COGNITIVE AND BEHAVIORAL CONSISTENCY IN A COMPUTER-BASED MARKETING-SIMULATION-GAME ENVIRONMENT: AN EMPIRICAL INVESTIGATION OF THE DECISION-MAKING PROCESS Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 12 COGNITIVE AND BEHAVIORAL CONSISTENCY IN A COMPUTER-BASED MARKETING-SIMULATION-GAME ENVIRONMENT: AN EMPIRICAL INVESTIGATION OF THE DECISION-MAKING PROCESS William Wellington and A. J. Faria, University of Windsor T. Rick Whiteley, Lawrence Technological University R. O. Nulsen Jr., Xavier University ABSTRACT Past research investigating participant adaptability to game parameters in computer-based, business simulation games has focused primarily on the nature of the decisions actually made (e.g., actual price set) in order to determine the validity of this experiential approach to management education and training. The present study moves back one step in the decision-making process and examines, as well, the cognitive nature of the decisions on which the behavioral responses were based (e.g., perceived importance of price to game success). A study of seven cognitive and seven behavioral measures obtained from 68 single-player competitive companies that were randomly assigned to two experimentally manipulated environments in a 10-period game was undertaken. The results indicate that between- environment differences were obtained but not always as expected. INTRODUCTION AND PAST RESEARCH Computer-based simulation games are extensively used in business programs as an active mode for learning (Faria 1 987). Over 95 percent of AACSB accredited business schools incorporate such games as part of their curricula (Faria 1 987), drawing from the over 200 such games available on the market (Horn and Cleaves 1980). As well, business firms make use of such simulations. Approximately 23 percent of all U.S. companies with over 1, 000 employees use simulation games in their training programs while an additional 11 percent have used them in the past (Faria 1 987). Game administrators assume that active participation allows players to develop and improve their decision-making skills. Traditionally, game performance outcomes, such as earnings per share, return on investment, or sales, are used as measures of decision-making skill. The relationship between skill level and performance level is considered to be positive in nature. When a player outperforms the competition, it is assumed that the “winner” has made decisions that are more consistent with the game parameters than those made by other simulation participants. By making decisions that are more consistent with the environment defined by the game parameters, it is assumed that the game player has learned how best to adapt to the simulation environment. Adapting to a simulation environment likely involves operant conditioning because participants learn to adjust their decision-making behaviors as a result of positive or negative consequences that are contingent on their previous decision-making. Operant conditioning applies to voluntary responses, which an organism performs willfully in order to produce a desired outcome. An organism operates on its environment in order to produce some desirable result, i.e., the law of effect... responses that are satisfying are more likely to be repeated, and those that are not satisfying are less likely to be repeated (Thorndike 1932). Thorndike’s early research formed the foundation for the work of B.F. Skinner who furthered the study of operant conditioning by illustrating how behavior varied as a result of alterations in the environment (Feldman 1990). Skinner contends that learning takes place in an effort to control the environment, i.e., to obtain favorable outcomes. Control is gained by means of a heuristic process during which one behavior results in a more favorable response than other behaviors. The reward (more favorable response) reinforces the behavior. As such, reinforcement is instrumental in teaching subjects a specific behavior that gives them more control over the outcome (Schiffman 1987). Not all learning takes place as a result of repeated heuristic activity, however. Much learning occurs as a result of individual thinking and problem solving. Cognitive learning theory, as this is known, suggests that the kind of learning most characteristic of human beings is “problem solving” which enables individuals to gain some control over their environment. Unlike behavioral learning theory, cognitive learning theory advances the idea that learning involves complex mental processing of information. Rather than emphasizing the importance of repetition or the association of rewards with a specific response, cognitive researchers stress the role of motivation and mental processes in producing a desired response (Schiffman 1987). Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 13 Learning theory would suggest that underlying the behavioral decisions made by a simulation participant (e.g., price setting, advertising expenditure level, sales force size, etc.) is a learning process that leads to the determination of what types of decisions work and what types of decisions do not work in a simulation competition. For example, if a player concludes that low price is important to game success, the appropriate behavioral response is to set a low price. This would suggest consistent cognitive-behavioral decision-making. The nature of this relationship in a simulation game context is illustrated in Figure 1. Given this expectation, it would be appropriate to analyze the cognitive and behavioral decision structures of game players in order to determine the nature of the behavioral responses expected based on the identified cognitive structures. While a number of studies have focused on the behavioral aspect of the decision-making process in simulation competitions (e.g., see Dickinson, Faria, and Whiteley 1988; Faria, Dickinson, and Whiteley 1991), research examining the cognitive decision-making process from the perspective identified here is relatively new (e.g., see Whiteley, Dickinson, and Faria 1992). Furthermore, in those studies examining the behavioral domain, the results indicate that game players do not seem to have made behavioral decisions which indicate that they had drawn correct cognitive conclusions about the nature of the simulation environment which they faced. PURPOSE OF STUDY The present study was designed to analyze the cognitive and behavioral structures of the decision-making process of game players to determine if they have understood the nature of the environment with which they had to contend. If the results indicate that the correct cognitive thought processes have occurred, then appropriate behavioral decisions are expected. The simulation game entitled LAPTOP: A Marketing Simulation (Faria and Dickinson 1987) was used to investigate the focus of the study since this game allows the game administrator to determine the importance (i.e., weight) of each parameter of the competition. In particular, the parameters were set such that two theoretically meaningful experimental environments were created. One of the experimental environments was designed to reward the use of a “pull” strategy while a second environment was designed to reward the use of a “push” strategy. Push and pull strategies are fundamental marketing concepts, which are taught to all students of marketing and are described, in all basic marketing textbooks. The focus of a “pull” strategy is consumer demand stimulation while the focus of a “push” strategy is the enlistment of channel cooperation in moving a product through the distribution system toward the consumer (Evans and Berman 1993, Bovee and Thill 1992). In order to create an environment, which would reward the use of a pull strategy, the importance (i.e., weight) of each of the marketing pull strategy elements in the simulation competition was set to 10. A weighting of 10 represents the highest (most important) that can be given to a strategy element in the LAPTOP FIGURE 1 A COGNITIVE-BEHAVIORAL PERSPECTIVE OF THE DECISION-MAKING PROCESS IN A BUSINESS SIMULATION GAME CONTEXT Pregame Analysis Cognitive Interpretation Behavioral Decision Game Parameter Structure Decision Processing Performance Results Postdecision Analysis Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 14 competition. The marketing strategy elements weighted at this level were price, broadcast and print advertising, and consumer sales promotional activities. Further, within the pull environment, traditional push strategy marketing variables were weighted at 1 • the lowest importance weighting possible. The strategy variables weighted at a level of 1 included trade advertising, dealer cooperative advertising allowances, sales force size, incentives, and dealer promotions. A