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Group & Organization Management

DOI: 10.1177/1059601102027003004 2002; 27; 374 Group Organization Management

Theresa K. Lant and Patricia F. Hewlin Competing Teams

Information Cuesand Decision Making: The Effects of Learning, Momentum, and Social Comparison in

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GROUP & ORGANIZATION MANAGEMENTLant, Hewlin / INFORMATION CUES AND DECISION MAKING

Information Cues and Decision Making

THE EFFECTS OF LEARNING, MOMENTUM,AND SOCIAL COMPARISON IN COMPETING TEAMS

THERESA K. LANT

PATRICIA F. HEWLINNew York University

The question of howmanagers make decisions, such as formulating competitive strategies, con-tinues to be a major theme in management literature. Cognitive models of organizational deci-sion making have benefited from research on individual-level information processing. Thisstudy explores the applicability of individual-levelmodels of information processing to teams ofdecision makers making decisions in simulated organizations. The article proposes that cogni-tive schemas and team decision-making structure will focus decision-maker attention on differ-ent types of information for different categories of decisions. The findings suggest that there areboth similarities and differences in the cues that influence tactical and strategic decisions.

In today’s information-rich environments, management teams engaged instrategic decision making are flooded with information. One of their keytasks is determining what information to attend to in order to make specificdecisions regarding resource investments and competitive positioning. Thisarticle investigates how the type of decision beingmade by a teamof decisionmakers focuses their attention on different types of information. We predictthat tactical types of decisionswill focus teams on information that tells them“how are we doing.” We predict that strategic types of decisions will focusteams on information that tells them “what are they doing.” Thus, tacticaldecisions focus a team’s attention internally, whereas strategic decisionsfocus their attention externally.We drawonmodels of individual and organi-zational information processing to identify types of information cues thathave been found to influence managerial decisions. We make predictions

The authors wish to thank Joel Baum, Rachel Davis, Raghu Garud, Greg Janicik, Joe Lampel,and Frances Milliken for their helpful comments on an earlier version of this article. We alsoappreciate the helpful comments from two anonymous Group & Organization Managementreviewers and editor P. Christopher Earley.

Group & Organization Management, Vol. 27 No. 3, September 2002 374-407© 2002 Sage Publications

374



about the relative impact of these information cues based on the type of deci-sion being made.Models of organizational decision making have benefited from research

on individual-level information processing (e.g., Kahneman, Slovic, &Tversky, 1982; Nisbett & Ross, 1980). This research suggests that decisionmakers have limited information-processing capabilities (Hogarth, 1987;March & Simon, 1958; Simon, 1957). Studies from cognitive psychology,organization theory, and strategicmanagement have demonstrated that whendecision makers are faced with complex tasks or ambiguous situations, theyattempt to simplify the decisions that confront them (Barnes, 1984;Kiesler&Sproull, 1982; March, 1978; Payne, 1976; Schwenk, 1984; Tversky &Kahneman, 1973, 1974, 1981). Simplification strategies include the use ofdecision heuristics (Barnes, 1984; Duhaime & Schwenk, 1985; Kahnemanet al., 1982; Schwenk, 1984), cognitive schemas (Brewer & Nakamura,1984; Neisser, 1976; Rumelhart, 1984; Taylor & Crocker, 1981), cognitivecategorization (Porac&Thomas, 1990; Rosch, 1978), subdividing decisionsinto manageable components (Kahneman & Tversky, 1979; Mintzberg,Raisinghani, & Theoret, 1976), and applying routinized decision rules suchas trial-and-error learning or incremental adjustment (Cyert &March, 1963;March & Shapira, 1992; Padgett, 1980) and social comparison (Greve,1998). Theories of information processing have provided a good basis forunderstanding how information cues and the schemas used to interpret suchcues underlie individual decision making.The major categories of information that appear to drive managerial stra-

tegic decisions are performance feedback (Greve, 1998; Lant, Milliken, &Batra, 1992), momentum (Amburgey & Miner, 1992; Miller & Friesen,1980), and social comparison (Porac, Thomas, & Baden-Fuller, 1989).Studies of organizational strategies have been the primary source of evidencefor the impact of these types of information. The actual managerial decisionprocesses that yield the observed organizational outcomes have beeninferred in these studies through an application of cognitive information-processingmodels (Daft&Weick, 1984; Ford&Baucus, 1987;Hitt&Tyler,1991; Jackson &Dutton, 1988; Milliken, 1990; Thomas &McDaniel, 1990;Weick, 1993).A direct application of individual information-processingmodels to orga-

nizational phenomena leaves a huge gap in our understanding about howindividual cognition aggregates to the organizational level of analysis(Walsh, 1995). Greve (1998) also raised this issue and noted the similar pat-tern of findings between individual and organizational risk taking, responseto performance feedback, and decisions to change behavior. However, moreresearch is needed to understand how teams of managers aggregate their

Lant, Hewlin / INFORMATION CUES AND DECISION MAKING 375



beliefs to yield organizational-level outcomes. Many key decisions in orga-nizations are made by teams of individuals rather than by individuals actingin isolation (Hambrick &Mason, 1984). Argote, Seabright, and Dyer (1986)found that groups tended to use the representative heuristic (Kahneman et al.,1982) as much as did individuals. Walsh, Henderson, and Deighton (1988)found that schema theory provided a good explanation for the information-processing characteristics of decision-making groups.Walsh andHenderson(1989) found that attributions in decision-making groups influenced theirdecisions to escalate or reduce commitments. Thus, to understand manage-rial decisions regarding organizational actions, it makes sense to study theinformation-processing patterns of management teams as well as the factorsthat may influence group decisions (Hambrick, Cho, & Chen, 1996; Michel& Hambrick, 1992; Wiersema & Bantel, 1992).This study examines directly the decision making of teams of individuals

making resource allocation and strategic decisions for simulated organiza-tions. This methodological approach allows us to closely assess how infor-mation drivers such as performance feedback, momentum, and social com-parison affect teams’ tactical and strategic decisions. In addition,we examinehow differences in how teams structured themselves tomake decisions influ-ence the information they attend to and how this information influences theirchoices.

