THE DESIGN OF A BUSINESS SIMULATION USING A SYSTEM-DYNAMICS-BASED APPROACH Developments in Business Simulation and Experiential Learning, Volume 30, 2003 THE DESIGN OF A BUSINESS SIMULATION USING A SYSTEM-DYNAMICS-BASED APPROACH Steven Gold Rochester Institute of Technology scgbbu@rit.edu ABSTRACT Research on business simulation design has been mostly on a sub-system level. Yet, the business environment is complex by nature, characterized by interconnected organizational elements with nonlinear feedback loops, and requires a systems approach to be modeled effectively. In this paper a system-dynamics based interactive model of a business enterprise simulation is developed, consisting of 18 equations. The model draws heavily upon the economic theory of the firm and the expansive body of prior research on the design of business simulations. The focus is on the linkages between the production, cost, revenues, profits and stock market value of the firm. A working model of the recommended system is tested and its empirical properties discussed. INTRODUCTION AND PURPOSE The design of business simulations is of high interest to the members of the Association of Business Simulations and Experiential Learning. Evidence of this interest is supported by a recent study by Peach and Platt (2002) titled “The ABSEL Research Heritage…”. In this study the authors review the ten most frequently cited ABSEL papers over the past 28 years, calling them “classics”, and conclude: “the top papers seem to fall into two groups: those dealing with the construction of simulations and those dealing with learning and simulations.” (p.261). This is not surprising as both users and developers of business simulations benefit from this type of research. Primary interest in the design and construction of business simulations may be traced to the path breaking article by Kenneth Goosen (1981), in which he stated: “The designing and developing of simulations at this time appears to be primarily an art form, a creative skill based on intuitive feel rather than acquired knowledge. There is a pressing need to construct a science of simulation design and development.” (p.41). A study by Gold and Pray (2001) found that since the Goosen (1981) article, about 50 studies were published on the algorithms used to model business simulations. Between 1982 and 1988, the algorithms centered on demand, marketing, and finance issues. In 1989 the focus shifted to the supply side, owing to the important work of Thavikulwat (1989), in which he pointed out that “The problem of modeling supply…has tended to be neglected. Yet, the supply side of modeling presents issues that are at least as involved as those of the demand side.” (p.37). The studies on the supply side focused on operations, quality, internal organization and human components of business simulation design. It was concluded by Gold and Pray (2001) that the open exchange, in which designers and users have shared their works, have resulted in improvements in newer simulations, such as: The Global Business Game (2000), and the Threshold Competitor (1999a & 1999b). Still the design and modeling of business simulations needs to be an ongoing effort. Goosen, Jensen, and Wells (1999) point out a perplexing, and probably chronic, design problem, i.e. there are conflicting theories and alternative procedures that may be used to model business behavior in the areas of: accounting, economics, finance, marketing, and operations. Some examples of these conflicts were identified by: Garrison & Noreen (1997) in accounting and the treatment of fixed production costs; Goosen (1994) in finance and the valuation of stock market returns; and Cannon, McGolwan & Sung-Joon Yoon (1994) in marketing and the impact of advertising. Goosen, Jensen, and Wells (1999) outlined the current conflicts in these major functional areas of business with respect to the modeling of computerized business simulations. Certainly, as new business theories develop and evolve, it will be necessary to design and re-design business simulations. SYSTEM-DYAMICS BASED MODELING: THE NEXT FRONTIER The design problems within the functional areas of business are exacerbated by the need for a systems- dynamics (SD) approach to modeling business simulations. The focus of business simulation design in the literature has been mostly on a sub-system level. Algorithms have been developed independently within the functional areas of marketing, accounting, finance, and operations without much attention to the system dynamics. However, Machuca (2000) argues, “A systematic and holistic perception of the firm is basic for an understanding of its behavior.” (p.231) It is pointed out that the business environment is characterized by interconnected