Group & Organization Management by Juan Pardo

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Information Cuesand Decision Making: The Effects of Learning, Momentum, and Social Comparison in Competing Teams Theresa K. Lant and Patricia F. Hewlin Group Organization Management 2002; 27; 374 DOI: 10.1177/1059601102027003004 The online version of this article can be found at: http://gom.sagepub.com/cgi/content/abstract/27/3/374

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GROUP Lant, Hewlin & ORGANIZATION / INFORMATION MANAGEMENT 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. HEWLIN New York University The question of how managers make decisions, such as formulating competitive strategies, continues to be a major theme in management literature. Cognitive models of organizational decision making have benefited from research on individual-level information processing. This study explores the applicability of individual-level models of information processing to teams of decision makers making decisions in simulated organizations. The article proposes that cognitive schemas and team decision-making structure will focus decision-maker attention on different types of information for different categories of decisions. The findings suggest that there are both similarities and differences in the cues that influence tactical and strategic decisions.

In today’s information-rich environments, management teams engaged in strategic decision making are flooded with information. One of their key tasks is determining what information to attend to in order to make specific decisions regarding resource investments and competitive positioning. This article investigates how the type of decision being made by a team of decision makers focuses their attention on different types of information. We predict that tactical types of decisions will focus teams on information that tells them “how are we doing.” We predict that strategic types of decisions will focus teams on information that tells them “what are they doing.” Thus, tactical decisions focus a team’s attention internally, whereas strategic decisions focus their attention externally. We draw on models of individual and organizational information processing to identify types of information cues that have 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 also appreciate the helpful comments from two anonymous Group & Organization Management reviewers and editor P. Christopher Earley. Group & Organization Management, Vol. 27 No. 3, September 2002 374-407 © 2002 Sage Publications

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about the relative impact of these information cues based on the type of decision 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 decision makers have limited information-processing capabilities (Hogarth, 1987; March & Simon, 1958; Simon, 1957). Studies from cognitive psychology, organization theory, and strategic management have demonstrated that when decision makers are faced with complex tasks or ambiguous situations, they attempt 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 of decision heuristics (Barnes, 1984; Duhaime & Schwenk, 1985; Kahneman et al., 1982; Schwenk, 1984), cognitive schemas (Brewer & Nakamura, 1984; Neisser, 1976; Rumelhart, 1984; Taylor & Crocker, 1981), cognitive categorization (Porac & Thomas, 1990; Rosch, 1978), subdividing decisions into manageable components (Kahneman & Tversky, 1979; Mintzberg, Raisinghani, & Theoret, 1976), and applying routinized decision rules such as 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 for understanding how information cues and the schemas used to interpret such cues underlie individual decision making. The major categories of information that appear to drive managerial strategic 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 evidence for the impact of these types of information. The actual managerial decision processes that yield the observed organizational outcomes have been inferred in these studies through an application of cognitive informationprocessing models (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-processing models to organizational phenomena leaves a huge gap in our understanding about how individual cognition aggregates to the organizational level of analysis (Walsh, 1995). Greve (1998) also raised this issue and noted the similar pattern of findings between individual and organizational risk taking, response to performance feedback, and decisions to change behavior. However, more research is needed to understand how teams of managers aggregate their

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beliefs to yield organizational-level outcomes. Many key decisions in organizations are made by teams of individuals rather than by individuals acting in 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 informationprocessing characteristics of decision-making groups. Walsh and Henderson (1989) found that attributions in decision-making groups influenced their decisions to escalate or reduce commitments. Thus, to understand managerial decisions regarding organizational actions, it makes sense to study the information-processing patterns of management teams as well as the factors that 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 organizations. This methodological approach allows us to closely assess how information drivers such as performance feedback, momentum, and social comparison affect teams’ tactical and strategic decisions. In addition, we examine how differences in how teams structured themselves to make decisions influence the information they attend to and how this information influences their choices.

SCHEMA-BASED INFORMATION PROCESSING Schema theory provides a framework for understanding what types of information managers will notice and how this information will be interpreted (Abelson, 1976; Neisser, 1976; Rumelhart, 1984). The schemas that a decision maker develops over time through past information processing affect the salience and relevance of information that the decision maker is exposed to subsequently. Schemas are cognitive representations of the world, based on historical experience, which contain rules that direct information processing (Kiesler & Sproull, 1982; Lord & Foti, 1986). Information that is relevant to an existing schema will be more salient to a decision maker and will be incorporated more easily than information that does not fit well within existing schemas (Kiesler & Sproull, 1982). Newly acquired information will be channeled into appropriate schemas depending on its relevance for 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 structure devoted specifically to the retention of context specific knowledge about

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events and event sequences and to the guidance of action on the basis of that knowledge” (p. 57), and “people possess categories of structured knowledge about . . . events, behavior, and actions that can be brought forth by situational cues [italics added] to facilitate understanding and action” (Abelson, 1976). Gioia and Poole (1984) suggested that the concept of scripts be applied to decision-making research. They argued that prior experiences relevant to a given situation will be remembered schematically. Script schemas guide behavior whenever individuals try to make sense of their organizational experience (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 different schemas that direct attention to different types of information cues. Specifically, we examine how information cues influence the following two broad categories of decisions that managers make: strategic and tactical, and whether the impact these decisions have are influenced by whether the team makes decisions collectively or by individual division of labor. INFORMATION CUES AS TRIGGERS OF ACTION FOR STRATEGIC AND TACTICAL DECISIONS