SCHEMA-BASED INFORMATION PROCESSING

Schema theory provides a framework for understanding what types ofinformation managers will notice and how this information will be inter-preted (Abelson, 1976; Neisser, 1976; Rumelhart, 1984). The schemas that adecision maker develops over time through past information processingaffect the salience and relevance of information that the decision maker isexposed to subsequently. Schemas are cognitive representations of theworld, based on historical experience, which contain rules that direct infor-mation processing (Kiesler&Sproull, 1982; Lord&Foti, 1986). Informationthat is relevant to an existing schemawill bemore salient to a decisionmakerand will be incorporated more easily than information that does not fit wellwithin existing schemas (Kiesler&Sproull, 1982). Newly acquired informa-tion will be channeled into appropriate schemas depending on its relevancefor that set of beliefs.According to Gioia (1986), the specific type of schema that is likely to

influence managerial action is the script schema: “A cognitive structuredevoted specifically to the retention of context specific knowledge about

376 GROUP & ORGANIZATION MANAGEMENT



events and event sequences and to the guidance of action on the basis of thatknowledge” (p. 57), and “people possess categories of structured knowledgeabout . . . events, behavior, and actions that can be brought forth by situa-tional cues [italics added] to facilitate understanding and action” (Abelson,1976). Gioia and Poole (1984) suggested that the concept of scripts beapplied to decision-making research. They argued that prior experiences rel-evant to a given situation will be remembered schematically. Script schemasguide behavior whenever individuals try to make sense of their organizationalexperience (Gioia, 1986; Gioia & Manz, 1985; McCabe & Dutton, 1993).In this article, we argue that the type of decisions teams are making, as

well as the decision-making structure of the team, will trigger differentschemas that direct attention to different types of information cues. Spe-cifically, we examine how information cues influence the following twobroad categories of decisions that managers make: strategic and tactical, andwhether the impact these decisions have are influenced by whether the teammakes decisions collectively or by individual division of labor.

INFORMATION CUES AS TRIGGERS OF ACTIONFOR STRATEGIC AND TACTICAL DECISIONS

Extensive evidence suggests that managers process information by cate-gorizing issues, competitors, and so forth (Dutton & Jackson, 1987; Poracet al., 1989). In addition to categorizing issues and competitors, managersmay also categorize the types of decisions they have to make. Although wehave little understanding of how managers might actually categorize deci-sions, implicit in the strategic management literature is the notion that deci-sions in organizations can be classified by the magnitude, purpose, or theextent of strategic change implicit in the decision (e.g., Miller & Friesen,1980; Tushman & Romanelli, 1985). Egelhoff (1982) suggested that deci-sions might be categorized based on the amount and type of information pro-cessing required to make the decisions. Tactical decisions, for instance, arefairly routine, require only narrow information processing, and can be easilymodified or reversed.Alternatively, strategic decisions are less routine,moresignificant, require broader information processing, and involve signifi-cant commitments that are difficult to modify or reverse (Egelhoff, 1982;Smith, Grimm, Gannon, & Chen, 1991). In an empirical study of morethan 900managers, Bacharach, Bamberger, andMundell (1995) providedevidence that managers seek decision criteria to justify their decisions.These criteria are based on the following two logics of justification: strate-gic and tactical. The results from their study indicate that strategic decisionsare macro-oriented, focusing on comprehensive organizational change.




Tactical decisions refer to day-to-day tasks, which maintain past practicesand achieve short-term goals.The study examines how performance feedback, prior decisions, and

competitive actions affect tactical and strategic decisions made by compet-ing teams. Our first predictions build on existing literature that addresses theimpact of these behavioral triggers (performance feedback, momentum, andsocial comparison) on organizational-level strategic change. We will exam-inewhether patterns expected at the aggregate, organizational level are foundat the team level of analysis. We then go further to examine the impact ofgroup structure on the tactical and strategic decisions made by the teams.It is important to note that although our primary emphasis is the role of

information cues and schema on howmanagement teamsmake decisions,werealize that other variables such as the level of affective states (Isen&Means,1983; Isen & Patrick, 1983) and personal efficacy (Wood & Bandura, 1989)may also influence decision making. Bandura and Wood (1989), for exam-ple, provided results that indicated a positive relationship between perceivedself-efficacy and the effective use of analytical strategies for achieving opti-mal managerial rules as well as for achieving optimal levels of personalgoals. Other studies have found motivational mechanisms such as socialidentity (Kramer, Brewer, &Hanna, 1996; Kramer, Shah, &Woerner, 1995)and feedback seeking (Ashford, 1986) to influence how individuals andorganizations make decisions. In this study, we have chosen to focus on theapplicability of traditional cognitive models of trial-and-error learning,momentum, and social comparison in explainingmanagement team decisionmaking in organizations.

INFORMATION CUES AND HYPOTHESIZEDRESPONSES OF TEAMS MAKING

TACTICAL AND STRATEGIC DECISIONS

The specific decisions this study examines are domain navigation deci-sions (Bourgeois, 1980), which are decisions about how to compete within agiven product-market domain. By focusing on domain navigation decisions,we examine decisions regarding specific commitments to action (Mintzberget al., 1976) within given product markets versus decisions to enter or exit aproduct market.These decisions differ in the extent and purpose of the actions taken.Deci-

sions to introduce a new product to the market or to withdraw an existingproduct from the market represent decisions to alter the way in which theorganization is competing in the industry. Because these choices represent




fairly major changes in domain-navigation strategy, they will be used to rep-resent strategic decisions. Decisions to modify or reposition existing prod-ucts represent tactical changes to the organization’s competitive position inthe industry. Tactical changes are achieved by making minor changes to aproduct’s physical characteristics or by changing consumer perceptions ofthe characteristics of the product (Smith et al., 1991).

INFLUENCE OF PERFORMANCE CUESON TACTICAL AND STRATEGIC DECISIONS

The argument that past performance is a salient cue that influences deci-sions is consistent with theories and observations about organizations(Greve, 1998; Lant et al., 1992). Performance information is used routinelyto detect problems and to determine if performance is satisfactory (Cyert &March, 1963).The recognition of satisficing behavior (March & Simon, 1958) by theo-

rists has also made aspiration levels for performance outcomes an importantvariable in organizational decision-making research (Lant, 1992; Lant &Montgomery, 1987; Mezias & Murphy, 1998). Aspiration levels serve ascognitive frames of reference for decision makers (Kahneman & Tversky,1979; Lant & Montgomery, 1987; Payne, Laughunn, & Crum, 1980).Research has found that performance relative to aspiration levels can influ-ence future goals (Lant, 1992; Murphy, Mezias, & Chen, 2001), problemsensing (Kiesler & Sproull, 1982), risk taking (Kahneman & Tversky, 1979;March & Shapira, 1987, 1992), organizational learning (Cyert & March,1963; Levinthal & March, 1981), and organizational change (Cyert &March, 1963; Greve, 1998; Lant &Mezias, 1992). Models of trial-and-errorlearning predict that when performance outcomes fall below aspiration lev-els, organizations will change current activities.Studies of managerial interpretations suggest that the relationship

between performance feedback and strategic action ismore complicated thansimple models of trial-and-error learning would predict (Lant et al., 1992).For instance, some studies have found that failure feedback may lead to per-sistence in strategy, rather than change, through the tendency to escalatecommitment to a failing course of action (Bobocel & Meyer, 1994; Staw &Ross, 1978) or through the generation of threat-rigidity behavior (Staw,Sandelands, & Dutton, 1981). Greve (1998) found that the riskiest strategicdecisions did not respond to performance feedback in the way that produc-tion decisions or less risky changes did. Strategic decisions may be influ-enced by long-term goals and plans, for which short-term feedback is lessmeaningful and less likely to produce immediate strategic change.




