A SEMINAL INVENTORY OF BASIC RESEARCH USING BUSINESS SIMULATION GAMES Developments in Business Simulation and Experiential Learning, Volume 31, 2004 A SEMINAL INVENTORY OF BASIC RESEARCH USING BUSINESS SIMULATION GAMES John R. Dickinson University of Windsor bjd@uwindsor.ca James W. Gentry University of Nebraska–Lincoln jgentry@unlnotes.unl.edu Alvin C. Burns Louisiana State University alburns@lsu.edu ABSTRACT The vast bulk of research in simulation gaming focuses on either the design of games or their use in education and training. Considerable basic research, though, makes use of games as a research environment. This paper provides a seminal inventory of the use of games for basic research in business. This inventory is anticipated to evolve into a more exhaustive data base and also to lead to more meaningful categorizations and characterizations of the inventory. Now and in the future this inventory may encourage researchers to make use of the real and numerous advantages of simulation games as a research platform. INTRODUCTION AND BACKGROUND The state of business simulation gaming is described from time to time from a variety of perspectives. Most recently, a special issue of Simulation & Gaming (Klabbers 2001) was dedicated to the state of the art and science of simulation/gaming generally. Most broadly, Wolfe & Crookall (1998) assessed the state of simulation/gaming as a scientific discipline. As a core reference, Gentry’s (1990) Guide to Business Gaming and Experiential Learning presents a foundation of business games. The Simulation and Gaming Yearbook (Saunders, Percival & Vartiainen, 1996) periodically updates key developments in gaming. More topically, Randel et al. (1992) summarized research on the effectiveness of games for educational purposes and, based on an extensive review of the literature, Feinstein & Cannon (2003, 2002, 2001) provided a comprehensive framework for the validation of simulation games. Faria (1998) and others (Biggs, 1979; Burgess, 1991; Chang, 2003; Eldredge & Watson, 1996) have reported the extent of usage of simulation games in academe and business. Overwhelmingly, the orientation of these “state of” works is either the design of simulation games or the use of games in education and training. Much less widely recognized (and applied) is the use of simulation games as a platform for basic research. Greenblat (1975, p. 320) characterized the shift in focus “...from research on games to games for research...” Less wide recognition notwithstanding, the use of games for basic research has to some extent been conceptualized and applied. The present review summarizes those conceptualizations and applications. In turn, the purposes of this summary are to inventory precedents for use of simulation games for basic research and to inspire researchers to consider possibilities and opportunities for the use of business simulation games as a platform for their basic researches. DELIMITING SIMULATION GAMING Simulation gaming, of course, is not limited to the computerized business simulation games of the modern day ilk. Virtually any laboratory experiment may be characterized as a simulation, the laboratory setting not being a natural setting. (For overviews of gaming methodologies in experimental research see Bass [1964], Klimoski [1978], Schlenker & Bonoma [1978], Schwenk [1982], and Shubik [1961].) Setting aside, simulation also subsumes early devices such as information display boards (Bettman, 1975; Jacoby, Speller & Kohn, 1974) and Hughes & Naert’s (1970) pioneering computer- controlled experiments, both of which were developed for use in basic research, as well as more mundane devices such as role- playing. Gaming, too, has a substantial modern history of use in basic research, the most prominent early developers being von Neumann & Morgenstern (1944), with Herrmann & Stewart (1957) being among the first to position gaming for basic research in management. The present review of the use of simulation games in basic research, then, is somewhat arbitrarily limited to (1) competitive (2) longitudinal (3) business games. All of the basic research applications inventoried here are computerized. The aspect of computerization, though, is usually incidental to the participation experience and to the basic research; computerization is but a (perhaps vital) facilitation. Our