default weighting of 5 was assigned to strategy elements that were neither of a push nor pull nature. In order to create an environment, which would reward the use of a push strategy, the importance values of 10 and 1 were assigned in a manner opposite to that used in the pull environment. The default value assignments were the same in both environments. METHODOLOGY The simulation competition used for this research involved 68 undergraduate students enrolled in two sections of a one- semester principles of marketing course. The students were advised that the game was worth 25 percent of their final course grade. The performance measures of relative (i.e., compared to the direct competition) earnings per share and relative market share were equally weighted for the purpose of grade determination. In addition to analyzing the actual decisions made in the simulation competition, a questionnaire was administered to each simulation participant to obtain the cognitive or game environment evaluation data needed for this study. The 68 simulation participants were randomly assigned to 14 industries, each consisting of 5 single-player companies. Seven of the industries (i.e., 35 companies) were randomly assigned to the “Push” environment and seven industries (i.e., 35 companies) were randomly assigned to the “Pull” environment. The participants were at no time informed about the nature of the environment to which they were assigned. [Two dummy companies were operated throughout the game in order to equalize the number of companies per industry. The data for these companies were not analyzed.] The first two (quarterly) decisions were made during weeks 3 and 4 of the course and served as trial decisions so as to provide the participants with the opportunity to become familiar with the technical aspects of the game and to try various strategies without risk. At the completion of the second trial period, a new competition was restarted. The marketplace environment was unchanged in the new start-up and competitors in each industry remained the same. The knowledge acquired during the trial periods, therefore, was relevant to the new game. The new game consisted of eight decisions (Real Periods 1 to 8), executed over a period of 9 weeks. Prior to receiving their results for Real Periods 1, 4, and 8, the game participants were given a questionnaire to indicate their perception of the importance [very important (10) to very unimportant (1)] of each of the decision variables in their competitions for stimulating marketplace demand (see variables in Table 1). This is the cognitive measure spoken of earlier. HYPOTHESES The general hypothesis for this study is that, if marketing strategy formulation in a simulated environment is an internally valid experience, then the cognitive and behavioral decisions of the simulation participants should be consistent with the environment with which they must contend. Thus, the cognitive and behavioral decisions should vary as a function of the environment in which a company/participant operates. Since learning occurs as a result of experience, it would be expected that the cognitions TABLE 1 PERCEIVED (COGNITIVE) IMPORTANCE OF VARIABLES ON DEMAND BY GAME ENVIRONMENT Environment R1 – Push (n = 33) Pull (n = 34) R4 – Push (n = 33) Pull (n = 32) R8 – Push (n = 31) Pull (n = 31) Decision Variable Period X SD X SD F-value H3a Low Price R1 50.65 8.13 49.45 11.62 .20 R4 49.48 10.2 50.53 9.93 .17 R8 49.94 9.8 50.06 10.36 .00 H3b High Broadcast R1 50.35 8.91 49.65 11.08 .08 Advertising Expenditure R4 50.64 8.09 49.33 11.75 .27 R8 50.53 9.29 49.47 10.80 .17 H3c High Print Advertising R1 49.77 10.05 50.22 10.10 .03 Expenditure R4 51.05 9.81 48.92 10.24 .75 R8 49.79 10.44 50.22 9.71 .03 H3d High Trade Advertising R1 50.98 9.02 49.05 10.91 .62 Expenditure R4 53.31 9.55 46.77 9.54 7.22 ** R8 52.57 10.35 47.43 9.01 4.31 ** H3e High Co-op Advertising R1 49.39 8.99 50.59 10.99 .23 Allowance Percent R4 52.62 8.98 47.29 10.40 4.91 ** R8 52.66 8.78 47.35 10.57 4.63 ** H3f High Sales Force Size R1 49.74 9.76 50.25 10.37 .04 R4 50.45 9.30 49.53 10.81 .13 R8 51.21 9.51 48.79 10.48 .90 H3g High Product Quality R1 50.86 8.2 49.17 11.55 .47 R4 48.69 10.11 51.35 9.86 1.15 R8 48.16 10.76 51.84 8.99 2.14 MANOVA RESULTS Round 1 Round 4 Round 8 Pillia’s Value .04234 .15399 .17876 Degree’s of Freedom 59 60 60 Exact F .37267 1.48211 1.67915 Significance .915 .192 .134 Notes * marginal significance at < .10 ** significant at < .05 Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 15 and behavior of the simulation participants in differing environments would be the same at the outset of the simulation and, as the simulation progresses and learning occurs, the cognitions and behavior would diverge. Although a 10-period game was executed, it was decided that analyzing the data for Real Periods 1, 4, and 8 would adequately serve the purpose of identifying changes in cognitions and behavior between the push and pull groups. This leads to the following eight hypotheses. For the present study, the specific cognitive expectations are as follows: H1: There will be no difference in cognitive expectations between push and pull participants in the first period of the simulation. H2: As the simulation progresses into period 4 and period 8, there will be a significant difference in cognitive expectations between push and pull participants. H3: As the simulation progresses into period 4 and period 8, push participants will perceive trade advertising (H3d), cooperative advertising (H3e), and the number of salespeople (H3f) as more important for demand stimulation than will pull participants. Pull participants will perceive broadcast advertising (H3b), print advertising (H3c), and low prices (H3a) as more important for demand stimulation than will push participants. Both push and pull participants will have the same perception of the impact of research and development (H3g) on demand. Similarly, in the present study, the specific behavioral expectations are as follows: H4: There will be no difference in actual decision-making behavior between push and pull participants in the first period of the simulation. H5: As the simulation progresses into period 4 and period 8, there will be a significant difference in actual decision-making behavior between push and pull participants. H6: As the simulation progresses into period 4 and 8, push participants will have higher prices (H6a), spend more on trade advertising (H6d), have higher cooperative advertising levels (H6e), and more salespeople (H6f) than pull participants. Pull participants will spend more on broadcast advertising (H6b) and print advertising (H6c) than push participants, and push and pull participants will spend the same amount on research and development (H6g). The expectation throughout the simulation is that there will be a relationship between the actual simulation decision behavior and the participants’ cognitions regarding which variables are important. This leads to the final two hypotheses: H7: Throughout the simulation competition there will be a relationship between the set of seven cognitive variables and the set of seven behavioral variables for both push and pull participants. H8: Throughout the simulation competition there will be a relationship between cognitions and actual behavior for both push and pull participants for each pair of cognitions and behaviors (prices, broadcast advertising, print advertising, trade advertising, co-op advertising, salesforce size and product research and development). In order to test these hypotheses, the perceptual and decision-making data had to be transformed in order to make scale free comparisons between the cognitive and behavioral variables. The data were standardized and transformed into T-scores (mean of 50 and standard deviation of 10) as suggested by Glass and Hopkins (1984). Hypotheses Hi and H2 were tested using SPSS MANOVA analysis to compare the overall cognitions of the push and pull groups as measured by the self reported demand influencing importance weightings for the seven push-pull decision variables (pricing, broadcast advertising, print advertising, trade advertising, co-operative advertising, product research and development and salesforce size) in periods one, four and eight. H3a through H3g were tested by looking at the univariate F-test results for each of the seven variables, which are produced by the MANOVA program. Hypotheses H4 and H5 were tested using SPSS MANOVA analysis to compare the behavior of push and pull groups as measured by actual participant decision-making for the seven variables in periods one, four and eight. H6a through H6g were again tested by looking at the univariate F-test results for each of the seven variables produced by the MAN OVA