Extensive evidence suggests that managers process information by categorizing issues, competitors, and so forth (Dutton & Jackson, 1987; Porac et al., 1989). In addition to categorizing issues and competitors, managers may also categorize the types of decisions they have to make. Although we have little understanding of how managers might actually categorize decisions, implicit in the strategic management literature is the notion that decisions in organizations can be classified by the magnitude, purpose, or the extent of strategic change implicit in the decision (e.g., Miller & Friesen, 1980; Tushman & Romanelli, 1985). Egelhoff (1982) suggested that decisions might be categorized based on the amount and type of information processing required to make the decisions. Tactical decisions, for instance, are fairly routine, require only narrow information processing, and can be easily modified or reversed. Alternatively, strategic decisions are less routine, more significant, require broader information processing, and involve significant commitments that are difficult to modify or reverse (Egelhoff, 1982; Smith, Grimm, Gannon, & Chen, 1991). In an empirical study of more than 900 managers, Bacharach, Bamberger, and Mundell (1995) provided evidence that managers seek decision criteria to justify their decisions. These criteria are based on the following two logics of justification: strategic and tactical. The results from their study indicate that strategic decisions are macro-oriented, focusing on comprehensive organizational change.

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Tactical decisions refer to day-to-day tasks, which maintain past practices and achieve short-term goals. The study examines how performance feedback, prior decisions, and competitive actions affect tactical and strategic decisions made by competing teams. Our first predictions build on existing literature that addresses the impact of these behavioral triggers (performance feedback, momentum, and social comparison) on organizational-level strategic change. We will examine whether patterns expected at the aggregate, organizational level are found at the team level of analysis. We then go further to examine the impact of group 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 how management teams make decisions, we realize 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 example, provided results that indicated a positive relationship between perceived self-efficacy and the effective use of analytical strategies for achieving optimal managerial rules as well as for achieving optimal levels of personal goals. Other studies have found motivational mechanisms such as social identity (Kramer, Brewer, & Hanna, 1996; Kramer, Shah, & Woerner, 1995) and feedback seeking (Ashford, 1986) to influence how individuals and organizations make decisions. In this study, we have chosen to focus on the applicability of traditional cognitive models of trial-and-error learning, momentum, and social comparison in explaining management team decision making in organizations.

INFORMATION CUES AND HYPOTHESIZED RESPONSES OF TEAMS MAKING TACTICAL AND STRATEGIC DECISIONS The specific decisions this study examines are domain navigation decisions (Bourgeois, 1980), which are decisions about how to compete within a given product-market domain. By focusing on domain navigation decisions, we examine decisions regarding specific commitments to action (Mintzberg et al., 1976) within given product markets versus decisions to enter or exit a product market. These decisions differ in the extent and purpose of the actions taken. Decisions to introduce a new product to the market or to withdraw an existing product from the market represent decisions to alter the way in which the organization is competing in the industry. Because these choices represent

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fairly major changes in domain-navigation strategy, they will be used to represent strategic decisions. Decisions to modify or reposition existing products represent tactical changes to the organization’s competitive position in the industry. Tactical changes are achieved by making minor changes to a product’s physical characteristics or by changing consumer perceptions of the characteristics of the product (Smith et al., 1991). INFLUENCE OF PERFORMANCE CUES ON TACTICAL AND STRATEGIC DECISIONS

The argument that past performance is a salient cue that influences decisions is consistent with theories and observations about organizations (Greve, 1998; Lant et al., 1992). Performance information is used routinely to detect problems and to determine if performance is satisfactory (Cyert & March, 1963). The recognition of satisficing behavior (March & Simon, 1958) by theorists has also made aspiration levels for performance outcomes an important variable in organizational decision-making research (Lant, 1992; Lant & Montgomery, 1987; Mezias & Murphy, 1998). Aspiration levels serve as cognitive 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 influence future goals (Lant, 1992; Murphy, Mezias, & Chen, 2001), problem sensing (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-error learning predict that when performance outcomes fall below aspiration levels, organizations will change current activities. Studies of managerial interpretations suggest that the relationship between performance feedback and strategic action is more complicated than simple models of trial-and-error learning would predict (Lant et al., 1992). For instance, some studies have found that failure feedback may lead to persistence in strategy, rather than change, through the tendency to escalate commitment 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 strategic decisions did not respond to performance feedback in the way that production decisions or less risky changes did. Strategic decisions may be influenced by long-term goals and plans, for which short-term feedback is less meaningful and less likely to produce immediate strategic change.

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Most studies of escalation of commitment have examined investment decisions characterized by large resource commitments, delayed feedback, and difficult adjustment or reversal of decisions. These are the same attributes that characterize strategic decisions, as we have defined them in this article. Because of the characteristics of strategic decisions, short-term performance feedback would not be expected to produce the pattern of incremental adjustment predicted by trial-and-error learning. Trial-and-error learning will be expected, however, for tactical decisions that involved small resource commitments with frequent feedback and opportunities to incrementally adjust previous decisions. Because of the features of tactical decisions, decision makers are less likely to respond to failure feedback with self-justifying actions 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 the schema associated with tactical decisions will use performance information to make trial-and-error adjustments in tactics. The schema associated with strategic decisions will not elicit a response from short-term feedback.1 Thus, this article predicts that performance below aspiration will elicit changes in tactical decisions but not strategic decisions. Hypothesis 1: Performance below aspiration will result in tactical changes but will not result in strategic changes. INFLUENCE OF CUES ABOUT PRIOR DECISIONS ON TACTICAL AND STRATEGIC DECISIONS