present delimitation of simulation gaming notwithstanding, there is no reason why 345 mailto:bjd@uwindsor.ca mailto:jgentry@unlnotes.unl.edu Developments in Business Simulation and Experiential Learning, Volume 31, 2004 complementary inventories of basic research applications using other types of simulation can not be compiled. Further, we invite advisements of basic research applications not reviewed here so that the inventory may grow to become truly comprehensive. CATEGORIZING BASIC RESEARCH APPLICATIONS The basis for organizing/categorizing the basic research applications using simulation games summarized here is the topic of the research. Interestingly, while this may seem to be the most obvious basis, other bases have been proposed. Greenblat (1975) developed a taxonomy comprising eight combinations among (1) the researcher’s purpose, (2) the kind of gaming-simulation employed, (3) the researcher’s role, (4) the participant’s role, and whether the game used was (5) existing, (6) a new game, or (7) a redesigning of a game. The basic research studies inventoried here are categorized into: cross sectional organizational behavior, longitudinal organizational behavior, management, decision-making, forecasting, and marketing. The studies and the specific business games used are summarized in Table 1. A CONCEPTUAL PERSPECTIVE The most recent work taking a comprehensive look at the potential of simulation games for basic research was published 20 years ago (Gentry et al., 1984). Among the advantages cited by Gentry et al. are: •“...sufficient control so as to ensure internal validity while at the same time being sufficiently realistic so as to have some external validity.” (p. 1) •the capacity to investigate subjects infeasible via questionnaire surveys and field studies due to complexity and time consumption, e.g., decision- making processes, infrequent environmental conditions, e.g., labor negotiations, or sensitivity, e.g., divestment strategies •high participant involvement •compression of longitudinal phenomena •ease of replicability Among the disadvantages of simulation games for basic research cited by Gentry et al. are: •limited mundane realism, i.e., face validity, •limited experimental realism, i.e., the propensity of participants to not behave realistically owing to no or nonenduring consequences •game construction resources required where a suitable game does not already exist •small sample sizes partly due to administrator and participant time requirements •confounding effects evolving from the longitudinal dynamism of the game, i.e., while experiment manipulations may remain constant, actual participation conditions vary as a function of differentially evolving conditions, e.g., performance success, as the game progresses. To the above list of advantages may be added “safely investigate potentially dangerous or costly situations and...provide a situation for players which offers its own rewards for participation” (Dukes, 1973, p. 4), the latter advantage being in contrast to the “no or nonenduring consequences” disadvantage in the above list. Also, in contrast to the “mundane realism” disadvantage listed above, McFarlane (1971, p. 150) cites as an advantage of simulation gaming “a setting more likely to be perceived as ‘realistic’ by the subjects.” TABLE 1: Games Used for Basic Research Game Used for basic research by... The Carnegie Tech Management Game Cangelosi & Dill (1965) Dairy business-management game Babb, Leslie & Van Slyke (1966) The Farm Game Gentry, Tice, Robertson & Gentry (1984) FINANSIM Biggs (1975) KUBSIM Urban (1977) The Management Game Etzion & Segev (1984) Segev (1987) Market Place Achrol & Gundlach (1999) Gundlach & Cadotte (1994) The Marketing Management Experience Dickinson (2002) 346 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 Markstrat Clark & Montgomery (1999) Clark & Montgomery (1998) Curren, Folkes & Steckel (1992) Glazer, Steckel & Winer (1992) Glazer, Steckel & Winer (1990) Glazer, Steckel & Winer (1989) Glazer, Steckel & Winer (1987) Hogarth & Makridakis (1981) Lant & Montgomery (1987) The Organization Game Cameron & Whetten (1981) Smith, Mitchell & Summer (1985) Purdue Farm Management Game Babb, Leslie & Van Slyke (1966) Purdue Farm Supply Center/Business Management Game Babb & Bohl (1975) Babb, Leslie & Van Slyke (1966) Purdue