program. H7 was tested using canonical correlation between the seven cognitive variables and the seven behavioral variables for periods one, four and eight. H8 was tested using correlation analysis between the seven pairs of cognitive and behavioral measures for the whole sample Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 16 and then by the push and pull strategy participants individually for periods one, four and eight. RESULTS The results of the cognitive data analyses are presented in Table i while the results of the behavioral analyses are shown in Table 2. The results of the combined analyses of cognitive and behavioral variables are shown in Tables 3 and 4. The MANOVA results shown at the bottom of Table 1 support the acceptance of Hi. In period one there were no differences in overall cognitions between the push and pull groups. H2, however, is not supported, as there were no significant differences in overall participant cognitions between the push and pull groups in either periods 4 or 8. While overall participant cognitions are not significantly different, the univariate F-tests for H3a through H3g do shown differences for two of the decision variables. The push groups did perceive trade advertising and cooperative advertising to be more important for demand stimulation than the pull groups, which supports H3d and H3e. However, there were no significant differences in the perceptions of the importance for any of the remaining push and pull variables. As such, hypotheses H3a, H3b, H3c and H3f are rejected. As expected, there was no difference in the perceptions of the importance of research and development and H3g is accepted. The MANOVA results shown at the bottom of Table 2 support the acceptance of both H4 and H5. In period one there were no differences in the behavioral actions (i.e., actual decisions) of the push and pull participants thus supporting H4. H5 is also supported, as there were significant differences in the overall decision-making strategy between the push and pull groups in each of periods 4 and 8. The univariate F-tests for H6a through H6g show that there are significant differences for two of the decision variables in simulation period 4 and on five of the decision variables in period 8. The push groups had higher prices and spent more than the pull groups on trade advertising in both periods 4 and 8 and, by period 8, had a higher sales force size thus supporting H6a, H6d and H6f. There were no differences in the behavioral decision-making between the push and pull groups on cooperative advertising, though, and H6e is rejected. It was expected that there would be differences between the push and pull groups on broadcast advertising and print advertising and this was found to be the case. However, the differences were not in the expected direction, the push groups spent more on print and broadcast advertising than the pull groups, and H6b and H6c are rejected. As expected, there was no difference in behavioral decisions for research and development and H6g is accepted. The results shown in Table 3 indicate that in period 1 there was no relationship between cognitions and behavior for either the push or pull participants. However, by period 8 there was a significant relationship between the cognitions and behavior undertaken (canonical correlation of .85i for push and .883 for pull participants). As such, H7 is accepted. The results shown in Table 4 indicate that by period 8 for the push respondents there was a significant correlation between cognitions and behavior for all of the variables except product quality. As for the pull respondents, there was a significant correlation in period 8 between behavior and cognitions on three variables (price, trade advertising and co-operative advertising). As such, there is partial support for H8. DISCUSSION AND CONCLUSION The results from this study are mixed. The evidence indicates that there was no significant difference in TABLE 1 PERCEIVED (COGNITIVE) IMPORTANCE OF VARIABLES ON DEMAND BY GAME ENVIRONMENT Environment Push (n = 33) Pull (n = 35) Decision Variable Period X SD X SD F-value H6a Low Price R1 50.70 8.79 49.33 11.11 .31 R4 53.03 11.60 47.14 7.29 6.35 ** R8 52.39 11.29 47.74 8.14 3.82 * H6b High Broadcast R1 49.68 10.03 50.29 10.10 .06 Advertising Expenditure R4 50.80 10.25 49.25 9.84 .40 R8 52.08 11.74 48.04 7.69 2.85 * H6c High Print Advertising