Most studies of escalation of commitment have examined investmentdecisions characterized by large resource commitments, delayed feedback,and difficult adjustment or reversal of decisions. These are the same attrib-utes that characterize strategic decisions, aswe have defined them in this arti-cle. Because of the characteristics of strategic decisions, short-term perfor-mance feedbackwould not be expected to produce the pattern of incrementaladjustment predicted by trial-and-error learning. Trial-and-error learningwill be expected, however, for tactical decisions that involved small resourcecommitments with frequent feedback and opportunities to incrementallyadjust previous decisions. Because of the features of tactical decisions, deci-sion makers are less likely to respond to failure feedback with self-justifyingactions or threat-rigidity behavior (Lant & Hurley, 1999).Thus, the response of managers to information about past performance

may depend on the type of decision being made. This study predicts that theschema associated with tactical decisions will use performance informationto make trial-and-error adjustments in tactics. The schema associated withstrategic decisionswill not elicit a response from short-term feedback.1 Thus,this article predicts that performance below aspiration will elicit changes intactical decisions but not strategic decisions.

Hypothesis 1:Performance below aspirationwill result in tactical changes butwillnot result in strategic changes.

INFLUENCE OF CUES ABOUT PRIOR DECISIONSON TACTICAL AND STRATEGIC DECISIONS

A large component of a manager’s interpretative task is to determine thecausal linkages between actions and performance outcomes (Milliken &Lant, 1991). Thus, not only will managers pay attention to information abouttheir performance, they will also recall and consider prior decisions. It is notobvious, however, the extent to which past decisions influence currentchoices.Perspectives on organizational evolution suggest that strategic decisions

exhibit momentum (Miller & Friesen, 1980; Tushman & Romanelli, 1985).That is, organizations will tend to repeat similar types of actions over time(Amburgey &Miner, 1992; Greve, 1998; Kelly & Amburgey, 1991; Miller,1990). Routines guide a wide range of an organization’s activities, from pro-duction procedures to “strategic heuristics that shape the approach of a firmto the nonroutine problems it faces” (Nelson & Winter, 1982, p. 15). Suchheuristics could include rules for making decisions of both a strategic and atactical nature. An evolutionary explanation for momentum is based on the




idea that competencies with certain types of activities increase with experi-ence (Ginsberg & Baum, 1993):

Each time an organization engages in a particular kind of change, it increasesits competency in making that type of change. The more experienced an orga-nization becomes with a particular type of change, the more likely it is to makefurther changes of a similar nature. (pp. 5-6)

This study proposes that there is also a cognitive element to decisionmomentum. Not only are organizational competencies likely to be stored inorganizational memory, but beliefs about competencies are likely to bereflected in the cognitive scripts that guide decisions. Such scripts will guidemanagerial attention toward decisions of the same type that have been madein the past. Evidence of decision momentum suggests that there should be apositive relationship over time between decisions of the same type.Decision momentummay characterize decisions involving some types of

tactical change. This is because beliefs about competencies in taking actionbecome embedded in associated scripts. Thus, this study predicts that deci-sions to fine-tune product positioning will be characterized by momentumand, thus, will be positively associated over time. Ifmanagers havemade tac-tical changes in the past, they are likely to do so in the future.

Hypothesis 2: Prior tactical changes will be positively associated with current tac-tical changes.

Like tactical decisions, strategic decisions of the same type are predictedto exhibit momentum. Thus, new product introductions should be positivelyassociated over time, and product withdrawals should be positively associ-ated over time.

Hypothesis 3: Prior strategic changes will be positively associated with currentstrategic changes of the same type.

INFLUENCE OF CUES ABOUT THE COMPETITIVE ENVIRONMENTON TACTICAL AND STRATEGIC DECISIONS

Theories of competitive strategy argue that strategic decision makersattend to the actions of their competitors in formulating their own strategies(Porac et al., 1989; Porter, 1980). Many studies of strategy formulation sug-gest that managers monitor the demands of their task environment, ofwhich competitors are a key component (Aguilar, 1967; Bourgeois, 1980;Dill, 1958; Jauch, Osborn, & Glueck, 1977; Keegan, 1974). The industrial




economics literature suggests that firms in competitive industries recognizetheir mutual interdependence and monitor each other’s actions in determin-ing their strategies (Chamberlin, 1933; Porter, 1980; Scherer & Ross, 1990).The actions of competitors are likely to influence managers’ decisions notonly because these actions may create opportunities or threats for strategicmanagers (Glueck, 1976;Hofer&Schendel, 1978) but also because they are,in and of themselves, salient and relevant pieces of information. As noted byKiesler and Sproull (1982, p. 556), “the behavior and outcomes of competi-tors, of course, are sharply drawn—a figure against the ground.”The actions of competitorsmay also serve as an important social compari-

son function, especially in environments characterized by uncertainty (Poracet al., 1989). The tendency to engage in social comparison has been demon-strated extensively at the individual level of analysis (Festinger, 1957;Schacter & Singer, 1962), including individuals in organizational contexts(Salancik & Pfeffer, 1978; Zucker, 1977). Processes of social comparisoncan also occur between organizations, through interorganizational imitation(DiMaggio & Powell, 1983; Haunschild & Miner, 1997; Levitt & March,1988; Mezias, 1990). Interfirm social comparison is likely to be most preva-lent within “primary competitive groups—a collection of firms that defineeach other as rivals” (Porac et al., 1989, p. 414; also see Lant & Baum, 1995;Porac, Thomas, Wilson, Paton, & Kanfer, 1995). Because the members of acompetitive group see themselves as strategically interdependent, the actionsof rivals and the characteristics of the competitive environment are morelikely to be components of the scripts that guide strategic decisions than ofthose that guide tactical decisions. In fact, the schemas of managers in firmswithin a competitive group may become very similar to one another overtime (Porac et al., 1989), resulting in similarities in the types of informationattended to and in the interpretations of and responses to information (Mor-gan & Milliken, 1992).Within competitive groups, the actions of the actors within the group are

likely to be watched closely. One type of competitive action that is likely toinfluence the strategic decisions of firms within the group is the introductionof new products that compete with products already being marketed. Theintroduction of products is a salient piece of information about competitoractivity that may affect firms significantly and provide information about thetype of strategies being pursued by one’s competitors. This article predictsthat competitors’ product introductions will be positively associated withstrategic changes by the focal firm. Firmsmay respond in this way to counterthe competitive threat of newproducts (Porter, 1980) or because they attemptto imitate the type of strategic actions they see their competitors making(Levitt & March, 1988).




Hypothesis 4: Competitor product introductions in one period will be positivelyassociated with strategic changes by the focal firm in the following period.