Supermarket Management Game Babb, Leslie & Van Slyke (1966) QUANTSIM, SIMQ Cosier & Rechner (1985) Slusher, Sims & Thiel (1978) Tycoon Gladstein & Reilly (1985) . CROSS SECTIONAL ORGANIZATIONAL BEHAVIOR Though not constituting reviews or inventories of such, numerous earlier works have recognized the potential of simulation games for basic research: Cohen & Rhenman (1961), Cohen & Cyert (1965), Babb, Leslie & van Slyke (1966), McFarlane (1971), Inbar & Stoll (1972), Seidner & Dukes (1976), Schlenker & Bonoma (1978), Sewall (1978), Nees (1983). More recently, the Journal of Business Research (1987) published a special issue devoted to basic research applications using Markstrat (Larr�ch� & Gatignon 1977) including observations on simulations in business education and research by Larr�ch� (1987). Decision makers, of course, must have information on which to base their decisions. One approach to obtaining information may be to designate, say, two committees: one charged with investigating a certain set of assumptions/conditions and the second charged with investigating a contrary set of assumptions/conditions, i.e., dialectical inquiry. Recommendations from the two different perspectives should prove informative to the decision maker. Alternatively, one committee may be charged with investigating a certain set of assumptions/conditions and a second charged with critiquing the work of the first committee, i.e., a devil’s advocate approach. Cosier & Rechner (1985) used Nichols & Schott’s SIMQ (1975) as a platform to compare the effectiveness of these two approaches. Group decision making processes may change as a function of external threats to the company, e.g., high risk of loss, and as a function of time pressure, some anticipated changes being a decrease in the amount of discussion and amount of information used and an increase in decision centrality. The Tycoon (Amos Tuck School of Business Administration 1979) management simulation was used by Gladstein & Reilly (1985) to test these types of propositions. Group decision making performance may also be related to the attitudes of the group members toward their task, the level of effort they exert, and the degree to which the more effective decision makers emerge from the group process. Glazer, Steckel & Winer (1987) found all of these relationships to hold among participants in a Markstrat (Larr�ch� & Gatignon, 1977) competition. The magnitude and symmetry of interdependence between parties to an exchange was hypothesized by Gundlach & Cadotte 347 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 (1994) to influence coerciveness of strategies, feelings of conflict, and business performance evaluation. They used Cadotte’s (1990) Market Place simulation, featuring exchange between manufacturers and distributors, to test these hypotheses. Extending this stream of research, Achrol & Gundlach (1999) hypothesized that an increase in comparative commitment by one party in an exchange, lower contractual safeguards, and lower levels of mutual interest would result in greater opportunism in an exchange relationship between organizations. They also used Cadotte’s (1990) Market Place simulation. Achrol & Gundlach (1999) examined the nomological, convergent, and discriminant validity of their measures and characterized these, respectively, as moderate, moderate, and reasonable (pp. 115, 116). Strategies for labor-management negotiations was the focus of a study by Slusher, Sims & Thiel (1978). Utilizing QUANTSIM (Nichols & Schott, 1972), they studied the effects of initial offers, first concession magnitudes, and number of concessions on wage settlements. In this same stream, Urban (1977) examined differences in management-union bargaining behavior of males and females using KUBSIM (Klatt & Urban, 1975). Organizational behaviorists distinguish between strategy (actions taken to match the organization with its environment) and strategy-making (the formulation and implementation process) and various taxonomies for each have been put forth. Segev (1987) hypothesized a relationship between two such taxonomies and that the fit between the two would be associated with high performance. Employing the Graduate School of Business Administration, New York University, Management Game (1972), he found support for the association hypothesis and partial support