discuss extensions and widerimplications of the proposed modeling framework. BACKGROUND Previous management research on the risk–returnrelationship may be broadly classified in accor-dance with three basic explanatory rationales:(1) contingencies that influence the risk behaviorof organizational decision-makers; (2) outcomesfrom strategic conduct; and (3) statistical artifacts.Appendix 1 provides an overview of different con-tributions organized in accordance with the threeclassifications, realizing that some studies mayincorporate multiple perspectives. The purpose of the Appendix is not to provide a full literaturereview but to highlight alternative explanations forthe negative risk–return relations. We will havemore to say about some of these explanationsdirectly below and at various points in the paper.For comprehensive reviews of the literature werefer the interested reader to Bromiley, Miller, andRau (2001) and Nickel and Rodriguez (2002).The first explanatory rationale, ‘contingenciesthat influence behavior of organizational decision-makers,’ includes a wide variety of explanations,as an examination of the Appendix shows. Promi-nent among these explanations is prospect the-ory (Kahneman and Tversky, 1979, 1984; Tver-sky and Kahneman, 1986). Prospect theory holdsthat the risk propensity of decision-makers isinfluenced by expected performance outcomes insuch a way that individuals are risk averse whenprospects are positive (expected gains) and risk seeking when prospects are negative (expectedlosses). These arguments were transposed to situ-ational framing where high performance is associ-ated with risk aversion and poor performance withrisk-seeking behavior. This perspective is consis-tent with behavioral models where the choice of actions, and hence risk behavior, is driven by firmperformance in relation to given aspiration levels(e.g., March and Shapira, 1987, 1992; Bromiley,1991). Accordingly, negative risk–return relation-ships arise as managers in the underperformingfirms decide to take riskier actions to increasereturns, thus implying that individual decisionbehaviors aggregate into organizational outcomeeffects (Bazerman, 1984; Hartman and Nelson,1996). Bromiley (1991) also found that higher risk seems to cause poorer performance, thus leading tovicious or virtuous performance cycles over time.Today these rationales constitute widely acceptedexplanations for Bowman’s risk–return paradox.Various empirical studies have tested theseprospect theory/behavioral model explanationsbased on split samples between above andbelow median performers (e.g., Fiegenbaum andThomas, 1988; Fiegenbaum, 1990; Jegers, 1991;Sinha, 1994; Gooding, Goel, and Wiseman,1996; Lehner, 2000). Firms operating belowtarget performance were on average found tohave inverse risk–return relationships, whilethose operating above target displayed positiverisk–return relationships as predicted. The below-target performers also showed a higher risk–returntrade-off than above-target performers as suggestedby theory. The prospect theoretical view has beenextended to consider both external and internalenvironmental conditions such as business cyclesand organizational life cycles that can framethe risk-taking behavior of strategic decision-makers. Fiegenbaum and Thomas (1986, 1988)proposed that the oil crisis of the 1970s withthe resulting economic uncertainty and increasedcompetition could explain the negative risk-return relationships. However, inverse risk-returnrelationships were also found in five-year intervalsduring 1960-79 including the steady growthscenarios of the 1960s (Bettis and Mahajan, 1985).Industry clusters characterized by intense rivalryamong members also constitute competitive envi-ronments where risk behaviors are associated withinverse risk–return relationships (e.g., Cool, Dier-ickx, and Jemison, 1989; Oviatt and Bauerschmidt,1991). Henderson and Benner (2000) further sug-gested that risk behaviors may be influenced bythe age of the organization as young agile firmsavoid losses and score gains, while aging iner-tial organizations have below-average performance Copyright  2007 John Wiley & Sons, Ltd. Strat. Mgmt. J. , 28 : 407–429 (2007)DOI: 10.1002/smj   Risk–Return and Responsiveness 409 and increase their propensity to risk. Finally, orga-nizational conditions may influence the perceptionof risk among individual decision-makers and theirrisk propensities (e.g., Fischoff, Watson, and Hope,1984; March and Shapira, 1987) and thereby affectthe organization’s ability to manage risk events(e.g., Sitkin and Pablo, 1992; Pablo, Sitkin, andJemison, 1996).The second explanatory rationale shown