The Meaning of Firm Demand in Business Simulations Page 375 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 ABSTRACT This traditional approach in business simulations to com- puting firm demand is to first compute a set of weights and then use these weights to compute market share percent- ages. Demand for each firm then is computed by multiply- ing market share percentages times industry demand. This approach is analyzed in this paper and the methodology is analyzed and criticized in terms of whether the approach has been logically explained. A new approach to comput- ing firm demand is presented. The new approach does not require that market share percentages be computed. Also, the new approach introduces the concept of potential cus- tomers and also introduces average purchases per poten- tial customer as an important value in determining firm demand. INTRODUCTION Business simulations are generally simulations involv- ing three or more firms in the same industry. Consequently, the economic term that best describes the market environ- ment of a business simulation is oligopoly. The theory of demand in an oligopoly for the past fifty years has been unsettled and even to this day remains unclear. There is still no generally accepted theory how an equilibrium price is achieved in an oligopoly industry; however, there ap- pears to be a theory within business simulations that ex- plains how this happens. In economic theory and consequently in business simulations there are two type of demand: firm demand and industry demand (market demand). Liebhafsky (1963) illustrated firm demand as shown in Figure 1. According to Liebhafsky, the dd line shows the firm’s belief as to how much it can sell at all possible prices pro- vided that the other firms keep their prices fixed at a given level. In other words even though the firm continually de- creases prices, the other firms do not change their price. The DD curve (Demand at the industry level) shows the amount the firm can in fact sell at all possible prices if all other firms always charge the same price as the dd firm. The problem with this brief explanation of firm de- mand is that there is no discussion or explanation by Liebhafsky as to what is firm demand if each firm has a different price. However, it appears that in business simula- tions the concept of firm demand as defined by Leibhafsky has been adopted in businesss simulations. Firm demand in business simulations appears to be based on the assumption of computing firm demand on the basis that other firms do not respond to prices changes and the resulting values are then used as weights to compute market share. The subject of firm and industry demand has not often been discussed in ABSEL papers. The most notable dis- cussions have been by Gold and Pray (1983), Goosen (1986), Carvalho (1991), Teach (1990), and Thakvikuwat (1988 ) Gold and Pray (1983) have chosen to refer to the proc- ess of determining firm demand as a ”firm demand func- tion” involving three steps. THE MEANING OF FIRM DEMAND IN BUSINESS SIMULATIONS Kenneth R. Goosen krgoosen@cei.net Figure 1 Industry and Firm Demand Illustrated mailto:krgoosen@cei.net Page 376 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 1. Computation of a firm demand weights 2. Computation of market share percentages 3. Computation of firm demand According to Gold and Pray and others, firm demand is determined by the following equation: QF i - firm demand of a specific firm Si - market share percentage of a specific firm QI - Industry demand The si values (market share percentages) are based on an equation frequently referred to as the firm demand equa- tion. PURPOSE OF RESEARCH In ABSEL, the mechanical or procedural steps in com- puting firm demand as previously referenced have been significantly discussed; however, the theoretical validity of the “firm demand function” has never been seriously ana- lyzed or explained. While many papers make references to firm demand the underlying theory and complexities of computing firm demand are not discussed. The procedure has just been accepted as being valid because it seems to work. The pa- pers on demand as noted above for the most part have cen- tered around the mechanics of computing firm demand and have not presented or discussed the theory behind the con- cept. The purpose of this paper is to analyze the procedures involved in computing firm demand in business simulations and to ask the question: what do the values generated by the firm demand function really mean? The conventional approach in business simulations regards firm demand de- termination as a process of first computing market share percentages. While this approach has much merit, there is another way of interpreting how firm demand is deter- mined. This paper will present this approach. CONVENTIONAL APPROACH TO COMPUTING INDUSTRY AND FIRM DEMAND In most business simulations, there are two demand equations involved in the total demand algorithm. Both equations are essentially identical except that the parameter values assigned to each are not the same. Assuming the use of a straight-line demand curve, these two equations are as follows: (1) QI - Industry demand Po - Y-intercept value for price K - Line slope coefficient Pa - Average industry price (2) QFi - Firm demand weight for each firm Equation 1 computes industry demand and equation 2 computes values which the conventional viewpoint calls firm demand weights (Gold and Pray, 1983). The lower the firm price and for a given firm, the greater is the firm