A large component of a manager’s interpretative task is to determine the causal linkages between actions and performance outcomes (Milliken & Lant, 1991). Thus, not only will managers pay attention to information about their performance, they will also recall and consider prior decisions. It is not obvious, however, the extent to which past decisions influence current choices. 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 production procedures to “strategic heuristics that shape the approach of a firm to the nonroutine problems it faces” (Nelson & Winter, 1982, p. 15). Such heuristics could include rules for making decisions of both a strategic and a tactical nature. An evolutionary explanation for momentum is based on the

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idea that competencies with certain types of activities increase with experience (Ginsberg & Baum, 1993): Each time an organization engages in a particular kind of change, it increases its competency in making that type of change. The more experienced an organization becomes with a particular type of change, the more likely it is to make further changes of a similar nature. (pp. 5-6)

This study proposes that there is also a cognitive element to decision momentum. Not only are organizational competencies likely to be stored in organizational memory, but beliefs about competencies are likely to be reflected in the cognitive scripts that guide decisions. Such scripts will guide managerial attention toward decisions of the same type that have been made in the past. Evidence of decision momentum suggests that there should be a positive relationship over time between decisions of the same type. Decision momentum may characterize decisions involving some types of tactical change. This is because beliefs about competencies in taking action become embedded in associated scripts. Thus, this study predicts that decisions to fine-tune product positioning will be characterized by momentum and, thus, will be positively associated over time. If managers have made tactical changes in the past, they are likely to do so in the future. Hypothesis 2: Prior tactical changes will be positively associated with current tactical changes.

Like tactical decisions, strategic decisions of the same type are predicted to exhibit momentum. Thus, new product introductions should be positively associated over time, and product withdrawals should be positively associated over time. Hypothesis 3: Prior strategic changes will be positively associated with current strategic changes of the same type. INFLUENCE OF CUES ABOUT THE COMPETITIVE ENVIRONMENT ON TACTICAL AND STRATEGIC DECISIONS

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

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economics literature suggests that firms in competitive industries recognize their mutual interdependence and monitor each other’s actions in determining their strategies (Chamberlin, 1933; Porter, 1980; Scherer & Ross, 1990). The actions of competitors are likely to influence managers’ decisions not only because these actions may create opportunities or threats for strategic managers (Glueck, 1976; Hofer & Schendel, 1978) but also because they are, in and of themselves, salient and relevant pieces of information. As noted by Kiesler and Sproull (1982, p. 556), “the behavior and outcomes of competitors, of course, are sharply drawn—a figure against the ground.” The actions of competitors may also serve as an important social comparison function, especially in environments characterized by uncertainty (Porac et al., 1989). The tendency to engage in social comparison has been demonstrated 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 comparison can 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 prevalent within “primary competitive groups—a collection of firms that define each other as rivals” (Porac et al., 1989, p. 414; also see Lant & Baum, 1995; Porac, Thomas, Wilson, Paton, & Kanfer, 1995). Because the members of a competitive group see themselves as strategically interdependent, the actions of rivals and the characteristics of the competitive environment are more likely to be components of the scripts that guide strategic decisions than of those that guide tactical decisions. In fact, the schemas of managers in firms within a competitive group may become very similar to one another over time (Porac et al., 1989), resulting in similarities in the types of information attended to and in the interpretations of and responses to information (Morgan & 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 to influence the strategic decisions of firms within the group is the introduction of new products that compete with products already being marketed. The introduction of products is a salient piece of information about competitor activity that may affect firms significantly and provide information about the type of strategies being pursued by one’s competitors. This article predicts that competitors’ product introductions will be positively associated with strategic changes by the focal firm. Firms may respond in this way to counter the competitive threat of new products (Porter, 1980) or because they attempt to imitate the type of strategic actions they see their competitors making (Levitt & March, 1988).

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

Not only will firms in a competitive group be affected by the specific actions of their competitors, but their decisions may also be affected by the degree of competition in their group. Another important type of information that may be relevant to managers’ scripts about making strategic decisions is the instability or amount of uncertainty surrounding the relative competitive positions of the members of the competitive group (Haunschild & Miner, 1997). Perceptions of a high degree of competitiveness in the group may lead to both uncertainty and strategic actions designed to fight for market share. Uncertainty is also likely to stimulate more social comparison behavior among competitors. Because product introductions are a typical response to high levels of competition (Porter, 1980), the combination of unstable competitive positions and perceptions of extreme competitiveness will result in an increase in strategic change. Hypothesis 5: Strategic changes will be positively associated with perceived high levels of competition as well as with positioning changes of competitors. INFLUENCE OF TEAM STRUCTURE ON TACTICAL AND STRATEGIC DECISIONS

In this section, we consider the impact of team information-processing structure on tactical and strategic decision making. Many alternative models exist in the literature that explore how individual beliefs aggregate to yield group-level decisions. These include voting rules (Greve, 1998), power relations and hierarchical position (Ilgen, 1988), and demographic distribution (Corner, Kinicki, & Keats, 1994). Our focus in this article is on how team structure influences information processing and, thus, responds to the following three decision drivers we have been examining: performance feedback, social comparison, and momentum. We define team as the collective actions of individuals in which group members are accountable for results (Hackman & Oldham, 1980). Hackman and Oldham (1980) defined this type of team as a “self-managing work group,” which accurately describes the type of teams in our study. Group members have the discretion to “handle internal processes as they see fit to generate a specific group product, service, or decision” (Hackman & Oldham, 1980, p.164). Because team members have the discretion to develop processes and to designate roles to accomplish its goals, the team is considered to be self-managing.

