Erratic Strategic Decisions

Strategic Management JournalStrat. Mgmt. J., 32: 683–704 (2011)

Published online EarlyView in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.905

Received 1 December 2008; Final revision received 6 November 2010

ERRATIC STRATEGIC DECISIONS: WHEN AND WHYMANAGERS ARE INCONSISTENT IN STRATEGICDECISION MAKING

J. ROBERT MITCHELL,1* DEAN A. SHEPHERD,2 and MARK P. SHARFMAN3

1 Richard Ivey School of Business, University of Western Ontario, London, Ontario,Canada2 Kelley School of Business, Indiana University, Bloomington, Indiana, U.S.A.3 Price College of Business, The University of Oklahoma, Norman, Oklahoma, U.S.A.

While decision makers in organizations frequently make good decisions rooted in stable andconsistent preferences, such consistency in outcomes is not always the case. In this study,we adopt a psychological perspective of judgment to investigate managers’ erratic strategicdecisions, which we define as a manager’s inconsistent judgments that can shape the directionof the firm. In a study of 2,048 decisions made by 64 CEOs of technology firms, we examinehow both metacognitive experience and perceptions of the external environment (hostility anddynamism) could affect the extent to which managers make erratic strategic decisions. Theresults indicate that managers with greater metacognitive experience make less erratic strategicdecisions. The results also indicate that in hostile environments managers make more erraticstrategic decisions. But contrary to our expectations, in dynamic environments managers makeless erratic strategic decisions. Similarly, hostility and dynamism interact in their effect on erraticstrategic decisions in that the positive relationship between environmental hostility and erraticstrategic decisions will be less positive for managers experiencing high environmental dynamismthan those experiencing low environmental dynamism. These results have important implicationsfor strategic decision-making research. Copyright 2010 John Wiley & Sons, Ltd.

INTRODUCTION

Strategic decisions are those choices made bymanagers that commit important resources, setimportant precedents, and/or direct important firm-level actions (Mintzberg, Raisinghani, and Theoret,1976). They are the decisions that shape a firm’sgeneral direction (Dean and Sharfman, 1996). Theprocesses that underlie effective strategic deci-sion making matter for organizational outcomes

Keywords: strategic decisions; entrepreneurship; psychol-ogy and metacognition; hostility and dynamism; conjointanalysis; field experiment∗ Correspondence to: J. Robert Mitchell, Richard Ivey School ofBusiness, University of Western Ontario, 1151 Richmond StreetNorth, London, Ontario N6A 3K7, Canada.E-mail: [email protected]

(Rajagopalan, Rasheed, and Datta, 1993), lead-ing to both organizational effectiveness (Deanand Sharfman, 1996) and organizational efficiency(Eisenhardt, 1989). These processes are influencedby the manager’s prior knowledge and experi-ences (Barr, Stimpert, and Huff, 1992; Kiesler andSproull, 1982; Walsh, 1995), the organizationalcontext in which they are embedded (Kaplan,2008; Ocasio, 1997; Simon, 1957), and the natureof the environment itself (Nadkarni and Barr,2008). Effective decision-making outcomes havebeen characterized in terms of reliability (Einhorn,1974; Shepherd, Zacharakis, and Baron, 2003),adaptability (Gigerenzer, 2001; Payne, Bettman,and Johnson, 1997), and performance (Camererand Johnson, 1991; Einhorn, Hogarth, and Klemp-ner, 1977; Shepherd et al., 2003).

Copyright 2010 John Wiley & Sons, Ltd.

684 J. Robert Mitchell, D. A. Shepherd, and M. P. Sharfman

Although previous research acknowledges theexpectation that high quality managerial decision-making processes are rooted in stable and con-sistent preferences (March, 1982; Shanteau, 1992;Shepherd et al., 2003), such consistency is notalways the case (Cohen, March, and Olsen, 1972;March, 1982; Masuch and LaPotin, 1989). Attimes strategic decision making is inconsistent(Eisenhardt and Zbaracki, 1992; March, 1994).This lack of consistency can reflect attempts toadapt to changing environmental conditions(Chakravarthy, 1982; Hogarth and Makridakis,1981). However, there is also evidence that deci-sions can be inconsistent absent changes in thedecision context; that is, decisions simply can beerratic (Kunreuther, 1969; Remus and Kottemann,1987; Remus, 1978). The notion that decisions canbe erratic has been discussed in economics (e.g.,Benhabib and Day, 1981), management informa-tion systems (e.g., Remus and Kottemann, 1987),operations management (e.g., Kunreuther, 1969),and psychology (e.g., Wood and Bandura, 1989).Erratic decisions have been linked to economicinefficiency (Bowman, 1963; Remus, 1978), andreduced performance (Bandura, 1989).

However, there has been little research on thesources of erratic decisions (a notable exceptionis Wood and Bandura [1989], which pointed tolow perceived self-efficacy as a source of erraticdecisions). While we acknowledge that decisionmaking in general can be unpredictable in rapidlychanging environments, where new informationis common (Eisenhardt and Martin, 2000; Miller,1992), we propose that strategic decisions can beerratic even with limited new information. In thispaper, we investigate the psychological founda-tions underlying erratic strategic decisions. Specifi-cally, we use a field experiment (Harrison and List,2004) to investigate cognitive and environmentalfactors (cf. Elsbach, Barr, and Hargadon, 2005)that lead managers to make more or less erraticstrategic decisions.

In doing so, we make four primary contribu-tions. First, previous research has acknowledgedthe importance of strategic decision making tofirm performance. This research has focused onthe systematic nature of those strategic decisionsin terms of content (Fahey and Christensen, 1986)(e.g., strategies and goals) and process (Hutzschen-reuter and Kleindienst, 2006) (e.g., speed [e.g.,Eisenhardt, 1989], biases [e.g., Hodgkinson et al.,

1999], and comprehensiveness [e.g., Atuahene-Gima and Li, 2004]). Herein, we explain howvariance in experience- and context-baseddecision-making processes can result in variance inthe extent to which strategic decisions are erratic,specifically providing a deeper understanding ofwhen and why managers’ decisions are likely tobe more erratic (Bowman, 1963; Remus, 1996).

Second, by investigating how strategic decision-making processes (which can be messy and ill-structured) lead to specific decision-making out-comes, we extend prior studies that focus on erraticdecisions that had utilized more structured set-tings (Kunreuther, 1969; Remus and Kottemann,1987; Remus, 1978). Moreover, we move beyonda focal emphasis on the development of boot-strapping models that exclude the erratic com-ponent of the decision-making process (Bowman,1963; Camerer, 1981; Shepherd et al., 2003) to anemphasis on capturing and explaining the process-based sources of erratic strategic decisions as out-comes.

Third, environmental conditions are an impor-tant consideration in making effective strategicdecisions (Bourgeois and Eisenhardt, 1988; Houghand White, 2003). We theorize and find that envi-ronmental conditions also influence the extent towhich managers make erratic strategic decisions.That is, when it comes to strategic decision mak-ing, we find that perceptions of the environ-ment appear to influence both the systematicand the erratic. Furthermore, individual differ-ences in strategic decision-making processes haveoften been attributed to experience (Forbes, 2005;Judge and Miller, 1991). We extend this researchby focusing on the processes that lead one typeof experience (metacognitive experience: whichrefers to a person’s conscious experiences that arecognitive and affective in nature [Flavell, 1987])to produce less erratic decision outcomes.

Finally, while this study is not a study ofstrategic change, the results nonetheless comple-ment research that has emphasized the importanceof managers’ strategic decisions in organizationaladaptation (Lant, Milliken, and Batra, 1992; Sharf-man and Dean, 1997). For example, studies haveinvestigated why some decision makers are moreeffective at deciding to change (or not) and oth-ers are less effective (Bourgeois and Eisenhardt,1988; Huy, 2001; Lant and Mezias, 1990). Regard-less of whether the decision is to change or staythe course, we explain why such a decision might

Copyright 2010 John Wiley & Sons, Ltd. Strat. Mgmt. J., 32: 683–704 (2011)DOI: 10.1002/smj

Erratic Strategic Decisions 685

be more or less erratic. This is particularly impor-tant given the findings that erratic decisions tend tobe associated with less optimal results (e.g., Bow-man, 1963; Hogarth and Makridakis, 1981; Remus,1978).1

In this paper, we first use a psychological per-spective of judgment to build a model explainingvariation in the extent to which managers’ strate-gic decisions are erratic. We then test a seriesof hypotheses using a sample of chief executiveofficers (CEOs) of technology firms. Finally, wediscuss the implications of our results from the per-spective of research on strategic decision making.

A MODEL OF ERRATIC STRATEGICDECISIONS

Erratic strategic decisions refer to a manager’sinconsistent judgments that can shape the direc-tion of the firm. Previous research in judgmentand decision making (e.g., Brunswik, 1952; Hog-arth and Karelaia, 2007; Karelaia and Hogarth,2008) has highlighted that judgment quality isa function of the matching index, environmentalpredictability, and response consistency. In judg-ment terms, the matching index refers to how wellthe weights and function forms that describe thecues of the environment model are represented bythe model of the decision maker (Karelaia andHogarth, 2008: 406). Environmental predictabilityreflects the extent to which the environment modelexplains variance in the environment. Responseconsistency reflects ‘the consistency with whichthe judge executes [his/her] decision rule’ in deci-sion making (Karelaia and Hogarth, 2008: 406).

To the extent that a strategic decision is erratic,response consistency is diminished, thereby low-ering judgment quality. Removing this responseinconsistency is the basis for bootstrapping mod-els that have been created directly from an expert’sdecision-making process. By applying thatdecision-making process consistently, the modelprovides ‘judgment’ superior to the expert fromwhich the bootstrapping model was created(Camerer, 1981). These findings have beenreplicated in a variety of judgment tasks: for

1 Hogarth and Makridakis (1981) also introduced the possibil-ity that inconsistent decision-making outcomes can sometimesserve a strategic purpose (e.g., it might be more difficult forcompetitors to predict future outcomes).

example, predicting violent behavior of newlyadmitted inmates (Cooper and Werner, 1990),clinical judgments of human health and behavior(Dawes, Faust, and Meehl, 1989), and judgmentsof venture capital portfolio company performance(Zacharakis and Meyer, 2000). In sum, judgmentquality appears to be enhanced when the responseinconsistency associated with erratic decisions iseliminated (for a meta-analysis see Karelaia andHogarth, 2008).

In our investigation of erratic strategic decisions,we adopt a psychological perspective of judgment(e.g., Goldstein and Hogarth, 1997; Walsh, 1995)to view managers’ decisions as the result of indi-vidual thinking that is embedded in the broaderenvironmental context (Elsbach et al., 2005). Thuswe focus on two important elements of themanager-environment interaction: (1) managers’ability to understand their thinking and (2) man-agers’ perception of the external environment as itaffects their thinking.