organizational elements. Actions taken in one area may impact the performance in other areas. Sometimes the relationships have strong feedback loops and may be nonlinear in nature. There may mailto:scgbbu@rit.edu Developments in Business Simulation and Experiential Learning, Volume 30, 2003 be delays and inertia in the production, sales, and distribution of products. There may be interaction with the external environment that may alter the internal operations of the firm. The message from the systems dynamics literature is clear. The business environment is complex by nature and requires a systems approach if it is to be modeled adequately. Machuca (2000) argues that real progress in management education will occur only when the use of SD (system-dynamics) becomes widespread. But the task is not simple. Davidsen (2000) states, “…the most fundamental challenge of all is that the field of system dynamics has yet to develop a consistent mathematical theory linking the behavior modes exhibited by a nonlinear feedback system to the underlying system structure.” The objective of this paper is to begin to develop a system-dynamics based interactive model for a business enterprise simulation. The model will draw heavily upon the economic theory of the firm and the expansive body of research on the design of business simulations. The focus will be on the linkages between the production, cost, revenues, and profits of the firm. Generally accepted economic principles on the relationships between these factors will be outlined. A working model of the recommended system will be tested as an illustrative example and its empirical properties discussed. The working model will draw upon algorithms developed in prior studies. THE BUSINESS SYSTEM-DYNAMICS MODEL A system-dynamic model is developed by drawing upon the fundamental market and firm relationships supported by economic theory. The market system is the starting point as shown in Figure 1. (C MARKET DEMA FIGURE 1: Market System Model The initial market structure establi nature of the market and the interdependence. Typical characteristic firms in the market, concentration ratio differentiation, and market segmenta demand depends on the total number number of customers is affected b population, demographics, income, a substitute and complement goods or ser is influenced is by several key factors advertising, product quality and servic firm are based on demand, and add to p the stock market value of the firm. Firm De Reve Related Markets: Substitute & Complements MARKET STRUCTURE ompetitive vs. Concentrated) ND MARKET SUPPLY shes the degree s includ s, degre tion. of cus y facto nd the vices. , like: r e. Rev rofits. mand Firm Supply nues Costs s Resource Markets: Labor/Capital Firm Profit e Stock Market Pric competitive of mutual e: number of e of product The market tomers. The rs such as: markets for Firm demand elative price, enues of the Profits affect Costs take away profits and are based on the firm’s supply of goods and services. The resource market determines the availability and costs of labor and capital. The ability to attract and retain workers is influenced by the firm’s HR policies with respect to wages and benefits, including training and management development. Production costs are affected by the productivity of the firm. Productivity is affected by the HR decisions of the firm as well as research in new technologies and capital investment in plant and equipment. Demand and supply on both the firm and market levels are mutually interdependent. Firm demand and supply will affect the competitive nature of the market. Relative Developments in Business Simulation and Experiential Learning, Volume 30, 2003 Where the variable definitions are: market shares and concentration ratios will change. In the long-run, the entire market is affected which feeds back to the firm’s performance and strategies. wij = weight used to calculate market share of firm i in segment j. The business market is a highly interactive and complex systems-dynamics model and is clearly a challenge to simulate. The methodology used in this paper is to break down the system into its major components and provide the linkages between the components. The major components include: market demand, firm demand, costs, production, profits, and stock market value. The algorithms for each component will draw upon models developed in previous studies. Σ wij = summation of weights (w) of all firms i in segment j. pij = price of firm i product in segment j (harmonic mean used) mij = marketing expenditures in of firm i in segment j dij = difference (d) between the actual product attributes from the ideal product attributes of firm i based in segment j MARKET DEMAND ALGORITHM