Not only will firms in a competitive group be affected by the specificactions of their competitors, but their decisions may also be affected by thedegree of competition in their group. Another important type of informationthat may be relevant to managers’ scripts about making strategic decisions isthe instability or amount of uncertainty surrounding the relative competitivepositions of the members of the competitive group (Haunschild & Miner,1997). Perceptions of a high degree of competitiveness in the groupmay leadto both uncertainty and strategic actions designed to fight for market share.Uncertainty is also likely to stimulate more social comparison behavioramong competitors. Because product introductions are a typical response tohigh levels of competition (Porter, 1980), the combination of unstable com-petitive positions and perceptions of extreme competitiveness will result inan increase in strategic change.

Hypothesis 5: Strategic changes will be positively associated with perceived highlevels of competition as well as with positioning changes of competitors.

INFLUENCE OF TEAM STRUCTUREON TACTICAL AND STRATEGIC DECISIONS

In this section, we consider the impact of team information-processingstructure on tactical and strategic decision making. Many alternative modelsexist in the literature that explore how individual beliefs aggregate to yieldgroup-level decisions. These include voting rules (Greve, 1998), power rela-tions and hierarchical position (Ilgen, 1988), and demographic distribution(Corner, Kinicki, & Keats, 1994). Our focus in this article is on how teamstructure influences information processing and, thus, responds to the fol-lowing three decision drivers we have been examining: performance feed-back, social comparison, and momentum.We define team as the collective actions of individuals in which group

members are accountable for results (Hackman&Oldham, 1980). Hackmanand Oldham (1980) defined this type of team as a “self-managing workgroup,” which accurately describes the type of teams in our study. Groupmembers have the discretion to “handle internal processes as they see fit togenerate a specific group product, service, or decision” (Hackman&Oldham,1980, p.164). Because team members have the discretion to develop pro-cesses and to designate roles to accomplish its goals, the team is considered tobe self-managing.




We investigate how decision-making structures influence tactical andstrategic decisionmaking by teams.We suggest that the way in which a teamstructures itself to make decisions (i.e., how it goes about processing infor-mation andmaking decisions based on this information) will focus the atten-tion of decision makers toward either tactical or strategic types of decisions.To the extent that there is a division of labor that focuses individual attention(vs. the collective) on information relevant to tactical decisions, and allowsindividuals the autonomy to make decisions independently, the morechanges in tactics we will see.When there is only one individual responsible for making decisions, the

benefit obtained from discourse among teammembers decreases and there isless exploration of alternative decisions (Hutchins, 1991). The notion ofsatisficing describes how decision makers are unable to explore all possiblealternatives (Cyert &March, 1963; Simon, 1997, p. 119). “Because adminis-trators satisfice rather than maximize, they can choose without first examin-ing all possible behavior alternatives and without ascertaining these are infact all the alternatives.” An individual decision maker is cognitively unableto survey as many alternatives as a group of decision makers (Hutchins,1991).We therefore predict that satisficingwill be expressed through a seriesof tactical decisions because tactical decisions require less cognitive labor onthe part of the decision maker.

Hypothesis 6: Teams with divisions of labor that permit individuals to make deci-sions independently will engage inmore tactical changes than teamswith divi-sions of labor that require collective decision making.

Teams with structures that require discussion for decisions to be mademay engage in more exploration of options because one individual is notresponsible for the final decision. In this situation the group becomes whatHutchins (1991) defined as a “cognitive system” in which the cognitive taskof considering various alternatives are socially distributed in the decision-making process. We expect that such structures will lead to more attentionbeing paid to strategic types of decisions rather than to tactical types ofdecisions.

Hypothesis 7: Teams with divisions of labor that require individuals to engage incollective discussion prior to making decisions will engage in more strategicchanges than teams with divisions of labor that permit individuals to makedecisions independently.

Figure 1 provides a summary of our predictions about how informationcues will affect changes in tactical and strategic decisions. The hypothesized




direction of each relationship is also indicated. The pattern of hypothesesillustrates our general prediction that tactical decisions elicit internallyfocused information processing, addressing the question,How arewe doing?Strategic decisions elicit externally focused information processing,addressing the question, What are they doing? We expect that decisionmomentum is a generic phenomenon, occurring for both tactical and strate-gic decisions. Finally, team structures can focus attention on different typesof decisions. Teams that allow individuals to make decisions will tend toexhibit more tactical changes, whereas teams that require collective decisionmaking will tend to be more focused on overall strategic issues.

METHODOLOGY

Research setting. To test the hypotheses described above, it is necessaryto track the performance and decisions of strategic decision makers overtime, aswell as the actions of their competitors. For reasons of external valid-ity, the setting should be complex enough so that decisions that are made aresimilar to those that are made in actual organizations. TheMarkstratmarket-ing strategy game (Larreche & Gatignon, 1977) provides such a setting.Markstrat was written as a comprehensive model of marketing dynamics tointegrate real-world experience of organizations and knowledge from existing


Figure 1: Summary of Hypothesized RelationshipsNOTE: H1 = Hypothesis 1; H2 = Hypothesis 2; H3 = Hypothesis 3; H4 = Hypothesis 4; H5 =Hypothesis 5; H6 = Hypothesis 6; H7 = Hypothesis 7.



marketing research. It is considered to be high on conceptual complexity andit yields experiences and research results that are relevant to awide variety ofenvironments and industries (Larreche, 1987). Markstrat’s relevance toorganizational life has led to its being adopted as a research as well as a peda-gogical resource in corporations and universities (Glazer, Steckel, &Winer,1987; Kinnear & Klammer, 1987; Larreche, 1987). For example, managerswithin a variety of large, successful corporations use Markstrat in theirin-house management training programs due to the high degree of externalvalidity (Kinnear & Klammer, 1987)Markstrat provides. Whereas businessgames are not one of the most commonly used settings in which to conductorganizational or strategy research, there is a substantial precedent for usingthem (Chesney & Locke, 1991; Earley, Northcraft, Lee, & Lituchy, 1990;Gladstein&Reilly, 1985; Segev, 1987). They are especially useful for study-ing the dynamics of decision making (Hogarth & Makridakis, 1981; Lant,1992; Lant & Hurley, 1999; Lant &Montgomery, 1987; Ross, 1987; Walshet al., 1988).A typical play ofMarkstrat consists of five teams, each representing the

marketing profit center of an organization, who compete with each other inone or two product categories over a period of time. In conjunction with theproduct-management decisions, the teams also make a wide range of deci-sions in a complex environment such as forecasting performance, analyzingtheir environment, thinking about their overall strategy, assessing their com-petitive position, and assessing their competitors. Complicated algorithmsthat simulate a competitive market in a multidimensional, interdependentworld control theMarkstrat game; the relationships between organizationalactions and outcomes are highly complex and nonlinear, capturing the com-plexity of the decisions facing organizational decision makers. As the gameis played, the competitive structure of the industry evolves, which is depend-ent on the moves of teams.Each decision-making session took about 2 hours to complete, and the

teams were studied once a week over seven weekly class periods. This studytherefore provides a longitudinal investigation of groups of decision makerssetting objectives, making strategic and resource allocation decisions, andreceiving feedback over several periods of time. Although the length of timeis limited in comparison to actual histories of real organizations, it representsa longer time period than is typically possible in studies of decisionmaking inreal organizations (e.g., Bourgeois, 1985; Eisenhardt & Bourgeois, 1988;Isabella, 1990; Mintzberg et al., 1976; Thomas, Clark, & Gioia, 1993).