for the high performance hypothesis. LONGITUDINAL ORGANIZATIONAL BEHAVIOR Several basic researches have exploited the longitudinal nature of a simulation game to study various evolutions in managers’ philosophies and strategies over the course of an organization’s “life cycle.” Cangelosi & Dill (1965) examined managers’ objectives and practices during different phases of organizational development using The Carnegie Tech Management Game (Cohen et al., 1964). They coupled their observations with a synthesis of extant theories of decision-making to formulate a comprehensive theory of organizational learning. Cameron & Whetten (1981) used The Organization Game (Miles & Randolph, 1979) to monitor the self-reported effectiveness of different levels of analysis–individual, departmental, divisional, organizational– over the course of the organization “life-cycle” and also the importance of input, internal processes, and output effectiveness at the different levels. Smith, Mitchell & Summer (1985) also used The Organization Game to track the change in importance of technical efficiency, political support, and organizational coordination over start-up, mobilization and turnaround, growth, and slow down stages of an organization. The longitudinal nature of some simulation games, of course, provides relevant “experience” for participants in a variety of forms. Indeed, it may be argued that it is that very experience that is the hallmark of intended learning in longitudinal games. Using Markstrat (Larr�ch� & Gatignon, 1977), Lant & Montgomery (1987) showed that the discrepancy between past aspiration and attainment levels enhances subsequent aspiration level, that past attainment discrepancy and past risk taking significantly explain future risk taking, and that past attainment discrepancy, proportion of unsuccessful R & D projects, and past innovativeness of search all positively affect current innovativeness of search. MANAGEMENT Managers whose roles within a company better fit their own interests and managers who have greater general business knowledge may be expected to perform better and to be more favorably evaluated by their peers. Etzion & Segev (1984) found these propositions to generally hold for participants in The Management Game (Graduate School of Business Administration, New York University, 1972). Rowland & Gardner (1973) used the Least-Preferred Coworker questionnaire to classify Marksim (Greenlaw & Kniffin, 1964) conglomerate- and firm-level “presidents” as relationship-oriented or task-oriented leaders. Generally, where both conglomerate- and firm-level presidents were relationship- oriented, team members’ perceptions of team atmosphere and of their immediate superior were more favorable. DECISION-MAKING The notion and application of automating “decision making” has appeared in various management/marketing decision support/information system guises for decades. Hogarth & Makridakis (1981) found simulation gaming, specifically Markstrat (Larr�ch� & Gatignon, 1977), to be a suitable research environment for quantifying the effectiveness of what they termed “arbitrary,” e.g., “Set level of advertising at 10% of estimated sales” (p. 97) decision rules vis-a-vis decisions made by humans. The mere accessibility of information may induce managers to focus on that information in their decision making, to some extent regardless of whether that information is most prescriptive for a larger plan; a “locally rational” but possibly ultimately suboptimal approach to decision making. Glazer, Steckel, & Winer (1992) manipulated the types of market research studies made available to participants in a Markstrat (Larr�ch� & Gatignon, 1977) simulation competition to demonstrate this phenomenon. A relative lack or overload of information on which to formulate decisions may lead to frustration (compared with a moderate amount of information) and lower satisfaction with the task. Biggs (1975) found these relationships to hold in an experiment using FINANSIM (Greenlaw & Frey, 1967). FORECASTING Simulation games, with their known or knowable and well- defined parameters and variables and their longitudinal natures, provide an ideal research platform for studying forecasting. 