in theAppendix is ‘strategic conduct’ as srcinally sug-gested by Bowman (1980). Although strategic con-duct explanations relate to managerial decision-making, they are different from contingency per-spectives. Rather than explaining on the basis of induced management behaviors, the strategic con-duct approach attempts to show that good manage-ment practices can make a difference; i.e., inverserisk–return relationships could be the result of firmheterogeneity of strategic management capabili-ties. Clearly, the ‘strategic conduct’ perspectiveimplies that managerial discretion matters. Thisstream of research has been less grounded in the-ory than the ‘contingency approach’ above, andhas tended to advocate normative approaches tostrategic management as an effective way to man-age both risk and return. However, there have alsobeen some empirical and theoretical contributions.An analysis of alternative risk measures in strat-egy research questioned the premises of prospecttheory and suggested that income variability canimpose incremental costs on the firm (Miller andBromiley, 1990). From this perspective, it wouldseem likely that managerial intervention to reduceperformance risk is associated with lower costand better performance, as illustrated by Millerand Chen (2003). Furthermore, decision-makersseem to have higher risk propensities in contextswhere they feel knowledgeable and competent(Heath and Tversky, 1991) and executives considerthese exposures to be manageable (Shapira, 1995).Accordingly, Palmer and Wiseman (1999) foundthat managerial choice has a significant influenceon performance risk. It has also been claimed thatthe associated risk management practices can leadto higher firm value as they induce investment infirm-specific rent-bearing resources (Miller, 1998;Wang, Barney, and Reuer, 2003) and empiricalstudies seem to corroborate this view (e.g., Sneierand Miccolis, 1998; Bartram, 2000).The third explanatory rationale detailed in theAppendix is ‘statistical artifacts.’ This rationaledeals with the possibility of misspecifications andspurious effects in empirical studies that havefound the negative association between risk andreturn. This explanation suggests that the nega-tive association may be due to flawed statisti-cal analyses. Since it is impossible to distinguishbetween time specific risk–return relationships andshifts in these relationships over time, the calcu-lations might indicate true relationships, althoughwe cannot be certain (e.g., Ruefli, 1990; Ruefliand Wiggins, 1994; Ruefli et al ., 1999). Henkel(2003) demonstrated that samples where account-ing measures are skewed toward negative returnslead to spurious effects of negative risk–return cor-relations. Nonetheless, after disentangling the trueeffects he still found inverse risk–return relation-ships across industries during 1970–79, the periodanalyzed by Ruefli and Wiggins (1994). Denrell(2004) also demonstrated that heterogeneity in risk propensity as well as serial correlation in perfor-mance can produce spurious u-shaped relationshipsbetween risk and return.As this brief overview indicates, research hassuggested a variety of possible explanations of the Bowman paradox. Nevertheless, most researchhas focused on prospect theory and the behavioraltheory of the firm, possibly because their implica-tions are well understood. In the next section weintroduce an alternative model that explicates theimplications of the strategic conduct perspective.We show formally how heterogeneity in the effec-tiveness of strategic management can provide analternative explanation of the Bowman paradox,with different empirical predictions MODEL In our model we pursue the view that high per-formance with low variability can be achievedthrough superior strategic conduct, and that lowperformance with high variability can result frominferior strategic conduct. More generally, hetero-geneity in the effectiveness of strategic manage-ment processes can result in an inverse relationshipbetween return and variability of return. Overview of the model Before developing the formal model, we pause inthis section to motivate it and embed it in somefundamental strategy literature. Copyright  2007 John Wiley & Sons, Ltd. Strat. Mgmt. J. , 28 : 407–429 (2007)DOI: 10.1002/smj  410 T. J. Andersen, J. Denrell, and R. A. Bettis First, our objective is to explain the nega-tive cross-sectional association between risk andreturn, which has been the focus of most of the lit-erature (e.g., Bowman, 1980; Fiegenbaum, 1990;Gooding et al ., 1996). Given this emphasis, andsince our model does not assume that low perfor-mance leads to risk taking, we pay