An explanation of how to solve for the parameters of the market and firm demand equations (g1..g7; k1…k7) are given in Gold and Pray (1984). The recommended demand algorithm combines the product attribute gravity flow model developed by Teach (1990) and later modified to fit the multiplicative demand function by Gold and Pray (1999). The equation set is listed below and briefly summarized. COST AND PRODUCTION ALGORITHMS A cost and production function, consistent with the economic principles of duality theory was derived using Sheppard’s lemma by Gold (1992) and is used in this model. The cost function possesses the desired properties of increasing and diminishing returns to the factors of production, economies and diseconomies of scale, and maintains a consistent relationship between production and costs. Non-separability is achieved between the variable and fixed factors, making variable costs dependent on the level of the fixed input. The cost function possesses the important duality property that maximum average (or marginal) production efficiency for each input corresponds to the point of minimum average (or marginal) cost. The market is divided into segments (j). For illustrative purposes, two demand factors (price and marketing expenditures) are included. Also, the difference (D) or gap between the attributes of the products offered for sale and the ideal product attributes desired by consumers is included as a demand factor. For a complete explanation of the product attribute variable see Teach (1990). The total market demand for each segment is: Qj = g1Pj -(g 2 +g 3 Pj) Mj +(g 4 +g 5 Mj) Dj -(g 6 +g 7 Dj) (1) Where the variable definitions are: The equation set for the cost function developed by Gold (1992) is listed below and briefly summarized. For illustrative purposes, two input variables (labor and materials) and one fixed input (capital) are used to specify the cost function. Qj = market demand for the segment j. Pj = average price of all products in segment j (harmonic mean used) Mj = average marketing expenditures in segment j Dj = average difference (d) or gap between the actual product attributes from the ideal product attributes based on customer preferences in segment j. TVCij= a1Pl a2 Pm a3 Qij (a4+a5Q-a6K) Kij a7 (5) TVCij = PlLij + PmMij (6) FIRM DEMAND ALGORITHM Where the variable definitions are: The demand of firm i in market segment j (qij) is based on its market share and the size of the total market segment (Qj). TVCij = total variable costs of firm i in segment j Pl = price of labor Pm = price of materials Qij = quantity produced of firm i. qij = Sij Qj (2) Kij = capital equipment and facilities of firm i. ai = parameters of system The market share of each firm i in segment j (Sij) depends on its relative price, relative marketing expenditures, and products attributes compared to the competition. Lij = Labor of firm i for segment j product Mij = Materials of firm i for segment j product Equation 5 shows total variable costs to be a non-linear multiplicative function of the level of output, the level of the fixed input, and the prices of the factor inputs. Equation 6 Sij = wij / Σ wij (3) wij = k1pjj -(k 2 +k 3 p j ) mij +(k 4 +k 5 Mj) dij -(k 6 +k 7 Dj) (4) Developments in Business Simulation and Experiential Learning, Volume 30, 2003 follows by definition. Total variable cost is the sum of the price of each input times the quantity of each input used. NIAT = (NOI – IE)(1.0 – T) (13) A homogeneity restriction of degree one must be imposed with respect to equation 5. NIPS = NIAT / NS (14) The future value of the net income per share (NIPSF) depends on the expected growth rate in net income (GR) and the number of future periods (FP). The expected growth rate is based on the historical growth in net income of the firm. a2 + a3 = 1.0 (7) The capital input, K, is fixed in the short-run. Fixed costs are: TFCij = PkKij (8) NIPSF = NIPS(1+GR)FP (15) Total costs of firm i in segment j (TCij) are the sum of fixed and variable costs. The market value per share (MVPS) is based on the current net income per share plus the discounted value of the incremental net income per share (DINIPS). TVCij = TVCij + TFCij (9) INIPS = NIPSF – NIPS (16) DINIPS = INIPS / (1.0 + ECC)FP (17) This guarantees that if factor prices (labor & materials) increase by, say 10%, then total variable costs (TVC) will increase by 10%. This forces a consistent relationship between equations 5 and 6. MVPS = (NIPS + DINIPS) / ECC (18) It is suggested that the NIPS used in the Goosen, Foote & Terry (1994) model be an exponentially smoothed average. This is needed to stabilize the system, since a one time significant drop in NIPS would have an unrealistic effect on the MVPS. It would also minimize