Participants. Data were gathered from four Markstrat industries com-posed of 10 teams of managers in an executive fellowship program and 10




teams of MBA students enrolled in a marketing strategy course at a majorbusiness school. A total of 87 individuals participated. Of those, 70 (80%)were men and 17 (20%) were women. The average age of the executives andMBAs is 37 and 26.7 years, respectively. The work experience of the execu-tives ranged from 10 to 12 years, whereas the averagework experience of theMBAs is 3.49 years. The teams ranged in size from 3 to 6 individuals. Seventeams were all male; 13 teams were of mixed gender. The participants wereinformed of the dynamics of the game and were encouraged to analyze theinformation in a realistic fashion.

Data sources. The following three data sources were used: group-decisionforms filled out by each team in each period; information generated by thegame and given to the participants at the beginning of each period; and an endof game questionnaire. The data gathered from each period of play are at theteam level of analysis. Decisions were reported as team decisions, and per-formance results were given for each team.The team decision forms were filled out each week. The question regard-

ing sales goals was worded as follows: “For each brand you are producing inthe current period, please indicate your sales objective (# of units sold).”These questionnaires were used to ensure that a systematic record of theseobjectives was kept. However, the teams had an incentive to think about per-formance objectives independent of the questionnaires. The teams are gradedbased on howwell they improve their company’s performance from its initialposition and the quality of the strategic plan they develop. They are taughtthat an important part of a good strategic plan is to set performance objec-tives. Thus, performance objectives become a natural part of strategic plan-ning,which is consistentwith empirical evidence that the teams pay attentionto performance goals in determining strategic actions. Lant andMontgomery(1987) found that performance relative to goals affected both the risk takingand search behavior of these teams.The game generated information that was provided to the teams in each

period. This included information on their own performance, the perfor-mance of other teams, and their competitive position relative to their com-petitors. The postgame questionnaire measured perceptual data based onindividual-level responses.

Dependent variables. The study examines the following two dependentvariables: tactical decision making (modifications of existing products) andstrategic decision making (new product introductions and withdrawals).Product modification is measured by the number of physical modifica-

tions made to existing products plus the number of attempts to reposition




consumer perceptions of an existing product in the market through changesin advertising content. New product introductions are measured as the num-ber of new products introduced by a team in a given period. Because productintroductions and withdrawals for any one team rarely exceeded one in anyperiod—and never exceeded two—for the purposes of analysis, these indica-tors are recoded as a binary variable, in which 1 indicates a product introduc-tion or withdrawal, and zero indicates no introduction or withdrawal in agiven period.

Predictor variables. There are four categories of predictor variables: pastperformance, information about prior decisions, information about the com-petitive environment, and team decision-making structure.Past performance was measured with two variables to grasp different

ways in which teams might determine if they were performing better orworse than their aspirations. The firstmeasure is perceived performance rela-tive to the team’s performance objectives. Performance objectives wereobtained from the forms each team completed during each decision-makingperiod. This form asked the team to indicate how well they thought they hadperformed in the prior period relative to the objectives they had set in thatperiod. They indicated this on a 5-point Likert-type scale that ranged frommuch better than expected tomuchworse than expected. The specific perfor-mance objective they were asked about was unit sales. The responses pro-vided a team-level assessment of how they had performed relative to theirperformance objectives. The second measure is an indicator of whether theteamhad performed above or below their industry average in the prior period.This is a binary variable, coded 1 if the team had unit sales higher than theindustry average, and coded zero if the team had unit sales lower than theindustry average. Thismeasurement is consistentwith other studies that haveexamined the effects of performance relative to aspirations with respect toaverage or median industry performance (Feigenbaum & Thomas, 1988;Lant et al., 1992).Two types of prior decisions are measured. These are equivalent to the

two dependent variables but lagged one period. Prior tactical changes aremeasured by the number of product modifications made to existing productsin the prior period. Prior strategic changes are indicators of whether therewere any product introductions or withdrawals made in the prior period.The competitive environment is measured by the following three vari-

ables: (a) the number of products introduced by a team’s competitors in theprior period, (b) the instability of competitive positions,which ismeasured asthe absolute value of the average change in market share among competitors




in an industry from period to period, and (c) perceptual measure of the levelof competition in the industry. This measure is derived from the postgamequestionnaire that asked individual participants to rate the level of competi-tiveness of theMarkstrat game they played. They indicated this on a scale of1 (extremely competitive) to 7 (not at all competitive).2 The average score ofindividuals within each teamwas used as the team-level indicator of percep-tion of competitiveness. The overall response rate for this questionnaire was82%. Of the women, 94% responded, whereas 79% of the men responded.There were at least 2 people per team who returned the questionnaire. Itshould be noted that there is little variance on this variable; the average teamperception of competitiveness varied only from 1 (extremely competitive) to3 (where 4 would indicate moderately competitive).Teams designed their own structure for making decisions and were asked

to provide brief descriptions of the decision-making structure used in theirgroup. These descriptions indicated that the following four basic designswere used: functional, product line, matrix, and consensus. Functional teamscreated vice president roles such as production, research and development(R &D), and marketing. There were 7 teams organized by function. Productteams divided responsibilities by creating product manager positions thatinvolved the management of all aspects of an individual product. There were4 teams organized by product. Matrix teams had responsibilities along bothfunctional and product dimensions. There were 3 matrix teams. Consensusteams did not divide responsibilities but made all decisions as a group. Therewere 6 consensus teams. These designs are fairly representative of the deci-sion structures that might be found in actual management teams. The charac-teristic that we were particularly interested in, theoretically, was whetherindividuals could make decisions without consulting other team members.Given the manner in which decisions must be made in theMarkstrat game,the only structure for which this was true is the product-line structure. Alldecisions on the decision forms must be recorded with respect to specificproducts, such as how much of a specific product to produce, how muchadvertising to do on that product, and so forth. For the three other team struc-tures, team discussion is necessary before filling out the decision forms.Thus, we calculated a binary variable that was coded 1 if the structure was byproduct-line, and zero otherwise.

Control variables. The controls used in the analysis are listed in Appen-dix A. The first category of controls includes decisions and performance out-comes that may be correlated with the dependent variables but are not of the-oretical interest in this study. These include change in production and sales.