348 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 Glazer, Steckel & Winer (1990) took advantage of these properties of Markstrat (Larr�ch� & Gatignon, 1977) to compare the rational expectations model of forecasting– essentially the relating of sales to the variables affecting sales– and the adaptive expectations model of forecasting, that “...assumes that changes in forecasts over time are functions of past errors” (p. 152) An earlier study by the same authors (1989) using the same simulation game examined the Rational Expectations Hypothesis specifically. An interesting aspect of this study is the analysis of data at three pooling levels: all firms, firm-type, and firm. BASIC RESEARCH USING SIMULATION GAMING Achrol, Ravi S. & Gundlach, Gregory T. (1999). “Legal and Social Safeguards Against Opportunism in Exchange,” Journal of Retailing, Vol. 75, Spring, 107-124. Biggs, William D. (1975). “Some Impacts of Varying Amounts of Information on Frustration and Attitudes in a Finance Game,” in Buskirk, Richard H. (Ed.), Simulation Games and Experiential Learning in Action, Volume 2. Statesboro, GA: Association for Business Simulation and Experiential Learning, 103-110. MARKETING Cameron, Kim S. & Whetten, David A. (1981). “Perceptions of Organizational Effectiveness Over Organizational Life Cycles,” Administrative Science Quarterly, Vol. 26, December, 525-544. It is widely recognized, though usually only implicitly so, that the basic “percent change in quantity divided by percent change in price” may be too simplistic for use as an estimator of elasticity. Most available “quantity” data are subject to influence by a great variety of influences not recognized in the simple formula. Quantifying the extent of estimation error was virtually impossible, though, until Dickinson (2002) employed The Marketing Management Experience (2000) to provide an environment in which true elasticity is known. Cangelosi, Vincent E. & Dill, William R. (1965). “Organizational Learning: Observation Toward a Theory,” Administrative Science Quarterly, Vol. 10, September, 175- 203. Clark, Bruce H. & Montgomery, David B. (1999). “Managerial Identification of Competitors,” Journal of Marketing, Vol. 63, July, 67-83. When performance is successful, marketing decision makers are more likely to attribute that success to themselves (or their team) and are also more likely to perceive that performance as being more controlled by themselves (or their team) than when performance is less successful. Curren, Folkes & Steckel (1992) used Markstrat (Larr�ch� & Gatignon, 1977) to test these and related hypotheses. Clark, Bruce H. & Montgomery, David B. (1998). “Deterrence, Reputations and Competitive Cognition,” Management Science, Vol. 44, January, 62-82. Clark, Bruce H. & Montgomery, David B. (1996). “Perceiving Competitive Reactions: The Value of Accuracy (and Paranoia),” Marketing Letters, Vol. 7, March, 115-129. In many competitive business simulation games the “competition” is nominally defined; a company is competing against other similar companies in an industry set by the game administrator and they are competing for a common market of customers. Nonetheless, among the nominal competitors some may be perceived as more formidable than others. Using the Markstrat2 game (Larr�ch� & Gatignon, 1990), Clark & Montgomery investigated the accuracy of perceptions that competitors had reacted to a company’s past decisions (1996), the credibility of competitors as defenders and, thus, less susceptible as targets (1998), and factors such as size of marketing effort and success that might identify more prominent competitors as well as a possible asymmetry in these identifications (1999). Cosier, Richard A. & Rechner, Paula L. (1985). “Inquiry Method Effects on Performance in a Simulated Business Environment,” Organizational Behavior and Human Decision Process, Vol. 36, August, 79-95. Dickinson, John R. (2002). “A Need to Revamp Textbook Presentations of Price Elasticity,” Journal of Marketing Education, Vol. 24, August, 143-149. CONCLUSION This paper compiles a seminal, and recognizably nonexhaustive, inventory of basic research using simulation games and develops a tentative framework for organizing those researches. It is apparent that simulation games may be employed for investigating a wide variety of management related topics and that games may, indeed, provide not only a ready and useful research platform, but also a platform that may not otherwise be