Erratic strategic decisions and managers’thinking

We seek to understand managers’ abilities relatedto understanding their own decision making. Todo so, we utilize the concept of metacognition,which refers to conscious reflection about one’sown thinking. Such thinking about thinking (Jost,Kruglanski, and Nelson, 1998) is particularly rel-evant to strategic decision making due to the cen-trality of metacognition in learning effectivenessand in goal planning, adaptation, and implemen-tation (Ford et al., 1998). Metacognition generallyrefers to a higher-order concept (Weinert, 1987)that involves ‘knowledge and cognition about cog-nitive phenomena’ (Flavell, 1979: 906) and hasbeen defined as an ability to reflect upon, under-stand, and control cognitive processes relating toa concrete goal or objective (Flavell, 1976: 232;Schraw and Dennison, 1994: 460).2

The idea of metacognition is useful in our inves-tigation because metacognition-based processes—which are instrumental in understanding the details

2 The concept of metacognition is not without controversy, fromcriticisms that the distinction between cognition and metacogni-tion is vague and arbitrary (Weinert, 1987) to criticisms of themultiplicity of uses of the concept (Brown, 1987; Reder, 1996).For our purposes, however, the notion of metacognition as sec-ond order cognitions (Weinert, 1987) is useful in that it describesgeneral processes that relate to ‘knowledge of and control overcognition’ (Ford et al., 1998: 220, italics added).



of a given situation vis-a-vis one’s own cog-nitions (Haynie et al., 2010)—may mitigate theconditions that give rise to erratic strategic deci-sions. That is, like other errors in thinking (Wil-son and Brekke, 1994), erratic decisions can resultfrom erroneous beliefs about cognition and inad-equate awareness of one’s decision-making pro-cesses. Importantly, metacognitive processes canallow individuals to exert cognitive control byenabling them to ‘generate multiple, alternativedecision frameworks focused on interpreting, plan-ning, and implementing goals’ (Haynie and Shep-herd, 2009: 697).

Because of the role of past experience in deci-sion making (Forbes, 2005; Judge and Miller,1991), we focus on metacognitive experience,3

which refers to a person’s conscious experiencesthat are cognitive and affective in nature (Flavell,1987). These experiences relate to believing thatone knows how to think about a task in order tosuccessfully accomplish that task. The duration ofthese experiences can be either brief or lengthyand their content either simple or complex (Flavell,1979). At the core of metacognitive experience isthe idea that previous experience can be used tomake sense of present situations (Flavell, 1987)and can trigger a belief that one knows how bestto approach the current situation. As an example:

A person has a metacognitive experience if heor she has the feeling that a particular task isdifficult to comprehend, and then draws on pastexperience with such difficult-to-comprehendproblems to inform the generation of a deci-sion framework for approaching this new, butrelated, task (Haynie and Shepherd, 2009: 699,italics in original).

Evident in this example, metacognitive experi-ences are often recognized as feelings/thoughts(Flavell, 1979) that relate relevant past experienceto current cognitive processes (Flavell, 1987). Indecision-making terms, those with greatermetacognitive experience are likely to recognizehow the current decision-making process isinformed by previous decision-making processes,

3 We acknowledge that metacognitive knowledge—a person’sunderstanding about the cognitive strategies they use to thinkabout a specific task (Flavell, 1987)—may also be important.And while we include only metacognitive experience, our resultsdid not differ substantially when metacognitive knowledge wasused as a control variable.

which can then be applied in a way that bestaddresses the decision task at hand (Haynie andShepherd, 2009: 697).

A critical element of metacognitive experienceis the accompanying self-awareness of one’s ownexperiences with mental processes. Prior researchsuggests that metacognitive experience affects theprocesses used to make decisions (Schwarz, 2004)by facilitating understanding of a given situa-tion (Haynie et al., 2010). This contextual elementis important in that metacognitive experiencesare especially likely to occur in situations thatrequire careful and conscious consideration, whichthen increases the extent to which they engen-der increased cognitive control (Flavell, 1979). Inthis way, metacognitive experience can allow man-agers to verify the usefulness of their decision-making processes, thus representing a type of cog-nitive ‘quality control’ (Flavell, 1979: 908). Justas metacognitive experience can enable adjust-ments in thinking processes (Ford et al., 1998)through the creation, adaptation, and abandonmentof knowledge and goals (Flavell, 1979), it canalso allow decision makers to preserve impor-tant knowledge and goals (Flavell, 1979). Previousexperience in decision making that is metacogni-tive in nature will accordingly allow managers toexert control in current decision-making processes.The expected result of this metacognitive experi-ence is that decisions become less erratic. Thus,

Hypothesis 1: Managers with higher metacogni-tive experience will make less erratic strategicdecisions than managers with lower metacogni-tive experience.

Erratic strategic decisions and managers’external environment

In our approach to understanding how the environ-ment affects the extent to which managers makeerratic strategic decisions, we focus on the impactof managers’ perceived experience in their envi-ronmental context (Duncan, 1972; Milliken, 1987).Specifically, we seek to understand the effect ofenvironmental contexts that are perceived as hos-tile and dynamic. This focus on the experience ofcognition is anchored in the role of experience ininterpretation of and response to changes in theenvironment (Anderson and Paine, 1975; Milliken,1990).



Hostile environmental context

By definition, hostile environments are danger-ous and threatening (Dean and Sharfman, 1993;Miller and Friesen, 1983). There has been consid-erable debate about whether threatening environ-mental conditions induce threat-rigidity (D’Aunnoand Sutton, 1992; D’Aveni and MacMillan, 1990;Staw, Sandelands, and Dutton, 1981) or change(Cyert and March, 1963; Kiesler and Sproull,1982; March and Simon, 1958; Tushman andRomanelli, 1985). There have been numerous stud-ies that reconcile these perspectives (Chattopad-hyay, Glick, and Huber, 2001; Ocasio, 1995;Thomas, Clark, and Gioia, 1993) finding, forexample, that the nature of threat-induced decision-making outcomes are contingent on the dimension-ality of the threat (Chattopadhyay et al., 2001), theattention given to the environment and the direc-tion of problemistic search (Ocasio, 1995), andthe interpretation of potentially threatening issues(Thomas et al., 1993).

In these studies, the emphasis is on reconcilingdifferences in decision-making outcomes: threat-rigidity versus failure-induced change (i.e., whenmight we expect ‘failures to alter response in theface of environmental change’ and when might wenot [Staw et al., 1981: 501]?). Like these studies,our research also addresses threatening environ-ments. It does not, however, address radical orincremental environmental change (although wecontrol for this); nor does it address rigidity- orchange-based decision-making outcomes (althoughwe control for this as well). Instead, our researchaddresses strategic decisions that are inconsistentabsent changes in the environmental context.

To illustrate the impact of environmental hos-tility on decision-making processes, we draw onstrategy process research (cf. Hutzschenreuter andKleindienst, 2006), which indicates that hostileenvironmental contexts decrease decision-makingspeed (Baum and Wally, 2003), reduce the effec-tiveness of strategic decision-making processes(Goll and Rasheed, 1997), decrease rationalityin strategic decision-making processes (Dean andSharfman, 1993), and result in negative perfor-mance consequences in terms of both profit andgrowth (Baum and Wally, 2003). A hostile envi-ronmental context can, thus, negatively affect man-agers’ strategic decision-making processes (Deanand Sharfman, 1993; Miller and Friesen, 1983;Nicholls-Nixon, Cooper, and Woo, 2000).

Decision making in the face of a threateningenvironment can lead to a process that is man-ifest as feverish desperation and panic (Millerand Friesen, 1983; Nicholls-Nixon et al., 2000;Staal et al., 2008). Such decision making reflectsthe disorganized and haphazard cognitive process-ing in response to a threat (Janis and Mann,1977; Staal et al., 2008: 271), which can leadto a degradation of judgment (Staal et al., 2008),disrupted and simplistic information processing(Janis and Mann, 1977), failure to consider alter-natives (Baradell and Klein, 1993), frantic search(Baradell and Klein, 1993; Staal et al., 2008), andpoor cognitive performance (Baradell and Klein,1993; Keinan, 1987). As Janis described, suchresponses to threats reflect a ‘defective coping pat-tern’ wherein decision makers ‘fail to carry outadequately the cognitive tasks that are essentialfor arriving at stable decisions’ (1982: 73, ital-ics in original). In the decision-making process,restricted information processing results in a fran-tic search for hastily contrived solutions amongfew alternatives (Janis and Mann, 1977: 51).

Our theorizing focuses on the extent to which,in the face of a threatening environment, decisionmakers’ information processing degrades such thatit leads to inconsistent decisions (Janis, 1982; Staalet al., 2008). Our expectation is that managers whoexperience a hostile environment will be moresusceptible to disrupted information processing intheir decision making (Baradell and Klein, 1993;Janis, 1982; Janis and Mann, 1977), leading to afailure to fully consider past, present, and futuredecision alternatives (Baradell and Klein, 1993).Our expectation is that these deficiencies (Keinan,1987) will lead to decisions that are increasinglyerratic. Thus,

Hypothesis 2: Managers experiencing greaterhostility in their environmental context will makemore erratic strategic decisions than managersexperiencing less hostility.

Dynamic environmental context

An environmental context that is dynamic is onewith a highly unpredictable and unstable rate ofchange (Aldrich, 1979; Dess and Beard, 1984;Duncan, 1972; Keats and Hitt, 1988; Miller andFriesen, 1983) and high levels of uncertainty(Achrol and Stern, 1988; Baum and Wally, 2003;Duncan, 1972) about the state of the context, the



means-ends relationships, and/or the outcomes ofactions (e.g., Milliken, 1987). To illustrate theimpact of environmental dynamism on decision-making processes, we draw on strategy processresearch (cf. Hutzschenreuter and Kleindienst,2006), which indicates that dynamic environmen-tal contexts lead to increased competitive aggres-siveness (Brouthers, Brouthers, and Werner, 2000),require more effort on the part of managers(Elenkov, 1997; Yasai-Ardekani and Nystrom,1996), necessitate the strategic reorientation of thefirm (Lant et al., 1992), and can result in dimin-ished performance if the firm is unable (or slow)to respond to the changed environment (Baum andWally, 2003; Bryson and Bromiley, 1993).

Managers in dynamic environments must alsograpple with the difficulties of understanding thecritical elements of the many, uncertain decision-making options they face (Baum and Wally, 2003;Hough and White, 2003). As a result, managers arerequired to engage in multiple cognitive activitiesat a given time (Gilbert and Osborne, 1989), whichdecreases cognitive functioning (Gilbert, Giesler,and Morris, 1995) and is manifest as a failure ofmore effortful cognitive processing (Gilbert andOsborne, 1989), diminished use of available infor-mation (Gilbert, Pelham, and Krull, 1988), a nar-rowing of attention (Ward and Mann, 2000), anda more pronounced susceptibility to distractions(Lavie et al., 2004). This challenging decision con-text is compounded by an increased difficulty todetect one’s own inconsistencies (Bargh and Thein,1985).