large fluctuation in the MVPS. The levels of labor (L) and material (M) inputs can be derived from the cost function, as demonstrated by Gold (1992). Lij = a2TVC / Pl (10) Mij = a3TVC / Pm (11) Variable definitions in the finance algorithm equations are: PROFIT AND STOCK MARKET VALUE ALGORITHM NOI = Net Operating Income IE = Interest Expense T = Tax rate Most business enterprise simulations measure student performance (success) by the increase in profits and stock market value. Surprisingly very limited research on the modeling of stock market valuation has been done with respect to business simulations. The one major work in this area is by Goosen, Foote & Terry (1994), who state: “How simulation designers model the complex cost of capital issues is a well kept secret.” (p.63). NS = Number of shares of stock NIAT = Net Income After Tax NIPS = Net Income Per Share after tax ( exponentially smoothed average) NIPSF = Future value of increase in net income per share GR = expected Growth Rate (based on historical growth in net income) FP = number of Future Periods The authors present a detailed financial valuation model based on modern cost of capital and capital structure principles. The major equations used to determine stock market value are presented below and used in this study. For a more detailed description and illustration of the functioning of this sub-system see the paper by Goosen, Foote & Terry (1994). INIPS = Incremental Net Income Per Share ECC = Cost of Equity Capital DINIPS = Discounted Incremental Net Income Per Share MVPS = Market Value Per Share It is pointed out by Goosen, Foote, & Terry (1994) that the dividend payout rate is not included. The authors argue that this is consistent with the branch of theory that dividend payout has no effect on the cost of capital and therefore the market value per share of stock. Profit is the starting point. Profit before interest expense is referred to as net operating income (NOI) and is the difference between total revenues and total variable costs (TVC). TESTING THE SYSTEM NOI = Total Revenue – TVC (12) Numerous scenarios could be used to test the dynamic behavior of the proposed system of equations. But only one scenario is simulated for sake of brevity. The objective in this study is to highlight and demonstrate: (1) the Subtracting interest expense (IE) and adjusting for tax rates we get net income after tax (NIAT). Dividing by the number of shares (NS) gives the net income per share after tax (NIPS). Developments in Business Simulation and Experiential Learning, Volume 30, 2003 functioning of the components of the system (i.e. demand, cost, production, profits and stock market value), (2) the interaction between the components of the system and (3) the system-based dynamics. The model developed to test the system is an oligopoly market composed of 6 firms. The parameters of the system were designed to be consistent with the characteristics of the standard oligopoly market as described by economic theory. The selected scenario is that one of the firms in the market lowers price and increases production to meet demand, holding other decisions constant (i.e. the prices of the other rival firms, marketing expenditures, product attributes, capital investment, etc.). The results of this scenario on the simulated system are show in Table 1. Table 1: Impact of a Firm Lowering Price and Increasing Production Price ($/unit) Sales Units (thousands) Market Share (%) Total Revenue (thou.$) Total Costs (thou.$) Net Income After Tax (thou.$) Stock Price ($/Share) $12.25 171 8.22 $2,098 $1,760 $203 $24.49 $12.00 185 8.83 $2,220 $1,871 $209 $25.04 $11.75 200 9.50 $2,350 $1,993 $215 $25.49 $11.50 216 10.21 $2,488 $2,124 $218 $25.81 $11.25 234 10.97 $2,634 $2,267 $220 $25.96 $11.00 253 11.79 $2,788 $2,422 $219 $25.91 $10.75 273 12.63 $2,937 $2,581 $214 $25.43 $10.50 294 13.47 $3,083 $2,744 $204 $24.56 $10.25 316 14.37 $3,236 $2,920 $190 $23.44 $10.00 339 15.32 $3,394 $3,115 $168 $21.80 $9.75 365 16.33 $3,558 $3,395 $98 $17.55 $9.50 392 17.39 $3,728 $3,901 -$104 $10.29 $9.25 422 18.70 $3,903 $4,675 -$463 $4.23 $160,000 $170,000 $180,000 $190,000 $200,000 $210,000 $220,000 $230,000 150 200 250 300 350 Thousands Quantity Sold Net Income FIGURE 2: Impact of Sales on Profits (Net Income) DISCUSSION OF RESULTS It can be seen that as price is lowered by only one firm in the market, there is a continuous increase in sales, market share, and total revenues. However, net income after tax (NIAT) and stock price increase up to a point (at a sales level of 273 units) and then decline. The relationship between NIAT and sales is shown in Figure 2. The interesting question is why net income (NIAT) declines after a point. To understand the behavior of the