R1 48.47 2.63 51.44 13.64 1.51 Expenditure R4 50.67 9.83 49.37 10.26 .29 R8 52.23 11.51 47.90 7.93 3.30 * H6d High Trade Advertising R1 49.53 8.45 51.44 13.64 1.51 Expenditure R4 52.81 11.64 47.35 7.39 5.40 ** R8 53.66 12.64 46.55 4.63 9.68 ** H6e High Co-op Advertising R1 50.16 9.21 49.85 10.82 .02 Allowance Percent R4 51.69 10.76 48.41 9.09 1.84 R8 50.46 10.07 49.56 10.06 .17 H6f High Sales Force Size R1 52.19 10.58 47.93 9.10 3.17 * R4 51.55 11.34 48.54 8.46 1.56 R8 52.52 12.39 47.63 6.37 4.26 ** H6g High Product Quality R1 49.31 9.88 50.65 10.21 .30 R4 50.22 11.23 49.79 8.41 .33 R8 50.32 12.32 49.70 7.36 .36 MANOVA RESULTS Round 1 Round 4 Round 8 Pillia’s Value .11249 .28483 .25289 Degree’s of Freedom 60 60 60 Exact F 1.08645 3.41374 2.90135 Significance .383 .004** .011** Notes * marginal significance at < .10 ** significant at < .05 Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 17 cognitions between push and pull participants in any of the three decision periods measured (see Table 1) but there were significant differences in decision-making behavior (see Table 2). This latter finding can be taken as evidence to support the contention that game participants understood the structural nature of the environment with which they had to contend and that push and pull participants developed and used different strategies. This would support, to a degree, the internal validity of the simulation exercise. When there was evidence that the structural nature of the simulation environment was understood, the behavioral decisions were, for the most part, consistent with participant cognitions. There was also evidence that the process of adaptive learning was occurring as the game progressed as more of the expected findings were realized during the later stages of the simulation. As well, while cognitive and behavioral differences were not significantly different in several decision-making areas, the direction of change in the participants’ decisions was as expected. With respect to actual behavior, the push participants seemed to adapt to their environment better than the pull participants. There is, in fact, no evidence (other than on the pricing variable) to indicate that the pull participants cognitively understood their environment. Yet, in many cases, their behavioral decisions moved in the right direction. The study undertaken, in retrospect, possessed a couple of important limitations which need to be addressed in future research. The finding that cognitions between push and pull participants were not always as expected could have been caused by measurement error. Cognitions, in this study, were measured by asking respondents to provide independent importance evaluations on the impact of each decision variable on demand stimulation. What might have been a more appropriate measure would be to ask respondents to estimate the relative influence of each variable in light of recognition that all the variables act together. With respect to actual behavior, and also a likely confound for cognitions, was the influence of the “pricing” variable. Making one environment price sensitive (the pull environment) while making the second environment less price sensitive (the push environment) meant that far greater revenues and profits could be earned in the push environment. Consequently, having more revenues and profits, the push firms spent more in all areas of demand creation than their pull counterparts whose revenues and profits were constrained by severe price competition. This critical difference was identified TABLE 3 CANONICAL CORRELATION BETWEEN COGNITIVE AND BEHAVIORAL VARIABLES MULTIVARIATE SIGNIFICANCE TESTS COMBINED PUSH AND PULL H7 H7 H7 Round 1 Round 4 Round 8 Root 1 Eigenvalue .370 1.051 1.282 Percent of Variance 43.758 48.327 37.393 Canonical Correlation .519 .716 .750 N 67 65 62 Pillia’s Value .68859 1.33083 1.92489 Degree’s of Freedom 49 49 49 Approximate F .91958 1.91153 2.92588 Significance .630 .000 ** .000 ** PUSH H7 H7 H7 Round 1 Round 4 Round 8 Root 1 Eigenvalue 1.635 3.873 2.623 Percent of Variance 49.847 56.291 39.679 Canonical Correlation .788 .891 .851 N 33 33 31 Pillia’s Value 1.67277 2.52347 2.62081 Degree’s of Freedom 49 49 49 Approximate F 1.12144 2.01326 1.96639 Significance .292 .001 ** .001 ** PULL H7 H7 H7 Round 1 Round 4 Round8 Root 1 Eigenvalue .616 2.297 3.547 Percent of Variance 42.113 56.303 48.117 