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We investigate how decision-making structures influence tactical and strategic decision making by teams. We suggest that the way in which a team structures itself to make decisions (i.e., how it goes about processing information and making decisions based on this information) will focus the attention 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 allows individuals the autonomy to make decisions independently, the more changes in tactics we will see. When there is only one individual responsible for making decisions, the benefit obtained from discourse among team members decreases and there is less exploration of alternative decisions (Hutchins, 1991). The notion of satisficing describes how decision makers are unable to explore all possible alternatives (Cyert & March, 1963; Simon, 1997, p. 119). “Because administrators satisfice rather than maximize, they can choose without first examining all possible behavior alternatives and without ascertaining these are in fact all the alternatives.” An individual decision maker is cognitively unable to survey as many alternatives as a group of decision makers (Hutchins, 1991). We therefore predict that satisficing will be expressed through a series of tactical decisions because tactical decisions require less cognitive labor on the part of the decision maker. Hypothesis 6: Teams with divisions of labor that permit individuals to make decisions independently will engage in more tactical changes than teams with divisions of labor that require collective decision making.

Teams with structures that require discussion for decisions to be made may engage in more exploration of options because one individual is not responsible for the final decision. In this situation the group becomes what Hutchins (1991) defined as a “cognitive system” in which the cognitive task of considering various alternatives are socially distributed in the decisionmaking process. We expect that such structures will lead to more attention being paid to strategic types of decisions rather than to tactical types of decisions. Hypothesis 7: Teams with divisions of labor that require individuals to engage in collective discussion prior to making decisions will engage in more strategic changes than teams with divisions of labor that permit individuals to make decisions independently.

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

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

direction of each relationship is also indicated. The pattern of hypotheses illustrates our general prediction that tactical decisions elicit internally focused information processing, addressing the question, How are we doing? Strategic decisions elicit externally focused information processing, addressing the question, What are they doing? We expect that decision momentum is a generic phenomenon, occurring for both tactical and strategic decisions. Finally, team structures can focus attention on different types of decisions. Teams that allow individuals to make decisions will tend to exhibit more tactical changes, whereas teams that require collective decision making will tend to be more focused on overall strategic issues.

METHODOLOGY Research setting. To test the hypotheses described above, it is necessary to track the performance and decisions of strategic decision makers over time, as well as the actions of their competitors. For reasons of external validity, the setting should be complex enough so that decisions that are made are similar to those that are made in actual organizations. The Markstrat marketing strategy game (Larreche & Gatignon, 1977) provides such a setting. Markstrat was written as a comprehensive model of marketing dynamics to integrate real-world experience of organizations and knowledge from existing

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marketing research. It is considered to be high on conceptual complexity and it yields experiences and research results that are relevant to a wide variety of environments and industries (Larreche, 1987). Markstratâ&#x20AC;&#x2122;s relevance to organizational life has led to its being adopted as a research as well as a pedagogical resource in corporations and universities (Glazer, Steckel, & Winer, 1987; Kinnear & Klammer, 1987; Larreche, 1987). For example, managers within a variety of large, successful corporations use Markstrat in their in-house management training programs due to the high degree of external validity (Kinnear & Klammer, 1987) Markstrat provides. Whereas business games are not one of the most commonly used settings in which to conduct organizational or strategy research, there is a substantial precedent for using them (Chesney & Locke, 1991; Earley, Northcraft, Lee, & Lituchy, 1990; Gladstein & Reilly, 1985; Segev, 1987). They are especially useful for studying the dynamics of decision making (Hogarth & Makridakis, 1981; Lant, 1992; Lant & Hurley, 1999; Lant & Montgomery, 1987; Ross, 1987; Walsh et al., 1988). A typical play of Markstrat consists of five teams, each representing the marketing profit center of an organization, who compete with each other in one or two product categories over a period of time. In conjunction with the product-management decisions, the teams also make a wide range of decisions in a complex environment such as forecasting performance, analyzing their environment, thinking about their overall strategy, assessing their competitive position, and assessing their competitors. Complicated algorithms that simulate a competitive market in a multidimensional, interdependent world control the Markstrat game; the relationships between organizational actions and outcomes are highly complex and nonlinear, capturing the complexity of the decisions facing organizational decision makers. As the game is played, the competitive structure of the industry evolves, which is dependent 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 study therefore provides a longitudinal investigation of groups of decision makers setting objectives, making strategic and resource allocation decisions, and receiving feedback over several periods of time. Although the length of time is limited in comparison to actual histories of real organizations, it represents a longer time period than is typically possible in studies of decision making in real 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 composed of 10 teams of managers in an executive fellowship program and 10

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teams of MBA students enrolled in a marketing strategy course at a major business school. A total of 87 individuals participated. Of those, 70 (80%) were men and 17 (20%) were women. The average age of the executives and MBAs is 37 and 26.7 years, respectively. The work experience of the executives ranged from 10 to 12 years, whereas the average work experience of the MBAs is 3.49 years. The teams ranged in size from 3 to 6 individuals. Seven teams were all male; 13 teams were of mixed gender. The participants were informed of the dynamics of the game and were encouraged to analyze the information in a realistic fashion. Data sources. The following three data sources were used: group-decision forms filled out by each team in each period; information generated by the game and given to the participants at the beginning of each period; and an end of game questionnaire. The data gathered from each period of play are at the team level of analysis. Decisions were reported as team decisions, and performance results were given for each team. The team decision forms were filled out each week. The question regarding sales goals was worded as follows: “For each brand you are producing in the current period, please indicate your sales objective (# of units sold).” These questionnaires were used to ensure that a systematic record of these objectives was kept. However, the teams had an incentive to think about performance objectives independent of the questionnaires. The teams are graded based on how well they improve their company’s performance from its initial position and the quality of the strategic plan they develop. They are taught that an important part of a good strategic plan is to set performance objectives. Thus, performance objectives become a natural part of strategic planning, which is consistent with empirical evidence that the teams pay attention to performance goals in determining strategic actions. Lant and Montgomery (1987) found that performance relative to goals affected both the risk taking and 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 performance of other teams, and their competitive position relative to their competitors. The postgame questionnaire measured perceptual data based on individual-level responses. Dependent variables. The study examines the following two dependent variables: tactical decision making (modifications of existing products) and strategic decision making (new product introductions and withdrawals). Product modification is measured by the number of physical modifications made to existing products plus the number of attempts to reposition