possible. Basic researchers may use this inventory as a consideration in designing their own studies. REFERENCES: 349 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 Gladstein, Deborah L. & Reilly, Nora P. (1985). “Group Decision Making Under Threat: The Tycoon Game,” Academy of Management Journal, Vol. 28, September, 613- 627. Glazer, Rashi, Steckel, Joel H. & Winer, Russell S. (1992). “Locally Rational Decision Making: The Distracting Effect of Information on Managerial Performance,” Management Science, Vol. 38, February, 212-226. Glazer, Rashi, Steckel, Joel H. & Winer, Russell S. (1990). “Judgmental Forecasts in a Competitive Environment: Rational vs. Adaptive Expectations,” International Journal of Forecasting, Vol. 6 , July, 149-162. Glazer, Rashi, Steckel, Joel H. & Winer, Russell S. (1989). “The Formation of Key Marketing Variable Expectations and Their Impact on Firm Performance: Some Experimental Findings,” Marketing Science, Vol. 8, Winter, 18-34. Glazer, Rashi, Steckel, Joel H. & Winer, Russell S. (1987). “Group Process and Decision Performance in a Simulated Marketing Environment,” Journal of Business Research, Vol. 15, December, 545-557 Gundlach, Gregory T. & Cadotte, Ernest R. (1994). “Exchange Interdependence and Interfirm Interaction: Research in a Simulated Channel Setting,” Journal of Marketing Research, Vol. XXXI, November, 516-532. Hogarth, Robin M. & Makridakis, Spyros (1981). “The Value of Decision Making in a Complex Environment: An Experimental Approach,” Management Science, Vol. 27, January, 93-107. Lant, Theresa K. & Montgomery, David B. (1987). “Learning from Strategic Success and Failure,” Journal of Business Research, Vol. 15, December, 503-517. Rowland, Kendrith M. & Gardner, David M. (1973). “The Uses of Business Gaming in Education and Laboratory Research,” Decision Sciences, Vol. 4, April, 268-283. Segev, Eli (1987). “Strategy, Strategy-Making, and Performance in a Business Game,” Strategic Management Journal, Vol. 8, November-December, 565-577. Slusher, E. Allen, Sims, Henry P., Jr. & Thiel, John (1978). “Bargaining Behavior in a Business Simulation Game,” Decision Sciences, Vol. 9, April, 310-321. Smith, Ken G., Mitchell, Terence R. & Summer, Charles E. (1985). “Top Level Management Priorities in Different Stages of the Organizational Life Cycle,” Academy of Management Journal, Vol. 28, December, 799-820. Urban, Thomas F. (1977). “Sex Differences in Bargaining Behavior,” in Nielson, Carl C. (Ed.), New Horizons in Simulation Games and Experiential Learning, Volume 4. Statesboro, GA: Association for Business Simulation and Experiential Learning, 231-236. GENERAL REFERENCES Bass, B. M. (1964). “Business Gaming for Organizational Research,” Management Science, Vol. 10, April, 545-556. Bettman, James R. (1975). “Issues in Designing Information Environments,” Journal of Consumer Research, Vol. 2, December, 169-177. Biggs, William D. (1979). “Who is Using Computerized Business Games?: A View from Publishers’ Adoption Lists,” in Certo, Samuel C. & Brenenstuhl, Daniel C. (Eds.), Insights into Experiential Pedagogy, Volume 6. Statesboro, GA: Association for Business Simulation and Experiential Learning, 202-206. Burgess, Thomas F. (1991). “The Use of Computerized Management and Business Simulation in the United Kingdom,” Simulation & Gaming, Vol. 22, June, 174-195. Chang, Jimmy (2003). “Use of Business Simulation Games in Hong Kong,” Simulation & Gaming: An International Journal, Vol. 34, September, 358-366. Cohen, Kalman J. & Cyert, Richard M. 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Statesboro, GA: Association for Business Simulation and Experiential Learning, 1-5. Greenblat, Cathy S. (1975). “Gaming-Simulation as a Tool for Social Research,” in Greenblat, Cathy S. & Duke, Richard D. (Eds.), Gaming-Simulation: Rationale, Design, and Applications. New York: John Wiley, 320-333. 