As Lavie (2005: 75) described: ‘the ability toremain focused on goal-relevant stimuli in thepresence of potentially interfering distractors iscrucial for any coherent cognitive function.’ Thissame logic likely applies to the strategic decision-making process. That is, managers who experi-ence a dynamic context will be more suscepti-ble to distractor interference (Engle, 2002; Lavie,2005; Lavie et al., 2004), which will split man-agers’ attention (Kahneman, 1973; Ocasio, 1997;Simon, 1957) such that they will pay less atten-tion to their previously established preferences indecision making (March, 1982; Ocasio, 1997). Asa result, current preferences will likely differ frompreviously established preferences (e.g., Cho andHambrick, 2006), and the decision maker will beless aware of the inconsistencies in these pref-erences (Bargh and Thein, 1985). The expected

impact of environmental dynamism on strategicdecisions is that they become more erratic. Thus,

Hypothesis 3: Managers experiencing greaterdynamism in their environmental context willmake more erratic strategic decisions than man-agers experiencing less dynamism.

Hostile and dynamic environmental context

Up to this point, we have focused on the hostileand dynamic aspects of the environmental contextin terms of their separate effects. And while hostileand dynamic environments are not always coupled,the combination of both is likely to be a potentone for strategic decision-making processes. In anenvironment that is experienced as hostile (i.e.,threatening [e.g., Dean and Sharfman, 1993; Millerand Friesen, 1983]) and dynamic (i.e., uncertainand frequently changing [e.g., Achrol and Stern,1988; Miller and Friesen, 1983]), a manager willanticipate hostile action (based on past experience)but will be unsure of its form and extent due tothe unpredictability of such environments as theresult of high levels of state, effect, and responseuncertainties (Milliken, 1987).

In a hostile and dynamic environment, there willbe a higher perceived risk of failure (due to hos-tility [Baum and Wally, 2003]) and greater uncer-tainty about the likely sources of such failure (dueto dynamism [Achrol and Stern, 1988]). Whereashostility alone is expected to make managers moresusceptible to disrupted information processing (inresponse to threats); and whereas dynamism aloneis expected to make managers more susceptible todistractor interference (in response to uncertaintyand change); the combination of both hostility anddynamism results in a kind of anxiety that feedson uncertainty about threats, which in turn leadsmanagers to perceive danger in perhaps innocu-ous situations (cf. Freeman and Freeman, 2008;Freeman et al., 2008a; Freeman et al., 2008b). Asa result, managers who experience such anxietyabout uncertain threats are more likely to make‘judgements on the basis of minimal data’ (Free-man and Freeman, 2008: 117) precisely becausethe data about environmental threats are missing.

Complicating matters, the rapid pace of changein dynamic environments may lead to numerousdecision-making options (Baum and Wally, 2003;Hough and White, 2003) that are likely unrealisticin a hostile environment (Castrogiovanni, 1991).



Combined with managers’ anxiety about uncertainthreats, environments that are both hostile anddynamic can thus lead to an inability on the part ofmanagers to make thoughtful and reasoned choices(even in contexts that are innocuous), which resultsin strategic decisions that are more erratic. Thus,

Hypothesis 4: Environmental hostility and envi-ronmental dynamism will interact in their effecton erratic strategic decisions, such that the posi-tive relationship between environmental hostilityand erratic strategic decisions will be more pos-itive for managers experiencing high environ-mental dynamism than for those experiencinglow environmental dynamism.

METHODS

Research participants

We identified a sample of CEOs using theCorpTech database of technology firms. Our expec-tation was that CEOs at firms in this database faceda frequent need to make strategic decisions dueto the changing nature of technology (Eisenhardt,1989; Hughes, 1990), but also faced a diverse setof operating environments, allowing for variancein our independent variables. From this database,we selected 459 companies based on three criteria.First, geographic location was important becausethe research required face-to-face interaction withCEOs. Thus, we only contacted companies in thesurrounding three area codes of a large Midwest-ern city (i.e., within a three hour drive). Sec-ond, because our focus was on the strategic deci-sion making of top managers, we only includedfirms for which information about the CEO ofthe company was provided. This meant that weexcluded firms that only provided contact informa-tion for a chairman of the board, a plant manager,a vice president, and so forth. Lastly, and relatedto the previous point, firm size was an impor-tant consideration in the research because, practi-cally speaking, we anticipated that CEOs in smallto medium-sized companies (10–500 employees)would be more open and available to discuss strate-gic decision making than CEOs of large companies(500+ employees). Therefore, we excluded firmsthat reported fewer than 10 and more than 500employees.

To arrive at the final sample and to ensurethat it was representative of the larger popula-tion, we randomly selected a subsample of theCEOs at these companies to contact (240 in total).So as to produce a constant but manageable flowof interviews, letters requesting participation weremailed in groups of approximately 25 (based ongeographical proximity to facilitate efficiency indata collection). Within a week of the mailing, afollow-up phone call was made to the letter recip-ients in order to set up a meeting time. Data werecollected over a five month period. Of the 240contacted, 127 CEOs agreed to participate in ourresearch4 and because of the nature of the design,64 of these were randomly assigned to partici-pate in this study. We used a logistic regression ofCEOs’ participation on firm age, firm size, and firmtype to test for differences between participantsand nonparticipants. None of the factors in theregression were significant, providing no signifi-cant evidence of participation bias. The mean ageof the CEOs’ firms was 33 years (median firm agewas 24 years) and the mean size was 88 employ-ees with $21 million in sales (median size was 30employees with $5 million in sales). The majorityof CEOs in the sample were men (94 percent), witha mean age of 51 years. Additionally, 55 percentwere firm founders.

Research task

Conjoint analysis

Capturing erratic strategic decisions is challeng-ing because variation in the decision weights inresponses to changes in the environment (inter-nal or external to the firm) must be distinguishedfrom changes in decision weights when there hasbeen no change in the environment. Conjoint anal-ysis provides the opportunity to focus on theCEO’s decision-making processes while ensuringthat the decision context remains constant. Aswe have described, strategic decisions are thosechoices made by managers that commit importantresources, set important precedents, and/or directimportant firm-level actions (Mintzberg et al.,1976). Because of the importance of resource allo-cation to strategy (Bower, Doz, and Gilbert, 2005;Ghemawat, 1991; Noda and Bower, 1996), and the

4 All but four of the participants were the CEO of the firm (thefour who were not participated at the request of the CEO, oncethe purpose of the study was made clear).



substantial commitment of resources often requiredfor the pursuit of new opportunities (Burgelman,1983), our field experiment required CEOs toengage in a decision-making task in which theyevaluated a series of hypothetical opportunitiesand decided whether or not to allocate resourcesto the full-scale exploitation of each opportunity.Through use of a field experiment design, we con-trol for changes in the environmental context (radi-cal or incremental) by ensuring that there are none.

In metric conjoint analysis, respondents makea series of decisions based on a set of theoreti-cally relevant attributes from which the underlyingstructure of their strategic decision policies canbe investigated (Shepherd and Zacharakis, 1997:211). In other words, metric conjoint analysisallowed us to better understand CEOs’ strategicdecisions (Priem, 1992; Priem and Harrison, 1994)such that we can investigate the factors that leadthese decisions to be more or less erratic.

Decision attributes and decision

The hypothetical opportunity profiles that partici-pants evaluated consisted of four theoretically rel-evant attributes (Mitchell and Shepherd, 2010).Because we cannot capture all attributes relevantto a decision, we based our choice of attributes ona model of entrepreneurial action that elaborateson the thinking processes underlying the decisionto allocate resources to the exploitation of oppor-tunities (McMullen and Shepherd, 2006). Consis-tent with other opportunity-focused research (e.g.,Baron, 2006; Krueger, 1993; Krueger, Reilly, andCarsrud, 2000), McMullen and Shepherd (2006)suggest that the allocation of resources for theexploitation of an opportunity involves an assess-ment of the extent to which the decision makeris motivated and knowledgeable to pursue theopportunity in an uncertain environment. Themotivation element of opportunity evaluation wasconceptualized as the potential value of an oppor-tunity, which reflects the profit predicted to resultfrom the decision to allocate resources to the full-scale exploitation of the opportunity (Venkatara-man, 1997). The knowledge element of opportu-nity evaluation was conceptualized as knowledgerelatedness, which reflects the extent to which theCEO believes he or she has the knowledge nec-essary to exploit the opportunity (Krueger andBrazeal, 1994). The environmental elements ofopportunity evaluation were conceptualized as the

window of opportunity and the number of poten-tial opportunities. These elements were selectedbecause they reflect the broader, uncertain environ-ment within which decision making about oppor-tunities takes place (Bourgeois and Eisenhardt,1988; McGrath and Nerkar, 2004). While theoret-ically grounded, these attributes were nonethelesspretested with CEOs of firms like those in our sam-ple. In these interviews, the CEOs were asked ifthe attributes were relevant to their strategic deci-sion making. Additionally, while we could haveincluded environmental hostility and environmen-tal dynamism as attributes in the conjoint exper-iment, doing so would have precluded us fromtesting the influence of these factors on erraticstrategic decisions, which are our focus.

Decisions

After viewing a structured combination of thesefour attributes, the CEOs decided their likelihoodof allocating resources to that opportunity, whichwas captured using a nine-point Likert-type scaleanchored by ‘very unlikely to invest in this oppor-tunity’ (1) and ‘very likely to invest in this oppor-tunity’ (9). When making these decisions, CEOswere asked to assume that: other than the infor-mation provided in the profiles, the hypotheticalopportunities presented are similar to other oppor-tunities they have ‘seen’ in all respects; they havethe resources (or access to the resources) to investin the opportunity, if they choose to do so; theyare making decisions about these opportunities fortheir current firm; and they are making decisionsabout these opportunities in their firm’s currentindustry and economic environment.

Orthogonal fractional factorial design

Each of the four opportunity attributes was var-ied at two levels (e.g., high and low knowledgerelatedness of an opportunity). Because a conjointexperiment with a fully crossed factorial designinvolving four attributes at two levels require 16(24) profiles, a fractional factorial design was usedto make the decision-making task more manage-able and shorter (see Green and Srinivasan, 1990).This resulted in eight profiles (Hahn and Shapiro,1966). Each of the eight profiles was fully repli-cated, permitting estimates of individual reliabilityfor use in subsequent analysis (Shepherd et al.,2003). The design was also orthogonal (i.e., no



correlation between attributes), which is consistentwith studies using metric conjoint analysis (Lou-viere, 1988). To test for order effects, four versionsof the profiles were created that varied the order ofthe attributes and the order of the profiles. Thereis no significant difference between the versions inthe level of erratic strategic decisions. Participantswere familiarized with the task with a practice pro-file that was not used in the analyses.

Measures and analysis

Erratic strategic decisions

To capture erratic strategic decisions, we asked theCEOs to engage in the same decision-making taskat two times (T1 and T2), separated by a distractortask that provided no new information to CEOs.In the distractor task, CEOs described how theirdecision making was similar to decisions that theymade in other areas of their life and discussed thedecision-making hierarchy at their firm (the meanduration of the distractor task was eight minutes).Because CEOs were making the same decisionsafter the distractor task as before the distractor taskand there was no ex ante reason for them to changetheir decisions, we represent any change in strate-gic decisions as erratic. One of the key benefits ofmetric conjoint analysis is that it allows researchersto calculate separate regression equations for eachset of decisions for each individual (both at T1 andT2). Each regression equation reflects an individ-ual’s strategic decision policy decomposed into itsvarious parts—the regression coefficients reflectthe weight given to a particular attribute in makingdecisions (Priem and Harrison, 1994). The regres-sion equation for each participant consisted of fourmain effects and three interaction effects (consis-tent with theory and empirical evidence [Mitchelland Shepherd, 2010]), which reflected each partic-ipant’s strategic decisions. Erratic strategic deci-sions were measured by calculating the extent towhich an individual’s T2 regression equations dif-fered from those at T1.