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consumer perceptions of an existing product in the market through changes in advertising content. New product introductions are measured as the number of new products introduced by a team in a given period. Because product introductions and withdrawals for any one team rarely exceeded one in any period—and never exceeded two—for the purposes of analysis, these indicators are recoded as a binary variable, in which 1 indicates a product introduction or withdrawal, and zero indicates no introduction or withdrawal in a given period. Predictor variables. There are four categories of predictor variables: past performance, information about prior decisions, information about the competitive 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 or worse than their aspirations. The first measure is perceived performance relative to the team’s performance objectives. Performance objectives were obtained from the forms each team completed during each decision-making period. This form asked the team to indicate how well they thought they had performed in the prior period relative to the objectives they had set in that period. They indicated this on a 5-point Likert-type scale that ranged from much better than expected to much worse than expected. The specific performance objective they were asked about was unit sales. The responses provided a team-level assessment of how they had performed relative to their performance objectives. The second measure is an indicator of whether the team had 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 the industry average, and coded zero if the team had unit sales lower than the industry average. This measurement is consistent with other studies that have examined the effects of performance relative to aspirations with respect to average 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 are measured by the number of product modifications made to existing products in the prior period. Prior strategic changes are indicators of whether there were any product introductions or withdrawals made in the prior period. The competitive environment is measured by the following three variables: (a) the number of products introduced by a team’s competitors in the prior period, (b) the instability of competitive positions, which is measured as the absolute value of the average change in market share among competitors

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in an industry from period to period, and (c) perceptual measure of the level of competition in the industry. This measure is derived from the postgame questionnaire that asked individual participants to rate the level of competitiveness of the Markstrat game they played. They indicated this on a scale of 1 (extremely competitive) to 7 (not at all competitive).2 The average score of individuals within each team was used as the team-level indicator of perception of competitiveness. The overall response rate for this questionnaire was 82%. Of the women, 94% responded, whereas 79% of the men responded. There were at least 2 people per team who returned the questionnaire. It should be noted that there is little variance on this variable; the average team perception of competitiveness varied only from 1 (extremely competitive) to 3 (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 their group. These descriptions indicated that the following four basic designs were used: functional, product line, matrix, and consensus. Functional teams created vice president roles such as production, research and development (R & D), and marketing. There were 7 teams organized by function. Product teams divided responsibilities by creating product manager positions that involved the management of all aspects of an individual product. There were 4 teams organized by product. Matrix teams had responsibilities along both functional and product dimensions. There were 3 matrix teams. Consensus teams did not divide responsibilities but made all decisions as a group. There were 6 consensus teams. These designs are fairly representative of the decision structures that might be found in actual management teams. The characteristic that we were particularly interested in, theoretically, was whether individuals could make decisions without consulting other team members. Given the manner in which decisions must be made in the Markstrat game, the only structure for which this was true is the product-line structure. All decisions on the decision forms must be recorded with respect to specific products, such as how much of a specific product to produce, how much advertising to do on that product, and so forth. For the three other team structures, team discussion is necessary before filling out the decision forms. Thus, we calculated a binary variable that was coded 1 if the structure was by product-line, and zero otherwise. Control variables. The controls used in the analysis are listed in Appendix A. The first category of controls includes decisions and performance outcomes that may be correlated with the dependent variables but are not of theoretical interest in this study. These include change in production and sales.

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The second set of control variables are those that are needed due to the characteristics of the game but are not particularly interesting from a theoretical viewpoint. Appendix B describes a number of group characteristics we examined for their potential impact on the dependent variables but did not include in the multivariate analysis. Given our focus on information processing in this study, these group characteristics, such as whether the group had a strong leader or used voting to make decisions, were not included in our predictions. However, other models of group process suggest that such factors may influence group outcomes (Stasser, Kerr, & Davies, 1989). Thus, we conducted exploratory analyses to determine whether any of these variables were related to our dependent variables, in an effort to avoid correlated omitted variables in our analysis. RESULTS

Our data are organized in a pooled cross-sectional time-series design. A panel of data that includes lagged independent variables can exhibit autocorrelated 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 product modification (tactical) decisions is based on such a maximum-likelihood procedure. For the analysis of product introductions and withdrawals (strategic decisions), in which the dependent variables are binary, logistic regression was used. The correlation matrix and descriptive statistics are given in Table 1. Information cues used in tactical decisions. Table 2 displays the model that analyzes the tactical decisions to modify existing products. Hypothesis 1 predicted that negative performance would be associated with tactical changes. Neither performance feedback variable had the predicted trial-and-error learning effect on product modifications. Prior decisions influenced product modifications as predicted in Hypotheses 2. Teams that made product modifications in the prior period were more likely to make subsequent modifications, reflecting decision momentum. The competitive environment was not predicted to have an effect on tactical decisions. We have argued that the schemas guiding tactical decisions would tend to focus on internal information such as performance feedback rather than external information such as the competitive environment. However, the analysis revealed that information about the competitive environment was used to make tactical decisions. Instability in the competitive positions of the teams led to more product modification. In addition, teams that perceived the game to be very competitive were more likely to engage in