Canonical Correlation .617 .835 .883 34 32 31 Pillia’s Value 1.05395 1.89245 2.56456 Degree’s of Freedom 49 49 49 Approximate F .65836 1.27035 1.89979 Significance .957 .135 .002 ** Notes. * marginal significance at < .10 ** Significant at < .05 TABLE 4 CORRELATION BETWEEN COGNITIVE AND BEHAVIORAL BARIABLE PAIRS H8 All Respondents Round 1 Round 4 Round 8 n Correlation n Correlation n Correlation Low Price 67 .0266 65 -.5249 ** 62 -.5707 ** Broadcast 67 -.0004 65 .1344 62 .2742 ** Print Advertising 67 -.1384 65 .2850 ** 62 .2703 ** Trade Advertising 67 -.0434 65 .4310 ** 62 .4507 ** Co-op Advertising 67 .0437 65 .0911 62 .4485 ** Sales Force Size 67 -.0117 65 .1870 62 .3765 ** Product Quality 67 -.0872 65 62 .1189 H8 Push Respondents Round 1 Round 4 Round 8 n Correlation n Correlation n Correlation Low Price 33 .2096 33 -.6612 ** 31 -.6648 ** Broadcast 33 .1164 33 .3159 * 31 .3488 * Print Advertising 33 -.1194 33 .4962 ** 31 .4200 ** Trade Advertising 33 .2162 33 .5223 ** 31 .4599 ** Co-op Advertising 33 .1591 33 .2396 31 .3570 ** Sales Force Size 33 -.0835 33 .1526 31 .4529 ** Product Quality 33 .1851 33 .2647 31 .1879 H8 All Respondents Round 1 Round 4 Round 8 n Correlation n Correlation n Correlation Low Price 34 -.0762 32 -.3514 ** 31 -.4934 ** Broadcast 34 -.0874 32 .0015 31 .1906 Print Advertising 34 -.1824 32 .0836 31 .0733 Trade Advertising 34 -.1886 32 .1737 31 .3217 * Co-op Advertising 34 -.0333 32 .5256 ** 31 .5203 ** Sales Force Size 34 .0808 32 .0108 31 .2679 Product Quality 34 -.2610 32 .1023 31 -.0023 * Significance < .10 ** Significance < .05 Developments In Business Simulation & Experiential Exercises, Volume 22, 1995 18 by both push and pull respondents and was likely what drove the competition and confounded the ability (and perhaps desire) of pull participants to understand and explore the impact of the other decision variables. In future research, the price variable should be kept neutral. In conclusion, this study provides some evidence of consistency between cognitions and simulation decision- making. However, a future study which deals with the two key limitations identified here needs to be undertaken to shed clearer light on this issue. REFERENCES Benjamin, L.T., Jr. (1988) A History of Teaching Machine American Psychologist, 43, 703-712 Biskin, B. H. (4 1980) Multivariate Analysis in Experimental Counseling Research The Counseling Psychologist, 8, 69-72. _______ (4 1983) Multivariate Analysis in Experimental Leisure Research Journal of Leisure Research, 15, 344- 358. Bovee, Courtland L. and Thill, John V. (1992) Marketing. New York, NY: McGraw-Hill. Cole, M. and Scribnerr, 5. (1974). Culture and Thought: A Psychological Introduction New York, NY: Wiley, p. 170. Dickinson, John R., Faria, A. 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A Cognitive Approach to the Measurement of Simulation-Game Participant Adaptability to Game Parameters Proceedings of the Ninth International Conference on Technology and Education, 9, (Vol. 2), 684-686. Table of Contents Volume 22, 1995 Simulation Performance, Learning and Struggle Are Good Simulation Performers Consistently Good? Cognitive and Behavioral Consistency in a Computer-Based Marketing-Simulation-Game Environment: An Empirical Investigation of the Decision-Making Process Chalk & Cheese: Executive Short-Course vs. Academic Simulations Revisiting Personality Bias in Total Enterprise Simulations Are Good Strategies Consistently Good? Investigating the Use of a Computer Simulation as an Effective Pedagogical Tool for the Application of a Strategic Model The Problem of determining an Individualized Simulation's Validity as an Assessment Tool A Simulation Based Analysis of the Value of Information in the Hrebiniak Joyce Typology of Adaptation Relative to Porter's Generic Strategies The Impact of Sales and Income Growth on Profitability and Market Measures in Actual and Simulated Industries A Comparison of a Stand Alone Version of a Simulation with the Traditional Competitive Version Computer-Assisted Gaming of International Business Analyzing Simulations with Computer-Based Programs and Applying the Experience to a Real-World Business A Preliminary Investigation of the Use of a Bankruptcy Indicator in a Simulation Environment Graduates' Views on the Use of Computer Simulation Games Versus Cases as Pedagogical Tools An Analytical Advertising Model Approach to the Determination