system, we look at each of the components, starting with the demand function. Developments in Business Simulation and Experiential Learning, Volume 30, 2003 DEMAND FUNCTION As price is lowered, the firm’s weight in equation 4 is increased. This will increase market share in equation 3 and firm demand in equation 2. Firm demand is market share multiplied by industry demand. The factors affecting industry demand are held constant except for the average industry price, which declines slightly, causing industry demand to increase (equation 1). The extend of the increase in demand, and firm revenues, is based on the price elasticity, which depends on the selected parameter values (gi and ki). Figure 3 shows the demand function with the corresponding marginal revenue schedule. The marginal revenues decline as price is lowered and quantity demanded increases. This means that although total revenues increase with sales, the incremental (marginal) gains in revenues to the firm are lowered each time price is lowered to increase sales. In part, this begins to explain the reason firm profit declines after a point. But the cost structure of the firm must be examined as well. $4.00 $5.00 $6.00 $7.00 $8.00 $9.00 $10.00 $11.00 $12.00 $13.00 150 250 350 450 Quantity Demanded (thousands) Demand Marginal Revenue FIGURE 3: Demand and Marginal Revenues TABLE 2: Average and Marginal Costs Production (thousands) Total Costs (thou.$) ATC MC 171 $1,760 9.33 xxx 185 $1,871 9.23 8.10 200 $1,993 9.15 8.08 216 $2,124 9.07 8.06 234 $2,267 8.99 8.04 253 $2,422 8.92 8.03 273 $2,581 8.85 8.01 294 $2,744 8.79 7.99 316 $2,920 8.73 7.97 339 $3,115 8.70 8.22 365 $3,395 8.86 10.98 392 $3,901 9.52 18.39 422 $4,675 10.69 26.17 Developments in Business Simulation and Experiential Learning, Volume 30, 2003 $7.50 $8.00 $8.50 $9.00 $9.50 $10.00 $10.50 $11.00 100 200 300 400 500 Units Produced (thousands) $/ un it ATC MC FIGURE 4: Average and Marginal Costs COST FUNCTION Table 1 shows that total costs increase with production, but the relationship is not linear. From equation 6 we can see that as production (Q) rises, a point will be reach where total variable costs (TVC) increase at an increasing rate. To understand the impact of production on costs, it is useful to derive the average and marginal costs. Table 2 shows that average total costs (ATC) initially declines, but after a point begin to increase, owing to the rise in marginal costs. This is shown graphically in Figure 4. The rise in total costs is another contributing factor to the eventual decline in profits. The rise in average and marginal costs can be explained by the production function of the firm. PRODUCTION FUNCTION The cost function is based on the production relationship of the firm. Equations 10 and 11 show the required levels of inputs (labor and materials) needed to produce a given level of output. The level of output determines the level of variable costs (TVC), based on equation 5. Table 3 shows the required level of labor, derived from equation 10, for each production level. From the labor hours, the average product of labor (APL) and the marginal product of labor (MPL) are calculated. Average product of labor initially rises, but after a point declines. This is shown graphically in Figure 5. The decline in the productivity of TABLE 3: Average and Marginal Productivity of Labor Production (thousands) Labor hours (thousands) APL MPL 171 183 0.93 xxx 185 196 0.94 1.08 200 210 0.95 1.09 216 225 0.96 1.10 234 241 0.97 1.11 253 258 0.98 1.12 273 275 0.99 1.14 294 293 1.00 1.15 316 312 1.01 1.16 339 333 1.02 1.12 365 366 1.00 0.77 392 433 0.91 0.41 422 568 0.74 0.22 Developments in Business Simulation and Experiential Learning, Volume 30, 2003 0.60 0.70 0.80 0.90 1.00 1.10 1.20 150 250 350 450 Labor Hours (thousands) O ut pu t p er h ou r Average Product Marginal Product FIGURE 5: Labor Productivity labor accounts for the rise in average and marginal costs, and is consistent with the duality theory in economics. This further explains the decline in profits as production increases to meet demand. PROFITS AND STOCK MARKET VALUE The change in the stock market value of the firm is directly related to the profits of the firm or net income after taxes (NIAT). As profits decline, the expected growth rate (GR) and expected future net income per share after tax (NIPSF) declines (see equation 15). CONCLUSIONS The design of computerized business simulations has focused on the development of its subsystems, such as: demand, marketing, operations, accounting and finance. However, it is clear from the system-dynamics literature that a more holistic approach is needed. The business environment is a highly interactive system. Decisions in one area, like marketing and sales, will