TABLE 1

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

1. Production change 2. Product modificationt 3. Production introductionst 4. Product withdrawalt 5. Performance versus aspirationt 6. Performance versus industryt a 7. Salest a 8. Production changet-1 9. Product modificationt-1 10. Production introductionst-1 11. Product withdrawalt-1 12. Competition product introductionst-1 13. Change in competitive positiont-1 14. Perceived competitiont-1 15. Perceived realismt-1 16. Team structure a 17. Research and development expenditurest-1 18. Success of research and development

.51 4.57 .50 .36 2.95 .42 6.56 .51 4.21 .45 .28

2.69 1.59 .50 .48 .91 .50 4.09 2.25 1.61 .50 .45

2.98

1.80 –.21*

1.07 4.67 5.33 .21

.98 –.18* .49 .18 .75 .04 .41 .09

30.58 26.90 .80

.40

.22* –.22* .20* –.11 .17 .39** .03 .33** .18 .33** .45** –.04 .05 –.05 .46** –.28** .08 –.06 –.07 .12

.54** –.11 –.09 –.12 –.10 .29** –.11 .03 .28** .61** –.09 –.17 .16 .30** .37** –.08 .14 .03 .12 .45** .18 .07 .29** –.19* –.21* –.11 –.33** –.02 .12 –.01 –.11 –.04 –.26** .14

.14 .21* .38** –.14 .19* .02 .22* .00

.16

–.11

–.03

–.10 –.04 –.01 –.03

–.13 .02 .03 .07

.04 –.15 .31** .39** .01 .08 –.22 –.04

.12

.38**

.36**

.19*

.08

.23*

.28**

.28** –.17

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

–.06

.25** –.10

.04

.39** .16

.10

.30** .26**

.55**

.14

.15

–.22* .43** –.01 –.01 –.01 –.01 –.01 –.05 .17 –.04 .02 .03 .02 –.11

.23*

–.04

.13

.10 .15

.12

.01

–.10 .13

–.01

.05

.06

.01

.05 –.01 .14 .27** .36** –.11 .12 .14 –.02 .08 .13 –.03 .24** –.07

.14 –.03 .01 –.07 .25**

391

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GROUP & ORGANIZATION MANAGEMENT TABLE 2

Maximum Likelihood Analysis of Product Modification Decisions (N = 116) Variable Performance Performance relative to team aspirationt-1 Performance above/below industry averaget-1 Prior decisions Product modificationt-1 Competitive environment Competitor product introductionst-1 Change in competitive positionst-1 Perceived competitiveness Independent decision-making structure Control variables Perceived realism a Salest-1 a Research and development expenditurest-1 a Change in production levelst-1 Intercept

–0.155 –0.014

0.130 0.290

0.322**

0.096

–0.052 0.317** 0.482* 0.895*

0.067 0.119 0.226 0.244

0.286* 0.119** 0.011* –0.155** –1.519

0.128 0.041 0.005 0.058 1.215

Adjusted R2 = .51 a. These measures are divided by 100,000 for rescaling. *p < .05. **p < .01, two-tailed tests.

product modifications. A possible explanation of these findings is that higher levels of competitiveness may lead to higher levels of uncertainty and increased 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 product modifications, as predicted by Hypothesis 6. This finding suggests more product modifications are made when individuals are responsible for decisions for a given product line than when collectives of group members are involved, such as in consensus, functional, and matrix structures. Information cues influencing strategic decisions. Tables 3 and 4 present the logistic regressions of the strategic decisions: product introductions and product withdrawals, respectively. Hypothesis 3 predicted that prior strategic decisions would influence current strategic decisions. This prediction was not supported; previous decisions to introduce a new product or to withdraw a product did not influence product introductions or product withdrawals, respectively. Consistent with expectations, neither performance relative to aspiration nor performance relative to industry average had an effect on

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TABLE 3

Logistic Regression Analysis of Product Introductions (N = 116) Variable Performance Performance relative to team aspirationt-1 Performance above/below industry averaget-1 Prior decisions Product introductionst-1 Competitive environment Competitor product introductionst-1 Change in competitive positionst-1 Perceived competitiveness Independent decision-making structure Control variables Product withdrawals Successful research and development projectt a Research and development expenditurest-1 Perceived realism a Salest-1 Intercept

0.179 0.477

0.359 0.944

–0.462

0.721

–0.081 0.849* –1.632* –0.007

0.207 0.406 0.795 0.744

3.560** 1.221 0.067** 0.103 –0.306* 3.673

0.737 0.812 0.021 0.409 0.159 3.968

Goodness of fit = 109.66 Model χ2 = 77.52** Percentage of cases classified correctly = 86.21% 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 product. Hypotheses 4 and 5 predicted that competitor product introductions and perceived high levels of competition, as well as changes in the positions of competitors, would be associated with strategic change. The competitive environment did influence strategic decisions, as expected. Product introductions by competitors did not influence product introductions but did make it more likely that teams would withdraw products from the market. This behavior may be an attempt to redirect resources toward developing new products that will counter their competitors’ new products. It is possible that competitive product introductions spur decision makers to start the process of new product introduction but that this process takes several periods before a new product can actually be introduced. The process may include old product withdrawal, investment in research development, followed by an eventual product introduction. Dropping existing products in response to competitor product introductions may be a first step toward funding new research and development projects and introducing new