To determine a score to reflect erratic strate-gic decisions, we followed a three-step process.First, we began by subtracting each significant(p < 0.05) T2 beta weight from the correspondingsignificant (p < 0.05) T1 beta weight (we includedonly the significant attributes so as to reduce error,but saw similar patterns in the results when non-significant beta weights were also used in the

calculation and included in the model—althoughwith reduced explanatory power). Second, we tookthe absolute value of each difference for eachbeta weight for each individual, resulting in upto seven separate scores reflecting the variance inthe significant beta weights underlying the strate-gic decisions. Third, we summed the scores foreach individual to result in our dependent variablethat describes the variance in strategic decisions.In the Appendix, we include an example of howwe calculated the erratic strategic decision scores.While this variable is intended to reflect a kindof change in decision outcomes, we suggest thatour variable does not suffer from the same prob-lems as the simple difference score as describedby Bergh and Fairbank (2002). Instead, our mea-sure captures what could be classified as the ‘true’variance (at the p < 0.05 level) in CEOs’ actualdecisions (Bergh and Fairbank, 2002), which is thefocus of this study.

Metacognitive experience

To capture metacognitive experience, we used areduced version of the scale developed by Haynieand Shepherd (2009). The items that make up thisscale are contained in the Appendix. Specifically,we measured metacognitive experience by ask-ing CEOs to indicate on a six-item, seven-pointLikert-type scale the extent to which they stronglydisagree (1) to strongly agree (7) with a series ofstatements regarding their experience with cogni-tion (α = 0.74). These items were designed to cap-ture the extent of the belief that one knows how tothink about a task in order to successfully accom-plish that task, such as, ‘I think about what I reallyneed to accomplish before I begin a task,’ ‘I orga-nize my time to best accomplish my goals,’ and ‘Iknow what kind of information is most importantto consider when faced with a problem.’

Hostility and dynamism

To measure the hostility and dynamism of theenvironment experienced by managers, we askedparticipants to answer a series of questions abouttheir principal industry (the industry that accountsfor the largest percentage of sales) in a way thatbest approximates the actual conditions in it. Weincluded the items that make up these scales in theAppendix. To measure hostility, we used Slevinand Covin’s (1997) six-item hostility scale, which



required CEOs to indicate on a seven-point Likert-type scale the extent to which they strongly dis-agree (1) to strongly agree (7) with a series ofstatements regarding the hostile nature of theirenvironmental context. The reliability of the hos-tility scale (α = 0.65) was similar to that of previ-ous research using this scale (e.g., Green, Covin,and Slevin, 2008 [α = 0.71]; Slevin and Covin,1997 [α = 0.70]). Moreover, consistent with otherresearch looking at environmental factors (e.g.,Ang and Cummings, 1997; Powell, 1996), environ-mental hostility can be categorized as a moderatelybroad construct (i.e., it contains a number of dis-tinct theoretical elements), the suggested range forwhich is alpha between 0.55 and 0.70 (Van de Venand Ferry, 1980). To measure dynamism, we used avariant of the Miller and Friesen (1982) dynamismscale—similar to that used by Green et al. (2008).This six-item measure required CEOs to indi-cate on a seven-point Likert-type scale the extentto which they strongly disagree (1) to stronglyagree (7) with a series of statements regarding thestability/dynamism of their environmental context(α = 0.83). Responses were coded so that higherdynamism had higher scores.

Control variables

We include a number of controls in our anal-ysis. First, because the threat-rigidity thesis (cf.Staw, 1981) suggests that decision making willbe more rigid for individuals who are in threat-ening environments compared to those who arenot, we controlled for participants’ rigidity- versuschange-based decision-making outcomes. To doso, we utilized the constant from each individual’sinitial regression equation (T1), which reflects aCEO’s overall propensity to allocate resources tonew opportunities.5 Individuals who were morerigid in their decision-making outcomes would beless likely to allocate resources to new opportuni-ties. Thus, lower scores reflected greater rigidity indecision-making outcomes.

Second, we expected that the level of effortgiven to the strategic decision-making task mighthave an effect on erratic strategic decisions (e.g.,Bargh and Chartrand, 2000). Thus, using a seven-point Likert-type scale, we asked participants ‘howmuch effort did you put in making decisions

5 A model controlling for the mean T1 and T2 regressionconstant did not change the results.

about hypothetical opportunities,’ anchored by(1) minimal and (7) considerable.

Third, because we expected decision makerswho were less reliable in their decision makingat either T1 or T2 to also make more erraticstrategic decisions, we controlled for reliability ofdecisions within the task at T1 and within the taskat T2 (a test-retest reliability assessment of theoriginal eight profiles vis-a-vis the replicated eightprofiles).

Fourth, to rule out the possibility that CEOswith complex decision policies make more erraticdecisions (because their decision policies are morecomplex, leaving more opportunity for inconsis-tencies) we also controlled for complexity ofstrategic decision policies, which reflected thenumber of criteria used in the decision (Nutt,1998). This was measured as the maximum num-ber of significant attributes for each CEO at T1or T2, which ranged from one to seven. As anexample, the CEO in the Appendix would have afairly complex decision policy with six significantattributes in the strategic decision policy.

Fifth, because experience can affect decisionmaking (e.g., Ashby and Maddox, 1992; Forbes,2005; Judge and Miller, 1991) we controlled foryears of industry experience (both in the primaryindustry and in similar industries) and CEO age.

Sixth, because perceived self-efficacy has beenlinked to erratic thinking (Bandura, 1989; Woodand Bandura, 1989), we also include a measure ofgeneral self-efficacy as a control. This was mea-sured using the eight-item scale (α = 0.81) devel-oped by Chen, Gully, and Eden (2001) measuredon a Likert-type scale anchored by strongly agree(1) and strongly disagree (7).

Lastly, because it has been suggested that firmcharacteristics affect strategic decision making(Hutzschenreuter and Kleindienst, 2006;Rajagopalan et al., 1993), we included firm ageand firm size (number of employees) as controlsin our investigation.

RESULTS

Table 1 shows the summary statistics and correla-tions of the variables in the model. All independentvariables were mean-centered. We examined thevariance inflation factors to check for multicol-inearity. All of the variables in the models were



Tabl

e1.

Mea

ns,

stan

dard

devi

atio

ns,

and

corr

elat

ions

a

Var

iabl

esM

ean

s.d

12

34

56

78

910

1112

1314

1.E

rrat

icst

rate

gic

deci

sion

s0.

640.

37

2.R

igid

ity

v.ch

ange

inou

tcom

es

4.47

0.91

0.08

3.Ta

skef

fort

5.41

0.97

0.20

0.01

4.R

elia

bilit

yat

tim

e1

0.86

0.12

−0.3

8∗∗−0

.35∗∗

0.21

5.R

elia

bilit

yat

tim

e2

0.89

0.18

−0.0

9−0

.39∗∗

0.05

0.34

∗∗

6.C

ompl

exity

ofst

rat.

deci

sion

polic

ies

3.50

1.21

0.26

∗−0

.43∗∗

∗0.

110.

250.

37∗∗

7.W

ork

expe

rien

ce:

sam

ein

dust

ry

21.8

810

.68

−0.0

4−0

.02

0.08

−0.1

3−0

.11

0.01

8.W

ork

expe

rien

ce:

sim

ilar

indu

stri

es

5.73

8.89

0.09

0.05

0.07

−0.0

4−0

.22

0.06

−0.0

3

9.C

EO

age

50.8

49.

35−0

.16

−0.0

7−0

.22

0.00

−0.0

40.

090.

66∗∗

∗0.

1810

.G

ener

alse

lf-e

ffica

cy0.

000.

560.

10−0

.11

0.29

∗−0

.04

0.07

0.09

0.10

0.23

−0.0

9

11.

Firm

age

32.9

527

.64

−0.2

0−0

.02

0.05

0.05

−0.1

3−0

.13

0.34

∗∗−0

.10

0.26

∗−0

.12

12.

Firm

size

(em

ploy

ees)

88.1

712

3.52

−0.4

8∗∗∗

0.04

0.04

0.23

0.12

−0.1

30.

100.

070.

130.

040.

31∗

13.

Met

acog

niti

veex

peri

ence

0.00

0.71

−0.1

5−0

.24

0.34

∗∗0.

170.

000.

160.

100.

130.

190.

41∗∗

0.08

0.12

14.

Env

iron

men

tal

host

ilit

y0.

001.

090.

180.

28∗

0.13

−0.1

7−0

.24

−0.3

7∗∗0.

130.

09−0

.12

0.14

0.00

−0.0

20.

08

15.

Env

iron

men

tal

dyna

mis

m0.

001.

34−0

.17

0.14

0.09

0.10

−0.0

2−0

.09

−0.1

50.

080.

04−0

.04

−0.1

00.

030.

210.

42∗∗

∗

an

=64

∗p

<0.

05∗∗

p<

0.01

∗∗∗

p<

0.00

1



Table 2. Results of regression analysis for erratic strate-gic decisions

Variables Model 1 Model 2 Model 3

Rigidity v. change inoutcomes

0.08 0.01 −0.02

Task effort 0.25∗ 0.38∗∗ 0.34∗∗

Reliability at time 1 −0.41∗∗ −0.39∗∗∗ −0.33∗∗

Reliability at time 2 0.00 −0.04 −0.11Complexity of

strategic decisionpolicies

0.33∗∗ 0.44∗∗∗ 0.48∗∗∗

Work experience:same industry

−0.01 −0.36∗ −0.23

Work experience:similar industries

0.08 −0.02 0.02

CEO age −0.09 0.28 0.15General self-efficacy −0.02 0.03 0.05Firm age 0.00 −0.02 −0.01Firm size (employees) −0.35∗∗ −0.30∗∗ −0.30∗∗

Metacognitiveexperience

−0.25∗ −0.32∗∗

Environmentalhostility

0.44∗∗∗ 0.34∗∗

Environmentaldynamism

−0.31∗∗ −0.34∗∗

Environmentalhostility ∗

environmentaldynamism

−0.29∗∗

�R2 0.14 0.05R2 0.49 0.63 0.68F 4.52∗∗∗ 5.98∗∗∗ 6.89∗∗∗

n 64 64 64

∗ p < 0.05 ∗∗ p < 0.01 ∗∗∗ p < 0.001.

considerably lower than the recommended valueof 10 (Neter et al., 1996). Table 2 provides theregression results. Of the controls listed in Model1, task effort (β = 0.25, p < 0.05) and the com-plexity of strategic decision policies (β = 0.33,p < 0.01) are positively related to erratic strate-gic decisions; and reliability of decisions at T1(β = −0.41, p < 0.01) and firm size (β = −0.35,p < 0.01) are negatively related to erratic strategicdecisions.