The second set of control variables are those that are needed due to the char-acteristics of the game but are not particularly interesting from a theoreticalviewpoint. Appendix B describes a number of group characteristics weexamined for their potential impact on the dependent variables but did notinclude in themultivariate analysis. Given our focus on information process-ing in this study, these group characteristics, such as whether the group had astrong leader or used voting to make decisions, were not included in our pre-dictions. However, other models of group process suggest that such factorsmay influence group outcomes (Stasser, Kerr, & Davies, 1989). Thus, weconducted exploratory analyses to determine whether any of these variableswere related to our dependent variables, in an effort to avoid correlated omit-ted variables in our analysis.

RESULTS

Our data are organized in a pooled cross-sectional time-series design. Apanel of data that includes lagged independent variables can exhibit auto-correlated disturbances across time periods. Following the advice in Johnston(1984), we use an iterative maximum-likelihood procedure, where possible,to estimate and correct for autocorrelation. The analysis of productmodifica-tion (tactical) decisions is based on such a maximum-likelihood procedure.For the analysis of product introductions and withdrawals (strategic deci-sions), in which the dependent variables are binary, logistic regression wasused. The correlation matrix and descriptive statistics are given in Table 1.

Information cues used in tactical decisions. Table 2 displays the modelthat analyzes the tactical decisions tomodify existing products. Hypothesis 1predicted that negative performance would be associated with tacticalchanges. Neither performance feedback variable had the predictedtrial-and-error learning effect on product modifications.Prior decisions influenced product modifications as predicted in Hypoth-

eses 2. Teams that made product modifications in the prior period were morelikely to make subsequent modifications, reflecting decision momentum.The competitive environment was not predicted to have an effect on tacti-

cal decisions. We have argued that the schemas guiding tactical decisionswould tend to focus on internal information such as performance feedbackrather than external information such as the competitive environment. How-ever, the analysis revealed that information about the competitive environ-ment was used to make tactical decisions. Instability in the competitive posi-tions of the teams led to more product modification. In addition, teams thatperceived the game to be very competitive were more likely to engage in




TABLE 1

Means, Standard Deviations, and Intercorrelation Matrix (N = 116)

Variable M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1. aProduction change .51 2.692. Product modificationt 4.57 1.59 .22*3. Production introductionst .50 .50 –.22* .20*4. Product withdrawalt .36 .48 –.11 .17 .54**5. Performance versus aspirationt 2.95 .91 .39** .03 –.11 –.096. Performance versus industryt .42 .50 .33** .18 –.12 –.10 .29**7. aSalest 6.56 4.09 .33** .45** –.11 .03 .28** .61**8. aProduction changet-1 .51 2.25 –.04 .05 –.09 –.17 .16 .30** .37**9. Product modificationt-1 4.21 1.61 –.05 .46** –.08 .14 .03 .12 .45** .1810. Production introductionst-1 .45 .50 –.28** .08 .07 .29** –.19* –.21* –.11 –.33** .30**11. Product withdrawalt-1 .28 .45 –.06 –.07 –.02 .12 –.01 –.11 –.04 –.26** .26** .55**12. Competition productintroductionst-1 2.98 1.80 –.21* .12 .14 .16 –.11 –.03 .04 .10 .14 .15 .01

13. Change in competitivepositiont-1 1.07 .98 –.18* .14 .21* –.10 –.13 .04 –.15 .05 –.01 .14 –.22* .43**

14. Perceived competitiont-1 4.67 .49 .18 .38** –.14 –.04 .02 .31** .39** .27** .36** –.11 –.01 –.01 –.0115. Perceived realismt-1 5.33 .75 .04 .19* .02 –.01 .03 .01 .08 .12 .14 –.02 –.01 –.01 –.05 .1716. Team structure .21 .41 .09 .22* .00 –.03 .07 –.22 –.04 .08 .13 –.03 –.04 .02 .03 .02 –.1117. aResearch and developmentexpenditurest-1 30.58 26.90 .12 .38** .36** .19* –.06 .25** .39** .23* .24** –.07 –.04 .13 .10 .15 .14 –.03

18. Success of researchand development .80 .40 .08 .23* .28** .28** –.17 –.10 .16 –.01 .05 .06 .12 .01 –.10 .13 .01 –.07 .25**

a. Variable has been divided by 100,000 for rescaling.*p < .05. **p < .01.

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productmodifications. A possible explanation of these findings is that higherlevels of competitiveness may lead to higher levels of uncertainty andincreased attempts to adapt incrementally to the competitive environment.The result for the team decision-making structure variable indicates that

teams that were organized by product line were more likely to make productmodifications, as predicted by Hypothesis 6. This finding suggests moreproduct modifications are made when individuals are responsible for deci-sions for a given product line than when collectives of group members areinvolved, such as in consensus, functional, and matrix structures.

Information cues influencing strategic decisions. Tables 3 and 4 presentthe logistic regressions of the strategic decisions: product introductions andproduct withdrawals, respectively. Hypothesis 3 predicted that prior strate-gic decisions would influence current strategic decisions. This predictionwas not supported; previous decisions to introduce a new product or to with-draw a product did not influence product introductions or product withdraw-als, respectively. Consistent with expectations, neither performance relativeto aspiration nor performance relative to industry average had an effect on


TABLE 2

Maximum Likelihood Analysisof Product Modification Decisions (N = 116)

Variable b SE

PerformancePerformance relative to team aspirationt-1 –0.155 0.130Performance above/below industry averaget-1 –0.014 0.290Prior decisionsProduct modificationt-1 0.322** 0.096Competitive environmentCompetitor product introductionst-1 –0.052 0.067Change in competitive positionst-1 0.317** 0.119Perceived competitiveness 0.482* 0.226Independent decision-making structure 0.895* 0.244Control variablesPerceived realism 0.286* 0.128aSalest-1 0.119** 0.041aResearch and development expenditurest-1 0.011* 0.005aChange in production levelst-1 –0.155** 0.058Intercept –1.519 1.215

Adjusted R2 = .51

a. These measures are divided by 100,000 for rescaling.*p < .05. **p < .01, two-tailed tests.



decisions to either introduce a new product or to withdraw an existing prod-uct. Hypotheses 4 and 5 predicted that competitor product introductions andperceived high levels of competition, as well as changes in the positions ofcompetitors, would be associated with strategic change.The competitive environment did influence strategic decisions, as

expected. Product introductions by competitors did not influence productintroductions but did make it more likely that teams would withdraw prod-ucts from the market. This behavior may be an attempt to redirect resourcestoward developing new products that will counter their competitors’ newproducts. It is possible that competitive product introductions spur decisionmakers to start the process of new product introduction but that this processtakes several periods before a new product can actually be introduced. Theprocess may include old product withdrawal, investment in research devel-opment, followed by an eventual product introduction. Dropping existingproducts in response to competitor product introductions may be a first steptoward funding new research and development projects and introducing new


TABLE 3

Logistic Regression Analysis of Product Introductions (N = 116)