de- mand weight. It would appear that the values generated by the use of equation 2 are firm demand in units. But technically this is not correct. These values as explained by Gold and Pray are used as weights in business simulations to compute market share percentage for each firm which are then used to compute market share percentages. In order to illustrate this point, let us assume the following demand parameters. Example 1 From this assumed values, we cane easily prepare the following price/quantity schedules To simplify the illustration, let us also assume that there are only two firms in the industry, Firm 1 and Firm 2. If both firms, Firm 1 and Firm 2, set price at $60, then av- erage industry price is also $60 and industry demand is 500. At prices of $60, the firm demand weights generated are 400 and 400 for firms 1 and 2 respectively. Total firm Industry Demand Schedule Firm Demand Schedule Po - 110 K - .1 Po - 80 K - .05 Industry Firm 110 100 90 80 70 60 50 40 30 20 10 0 100 200 300 400 500 600 700 800 900 1,000 110 100 90 80 70 60 50 40 30 20 10 0 0 0 0 200 400 600 800 1,000 1,200 1,400 Page 377 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 value weights would be 800. The allocation percentages (market share percentages) for Firm 1 and Firm 2 respec- tively are .5 and .5. The problem then is this. It would appear that the val- ues generated by equation 2 of the firm demand function represent some type of demand and more than simple num- bers to be used as weights. In principle, equations 1 and 2 are identical. Clearly, equation 1 generates overall or in- dustry demand. However, equation 1 does not indicate how much of the 500 industry demand belongs to Firm 1 and Firm 2. In our example though, it appears obvious that if both firms have the same price, the industry demand should be allocated equally. However, what if price is not the same? To create a more dynamic example, let us now assume the following: Example 2: Given these price values and using the same parame- ters as before, the following may be computed Based on the above values, market share percentages are .75 and .25 for firms 1 and 2 respectively. Allocated industry demand is then: F-1 500 x .75 = 375 F-2 500 x .25 = 125 At the moment, the question of the validity of equation 2 is not under scrutiny but rather the question being asked is: what is the meaning of the values generated by equation 2? To simply call these values demand weights does not seem adequate. More specifically, how do we interpret the 600 value generated for Firm 1 and the 200 value generated for Firm 2? Are they units of product or something else? It appears that the conventional approach treats them as units of product that would be purchased under certain circum- stances. However, the issue as to what they represent is avoided by simply referring to them as weights or numbers used in a proportional manner to compute market share percentages and then firm demand. If we look at the end results of the price changes, we see that Firm1 increased its sales from 250 to 375 units or an increase of 125 units. Firm 2's sales decreased by 125 from 250 to 125. If the firm demand of 375 for Firm 1 and the demand of 125 for Firm 2 represent firm demand, then what do the values of 600 and 200 represent? It seems ob- vious that these two values represent some type of demand since the equation that generated these values is in fact a demand equation. However, to simply describe these values as numbers or weights needed to compute market share does not seem adequate. Surely, there is some more logical explanation. A NEW THEORY OF EQUATION 2 GE- NEARATED VALUES It will now be proposed that what is commonly called as market share weights may be called “potential custom- ers”. For the moment, let us accept this proposition as be- ing true. Then initially when both firms had identical prices of $60, the potential customers of each firm was 400 each. Now when Firm 1 lowered its price to $50 and Firm 2 increased its price to $70, Firm 1 gained 200 additional potential customers and firm 2 lost 200 potential customers. The underlying idea then is that the firm which can gener- ate the greater number of potential customers logically would have a greater market share. Because what we are calling potential customers can be greater than firm de- mand it is apparent that not all potential customers actually purchase. The term “potential” obviously implies that a potential customer may elect to buy or not buy. In fact, it is unrealis- tic to assume that all potential customers will purchase. Also, it seems quite normal to expect that for each firm the number of potential customers can greatly exceed the ac- tual number of customers buying. Also, for the industry as a whole, it likewise seems logical that the total number of potential customers can exceed the number of customers actually buying. It is possible for a the same customer to be a potential customer of both Firm 1 and Firm 2? The answer is yes. At a minimum, a potential customer is somewhat who is  Aware of the business  Aware of the product  Is debating in his mind whether to buy or not buy  Is price conscious  May consider two or more firms to purchase from  Has not yet made a decision as to which firm to purchase from Originally, at a price of $60, firms 1 and 2 had the same number of potential customer--400. Of this number only 250 customers actually made a purchase. For each firm, 150 potential customers did not purchase. At the mo- ment the assumption is that each buying customer