As evident in Model 2, the coefficient formetacognitive experience is significant and neg-ative (β = −0.25, p < 0.05). This result indicatesthat greater metacognitive experience is associatedwith less erratic strategic decisions, supportingHypothesis 1. Model 2 also indicates that the coef-ficient for hostility of the environmental contextis significant and positive (β = 0.44, p < 0.001).This result indicates that greater environmental

hostility is associated with more erratic strategicdecisions, supporting Hypothesis 2. The coeffi-cient for dynamism of the environmental contextis significant and negative (β = −0.31, p < 0.01).This result suggests that those managers experi-encing a more dynamic environment make lesserratic strategic decisions. This result runs con-trary to Hypothesis 3. Thus, Hypothesis 3 is notsupported.

Model 3 includes the interaction term for envi-ronmental hostility × environmental dynamism.The coefficient for the interaction between hos-tility and dynamism is significant and negative(β = −0.29, p < 0.01). To interpret this interac-tion, we plotted the nature of the relationshipsin Figure 1, consistent with the techniques rec-ommended by Cohen et al. (2003). The extentto which managers make erratic strategic deci-sions is plotted on the Y-axis, environmental hos-tility is plotted on the X-axis, and the plottedlines represent one standard deviation above andbelow the mean for environmental dynamism.Figure 1 illustrates how erratic strategic deci-sions increase with environmental hostility, but doso more when environmental dynamism is lowthan when it is high. This finding is contrary toour expectation in Hypothesis 4 that the positiverelationship between environmental hostility anderratic strategic decisions will be more positivefor managers who experience high environmen-tal dynamism than for those who experience lowenvironmental dynamism. Thus, Hypothesis 4 isnot supported.

Low High

Err

atic

str

ateg

ic d

ecis

ions

—— Low env. dynamism High env. dynamism

Environmental hostility

Figure 1. Interaction effect



DISCUSSION

In this study, we investigated the processes under-lying managers’ erratic strategic decisions. Specifi-cally, we examined whether metacognitiveexperience and/or the environmental contextexplains the extent to which managers make erraticstrategic decisions in allocating resources to oppor-tunity exploitation.

Theoretical implications

Prior research has demonstrated that strategic deci-sion making is important to performance (Deanand Sharfman, 1996). The findings that we reportextend this line of inquiry by highlighting howstrategic decisions themselves can be erratic.Specifically, our findings illustrate the conditionsunder which the order that underlies success-ful strategic decision making is diminished (cf.Mintzberg, 1987; Mintzberg et al., 1976).

By explaining variance in erratic strategic deci-sions, we further contribute to decision-makingresearch by investigating the antecedents of erraticdecisions. Because we investigate the sources ofstrategic decision making (which can be messyand ill-structured [e.g., Mintzberg et al., 1976]),we extend previous research that has investigatederratic decisions in more structured (less strate-gic) settings (Kunreuther, 1969; Remus and Kot-temann, 1987; Remus, 1978). In this way, webroaden the applicability of erratic decisions as aresearch construct in the study of organizations.

Our results also contribute to extant decision-making research in that they further highlightthe importance of environmental conditions ineffective strategic decision making (Bourgeois andEisenhardt, 1988; Hough and White, 2003). Specif-ically, we hypothesize and find that environ-mental hostility increases the degree to whichmanagers’ strategic decisions are erratic. We alsofind that, contrary to our expectations, environmen-tal dynamism is negatively related to erratic strate-gic decisions and that environmental dynamismtempers the positive relationship between environ-mental hostility and erratic strategic decisions. Tous, these contingent results underscore the nuancedimportance of the environment in strategic decisionmaking (Hutzschenreuter and Kleindienst, 2006).Therefore, not only does the environment moder-ate the relationships between decision-level factorshighlighted in numerous studies (e.g., Dean and

Sharfman, 1996; Hough and White, 2003; Judgeand Miller, 1991), we found that it also impactsthe consistency of the decisions themselves.

Individual differences in strategic decision mak-ing have often been attributed to past experience(Forbes, 2005; Judge and Miller, 1991). We extendthis research by focusing on the role of metacog-nitive experience in producing less erratic deci-sion outcomes. While consistent with our expec-tations, the finding that metacognitive experiencedecreases the extent to which managers makeerratic strategic decisions warrants further dis-cussion. Prior research in the area of judgmentand decision making (e.g., Brunswik, 1952; Hog-arth and Karelaia, 2007; Karelaia and Hogarth,2008) illustrates how bootstrapping models (cre-ated directly from an expert’s decision-makingprocess) can result in better decisions (Camerer,1981; Karelaia and Hogarth, 2008). The results ofHypothesis 1 suggest that metacognitive experi-ence also has a positive impact on decision out-comes. Given these results, and considering thelearned nature of metacognition (Nelson, 1996),future research should further investigate the pro-cesses managers can utilize to invoke ‘thinkingabout strategic thinking’ as a way to develop suchmetacognitive experience, which can result in lesserratic strategic decisions.

Although this study is not a study of strategicchange, our findings regarding dynamic environ-ments are nevertheless relevant to the strategicadaptation literature, which suggests that strategyinvolves both adapting to environmental changesand threats and enabling pursuit of new oppor-tunities (Ginsberg, 1988; Gioia and Chittipeddi,1991; Smith and Grimm, 1987). According to thisperspective, adaptive strategic decision making isbased in a belief that change can somehow improvethe performance of the organization (de Rond andThietart, 2007). Because of a focus on organiza-tional outcomes in change research (Rajagopalanand Spreitzer, 1996) and because of results thatseem to positively support change-driven perfor-mance effects (e.g., Haveman, 1992; Smith andGrimm, 1987; Zajac and Kraatz, 1993), changeis often viewed to be necessary and even desir-able (e.g., Kotter, 1996; Teece, Pisano, and Shuen,1997; Zhou, Tse, and Li, 2006). Our results onstrategic decisions help further clarify a boundarycondition on the desirability of change.

Change is more likely to be maladaptive if itresults from strategic decisions that are themselves



erratic. This is particularly important given find-ings that erratic decisions can lead to less desirabledecision outcomes (e.g., Bowman, 1963; Hoga-rth and Makridakis, 1981; Remus, 1978). Thus,it may be that in dynamic environments maladap-tive change would be less likely, but in hostileenvironments it would be more likely. In thisway, our findings related to environmental hostil-ity and dynamism may both inform research thathas sought to understand why maladaptive changeoccurs and what might be done to effect adap-tive change (e.g., Rajagopalan and Spreitzer, 1996;Sastry, 1997) as well as provide opportunities forfuture research.

Implications of unexpected findings

As we note in our results section, we were sur-prised with findings that dynamism actually leadsto less erratic strategic decisions (Hypothesis 3 andHypothesis 4). In speculating about a theoreticalexplanation for the unexpected results concerningdynamism, we wonder whether managers operat-ing in highly dynamic environments are less erraticin their decisions because of accumulated expe-rience in that environment (Nelson and Winter,1982; Zollo and Winter, 2002). While our expec-tation was that experience in an uncertain andfrequently changing environment would makemanagers more susceptible to distractor interfer-ence (e.g., Achrol and Stern, 1988; Miller andFriesen, 1983), it may be that managers in such anenvironment instead develop the ability to ‘tuneout’ distractions and thus reduce the extent towhich they make erratic strategic decisions. Inlight of the results for Hypothesis 4, it may also bethat environmental hostility complicates learning,hence reducing the likelihood that such capabili-ties are accumulated through experience (Nicholls-Nixon et al., 2000).

As potential responses to a dynamic environ-ment, capabilities that reduce the extent to whichmanagers make erratic strategic decisions mightbe similar to the higher-level patterns of behaviordiscussed by Winter (2003) that increase effec-tiveness, are learned, and are akin to routines(Eisenhardt and Martin, 2000; Winter, 2003). Butinstead of being focused on change, as is the casefor dynamic capabilities (Eisenhardt and Martin,2000; Helfat et al., 2007; Teece et al., 1997; Win-ter, 2003), the learned capabilities that lead to lesserratic strategic decisions may form a foundation

for the purposeful action that underlies all capabil-ities (Helfat et al., 2007).

The unexpected results regarding dynamism mayalso have implications for the strategic processliterature in that they contribute to the ongoingconversation that explores the role of environ-mental context in strategic decision making (see,e.g., Brouthers et al., 2000; Bryson and Bromiley,1993; Dean and Sharfman, 1993, 1996; Eisen-hardt and Zbaracki, 1992; Elenkov, 1997; Goll andRasheed, 1997; Hutzschenreuter and Kleindienst,2006; Papadakis, Lioukas, and Chambers, 1998;Rajagopalan et al., 1993). Whereas prior researchhas related a changing and dynamic environmentto higher levels of uncertainty (Achrol and Stern,1988; Baum and Wally, 2003; Duncan, 1972),increased competitive aggressiveness (Broutherset al., 2000), a requirement of greater effort on thepart of managers (Elenkov, 1997; Yasai-Ardekaniand Nystrom, 1996), a need for strategic reorien-tation (Lant et al., 1992), and diminished perfor-mance if the firm is unable (or slow) to respond tothe changed environment (Baum and Wally, 2003;Bryson and Bromiley, 1993), our results highlighthow the environmental context can affect strategicdecisions absent change and indicate how envi-ronmental dynamism and environmental hostilityinteract in their strategic decision-making effects.

Moreover, while contrary to expectations, ourfinding that dynamism leads to less erratic strategicdecisions also contributes to the richness of under-standing about the effect of dynamism on strate-gic decision making (cf. Baum and Wally, 2003;Bourgeois and Eisenhardt, 1988; Judge and Miller,1991). For instance, prior strategic decision-making research has linked environmentaldynamism to reduced performance benefits fromdecision-making comprehensiveness (Fredricksonand Iaquinto, 1989; Fredrickson and Mitchell,1984; Hough and White, 2003), while at thesame time finding that successful decision makingin dynamic environments is related to increasedcomprehensiveness (Eisenhardt, 1989; Goll andRasheed, 1997). Like these latter studies, theresults we present herein have challenged conven-tional thinking: where we expected to find thatdynamism increased the extent to which managersmake erratic strategic decisions, we actually foundthe opposite. While we have speculated on the rea-sons for why the results were contrary to our initialexpectations, this represents a fruitful avenue forfuture research.



Limitations

While the absolute magnitude of the sample issmall, it is nonetheless large enough to providesufficient statistical power to test the hypotheses.Moreover, the study involved CEOs, who are gen-erally the most skilled strategic decision makers intheir particular firm. The average participant washighly experienced, running a relatively maturefirm. While the firms on average were small tomedium sized, CEOs from such a population offirms likely will be more in touch with the typesof strategic decision making that we used in ourfield experiment. In this regard, we have a rich datasource that is well suited to inform the processesand outcomes we study.

Managers in our study were asked to make deci-sions about ‘hypothetical’ as opposed to ‘actual’opportunities. One benefit of conjoint analysis,however, is that it provides data on decision mak-ing that is not affected by retrospective and self-report biases (Shepherd and Zacharakis, 1997). Wealso highlight previous research that suggests that‘hypothetical’ conjoint-analysis-type decisions aresimilar to ‘actual’ decisions (Brown, 1972; Ham-mond and Adelman, 1976) and that the use ofconjoint analysis can improve the practical rele-vance of strategy research (Priem and Harrison,1994). Related to this point, the content of thedecisions made by the managers was exclusivelywithin the realm of opportunity choice and may becriticized as being narrow. Our view, however, isthat opportunity pursuit is a core area of strategicdecision making because such choices are criticalto the long-term success of the firm (e.g., Dutton,Fahey, and Narayanan, 1983; Jackson and Dutton,1988; Teece, 2007).