350 Developments in Business Simulation and Experiential Learning, Volume 31, 2004 351 Herrmann, Cyril C. & Stewart, John B. (1957). “The Experimental Game,” Journal of Marketing, Vol. 22, July, 12-20. Hughes, G. David & Naert, Phillipe A. (1970). “A Computer- Controlled Experiment in Consumer Behavior,” Journal of Business, Vol. 43, July, 354-372. Inbar, Michael & Stoll, Clarice S. (1972). Simulation and Gaming in Social Science. New York: The Free Press. Jacoby, Jacob, Speller, Donald E. & Kohn, Carol A. (1974). “Brand Choice Behavior as a Function of Information Load,” Journal of Marketing Research, Vol. XI, February, 63-69. Journal of Business Research (1987). Volume 15, December. Klabbers, Jan H. G. (Ed.) (2001). “Symposium: State of the Art and Science of Simulation/Gaming,” Simulation & Gaming, Vol. 32, December, 469-551. Klimoski, Richard J. (1978). “Simulation Methodologies in Experimental Research on Negotiations by Representatives,” Journal of Conflict Resolution, Vol. 22, March, 61-77. Larr�ch�, Jean-Claude (1987). “On Simulations in Business Education and Research,” Journal of Business Research, Vol. 15, December, 559-571. McFarlane, Paul A. (1971). “Simulation Games as Social Psychological Research Sites,” Simulations & Games, Vol. 2, June, 149-161. Nees, Danielle B. (1983). “Simulation: A Complementary Method for Research on Strategic Decision-Making Processes,” Strategic Management Journal, Vol. 4, March- June, 175-185. Randel, Josephine M., Morris, Barbara A., Wetzel, C. Douglas & Whitehill, Betty V. (1992). “The Effectiveness of Games for Educational Purposes: A Review of Recent Research,” Simulation & Gaming, Vol. 23, September, 261-276. Saunders, Danny, Percival, Fred & Vartiainen, Matti (Eds.) (1996). The Simulation and Gaming Yearbook, Volume 4. London: Kogan Page Limited. Schlenker, Barry R. & Bonoma, Thomas V. (1978). “The Validity of Games for the Study of Conflict,” Journal of Conflict Resolution, Vol. 22, March, 7-38. Schwenk, Charles R. (1982). “Why Sacrifice Rigour for Relevance? A Proposal for Combining Laboratory and Field Research in Strategic Management,” Strategic Management Journal, Vol. 3, 213-225. Seidner, Constance J. & Dukes, Richard L. (1976). “Simulation in Social-Psychological Research: A Methodological Approach to the Study of Attitudes and Behavior,” Simulations & Games, Vol. 7, 3-20. Sewall, Murphy (1978). “Simulation and Experiential Learning as Human Subject Research,” in Brenenstuhl, Daniel G. & Certo, Samuel C. (Eds.), Exploring Experiential Learning: Simulation and Experiential Exercises, Volume 5. Statesboro, GA: Association for Business Simulation and Experiential Learning, 283-287. Shubik, Martin (1961). “Approaches to the Study of Decision- Making Relevant to the Firm,” Journal of Business, Vol. 34, April, 101-118. von Neumann, John & Morgenstern, Oskar (1944). Theory of Games and Economic Behavior, First Edition. Princeton, NJ: Princeton University Press. Wolfe, Joseph & Crookall, David (1998). “Developing a Scientific Knowledge of Simulation/Gaming,” Simulation & Gaming, Vol. 29, March, 7-19. SIMULATION GAMES Amos Tuck School of Business Administration (1979). Tycoon. Cadotte, Ernest R. (1990). Market Place. Homewood, IL: Richard D. Irwin. Cohen, Kalman J., Dill, William R., Kuehn, Alfred A. & Winters, Peter R. (1964). The Carnegie Tech Management Game: An Experiment in Business Education. Homewood, IL: Richard D. Irwin. Dickinson, John R. (2000). The Marketing Management Experience. Windsor, Ontario: Management Experiences. Graduate School of Business Administration, New York University (1972). The Management Game: Player’s Manual. New York: Author. Greenlaw, Paul S. & Frey, M. W. (1967). FINANSIM: A Financial Management Simulation. Scranton, PA: International Textbook. Klatt, L. A. & Urban, Thomas F. (1975). KUBSIM: A Collective Bargaining Simulation. Columbus, Ohio: Grid. Larreche, Jean-Claude & Gatignon, Hubert (1990). Markstrat2. Palo Alto or Redwood, CA: The Scientific Press. Larreche, Jean-Claude & Gatignon, Hubert (1977). Markstrat. Palo Alto or Redwood, CA: The Scientific Press. Miles, Robert H. & Randolph, Alan (1979). The Organization Game. Santa Monica, CA: Goodyear. Nichols, A. C. & Schott, B. (1975; 1972). SIMQ: A Business Simulation Game for Decision Science Students. Dubuque, IA: Kendall-Hunt Table of Contents Volume 31, 2004 Controlling the Complexity and Orenting Target Groups by a Modular, Server-Based Business Game System Learning Network Demonstration: Delivering Business Education in a Distance Learning Environment Economic Evolution, Human Capital Investment, and Adult Distributed Electronic Learning: A Literature Review Designing a Globalization Simulation to Teach Corporate Social Responsibility Developing and Teaching an Online / In-Class Hybrid: A Demonstration A Model for