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Abstract

This article describes the results of an experiment that investigated the link between the ability of business simulation team participants to forecast financial and/or market-related outcomes, and links this forecasting ability to the actual results of their decision making. This experiment was repeated six times over the course of an academic year. Each replication involved 12 consecutive decision periods. The experiment required each member of the student teams to forecast either the expected market share and sales of the product for which they were making decisions or to forecast the cash flows and profits of the simulated firm. The managerial position that the participant held during the simulation determined the type of forecast (market share, cash flow, etc.) he or she was assigned. The experiment showed a very strong link between the ability of the management team to forecast outcomes and their firm's performance, as measured by profitability.

Keywords

business simulations decision making forecasting measuring performance performance evaluation strategic management

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References

Anderson, P. , & Lawton, L. (1988). Assessing student performance on a business simulation exercise. In P. Sanders & T. Pray (Eds.), Developments in business simulation and experiential exercises (Vol. 15, pp. 241-245). Stillwater: Oklahoma State University Press.

Chiesl, N. (1987). The use of simple forecasting techniques during an interactive computerized business game. In L. Kelly & P. Sanders (Eds.), Developments in business simulation and experiential exercises (Vol. 14, pp. 39-42). Stillwater: Oklahoma State University Press.

Gold, S. C. (1992). Modeling short-run cost and production functions in computerized business simulations. Simulation & Gaming, 23(4), 417-430.

Gosenpud, J. (1987). Research on predicting performance in the simulation. In L. Kelly & P. Sanders (Eds.), Developments in business simulation and experiential exercises (Vol. 14, pp. 75-79). Stillwater: Oklahoma State University Press.

Gosenpud, J. , & Miessing, P. (1983). Determinants of performance in the simulation. In L. Graf & D. M. Cure (Eds.), Developments in business simulation and experiential exercises (Vol. 10, pp. 53-65). Stillwater: Oklahoma State University Press.

Gosenpud, J. , Milton, C. , & Larson, A. (1985). Predicting performance over the course of the simulation. In J. Gentry & A. Burns (Eds.), Developments in business simulation and experiential exercises (Vol. 12, pp. 5-10). Stillwater: Oklahoma State University Press.

Hand, H. H. , & Sims, H. P., Jr. (1975). Statistical evaluation of complex gaming performance. Management Science, 21(6), 708-717.

Hornaday, R. , & Curran, K. (1987). Decision styles and student simulation performance. In L. Kelly & P. Sanders (Eds.), Developments in business simulation and experiential exercises (Vol. 14, pp. 95-99). Stillwater: Oklahoma State University Press.

Hornaday, R. , & Wheatley, W. (1986). Four factors affecting group performance in business policy simulations. In A. Burns & L. Kelly (Eds.), Developments in business simulation and experiential exercises (Vol. 13, pp. 17-21). Stillwater: Oklahoma State University Press.

10.

House, W. , & Napier, H. (1988). A comparative study of strategic performance factors in actual and simulated business environments. In P. Sanders & T. Pray (Eds.), Developments in business simulation and experiential exercises (Vol. 15, pp. 50-55). Stillwater: Oklahoma State University Press.

11.

MARKETING IN ACTION. (1986). Ness, T. , & Day, R. (5th ed., microcomputer version). Homewood, IL: Irwin.

12.

Picket, G. , & Stell, R. (1987). Teaching forecasting to the masses: Little decision support training, but a lot of answers. In L.'Kelly & P. Sanders (Eds.), Developments in business simulation and experiential exercises (Vol. 14, pp. 165-188). Stillwater: Oklahoma State University Press.

13.

Pray, T. , & Gold, S. (1991). Goal setting and performance evaluating with different starting positions: The modeling dilemma. Simulation & Gaming, 22(4), 476-489.

14.

Teach, R. D. (1990). Profits: The false profit. Simulation & Gaming, 21(1), 12-26.

15.

Teach, R. D. (1993). Forecasting and management ability. Simulation & Gaming, 24(1), 63-72.

16.

Wolfe, J. (1993a). Forecasting and management ability: Rebuttal to Teach's response. Simulation & Gaming, 24(1), 73-75.

17.

Wolfe, J. (1993b). On the propriety of forecasting accuracy as a measure of team management ability: A preliminary investigation. Simulation & Gaming, 24(1), 47-62.

18.

Wolfe, J. , & Box, T. (1988). Team cohesion effects on business game performance. Simulation & Gaming, 19(1), 82-98.

19.

Wolfe, J. , & Chacko, T. L. (1983). Team-size effects on business game performance and decision making behaviors. Decision Science, 14, 121-133.

Forecasting Accuracy as a Performance Measure in Business Simulations

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