Conjoint analysis is also limited in the num-ber of profiles that individuals can manage: themore attributes (cues) that are included, the greaterthe number of profiles each individual must eval-uate (Hahn and Shapiro, 1966). To ensure that thetask was manageable for managers (see Green andSrinivasan, 1990), we included only four attributesin the hypothetical opportunities and asked theCEOs to make a series of assumptions regard-ing the opportunity profiles: they were making thedecisions in their firm’s current industry and eco-nomic environment; they had access to resourcesneeded to invest; and so on. In essence, thesefactors were controlled by being ‘set’ at a spe-cific level. But this meant that interesting questions

went untested. Future research is, thus, needed tobetter understand other elements of strategic deci-sion making, which were controlled for in thisstudy.

CONCLUSION

Strategic decisions are of central concern for mod-ern organizations. But to be effective at strate-gic decision making, there must be, as we havedescribed, some consistency in strategic decision-making outcomes (Mintzberg, 1987). In this study,we endeavored to explain the factors that bothinhibit and enable managers to reflect consistencyin their own strategic decisions. We have doneso by investigating the sources of erratic strategicdecisions. Whereas previous research has inves-tigated the effects of erratic decisions, we haveendeavored to explain the source of such decisions.Our results suggest that metacognitive experienceand perceptions of the external environment mat-ter. Erratic strategic decisions are less likely frommanagers with greater metacognitive experienceand for managers who operate in more dynamicenvironments. Conversely, erratic strategic deci-sions are more likely from managers in more hos-tile environments, especially when dynamism inthat environment is low. Importantly, these resultsinform understanding of when managers might bemore susceptible to having erratic strategic deci-sions and why.

ACKNOWLEDGEMENTS

The authors thank Editors Richard Bettis andThomas Powell, and two anonymous reviewers fortheir insightful comments on earlier drafts of themanuscript. We also acknowledge and thank theJohnson Center for Entrepreneurship and Innova-tion at Indiana University for its generous sup-port of this research. An earlier version of thispaper was presented at the Babson Entrepreneur-ship Research Conference.

REFERENCES

Achrol RS, Stern LW. 1988. Environmental determinantsof decision-making uncertainty in marketing channels.Journal of Marketing Research 25(1): 36–50.



Aldrich H. 1979. Organizations and Environments .Prentice-Hall: Englewood Cliffs, NJ.

Anderson CR, Paine FT. 1975. Managerial perceptionsand strategic behavior. Academy of ManagementJournal 18(4): 811–823.

Ang S, Cummings LL. 1997. Strategic response toinstitutional influences on information systemsoutsourcing. Organization Science 8(3): 235–256.

Ashby FG, Maddox WT. 1992. Complex decision rulesin categorization: contrasting novice and experiencedperformance. Journal of Experimental Psychology:Human Perception and Performance 18(1): 50–71.

Atuahene-Gima K, Li H. 2004. Strategic decisioncomprehensiveness and new product developmentoutcomes in new technology ventures. Academy ofManagement Journal 47(4): 583–597.

Bandura A. 1989. Human agency in social cognitivetheory. American Psychologist 44(9): 1175–1184.

Baradell JG, Klein K. 1993. Relationship of life stressand body consciousness to hypervigilant decisionmaking. Journal of Personality and Social Psychology64(2): 267–273.

Bargh JA, Chartrand TL. 2000. The mind in the middle:a practical guide to priming and automaticityresearch. In Handbook of Research Methods in Socialand p\Personality Psychology , Reis HT, Judd CM(eds). Cambridge University Press: Cambridge, UK;253–285.

Bargh JA, Thein RD. 1985. Individual construct accessi-bility, person memory, and the recall-judgment link:the case of information overload. Journal of Person-ality and Social Psychology 49(5): 1129–1146.

Baron RA. 2006. Opportunity recognition as patternrecognition: how entrepreneurs “connect the dots”to identify new business opportunities. Academy ofManagement Perspectives 20(1): 104–119.

Barr PS, Stimpert JL, Huff AS. 1992. Cognitive change,strategic action, and organizational renewal. StrategicManagement Journal , Summer Special Issue 13:15–36.

Baum JR, Wally S. 2003. Strategic decision speed andfirm performance. Strategic Management Journal24(11): 1107–1129.

Benhabib J, Day RH. 1981. Rational choice and erraticbehaviour. Review of Economic Studies 48(3):459–471.

Bergh DD, Fairbank JF. 2002. Measuring and testingchange in strategic management research. StrategicManagement Journal 23(4): 359–366.

Bourgeois LJ III, Eisenhardt KM. 1988. Strategic deci-sion processes in high velocity environments: fourcases in the microcomputer industry. Management Sci-ence 34(7): 816–835.

Bower JL, Doz YL, Gilbert CG. 2005. Linking resourceallocation to strategy. In From Resource Allocationto Strategy , Bower JL, Gilbert CG (eds). OxfordUniversity Press: Oxford, UK; 3–25.

Bowman EH. 1963. Consistency and optimality inmanagerial decision making. Management Science9(2): 310–321.

Brouthers KD, Brouthers LE, Werner S. 2000. Influenceson strategic decision-making in the Dutch financial

services industry. Journal of Management 26(5):863–883.

Brown A. 1987. Metacognition, executive control, selfregulation and other more mysterious mechanisms.In Metacognition, Motivation and Understanding ,Weinert FE, Kluwe RH (eds). Erlbaum: Hillsdale, NJ;65–117.

Brown TR. 1972. A comparison of judgmental policyequations obtained from human judges under naturaland contrived conditions. Mathematics Bioscience 15:205–230.

Brunswik E. 1952. The Conceptual Framework ofPsychology . University of Chicago Press: Chicago, IL.

Bryson JM, Bromiley P. 1993. Critical factors affectingthe planning and implementation of major projects.Strategic Management Journal 14(5): 319–337.

Burgelman RA. 1983. A process model of internalcorporate venturing in the diversified major firm.Administrative Science Quarterly 28(2): 223–244.

Camerer C. 1981. General conditions for the success ofbootstrapping models. Organizational Behavior andHuman Performance 27: 411–422.

Camerer C, Johnson EJ. 1991. The process-performanceparadox in expert judgment: how can experts knowso much and predict so badly? In Toward aGeneral Theory of Expertise: Prospects and Limits ,Ericsson KA, Smith J (eds). Cambridge UniversityPress: Cambridge, UK; 195–217.

Castrogiovanni GJ. 1991. Environmental munificence:a theoretical assessment. Academy of ManagementReview 16(3): 542–565.

Chakravarthy BS. 1982. Adaptation: a promisingmetaphor for strategic management. Academy of Man-agement Review 7(1): 35–44.

Chattopadhyay P, Glick WH, Huber GP. 2001. Organiza-tional actions in response to threats and opportunities.Academy of Management Journal 44(5): 937–955.

Chen G, Gully SM, Eden D. 2001. Validation of a newgeneral self-efficacy scale. Organizational ResearchMethods 4(1): 62–83.

Cho TS, Hambrick DC. 2006. Attention as the mediatorbetween top management team characteristics andstrategic change: the case of airline deregulation.Organization Science 17(4): 453–469.

Cohen J, Cohen P, West SG, Aiken LS. 2003. AppliedMultiple Regression/Correlation Analysis for theBehavioral Sciences. (3rd edn). Erlbaum: Mahwah,NJ.

Cohen MD, March JG, Olsen JP. 1972. A garbage canmodel of organizational choice. Administrative ScienceQuarterly 17(1): 1–25.

Cooper RP, Werner PD. 1990. Predicting violence innewly admitted inmates: a lens model analysis ofstaff decision making. Criminal Justice and Behavior17(4): 431–447.

Cyert RM, March JG. 1963. A Behavioral Theory of theFirm . Prentice-Hall: Englewood Cliffs, NJ.

D’Aunno T, Sutton RI. 1992. The responses of drugabuse treatment organizations to financial adversity:a partial test of the threat-rigidity thesis. Journal ofManagement 18(1): 117–131.



D’Aveni RA, MacMillan IC. 1990. Crisis and the contentof managerial communications: a study of the focusof attention of top managers in surviving andfailing firms. Administrative Science Quarterly 35(4):634–657.

Dawes RM, Faust D, Meehl PE. 1989. Clinical versusactuarial judgment. Science 243(4899): 1668–1674.

Dean JW Jr, Sharfman MP. 1993. Procedural rationalityin the strategic decision-making process. Journal ofManagement Studies 30(4): 587–610.

Dean JW Jr, Sharfman MP. 1996. Does decisionprocess matter? A study of strategic decision-makingeffectiveness. Academy of Management Journal 39(2):368–396.

de Rond M, Thietart R-A. 2007. Choice, chance,and inevitability in strategy. Strategic ManagementJournal 28(5): 535–551.

Dess GG, Beard DW. 1984. Dimensions of organi-zational task environments. Administrative ScienceQuarterly 29(1): 52–73.

Duncan RB. 1972. Characteristics of organizational envi-ronments and perceived environmental uncertainty.Administrative Science Quarterly 17(3): 313–327.

Dutton JE, Fahey L, Narayanan VK. 1983. Towardunderstanding strategic issue diagnosis. StrategicManagement Journal 4(4): 307–323.

Einhorn HJ. 1974. Expert judgment: some necessaryconditions and an example. Journal of AppliedPsychology 59(5): 562–571.

Einhorn HJ, Hogarth RM, Klempner E. 1977. Qualityof group judgment. Psychological Bulletin 84(1):158–172.

Eisenhardt KM. 1989. Making fast strategic decisions inhigh-velocity environments. Academy of ManagementJournal 32(3): 543–576.

Eisenhardt KM, Martin JA. 2000. Dynamic capabilities:what are they? Strategic Management Journal ,October–November Special Issue 21: 1105–1121.

Eisenhardt KM, Zbaracki MJ. 1992. Strategic decisionmaking. Strategic Management Journal , WinterSpecial Issue 13: 17–37.

Elenkov DS. 1997. Strategic uncertainty and environ-mental scanning: the case for institutional influenceson scanning behavior. Strategic Management Journal18(4): 287–302.

Elsbach KD, Barr PS, Hargadon AB. 2005. Identifyingsituated cognition in organizations. OrganizationScience 16(4): 422–433.

Engle RW. 2002. Working memory capacity as executiveattention. Current Directions in Psychological Science11(1): 19–23.

Fahey L, Christensen HK. 1986. Evaluating the researchon strategy content. Journal of Management 12(2):167–183.

Flavell JH. 1976. Metacognitive aspects of problemsolving. In The Nature of Intelligence, Resnick LR(Ed). Erlbaum: Hillsdale, NJ; 231–235.

Flavell JH. 1979. Metacognition and cognitive monitor-ing: a new area of cognitive-developmental inquiry.American Psychologist 34(10): 906–911.