affect the other functional areas of the business. Complex feedback loops exist and may give rise to unexpected results that are difficult to understand and interpret. Users of business simulations, both students and faculty, are often perplexed concerning the outcomes associated with particular simulations. For these reasons it is important, from a management education perspective, to model and view the business system as a whole. In this study, a system-dynamics based interactive model of a business enterprise simulation was developed and tested. The model utilized several algorithms of subsystems (demand, operations, costs, etc.) that were developed in prior studies. The purpose was to show the linkage and interactions between the subsystems previously developed. A relatively simple example was simulated to illustrate and explain the behavior of the system. In the example, a change in price and production created a non-linear profit function that initially increased, reached a maximum, and then declined. Profits declined despite the fact that sales and revenues continued to increase. The system results were explained by examining the interactive effects between the demand, revenue, production and cost functions of the firm. After a point, it was shown that productivity declined, causing unit costs to increase more than revenues, and bring about the decline in profits. The example shows that a holistic perception of the firm is needed to truly understand and explain the outcome of the business system. This supports the conclusion by Machuca (2000) that “management education will make real progress both in effectiveness and scope only when the use of SD (system- dynamics) becomes widespread.” (p.233) To continue to advance the state of business simulation design and use, and management education, it is critical to open the “black box” and debate the equations and algorithms used to model the business system. Clearly, more research emphasis needs to be placed on the system level rather than the subsystem, but both need to be addressed in a coordinated effort. Goosen, Jensen, and Wells (1999) point out that in the core business disciplines, conflicting theories exist that may require the simulation designer to make difficult choices concerning how to model a particular subsystem. The algorithms selected in this study to model the business system should be viewed as part of an ongoing effort to further develop the state of business simulation design. Further scrutiny of the system in this paper is encouraged, along with the development of more advanced business systems. Developments in Business Simulation and Experiential Learning, Volume 30, 2003 REFERENCES Anderson, P., Scott, T., Beveridge, D., & Hofmeister, D. (1999a). Threshold Competitor: Solo Version. Englewood Cliffs, NJ: Prentice-Hall. Anderson, P., Beveridge, D., Scott, T., & Hofmeister, D. (1999b). Threshold Competitor: Team and Solo Versions. Englewood Cliffs, NJ: Prentice-Hall. Cannon, Hugh M, McGowan, Laura & Yoon, Sung-Joon (1994). “Incorporating advertising strategy into computer-based business simulations: a validation study.” Developments in Business Simulation and Experiential Learning, 21. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org ]. Garrison, Ray H. & Noreen, Eric W. (1997). Managerial Accounting, Richard D. Irwin. Wolfe, J. (2000). The Global Business Game: A strategic management and international simulation. Cincinnati, OH: South-Western. Gold, S. and Pray, T. (1984) "Modeling Market and Firm Level Demand Functions in Computerized Business Simulations." Simulation & Games: An International Journal of Theory, Design, and Research, Vol. 15, No. 3, 346 - 363. Gold, Steven C.(1992) "Modeling Short-run Cost and Production Functions in Computerized Business Simulations." Simulation & Games: An International Journal of Theory, Design, and Research, Vol. 23, No. 4, 417-430. Gold, S.C. and Pray, T.F. (1999) “Changing Customer Preferences and Product Characteristics in the Design of Demand Functions.” Simulation & Gaming: An International Journal of Theory, Design, and Research, Vol.30, No.3, 264-282. Gold, Steven C., & Pray, Thomas, F. (2001) “Historical Review of Algorithm Development for Computerized Business Simulations.” Simulation and Gaming: An Interdisciplinary Journal of Theory, Practice, and Research, 32(1), 66-83. Goosen, Kenneth R. (1981) “A Generalized Algorithm For Designing and Developing Business Simulation.” Developments in Business Simulation and Experiential Learning, 8, 41-47. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org]. Goosen, Kenneth R. (1994) “Increasing the effectiveness of performance evaluation through the design and development of realistic finance algorithms. “ Developments in Business Simulation and Experiential Learning, 21, 63-69. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org]. Goosen, Kenneth R; Jensen, Ron, & Wells, Robert (1999) “Purpose and Learning Benefits of Business Simulations: A Design and Development Perspective.” Developments in Business Simulation and Experiential Learning, 26, 133-145. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org ]. Machuca, Jose A. D. (2000) “Transparent-box business simulators: An aid to manage the complexity of organizations.” Simulation and Gaming: An Interdisciplinary Journal of Theory, Practice, and Research, 31(2), 230-239. Platt, R.G., & E.B. Peach (2002) “The ABSEL Research heritage and the BKL: Leveraging Their Value for Future Research.” Developments in Business Simulation and Experiential Learning, 29, 260-264. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org ]. Teach R. D. (1990) “Demand Equations which include Product Attributes.” Developments in Business Simulation and Experiential Learning, 17, 161-166. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org]. Thavikulwat, P. (1989) “Modeling the human component of computerized business simulations.” Developments in Business Simulation and Experiential Learning, 16, 37- 40. Reprinted in The Bernie Keys Library, 2nd Ed., Hugh M. Cannon (ed). [Available from http://www.ABSEL.org ]. http://www.absel.org/ http://www.absel.org/ http://www.absel.org/ http://www.absel.org/ http://www.absel.org/ http://www.absel.org/ http://www.absel.org/ Table of Contents Volume 30, 2003 The Optimal Timing For Introducing Business Simulations Can Handicapped Students Access Your Class Web Site? The Competition Game: Decision Making In A Dynamic Environment Pan-Pacific Enterprises: Strategic Decision Making Simulation Study Of Stochastic Channel Redistribution The Impact Of Business War Games: Quantifying Training Effectiveness Experiential Learning: Introducing Faculty And Staff To A University Leadership Development Program The Feasibility Of The Balanced Scorecard For Business Games A Misuse Of Pims For The Validation Of Marketing Management Simulation Games Incorporating Technology Into The 21st Century Classroom: Are We Facilitating Academic Dishonesty? Improving The Effectiveness Of Peer Evaluations The Use Of A Simulation In An Integrated Mba Curriculum Student Portfolios In Business Education Student Portfolios In Business Education At Ashland University Using SAP ERP Technology To Integrate The Undergraduate Business Curriculum Board Games And Teaching Textile Marketing And Finance Blogging: A New Threat To Student Research? The Way We Talk! Take II Strategic Management: An Evaluation Of The Use Of Three Learning Methods In Hong Kong Adoption Of Discussion-Based Teaching And Assessment In Teaching Strategic Management In Hong Kong The Tobin Q As A Company Performance Indicator Using Representative Nominal Group Technique For Course Review And An Interactive Solicitation Of Ways To Enhance Absel's Image Making Teaching Matter: The Art And Science Of Teaching Business Communication Beyond Sex, Age, And Race: Exploring The Deeper Contents Of Diversity Teaching & Learning The Facilitation Process A Brief On Debriefing: What It Is And What It Isn't Changing Perceptions Of The Importance Of Leadership: The Contribution Of Individual Spirit Harmonics In Leadership Knowledge, Skills And Sustainable Values In Learning Organizations: Some Implications From The Multicultural Virtual Classroom Challenges Of Teaching Undergraduate Organizational Behavior In A Nontraditional Time Format Interactive Online Positioning With The Web-Based Product Positioning Map Graphics Package The Longitudinal Effects Of Entrepreneurship Training On Risk Tolerance: A Look At Similarities And Differences Between Male And Female Undergraduate Students Revisiting Strategy Learning In A Total Enterprise Simulation What Are Simulations For?: Learning Objectives As A Simulation Selection Device Gaming Agency Markets Cooperate For Profits Or Compete For Market? Study Of Oligopolistic Pricing With A Business Game The Design Of A Business Simulation Using A System-Dynamics-Based Approach Modeling The Product Development Function For An Entrepreneurial Firm Simulation Performance And Forecast Accuracy? Is That All? Business Manager Identification Of Competitors In Real World And Simulation Settings Monte Carlo Simulation Analysis On The Costs Reduction Argument Of Interest Rate Swaps Ebiz Game: A Scalable Online Business Simulation Game For Entrepreneurship Training A Model For Online Education Delivery And A Look At Online Delivery Effectiveness Incorporating "Company Reputation" into Total Enterprise Simulations The Genesis And Future Of The Absel "Classicos" Initiative