394

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Logistic Regression Analysis of Product Withdrawals (N = 116) Variable Performance Performance relative to team aspirationt-1 Performance above/below industry averaget-1 Prior decisions Product withdrawalst-1 Competitive environment Competitor product introductionst-1 Change in competitive positionst-1 Perceived competitiveness Independent decision-making structure Control variables Product withdrawals Successful research and development projectt a Research and development expenditurest-1 Perceived realism a Salest-1 Intercept Goodness of fit = 108.32 Model χ2 = 55.83** Percentage of cases classified correctly = 81.90%

–0.174 –0.332

0.314 0.773

0.392

0.601

0.374* –0.936** 0.097 –0.193

0.171 0.364 0.599 0.674

3.379** 1.373 –0.008 –0.339 0.082

0.694 0.989 0.011 0.368 0.092

–2.358

3.329

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

products. Examination of a longer lag time than one period would be necessary to explore this possibility. If such a delayed effect occurred, a possible explanation for the long lag time is that teams wanted to develop a new product that directly countered their competitor’s products. If they did not have such a product waiting on the shelf, they would have to use the research and development process to develop one. Responding to specific moves (product introductions) by competitors may require planning and product development and, thus, results in product withdrawals in the short run to redirect needed resources. Changes in competitive positions made it both more likely that teams would introduce a new product and less likely that they would withdraw an existing product. Thus, instability and uncertainty in the environment led teams to make strategic changes but also led them to persist with prior strategies by keeping existing products. This finding suggests that the general uncertainty created by competitive instability led teams to introduce new products that they already had on the shelf but not to simultaneously drop old

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products. Similar patterns were found in Tables 3 and 4, in which high levels of competition led to more product modification. Thus, general uncertainty may lead teams to make whatever strategic and tactical changes they are able to implement fairly quickly, while maintaining their dependence on their existing 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 be moderately competitive. The very high level of uncertainty associated with extreme competitiveness may discourage teams from taking risks in the form of new products; the analysis of tactical decisions suggests that under these conditions, they prefer the less risky tactic of modifying existing products. This effect might also suggest that teams experiencing an extreme level of competitiveness are too preoccupied with trying to keep pace with the environment to do the long-term planning required to develop and introduce new products. These teams emphasize product modification, which requires less long-term planning. Last, we found no support for Hypothesis 7, which predicted that a collective decision-making structure would be positively associated with strategic decision making. The decision-making structure variable had no effect on product introductions or withdrawals.

DISCUSSION The purpose of this article was to investigate how the type of decisions managers make might elicit different schemas, directing attention to different types of information cues. We applied individual-level informationprocessing theories to examine two categories of decisions that managers make: tactical and strategic. Our results provided evidence that there is some difference in the information cues that influenced tactical and strategic decisions but there are also similarities. Specifically, prior decisions influenced tactical decision making; the competitive environment influenced both tactical and strategic decision making. A summary of these findings is shown in Figure 2. A surprising finding was the lack of influence of past performance outcomes on tactical decisions. A fundamental prediction of trial-and-error learning is that negative feedback will trigger change in actions. We did not find this relationship holding with respect to tactical decisions. Other empirical studies have suggested that actual behaviors in response to performance feedback seem to be more complex than our commonsense notions of trialand-error learning (Lant et al., 1992; Lant & Hurley, 1999). This research on performance relative to aspiration level has found a combination of

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

trial-and-error learning and persistence or escalation effects. It may be that certain types of decisions are more likely to exhibit trial-and-error responses. Empirical evidence from fieldwork suggests that adjustments in aspirations by teams seem to follow this pattern (Mezias & Murphy, 1998; Murphy et al., 2001). In additional analyses that we conducted, we found that changes in production demonstrated the trial-and-error effect that we had predicted for tactical decisions. We can speculate from this pattern of findings that trial-and-error responses to performance feedback are most likely to occur when decision makers are setting levels of a variable, such as performance goals or production units. The tactical and strategic decisions that we studied were more context-specific choices that teams made when faced with a broad array of possible alternatives. The overall pattern that we had predicted was that tactical decisions would use internally focused information whereas strategic decisions would use externally focused information. Our pattern of findings suggests that both types of decisions were influenced by external information about the competitive environment but neither was influenced by internal performance feedback. Changes in tactics did exhibit a routinized characteristic, in that once decision makers start making changes in tactics, they are likely to continue to do so. Furthermore, when individuals on the teams had the autonomy to make tactical decisions alone, more changes in tactics were made. We can speculate that tactical decisions are more easily influenced by routines and the decision structure of teams than are strategic decisions. Tactical decisions also appear to be influenced by the overall uncertainty of the competitive environment rather than specific moves by competitors.