Variable b SE

PerformancePerformance relative to team aspirationt-1 0.179 0.359Performance above/below industry averaget-1 0.477 0.944Prior decisionsProduct introductionst-1 –0.462 0.721Competitive environmentCompetitor product introductionst-1 –0.081 0.207Change in competitive positionst-1 0.849* 0.406Perceived competitiveness –1.632* 0.795Independent decision-making structure –0.007 0.744Control variablesProduct withdrawals 3.560** 0.737Successful research and development projectt 1.221 0.812aResearch and development expenditurest-1 0.067** 0.021Perceived realism 0.103 0.409aSalest-1 –0.306* 0.159Intercept 3.673 3.968

Goodness of fit = 109.66Model χ2 = 77.52**Percentage of cases classified correctly = 86.21%




products. Examination of a longer lag time than one period would be neces-sary to explore this possibility. If such a delayed effect occurred, a possibleexplanation for the long lag time is that teams wanted to develop a new prod-uct that directly countered their competitor’s products. If they did not havesuch a product waiting on the shelf, they would have to use the research anddevelopment process to develop one. Responding to specificmoves (productintroductions) by competitors may require planning and product develop-ment and, thus, results in product withdrawals in the short run to redirectneeded resources.Changes in competitive positions made it both more likely that teams

would introduce a new product and less likely that they would withdraw anexisting product. Thus, instability and uncertainty in the environment ledteams to make strategic changes but also led them to persist with prior strate-gies by keeping existing products. This finding suggests that the generaluncertainty created by competitive instability led teams to introduce newproducts that they already had on the shelf but not to simultaneously drop old


TABLE 4

Logistic Regression Analysis of Product Withdrawals (N = 116)

Variable b SE

PerformancePerformance relative to team aspirationt-1 –0.174 0.314Performance above/below industry averaget-1 –0.332 0.773Prior decisionsProduct withdrawalst-1 0.392 0.601Competitive environmentCompetitor product introductionst-1 0.374* 0.171Change in competitive positionst-1 –0.936** 0.364Perceived competitiveness 0.097 0.599Independent decision-making structure –0.193 0.674Control variablesProduct withdrawals 3.379** 0.694Successful research and development projectt 1.373 0.989aResearch and development expenditurest-1 –0.008 0.011Perceived realism –0.339 0.368aSalest-1 0.082 0.092

Intercept –2.358 3.329Goodness of fit = 108.32Model χ2 = 55.83**Percentage of cases classified correctly = 81.90%




products. Similar patterns were found in Tables 3 and 4, in which high levelsof competition led to more product modification. Thus, general uncertaintymay lead teams tomakewhatever strategic and tactical changes they are ableto implement fairly quickly, while maintaining their dependence on theirexisting product portfolio.Teams that perceived the game to be extremely competitive were less

likely to introduce new products than teams that perceived the game to bemoderately competitive. The very high level of uncertainty associated withextreme competitivenessmay discourage teams from taking risks in the formof new products; the analysis of tactical decisions suggests that under theseconditions, they prefer the less risky tactic of modifying existing products.This effect might also suggest that teams experiencing an extreme level ofcompetitiveness are too preoccupied with trying to keep pace with the envi-ronment to do the long-term planning required to develop and introduce newproducts. These teams emphasize product modification, which requires lesslong-term planning. Last, we found no support for Hypothesis 7, which pre-dicted that a collective decision-making structurewould be positively associ-ated with strategic decision making. The decision-making structure variablehad no effect on product introductions or withdrawals.

DISCUSSION

The purpose of this article was to investigate how the type of decisionsmanagers make might elicit different schemas, directing attention to differ-ent types of information cues. We applied individual-level information-processing theories to examine two categories of decisions that managersmake: tactical and strategic. Our results provided evidence that there is somedifference in the information cues that influenced tactical and strategic deci-sions but there are also similarities. Specifically, prior decisions influencedtactical decisionmaking; the competitive environment influenced both tacti-cal and strategic decision making. A summary of these findings is shown inFigure 2.A surprising finding was the lack of influence of past performance out-

comes on tactical decisions. A fundamental prediction of trial-and-errorlearning is that negative feedback will trigger change in actions. We did notfind this relationship holdingwith respect to tactical decisions. Other empiri-cal studies have suggested that actual behaviors in response to performancefeedback seem to be more complex than our commonsense notions of trial-and-error learning (Lant et al., 1992; Lant &Hurley, 1999). This research onperformance relative to aspiration level has found a combination of




trial-and-error learning and persistence or escalation effects. It may be thatcertain types of decisions aremore likely to exhibit trial-and-error responses.Empirical evidence from fieldwork suggests that adjustments in aspirationsby teams seem to follow this pattern (Mezias&Murphy, 1998;Murphy et al.,2001). In additional analyses that we conducted, we found that changes inproduction demonstrated the trial-and-error effect that we had predicted fortactical decisions. We can speculate from this pattern of findings thattrial-and-error responses to performance feedback are most likely to occurwhen decision makers are setting levels of a variable, such as performancegoals or production units. The tactical and strategic decisions that we studiedweremore context-specific choices that teamsmadewhen facedwith a broadarray of possible alternatives.The overall pattern thatwe had predictedwas that tactical decisionswould

use internally focused information whereas strategic decisions would useexternally focused information. Our pattern of findings suggests that bothtypes of decisionswere influenced by external information about the compet-itive environment but neither was influenced by internal performance feed-back. Changes in tactics did exhibit a routinized characteristic, in that oncedecisionmakers start making changes in tactics, they are likely to continue todo so. Furthermore,when individuals on the teams had the autonomy tomaketactical decisions alone, more changes in tactics were made. We can specu-late that tactical decisions are more easily influenced by routines and thedecision structure of teams than are strategic decisions. Tactical decisionsalso appear to be influenced by the overall uncertainty of the competitiveenvironment rather than specific moves by competitors.


Figure 2: Summary of ResultsNOTE: * = Relationship not hypothesized in the original model; H2 = Hypothesis 2; H4 =Hypothesis 4; H5 = Hypothesis 5; H6 = Hypothesis 6.