bought only one unit. Now when Firm1 lowered price from $60 to $50 (see example 2) it appears that 200 of the potential customers of F-2 became also potential customers of F-1. Some of the potential customers of Firm 2 could not accept Firm 2's price increase from $60 to $70. Consequently, the decrease in price by Firm 1 from $60 to $50 caused some potential customers of Firm 2 to seriously consider buying from Firm 1, which in fact happened. Of the 200 that switched, 125 or 62.5% did purchase. The question needs to be asked: did in fact, some of the potential customers of Firm 2 really switch to Firm 1? Could it not be logically argued that the increase in the potential customers of Firm 1 are totally new individuals that have never been potential customers before? Could it Industry Firm Industry demand 500 Average price $60 Firm 1 demand weight 600 Firm 2 demand weight 200 Page 378 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 be argued that the decrease in potential customers by F-2 were individuals that totally lost interest and did not even consider buying from F-1? The answer is yes. A decrease in price can have two effects: 1. Cause potential customers of one firm to become po- tential customers of another firm. 2. Attract new individuals that can be classified as poten- tial customers. Let us now assume the following: Firm 1 decreases price from $60 to $50 but Firm 2 lets price remain at $60. Then we have the following results: Example 3 Based on the above values, market share percentages are .6 and .4 for firms 1 and 2 respectively. Allocated in- dustry demand is then: F-1 550 x .60 = 330 F-2 550 x .40 = 220 When both firms have the same price at $60 as in ex- ample 1, potential customers would be 400 for each firm. Now we see that Firm 2 did not lose any potential custom- ers which remained at 400 but Firm 1 did gain 200 poten- tial customers. If Firm 2 did not lose any potential custom- ers, why then did Firm 2's sales decrease from 250 to 220? Remember that a potential customer is not necessarily a purchasing customer. A potential customer may cease to be regular customer and become a standby customer. Also, the same person can be a potential customer of more than one business. Therefore, some of the firm’s potential customers may have also become potential customers of Firm 1 be- cause Firm 1 now had a lower price. Of this number that also became potential customers of firm 1, a certain per- centage decided to buy from Firm 1 rather than Firm 2. In terms of firm demand, it is clear that the purpose of lowering price or increasing advertising is to initially at- tract new potential customers. Each firm will deliberately seek to make potential customers of one firm their own potential customer. Based on the conditions specified and the assumptions made, a potential customer is definitely not someone who has never heard of the firm. Knowledge or awareness of the firm seems essential. Potential customers can be created by advertising, There are two ways two ways advertising can create new potential customers, Advertising can attract potential customers from another firm or bring in new po- tential customers. However, it is beyond the scope of this paper to deal directly with the effect of advertising on po- tential customers and firm demand. A NEW APPROACH TO UNDERSTAND- ING AND COMPUTING FIRM DEMAND Theoretically, total potential customers can outnumber the customer actually buying. How in a business simulation can potential customers be converted to actual customers that purchase? Can this be done without treating the so- called firm demand weights as values necessary to compute market share? As almost always done, is it necessary to actually compute market share of each firm and then multi- ply these allocation percentages times industry demand? Surprisingly, the answer is no! Computing market share of each firm is not necessary. How this is possible will now be illustrated: The conventional approach is to compute firm demand as follows: (3) FDi - Firm demand of a specific firm FDWi - The firm demand weight values generated by used of equation 2 TDFW - Total of the individual firm demand weights QI - Industry demand This conventional approach involves actually the fol- lowing steps: 1. Compute of the demand weight of each firm in the industry using equation 2. 2. Compute total firm weights 3. Compute market share percentages by dividing each weight by total of all weights. 4. Compute firm demand by multiplying the market share percentages times industry demand This conventional approach for the remainder of the paper will be called method 1. There is a second approach that may be used. This approach apparently has apparently never been discussed in ABSEL papers. No articles could be found that described or even mentioned this approach. The significance of this approach is that is gives validity to the notion to the idea that the market share weights are more appropriately described as potential customers. Equation 3 may be mathematically expressed as fol- lows: (4) Industry Industry demand Average price Firm 1 price - - - 550 $55 $50 Firm Firm 1 demand weight Firm 2 demand weight Firm 2 price - - - 600 400 $60 Page 379 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 FDi - firm demand of a specific firm FPCi - potential consumers of a specific firm TPC - Total potential customers QI - Industry demand In this approach the ratio of QI to TPC is computed The denominator is now called total potential customers instead of the total weights. This approach involves the following steps: 1. Compute the potential customers of each firm by using equation 2. 