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CONCLUSION The design of this study is simultaneously a risk and a potential contribution: Our study was designed to test for information-processing patterns that are attributable to the cognition of small groups. These groups were faced with information input that is similar to that which organizational decision makers would experience; however, the groups made their choices in the absence of a real organization. Thus, the relationships that were found are not attributable to factors such as organizational structure, organizational inertia, or implementation issues. Thus, we can say with some confidence that the observed relationships are due to the cognition of the decision makers. For instance, the observed momentum in tactical decisions suggests that, in addition to the organizational routines that may develop through the implementation of product modifications, routines also develop in the decision-making process itself. This suggests that decision momentum may have a cognitive as well as an organizational component. The results found in this setting also suggest that outcomes of social comparison, such as the effect of uncertainty on judgment (Schacter & Singer, 1962; Zucker, 1977), and the imitative tendencies noted by institutional theorists (Haunschild & Miner, 1997; Haveman, 1993; Mezias, 1990), may have their origin in the cognition of decision makers. This study also found that the way in which groups organize themselves to make decisions can affect their decisions significantly. In this study, the patterns of decisions made by teams structured by product line were significantly different from those of other teams. Product managers tended to engage in high levels of product modification. Other characteristics of the teams, such as size, experience, and demography, did not appear to affect the types of decisions investigated in this study. However, it is always important to control for these characteristics when studying group decision making. A key limitation in this study is the assumption that the effects of information cues on managerial decision making are independent. Managerial scripts are likely to be complex. It would be reasonable to expect that the affect of prior decisions or the level of competition, for example, may be influenced by past performance. In particular, there is much in the literature that suggests that good performance leads to complacency, reduced attentiveness to the environment, and lowered probabilities of change (Milliken & Lant, 1991). This reduced responsiveness can create both strategic persistence and even reduce attempts at tactical changes. Similarly, past performance may influence the affect of competitiveness on the likelihood of strategic change. Although competitiveness should increase the likelihood of change, in the presence of good performance and complacency, decision makers may be less likely to respond to this information. Future empirical research should

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explore such potential interactions among information cues and how decision 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 decision making should explore more directly how groups use scripts and how these collective cognitions emerge. The study conducted by Walsh et al. (1988) is a good example of this type of research. This study cannot determine whether the scripts that we have inferred from the pattern of findings are the result of similarity in scripts that the participants brought with them into the game or whether shared scripts developed and were negotiated during the game. Given the background, experience, and training of the participants, it would not be surprising to see a high level of homogeneity in scripts among the participants. Finally, although this study has provided some insights into information processing by decision-making groups, it is only suggestive of informationprocessing patterns that might occur in actual organizations. Organizational structure, inertia, and implementation problems, among other factors, may all affect the cognitive information processing of managers. Furthermore, the findings may be less suggestive of high-impact strategic decisions in organizations. The strategic decisions in the game were quite limited, given the constraints on numbers of products, market segments, and no opportunities for industry entry or exit. Major strategic decisions, such as entering or leaving certain markets, may involve extensive long-term planning. Although this planning may in itself be affected by information-processing limitations, the effect of such limitations on these planning processes cannot be explored within this context. However, the findings of the study are suggestive of the types of information cues that should be investigated in future studies of strategic decision making.

APPENDIX A Description of Control Variables RESOURCE EFFECTS Changes in Production Changes in production are likely to oscillate up and down as decision makers attempt to find the correct level of production given market demand. This is similar to regression to the mean effect. That is, increases in resource commitments in a prior period will make increases less likely in the following period. Changes in production,

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on the other hand, which are tactical decisions aimed at finding an equilibrium at which production matches demand, exhibit an oscillation similar to regression to the mean. Sales Higher levels of sales were also associated with increases in production. This might be a resource effect; because sales bring revenues, teams that have higher sales will be better able to afford increase in production. GAME CHARACTERISTICS Perceived Realism Because the research setting is a simulation, it is important to determine whether participants took the game seriously enough to make decisions in the way that is similar to what they would do in reality. One of the questions in the postgame questionnaire 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 within each team was 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 realism scores ranged from 1 to 5.25. Product Limitations One constraint of the game is that teams cannot have more than five products on the market at the same time. If a team already has five products on the market, they will have to drop a product to introduce a new one. Thus, in the analysis of product introductions and product withdrawals, a concurrent indicator of product withdrawals and 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 project is required before a new product can be introduced. Thus, an indicator of whether a research and development project has been successfully completed is included in the analysis of product introductions and product withdrawals. Prior Research and Development Expenditures Prior research and development expenses on both tactical decisions and strategic decisions are also included due to the potential impact of these expenses on both types of decisions.

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APPENDIX B Group Characteristics: Examined but Not Included in the Multivariate Analysis Theoretically, this study was interested in the impact of team structure on information processing. However, other team characteristics could affect decisions, such as team demography and the team’s perceptions of their decision-making process. In terms of demography, the potential impact of gender mix and managerial experience were explored. As noted earlier, 80% of the participants were men and 20% were woman. Thus, some of the teams were composed of only male participants and others had both men and women. The potential difference between all male versus mixed gender groups is explored with a binary variable, coded 1 if the team was composed of both men and women, and zero if the team was composed of all men. Half of the teams were composed of managers enrolled in the fellowship program; the other half of the teams were composed of MBA students. The executives were, on average, older and had more managerial experience than the MBAs. The potential impact of these differences is explored with a binary variable, coded 1 if the team is composed of executive and zero if the team is composed of MBAs. There were no significant differences found as a result of any of these variables. Because real-time observation of the group decision-making process in each team was not possible (teams met at the same time), several variables that measure perceptions of group processes are used as proxies for actual group process. These variables were constructed from a postgame questionnaire. The items were 7-point Likert-type scales that assessed the participants’ perceptions of whether they had a strong leader, their effectiveness in terms of the group’s performance, and decision-making procedures. To construct a team-level variable, the average response of members of each team was calculated. None of these group characteristics had an impact on the dependent variables in this study. Thus, they are not included in the multivariate analysis that is reported.

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

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

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Patricia F. Hewlin is a Ph.D. candidate in the management and organizational behavior department at New York University. Her research interests are in managerial cognition, identity, and impression management.