CONCLUSION

The design of this study is simultaneously a risk and a potential contribu-tion: Our study was designed to test for information-processing patterns thatare attributable to the cognition of small groups. These groups were facedwith information input that is similar to that which organizational decisionmakers would experience; however, the groups made their choices in theabsence of a real organization. Thus, the relationships that were found are notattributable to factors such as organizational structure, organizational inertia,or implementation issues. Thus, we can say with some confidence that theobserved relationships are due to the cognition of the decision makers. Forinstance, the observedmomentum in tactical decisions suggests that, in addi-tion to the organizational routines that may develop through the implementa-tion of product modifications, routines also develop in the decision-makingprocess itself. This suggests that decision momentummay have a cognitive aswell as an organizational component. The results found in this setting alsosuggest that outcomes of social comparison, such as the effect of uncertaintyon judgment (Schacter&Singer, 1962; Zucker, 1977), and the imitative tenden-cies noted by institutional theorists (Haunschild & Miner, 1997; Haveman,1993;Mezias, 1990),mayhave their origin in thecognitionofdecisionmakers.This study also found that theway inwhich groups organize themselves to

make decisions can affect their decisions significantly. In this study, the pat-terns of decisions made by teams structured by product line were signifi-cantly different from those of other teams. Product managers tended toengage in high levels of product modification. Other characteristics of theteams, such as size, experience, and demography, did not appear to affect thetypes of decisions investigated in this study. However, it is always importantto control for these characteristics when studying group decision making.A key limitation in this study is the assumption that the effects of informa-

tion cues onmanagerial decisionmaking are independent.Managerial scriptsare likely to be complex. It would be reasonable to expect that the affect ofprior decisions or the level of competition, for example, may be influencedby past performance. In particular, there is much in the literature that sug-gests that good performance leads to complacency, reduced attentiveness tothe environment, and lowered probabilities of change (Milliken & Lant,1991). This reduced responsiveness can create both strategic persistence andeven reduce attempts at tactical changes. Similarly, past performance mayinfluence the affect of competitiveness on the likelihood of strategic change.Although competitiveness should increase the likelihood of change, in thepresence of good performance and complacency, decision makers may beless likely to respond to this information. Future empirical research should




explore such potential interactions among information cues and how deci-sion making is influenced by them.It was beyond the scope of this study to explore how individual-level

cognitions are translated into collective cognitions. Future research on deci-sion making should explore more directly how groups use scripts and howthese collective cognitions emerge. The study conducted by Walsh et al.(1988) is a good example of this type of research. This study cannot deter-minewhether the scripts thatwe have inferred from the pattern of findings arethe result of similarity in scripts that the participants brought with them intothe game orwhether shared scripts developed andwere negotiated during thegame. Given the background, experience, and training of the participants, itwould not be surprising to see a high level of homogeneity in scripts amongthe participants.Finally, although this study has provided some insights into information

processing by decision-making groups, it is only suggestive of information-processing patterns that might occur in actual organizations. Organizationalstructure, inertia, and implementation problems, among other factors, mayall affect the cognitive information processing ofmanagers. Furthermore, thefindings may be less suggestive of high-impact strategic decisions in organi-zations. The strategic decisions in the gamewere quite limited, given the con-straints on numbers of products, market segments, and no opportunities forindustry entry or exit. Major strategic decisions, such as entering or leavingcertain markets, may involve extensive long-term planning. Although thisplanning may in itself be affected by information-processing limitations, theeffect of such limitations on these planning processes cannot be exploredwithin this context. However, the findings of the study are suggestive of thetypes of information cues that should be investigated in future studies of stra-tegic decision making.

APPENDIX ADescription of Control Variables

RESOURCE EFFECTS

Changes in Production

Changes in production are likely to oscillate up and down as decision makersattempt to find the correct level of production givenmarket demand. This is similar toregression to the mean effect. That is, increases in resource commitments in a priorperiodwill make increases less likely in the following period. Changes in production,




on the other hand, which are tactical decisions aimed at finding an equilibrium atwhich production matches demand, exhibit an oscillation similar to regression to themean.

Sales

Higher levels of sales were also associated with increases in production. Thismight be a resource effect; because sales bring revenues, teams that have higher saleswill be better able to afford increase in production.

GAME CHARACTERISTICS

Perceived Realism

Because the research setting is a simulation, it is important to determine whetherparticipants took the game seriously enough tomake decisions in theway that is simi-lar to what they would do in reality. One of the questions in the postgame question-naire asked participants to rate the realism of the game on a Likert-type scale from 1(totally realistic) to 7 (not at all realistic). The average score of individuals withineach teamwas used as the team-level indicator of perception of realism. Of the teams,75% perceived that game to be at least moderately realistic. The average realismscores ranged from 1 to 5.25.

Product Limitations

One constraint of the game is that teams cannot have more than five products onthe market at the same time. If a team already has five products on the market, theywill have to drop a product to introduce a new one. Thus, in the analysis of productintroductions and productwithdrawals, a concurrent indicator of productwithdrawalsand product introductions, respectively, is included in the multivariate analysis.

Successful Research and Development Projects

Another constraint of the game is that a successful research and development pro-ject is required before a new product can be introduced. Thus, an indicator of whethera research and development project has been successfully completed is included inthe analysis of product introductions and product withdrawals.

Prior Research and Development Expenditures

Prior research and development expenses on both tactical decisions and strategicdecisions are also included due to the potential impact of these expenses on both typesof decisions.




APPENDIX BGroup Characteristics:

Examined but Not Included in the Multivariate Analysis

Theoretically, this studywas interested in the impact of team structure on informa-tion processing. However, other team characteristics could affect decisions, such asteam demography and the team’s perceptions of their decision-making process. Interms of demography, the potential impact of gender mix and managerial experiencewere explored. As noted earlier, 80% of the participants were men and 20% werewoman. Thus, some of the teamswere composed of onlymale participants and othershad both men and women. The potential difference between all male versus mixedgender groups is exploredwith a binary variable, coded 1 if the teamwas composed ofbothmen andwomen, and zero if the teamwas composed of allmen.Half of the teamswere composed of managers enrolled in the fellowship program; the other half of theteams were composed of MBA students. The executives were, on average, older andhadmoremanagerial experience than theMBAs. The potential impact of these differ-ences is exploredwith a binary variable, coded 1 if the team is composed of executiveand zero if the team is composed of MBAs. There were no significant differencesfound as a result of any of these variables.Because real-time observation of the group decision-making process in each team

was not possible (teamsmet at the same time), several variables that measure percep-tions of group processes are used as proxies for actual group process. These variableswere constructed from a postgame questionnaire. The items were 7-point Likert-typescales that assessed the participants’ perceptions of whether they had a strong leader,their effectiveness in terms of the group’s performance, and decision-making proce-dures. To construct a team-level variable, the average response of members of eachteam was calculated. None of these group characteristics had an impact on thedependent variables in this study. Thus, they are not included in themultivariate anal-ysis that is reported.

NOTES

1. Strategic decisions might be influenced by trends in feedback over time; a series of nega-tive outcomes might elicit either escalation or change. An examination of long-term effects ofperformance is beyond the scope of this article.2. This variable has been recoded for ease of interpretation; high values indicate higher com-

petitiveness in the multivariate analysis.




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Theresa K. Lant is an associate professor of management and organizational behaviorat New York University. Her research interests are in organizational learning, strategicdecision making, and managerial cognition.




Patricia F.Hewlin is a Ph.D. candidate in themanagement and organizational behaviordepartment at New YorkUniversity. Her research interests are inmanagerial cognition,identity, and impression management.




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