Flavell JH. 1987. Speculations about the nature anddevelopment of metacognition. In Metacognition,

Motivation and Understanding , Weinert FE, KluweRH (eds). Erlbaum: Hillsdale, NJ; 21–29.

Forbes DP. 2005. Managerial determinants of decisionspeed in new ventures. Strategic Management Journal26(4): 355–366.

Ford JK, Smith EM, Weissbein DA, Gully SM, Salas E.1998. Relationships of goal orientation, metacognitiveactivity, and practice strategies with learning outcomesand transfer. Journal of Applied Psychology 83(2):218–233.

Fredrickson JW, Iaquinto AL. 1989. Inertia and creepingrationality in strategic decision processes. Academy ofManagement Journal 32(3): 516–542.

Fredrickson JW, Mitchell TR. 1984. Strategic decisionprocesses: comprehensiveness and performance in anindustry with an unstable environment. Academy ofManagement Journal 27: 399–423.

Freeman D, Freeman J. 2008. Paranoia: The Twenty-First Century Fear . Oxford University Press: Oxford,UK.

Freeman D, Gittins M, Pugh K, Antley A, Slater M,Dunn G. 2008a. What makes one person paranoid andanother person anxious? The differential predictionof social anxiety and persecutory ideation in anexperimental situation. Psychological Medicine 38(8):1121–1132.

Freeman D, Pugh K, Antley A, Slater M, Bebbing-ton PE, Gittins M, Dunn G, Kuipers E, Fowler D,Garety PA. 2008b. Virtual reality study of paranoidthinking in the general population. British Journal ofPsychiatry 192(4): 258–263.

Ghemawat P. 1991. Commitment: The Dynamics ofStrategy . Free Press: New York.

Gigerenzer G. 2001. The adaptive toolbox. In BoundedRationality: The Adaptive Toolbox , Gigerenzer G,Selten R (eds). MIT Press: Cambridge, MA; 37–50.

Gilbert DT, Giesler RB, Morris KA. 1995. When com-parisons arise. Journal of Personality and Social Psy-chology 69(2): 227–236.

Gilbert DT, Osborne RE. 1989. Thinking backward:some curable and incurable consequences of cognitivebusyness. Journal of Personality and Social Psychol-ogy 57(6): 940–949.

Gilbert DT, Pelham BW, Krull DS. 1988. On cognitivebusyness: when person perceivers meet persons per-ceived. Journal of Personality and Social Psychology54(5): 733–740.

Ginsberg A. 1988. Measuring and modelling changesin strategy: theoretical foundations and empiricaldirections. Strategic Management Journal 9(6):559–575.

Gioia DA, Chittipeddi K. 1991. Sensemaking andsensegiving in strategic change initiation. StrategicManagement Journal 12(6): 433–448.

Goldstein WM, Hogarth RM. 1997. Judgment and deci-sion research: some historical context. In. Researchon Judgment and Decision Making: Currents, Connec-tions, and Controversies , Goldstein WM, Hogarth RM(eds). Cambridge University Press: Cambridge, UK;3–65.

Goll I, Rasheed AMA. 1997. Rational decision-makingand firm performance: the moderating role of



environment. Strategic Management Journal 18(7):583–591.

Green KM, Covin JG, Slevin DP. 2008. Exploringthe relationship between strategic reactiveness andentrepreneurial orientation: the role of structure–stylefit. Journal of Business Venturing 23(3): 356–383.

Green PE, Srinivasan V. 1990. Conjoint analysis inmarketing: new developments with implications forresearch and practice. Journal of Marketing 54(4):3–19.

Hahn G, Shapiro S. 1966. A Catalogue and ComputerProgram for the Design and Analysis of OrthogonalSymmetric and Asymmetric Fractional FactorialDesigns . General Electric Corporation: Schenectady,NY.

Hammond KR, Adelman L. 1976. Science, values, andhuman judgment. Science 194(4263): 389–396.

Harrison GW, List JA. 2004. Field experiments. Journalof Economic Literature 42(4): 1009–1055.

Haveman HA. 1992. Between a rock and a hard place:organizational change and performance under condi-tions of fundamental environmental transformation.Administrative Science Quarterly 37(1): 48–75.

Haynie JM, Shepherd D, Mosakowski E, Earley PC.2010. A situated metacognitive model of theentrepreneurial mindset. Journal of Business Venturing25(2): 217–229.

Haynie M, Shepherd DA. 2009. A measure of adaptivecognition for entrepreneurship research. Entrepreneur-ship Theory and Practice 33(3): 695–714.

Helfat CE, Finkelstein S, Mitchell W, Peteraf MA,Singh H, Teece D, Winter SG. 2007. Dynamic Capa-bilities: Understanding Strategic Change in Organiza-tions . Blackwell: Malden, MA.

Hodgkinson GP, Bown NJ, Maule AJ, Glaister KW,Pearman AD. 1999. Breaking the frame: an analysisof strategic cognition and decision making underuncertainty. Strategic Management Journal 20(10):977–985.

Hogarth RM, Karelaia N. 2007. Heuristic and linearmodels of judgment: matching rules and environments.Psychological Review 114(3): 733–758.

Hogarth RM, Makridakis S. 1981. The value of decisionmaking in a complex environment: an experimentalapproach. Management Science 27(1): 93–107.

Hough JR, White MA. 2003. Environmental dynamismand strategic decision-making rationality: an exam-ination at the decision level. Strategic ManagementJournal 24(5): 481–489.

Hughes GD. 1990. Managing high-tech product cycles.Academy of Management Executive 4(2): 44–55.

Hutzschenreuter T, Kleindienst I. 2006. Strategy-processresearch: what have we learned and what is still to beexplored. Journal of Management 32(5): 673–720.

Huy QN. 2001. Time, temporal capability, and plannedchange. Academy of Management Review 26(4):601–623.

Jackson SE, Dutton JE. 1988. Discerning threats andopportunities. Administrative Science Quarterly 33(3):370–387.

Janis IL. 1982. Decisionmaking under stress. In Hand-book of Stress: Theoretical and Clinical Aspects, Gold-berger L, Breznitz S (eds). Free Press: New York;69–80.

Janis IL, Mann L. 1977. Decision Making: A Psycholog-ical Analysis of Conflict, Choice, and Commitment .Free Press: New York.

Jost JT, Kruglanski AW, Nelson TO. 1998. Socialmetacognition: an expansionist review. Personalityand Social Psychology Review 2(2): 137–154.

Judge WQ, Miller A. 1991. Antecedents and outcomesof decision speed in different environmental context.Academy of Management Journal 34(2): 449–463.

Kahneman D. 1973. Attention and Effort . Prentice Hall:Englewood Cliffs, NJ.

Kaplan S. 2008. Cognition, capabilities and incentives:assessing firm response to the fiber-optic revolution.Academy of Management Journal 51(4): 672–695.

Karelaia N, Hogarth RM. 2008. Determinants of linearjudgment: a meta-analysis of lens model studies.Psychological Bulletin 134(3): 404–426.

Keats BW, Hitt MA. 1988. A causal model of linkagesamong environmental dimensions, macro organiza-tional characteristics, and performance. Academy ofManagement Journal 31(3): 570–598.

Keinan G. 1987. Decision making under stress: scanningof alternatives under controllable and uncontrollablethreats. Journal of Personality and Social Psychology52(3): 639–644.

Kiesler S, Sproull L. 1982. Managerial response tochanging environments: perspectives on problemsensing from social cognition. Administrative ScienceQuarterly 27(4): 548–570.

Kotter JP. 1996. Leading Change (1st edn). HarvardBusiness School Press: Boston, MA.

Krueger NF. 1993. The impact of prior entrepreneurialexposure on perceptions of new venture feasibility anddesirability. Entrepreneurship Theory and Practice18(1): 5–21.

Krueger NF, Brazeal DV. 1994. Entrepreneurial potentialand potential entrepreneurs. Entrepreneurship Theoryand Practice 18(3): 91–104.

Krueger NF Jr, Reilly MD, Carsrud AL. 2000. Compet-ing models of entrepreneurial intentions. Journal ofBusiness Venturing 15(5/6): 411–432.

Kunreuther H. 1969. Extensions of Bowman’s theoryon managerial decision-making. Management Science15(8): B415–B439.

Lant TK, Mezias SJ. 1990. Managing discontinuouschange: a simulation study of organizational learningand entrepreneurship. Strategic Management Journal ,Summer Special Issue 11: 147–179.

Lant TK, Milliken FJ, Batra B. 1992. The role ofmanagerial learning and interpretation in strategicpersistence and reorientation: an empirical exploration.Strategic Management Journal 13(8): 585–608.

Lavie N. 2005. Distracted and confused? Selectiveattention under load. Trends in Cognitive Sciences9(2): 75–82.

Lavie N, Hirst A, de Fockert JW, Viding E. 2004. Loadtheory of selective attention and cognitive control.



Journal of Experimental Psychology: General 133(3):339–354.

Louviere JJ. 1988. Analyzing Decision Making: MetricConjoint Analysis . Sage: Newbury Park, CA.

March JG. 1982. Theories of choice and makingdecisions. Society 20(1): 29–39.

March JG. 1994. A Primer on Decision Making: HowDecisions Happen . Free Press: New York.

March JG, Simon HA. 1958. Organizations . Wiley: NewYork.

Masuch M, LaPotin P. 1989. Beyond garbage cans: an AImodel of organizational choice. Administrative ScienceQuarterly 34(1): 38–67.

McGrath RG, Nerkar A. 2004. Real options reasoningand a new look at the R&D investment strategies ofpharmaceutical firms. Strategic Management Journal25(1): 1–21.

McMullen JS, Shepherd DA. 2006. Entrepreneurialaction and the role of uncertainty in the theory of theentrepreneur. Academy of Management Review 31(1):132–152.

Miller D. 1992. Environmental fit versus internal fit.Organization Science 3(2): 159–178.

Miller D, Friesen PH. 1982. Innovation in conservativeand entrepreneurial firms: two models of strategicmomentum. Strategic Management Journal 3(1):1–25.

Miller D, Friesen PH. 1983. Strategy-making and envi-ronment: the third link. Strategic Management Journal4(3): 221–235.

Milliken FJ. 1987. Three types of perceived uncertaintyabout the environment: state, effect, and responseuncertainty. Academy of Management Review 12(1):133–143.

Milliken FJ. 1990. Perceiving and interpreting environ-mental change: an examination of college admin-istrators’ interpretation of changing demographics.Academy of Management Journal 33(1): 42–63.

Mintzberg H. 1987. Crafting strategy. Harvard BusinessReview 65(4): 66–75.

Mintzberg H, Raisinghani D, Theoret A. 1976. The struc-ture of “unstructured” decision processes. Administra-tive Science Quarterly 21(2): 246–275.

Mitchell JR, Shepherd DA. 2010. To thine own selfbe true: images of self, images of opportunity, andentrepreneurial action. Journal of Business Venturing25(1): 138–154.

Nadkarni S, Barr PS. 2008. Environmental context,managerial cognition, and strategic action: anintegrated view. Strategic Management Journal29(13): 1395–1427.

Nelson RR, Winter SG. 1982. An Evolutionary Theory ofEconomic Change. Belknap: Cambridge, MA.