less attentionto the longitudinal association, i.e., the direction of causal relationships between risk and performanceand vice versa (Bromiley, 1991; Wiseman andBromiley, 1996; Wiseman and Catanach, 1997).Second, our ambition is to show that an inverserisk–return relationship can emerge even if it isnot assumed, as in financial models, that thereis an association in equilibrium between risk andreturn due to risk aversion. For this reason, wemeasure performance by the return rather than therisk-adjusted return.Our model is based on the long-standing con-cept of strategic fit (e.g., Andrews, 1971; Hoferand Schendel, 1978; Fiegenbaum, Hart, and Schen-del, 1996; Siggelkow, 2001). According to thisview, high performance is achieved by aligningthe strategy content and organizational structureof the firm with prevailing environmental con-ditions. As environmental conditions change, thealignment to obtain strategic fit will also needto change appropriately. Furthermore, the ‘farther’one deviates from achieving optimal fit, the moresevere the performance penalties. We conceive of strategic fit broadly to include external environ-mental conditions, such as competitive structure,customer demand, and stakeholder relationships,as well as internal organizational structure andresource mobilization (Barney, 1991; Drazin andVan de Ven, 1985; Porter, 1996).The concept of strategic fit is consistent withseveral different approaches to strategy, includingPorter’s (1996) idea of achieving third-order strate-gic fit to optimize organizational effort. The modelis also consistent with both the resource-basedview and with the concept of dynamic capabilities.It incorporates the basic tenet of the resource-basedview that superior performance comes from mak-ing the best use of unique resources that a firm maypossess (Barney, 1986, 1991; Winter, 1995; Den-rell, Fang, and Winter, 2003). In a changing envi-ronment obtaining strategic fit over time requirescapabilities (and other resources) that allow a firmto accurately assess environmental changes, for-mulate an appropriate response, and then reconfig-ure internal resources appropriately. This is in linewith the conclusions of the resource-based view of the firm (e.g., Barney, 1986, 1991, 2001). It is alsoconsistent with the conceptualization of dynamiccapabilities as the organization’s ability to identifyavailable resources, and apply them in effectiveresponses to uncertain and changing environmentalconditions (e.g., Teece, Pisano, and Shuen, 1997;Eisenhardt and Martin, 2000; Adner and Helfat,2003).We will refer to this bundle of capabilities toassess the environment, identify firm resources,and mobilize them in effective responsive actions(achieving strategic fit over time) as strategicresponsiveness. Our concern here is not withand of the individual capabilities, but with theoverall concept of strategic responsiveness as theaccomplishment of fit. Finally, it should be notedthat strategic responsiveness is almost always dis-tributed heterogeneously in the population of firms. Model specification To narrow the focus on an analysis of risk–returneffects, we project strategic responsiveness as a rel-atively simple periodic adaptation process. From astrategic fit perspective, firm performance dependson how well management is able to impose strate-gic responsiveness on the organization. A simpleway to model this is to assume that, given an ini-tial resource endowment of the firm, it can achievean optimal performance level at a given point intime expressed by a value K . The optimal perfor-mance, K , is achieved when the environment thatcircumscribes the firm is accurately assessed andresponded to so as to achieve the best strategicfit available to the firm. This approach is compa-rable to rationales that define production frontiersin classical economics and investment opportunitysets in financial economics where periodic choicesare limited by upper bounds determined by pre-vailing technologies and investments. Similar ideashave been applied in the introduction of conceptslike productivity frontiers (e.g., Porter, 1996) andefficient frontiers (Devinney, Midgley, and Venaik,2000) in organizational studies. The value of  K candiffer between firms, depending on their individ-ual business prospects and resource endowments.A constant K , as an indicator of optimal perfor-mance, is obviously restrictive since performanceusually will be influenced by a large number of exogenous variables and can change over time, but Copyright  2007 John Wiley & Sons, Ltd. Strat. Mgmt. J. , 28 : 407–429 (2007)DOI: 10.1002/smj
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