of Market Demand Dealing with the Complexity Paradox in Business Simulation Games A Prototyping Approach for Incorporating Large Data Bases into Media Planning Simulations: An Example Using Magazine Media Through the Looking Glass, Inc: Superior-Subordinate Personality Type and the Leniency effect Evaluating the Effectiveness of Role Playing Simulation and Other Methods in Teaching Managerial Skills Student and Teacher Perceptions of a Management Simulation Course Performance Evaluation: The Effect on the Propensity to Create Budgetary Slack Management Team Formation for Large Scale Simulations Comparative Static Analysis with the Complete PPA Package: A Strategic Market Planning Tool Consistency in Intent: Learning Objectives at the 1994 Intercollegiate Business Policy Competition A New Twist on an Old Game: The Business Strategy Game: A Global Industry Simulation 3ed Evaluation of Performance in Management Simulation: A Management Coefficients Model Building SimuWorlds: Strategic Management Games of the Future Bulls and Bears: A Stock Market Simulation A Systems Thinking Paradigm and Think Computer Simulation Model of Broadcast and Cable Television Industry Competition Jacket Factory The Sales Management Simulation The Marketing Management Simulation A Demonstration of Promodel Demonstrating A New, Cross-Functional Business Simulation: Vision+ A Cost Chain For The Business Strategy Game Simulation Special Session On Experiential Teaching Compensation Dilemmas: An Exercise In Ethical Decision-Making Organizational Storytelling: Telling Tales In The Business Classroom Evaluating Experiential Training: Case Study And Recommendations The Internship Portfolio: An Innovative Tool For Experiential Learning, Critical Thinking, And Communication An Ethnographic Analysis Of The Pedagogical Impact Of Cooperative Communicating Consumer Behavior: A Long-Term Integrated Exercise Using Personal Consumption Journals And Consumer Analysis Papers An Experiential Paradigm For Teaching Business Problem Solving Developing Leadership Skills The Spss® Student Assistant: The Integration of A Statistical Analysis Program Into A Marketing Research Textbook Negotiating With Your Students Using TQM Principles To Transform Accounting Systems Into An Experiential Exercise Enhancing The Effectiveness Of Outdoor-Based Experiential Training Using Virtual Reality Concepts Case Writing In A Developing Country: An Indonesian Example Experiential Learning Using Focus Groups How Real Should Experiential Pedagogy Be? A Viewpoint From Our Students Reengineering The Internship: A New Approach To Experiential Learning Utilizing The Cosmopolitan/Local And Marginal Man Constructs To Measure Students' Propensity For Creativity Developing Experiential Processes For Teaching Quantitative Techniques For Business Team Learning Roles: A Cooperative Learning Technique Creating the Ultimate Small Business Student Experience: Melding Score/Ace with SBI The Development of Trust in Work Teams: The Impact of Touch Some Outcomes of Experiential Learning: How the Cultural Dynamics of Different Countries are reflected in Workplace Norms & Values Incorporation of Job Analysis Results in Various Forms of Selection Interviews Chudesno, Inc.: An Evaluation of an Experiential Training and Development Simulation An Exercise for Exploring the Relationship between Jungian Psychological Types and Organizational Dynamics Partnership: A Radical Approach to Experiential Learning Partnership: A Nice Idea, But How Do I Get Started? Recognizing Discrimination at Work Using Critical Incident Skills Questions to Help Students Become More Successful at Job Interviewing The Video Project Introduction to Psychological Type Theory Come On Down Understanding Facilitation for Development and Continuous Learning: A Micro-Workshop Leadership and Empowerment: An Experiential Exercise in Decision Making Experiential Exercises and Pedagogy Track Workshop: Selecting a Manager for Maquiladora, Inc. Experiential Training In Multi-Cultural Corporate Settings The Role of Facilitation as an Aid to Complete Learning An Experiential Exercise to Illustrate Difference in Information Processing Behaviors and Styles How to Deliver Accessible Survey Results Age Diversity in the Workplace- Family Feud Style Nafta Standoff: A Cross-Cultural negotiating Role-Play Three Strikes and You're Out!: A Downsizing Experiential Exercise