Nelson TO. 1996. Consciousness and metacognition.American Psychologist 51(2): 102–116.

Neter J, Kutner MH, Nachtsheim CJ, Wasserman W.1996. Applied Linear Statistical Models (4th edn).WCB/McGraw-Hill: Boston, MA.

Nicholls-Nixon CL, Cooper AC, Woo CY. 2000. Strate-gic experimentation: understanding change and perfor-mance in new ventures. Journal of Business Venturing15: 493–521.

Noda T, Bower JL. 1996. Strategy making as iteratedprocesses of resource allocation. Strategic Manage-ment Journal , Summer Special Issue 17: 159–192.

Nutt PC. 1998. How decision makers evaluate alterna-tives and the influence of complexity. ManagementScience 44(8): 1148–1166.

Ocasio W. 1995. The enactment of economic adversity:a reconciliation of theories of failure-induced changeand threat rigidity. In Research in OrganizationalBehavior (Volume 17), Cummings LL, Staw BM (eds).JAI Press: Greenwich, CT; 287–331.

Ocasio W. 1997. Towards an attention-based view of thefirm. Strategic Management Journal , Summer SpecialIssue 18: 187–206.

Papadakis VM, Lioukas S, Chambers D. 1998. Strategicdecision-making processes: the role of managementand context. Strategic Management Journal 19(2):115–147.

Payne JW, Bettman JR, Johnson EJ. 1997. The adap-tive decision maker: effort and accuracy in choice.In Research on judgment and decision making: Cur-rents, connections and controversies, Goldstein WM,Hogarth RM (eds). Cambridge University Press: Cam-bridge, UK; 181–204.

Powell TC. 1996. How much does industry matter?An alternative empirical test. Strategic ManagementJournal 17(4): 323–334.

Priem RL. 1992. An application of metric conjointanalysis for the evaluation of top managers’ individualstrategic decision making processes: a research note.Strategic Management Journal , Summer Special Issue13: 143–151.

Priem RL, Harrison DA. 1994. Exploring strategic judg-ment: methods for testing the assumptions of pre-scriptive contingency theories. Strategic ManagementJournal 15(4): 311–324.

Rajagopalan N, Rasheed AMA, Datta DK. 1993. Strate-gic decision processes: critical review and future direc-tions. Journal of Management 19(2): 349–384.

Rajagopalan N, Spreitzer GM. 1996. Toward a theoryof strategic change: a multi-lens perspective andintegrative framework. Academy of ManagementReview 22(1): 48–79.

Reder LM. 1996. Different research programs onmetacognition: are the boundaries imaginary? Learn-ing and Individual Differences 8(4): 383–390.

Remus WE. 1978. Testing Bowman’s managerial coeffi-cient theory using a competitive gaming environment.Management Science 24(8): 827–835.

Remus W. 1996. Will behavioral research on managerialdecision making generalize to managers? Managerialand Decision Economics 17(1): 93–101.

Remus W, Kottemann JE. 1987. Semi-structured recur-ring decisions: an experimental study of decisionmaking models and some suggestions for DSS. MISQuarterly 11(2): 233–243.

Sastry MA. 1997. Problems and paradoxes in a modelof punctuated organizational change. AdministrativeScience Quarterly 42(2): 237–275.

Schraw G, Dennison RS. 1994. Assessing metacognitiveawareness. Contemporary Educational Psychology19(4): 460–475.



Schwarz N. 2004. Metacognitive experiences in con-sumer judgment and decision making. Journal of Con-sumer Psychology 14(4): 332–348.

Shanteau J. 1992. Competence in experts: the role of taskcharacteristics. Organizational Behavior and HumanDecision Processes 53(2): 252–266.

Sharfman MP, Dean JW Jr. 1997. Flexibility in strategicdecision making: informational and ideologicalperspectives. Journal of Management Studies 34(2):191–217.

Shepherd DA, Zacharakis AL. 1997. Conjoint analysis: awindow of opportunity for entrepreneurship research.In Advances in Entrepreneurship, Firm Emergenceand Growth (Volume 3), Katz JA (ed). JAI Press:Greenwich, CT; 203–248.

Shepherd DA, Zacharakis A, Baron RA. 2003. VCs’decision processes: evidence suggesting more expe-rience may not always be better. Journal of BusinessVenturing 18(3): 381–401.

Simon HA. 1957. Administrative Behavior: A Studyof Decision-Making Processes in AdministrativeOrganizations (2nd edn). MacMillan: New York.

Slevin DP, Covin JG. 1997. Strategy formation patterns,performance, and the significance of context. Journalof Management 23(2): 189–209.

Smith KG, Grimm CM. 1987. Environmental variation,strategic change and firm performance: a study ofrailroad deregulation. Strategic Management Journal8(4): 363–376.

Staal MA, Bolton AE, Yaroush RA, Bourne LE Jr. 2008.Cognitive performance and resilience to stress. InBiobehavioral Resilience to Stress , Lukey BJ, Tepe V(eds). CRC Press: Boca Raton, FL; 259–299.

Staw BM. 1981. The escalation of commitment to acourse of action. Academy of Management Review6(4): 577–587.

Staw BM, Sandelands LE, Dutton JE. 1981. Threat-rigidity effects in organizational behavior: a multilevelanalysis. Administrative Science Quarterly 31(4):501–524.

Teece DJ. 2007. Explicating dynamic capabilities: thenature and microfoundations of (sustainable) enter-prise performance. Strategic Management Journal28(13): 1319–1350.

Teece DJ, Pisano G, Shuen A. 1997. Dynamic capabili-ties and strategic management. Strategic ManagementJournal 18(7): 509–533.

Thomas JB, Clark SM, Gioia DA. 1993. Strategicsensemaking and organizational performance: linkagesamong scanning, interpretation, action, and outcomes.Academy of Management Journal 36(2): 239–270.

Tushman ML, Romanelli E. 1985. Organizational evo-lution: a metamorphosis model of convergence and

reorientation. In Research in Organizational Behavior(Volume 7), Cummings LL, Staw BM (eds). JAI Press:Greenwich, CT; 171–222.

Van de Ven AH, Ferry DL. 1980. Measuring andAssessing Organizations . Wiley: New York.

Venkataraman S. 1997. The distinctive domain ofentrepreneurship research. In Advances inEntrepreneurship, Firm Emergence and Growth (Vol-ume 3), Katz JA (ed). JAI Press: Greenwich, CT;119–138.

Walsh JP. 1995. Managerial and organizational cognition:notes from a trip down Memory Lane. OrganizationScience 6(6): 280–321.

Ward A, Mann T. 2000. Don’t mind if I do: disinhibitedeating under cognitive load. Journal of Personality andSocial Psychology 78(4): 753–763.

Weinert FE. 1987. Introduction and overview: metacog-nition and motivation as determinants of effectivelearning and understanding. In Metacognition, Moti-vation and Understanding , Weinert FE, Kluwe RH(eds). Erlbaum: Hillsdale, NJ; 1–15.

Wilson TD, Brekke N. 1994. Mental contaminationand mental correction: unwanted influences onjudgments and evaluations. Psychological Bulletin116(1): 117–142.

Winter SG. 2003. Understanding dynamic capabilities.Strategic Management Journal , October Special Issue24: 991–995.

Wood R, Bandura A. 1989. Impact of conceptions ofability on self-regulatory mechanisms and complexdecision making. Journal of Personality and SocialPsychology 56(3): 407–415.

Yasai-Ardekani M, Nystrom PC. 1996. Designs for envi-ronmental scanning systems: tests of a contingencytheory. Management Science 42(2): 187–204.

Zacharakis AL, Meyer GD. 2000. The potential ofactuarial decision models: can they improve theventure capital investment decision? Journal ofBusiness Venturing 15(4): 323–346.

Zajac EJ, Kraatz MS. 1993. A diametric forces modelof strategic change: assessing the antecedents andconsequences of restructuring in the higher educationindustry. Strategic Management Journal , SummerSpecial Issue 14: 83–102.

Zhou KZ, Tse DK, Li JJ. 2006. Organizational changesin emerging economies: drivers and consequences.Journal of International Business Studies 37(2):248–263.

Zollo M, Winter SG. 2002. Deliberate learning andthe evolution of dynamic capabilities. OrganizationScience 13(3): 339–351.



APPENDIX: ERRATIC STRATEGICDECISION ILLUSTRATION

We illustrate how erratic strategic decision is cal-culated using one of our cases for reference. Asnoted in the text, we use a three-step process,beginning with two regression equations per indi-vidual: one for the 16 decisions at T1 and one forthe 16 decisions at T2.

Starting point: determining the usable (significant atp < 0.05) b’s for each individual

T1 Regression weights and significance

T1 β T1 Usable

β1 = 0.790∗ → β1 = 0.790β2 = 0.503∗ → β2 = 0.503β3 = 0.216∗ → β3 = 0.216β4 = −0.168∗ → β4 = −0.168β5 = 0.120∗ → β5 = 0.120β6 = 0.120∗ → β6 = 0.120β7 = −0.072

∗ p < 0.05

T2 regression weights and significance

T2 β T2 Usable

β1 = 0.702∗ → β1 = 0.702β2 = 0.521∗ → β2 = 0.521β3 = 0.294∗ → β3 = 0.294β4 = −0.249∗ → β4 = −0.249β5 = 0.204∗ → β5 = 0.204β6 = 0.158∗ → β6 = 0.158β7 = −0.023

∗ p < 0.05

Step 1: Subtract each significant T2 b from thecorresponding significant T1 b

Step 1

T1 − T2 = X

0.790 − 0.702 = 0 .0880.503 − 0.521 = 0 .0180.216 − 0.294 = 0 .078

(0.168) − (0.249) = 0 .0810.120 − 0.204 = 0 .0840.120 − 0.158 = 0 .038

Step 2: Take the absolute value of each difference

Step 2

|X||0 .088 ||0 .018 ||0 .078 ||0 .081 ||0 .084 ||0 .038 |

Step 3: Sum the scores for each individualto result in our dependent variable

Step 3

Erratic strategic decision: 0.387



APPENDIX: SCALE ITEMS

Metacognitive experience items (see Haynie and Shepherd, 2009: 710)I think about what I really need to accomplish before I begin a task.I use different strategies depending on the situation.I organize my time to best accomplish my goals.I am good at organizing information.I know what kind of information is most important to consider when faced with a problem.I consciously focus my attention on important information.

Environmental hostility items (see Green et al., 2008: 378; Slevin and Covin, 1997: 205–206)The failure rate of firms in my industry is high.My industry is very risky, such that one bad decision could easily threaten the viability of my business unit.Competitive intensity is high in my industry.Customer loyalty is low in my industry.Severe price wars are characteristic of my industry.Low profit margins are characteristic of my industry.

Environmental dynamism items (see Green et al., 2008: 378–379; Miller and Friesen, 1982: 17–18)My business unit must rarely change its marketing practices to keep up with competitors.The rate at which products are becoming obsolete in my industry is very slow.Actions of competitors are quite easy to predict.The set of competitors in my industry has remained relatively constant over the last 3 years.Product demand is easy to forecast.Customer requirements/preferences are easy to forecast.


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