Evaluating Online Instruction An Evaluation of a Distributed Learning Course: A Students'-Eye Perspective Blended Learning Strategy Improved Business Writing Skills How to Receive and Process Attachemnts while Greatly Reducing the Risk of Viruses and Trojans Introducing Online Components to a Class: How to Increase teh Likelihood of Success Teaching Strategic Communications Online: Using Learning Outcomes to Develop a Case-Based Course Implementing Distance Approaches to Education: A Panel Discussion for ABSEL: Las Vegas, 2004 MANDI: Learning Management Through Field Sales Experience An International Capital budgeting Experiential Exercise A Primer To Combating Terrorism: Playing It Safe While On Overseas Assignment (An Experiential Exercise) Integrating The Business Curriculum With A Comprehensive Case Study: A Prototype The Case Brief: A Model For Case Analysis, Writing And Discussion Technology Infused Pedagogy And Delivery – A Sure Bet? A Proposal For Panel Discussion Absel Conference 2004 Research Strategy And The Bkl: Getting The Most From The Absel Archives Simple But Effective: Rediscovering The Class Discussion Needle And Thread: An Activity For Examining Various Management Behaviors A Customized Excel Data Analysis System For Use In Undergraduate Marketing Research Team Leader Selection - Does It Matter? The Power Of Perspective: Reframing Your Framing Skills For Innovative Instruction In Leadership And Influence Exercise: How Should Merit Raises Be Allocated? An Online Situation For Problem-Based Learning In A Junior-Level Management Course The Eden Alternative As A Roadway For Change: A Service Learning Quality Improvement Project Avoiding Catastrophe: The Role Of Individual Accountability In Team Effectiveness Omega Systems: A Change Management Exercise The Risks And Rewards Of Providing Students A Structured Cheating Opportunity Experimentation With Assessment Techniques: A Proposal For Panel Discussion Using A 2 - Page Case To Introduce Concepts Of Business Strategy Interactive Session The Integration Of Appreciative Inquiry And Experiential Learning For Peak Performance Appreciative Inquiry Case Story: New York City Leadership Challenge Individual Achievement Versus Team Performance: An Empirical Study With Business Games Some Strategists Don't Learn Or Can't Learn Computer Simulation: A Design Architectonic On The Value Of Bugs In Simulation Environments Online Sales Forecasting With The Multiple Regression Analysis Data Matrices Package Simulation Exercises And Problem Based Learning: Is There A Fit? A Study Of Business Game Stock Price Algorithms Assessing Individual Performance In A Total Enterprise Simulation Information Use In A Business Game Determining The Value Of A Firm Unsorting Algorithms For An Ordered List And Its Application To Business Simulations Teaching Public Finance Management Through Simulation Antecedents Of Game Performance Student Expectations Of Classroom Teaching Practices In Developing And Presenting Course Information In Hong Kong Implementation And Impacts Of The Balanced Scorecard: An Experiment With Business Games Impact: Shocking The Legacy Mindset Implementation Of The Eepad Framework Of Business Processes In An Accounting Information Systems Course Are Business Games Really Delivering What Students Are Led To Believe?? Reporting Lessons Learned: What Gets Reported; Who Gains Value Teacher Expectations Of Classroom Teaching Practices In Developing And Presenting Course Information In Hong Kong Student Reactions To The Use Of A Computer-Based Simulation As An Integrating Mechanism For A Mba Curriculum A Cognitive Investigation Of The Internal Validity Of A Management Strategy Simulation Game The Casino Challenge: Making Simulation Delivery A Safe Bet! Accounting For Company Reputation: Variations On The Gold Standard Foreign Currency Hedging: A Simulation The Influence Of Variables Easily Controlled By The Instructor/Administrator On Simulation Outcomes: In Particular, The Variable, Reflection. Absel Awareness Among Business School Faculty Validating Business Simulations: Does High Market Share Lead To High Profitability? Simulation Debriefing Procedures Coaching And Business Simulations: A Formula For Success? A Seminal Inventory Of Basic Research Using Business Simulation Games The Influence Of Scorecard Evaluation On Decisions And Outcomes