2. Compute total potential customers in the industry. 3. Compute average units purchased by each poten- tial customer in the industry 4. For each firm multiply, average units purchased times the potential customers in each firm. In example 2, we had the following values: If we use these values previously computed, then we may compute firm demand as follows: For Firm 1 we would have: For Firm 2, we would have: This approach which we will now call method 2, com- putes QI/TPC, which represents the average purchase in units per potential customer. Computing firm demand is then simply a matter of multiplying average purchase size times the number of potential customers per firm as op- posed to method 1 which computes market share. Method 2 does not require at all that market share be computed and if market share is desired then that is simply a matter of di- viding firm demand by total industry demand. Now is should be noticed that both interpretations give exactly the same results; that is, allocated industry demand is the same. In other words, firm demand for each firm is the same regardless of which method is used. However, the traditional approach requires computing market share percentages before firm demand can be computed. It is somewhat illogical to know market share before knowing the demand of each firm. Intuitively, one would think that it is the relationship of firm demand to total demand that determines market share. Consequently, it seems more logi- cal that firm demand should be computed before market share is computed. The traditional approach reverses this procedure and assumes that market share is known before firm demand is known. Given that the results between method 1 and method 2 are the same, the question becomes then which interpreta- tion is the most meaningful. Should the results of equation 2 be interpreted as market share weights or as potential customers? While not necessarily important, method 2 involves less computations. Assuming an industry of eight firms, the following computations are necessary: 1. Compute the potential customers for each firm 2. Compute the total potential customers 3. Divide the industry demand by the total potential customers 4. Multiply the potential customers of each firm by the value computed in step 3. Consequently, given an industry of 8 firms, 18 compu- tations are required. If the conventional technique is used, the following steps are required: 1. Compute the market share weights of each firm 3. Compute the total of the market share weights 4. Compute the market share allocation percentages 5. Multiply industry demand by each market share allocation percentage In this approach, 25 computations are required. A possible weakness of the proposed approach is that average sales per potential customer is the same for all firms regardless of differences in price.. If Firm 1 has a lower price, then is seems reasonable to assume that the average purchase rate for Firm 1 should be greater than for Firm 2. Whether this is a serious problem can not be examined here; however, an examination of this issue in the future might prove to be profitable. To illustrate, assume that Firm 1 has lowered price and as a result potential customers are now 1,000. Firm 2 does not change price and its potential customers remain at 500. Assume industry demand is 1,000. If the average sales rate per potential customer to convert potential customers to firm demand is .67, then firm demand for Firm 1 is 667 (.67 x 1,000) and 333 for Firm 2 (500 x .67)? If Firm 1 had the lower price, would it not be reason- able to expect that the average units purchased by potential customers of Firm 1 would be greater than the purchase rate of potential customers in Firm 2? This is an issue that should be explored in another paper. It should be pointed out that the average purchase rate can be less than one or greater than 1. For example, assume that industry demand is 1,000 and the total number of po- tential customers is 500. The average purchase rate per potential customer then would be 2 (1,000/500). However, if the industry demand is 500, then the average purchase rate is .5 units (250/500). In this instance, it can be assumed that 50% of the potential customers did not purchase. QI 500 F-1 demand 375 F-2 demand 125 Firm 1 market share weight (now called potential customers) 600 Firm 2 market share weight (now called potential customers) 200 Page 380 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 One of the weaknesses of the conventional approach is that there is no measure of market size or the number of customers in the market. If allocated demand to a specific firm, for example, is 10,000, then how many buying cus- tomers did the firm have. The answer, of course, depends on how many units each customer buys in a given period of time. If the average number of units purchased were 5, then the number of buying customers would be 2000 (10,000/5). The new approach proposed in this paper then makes relevant the number of units purchased per potential customer and the number of potential customers. DISCUSSION OF POTENTIAL CUSTOMERS IN ABSEL PAPERS A word search in ABSEL papers of the term “potential customers” revealed that the term appeared at least once in sixteen papers. Of these sixteen papers, the term “potential customer” appeared only one time in fourteen of these pa- pers. The use of the term was more or less just a casual use of the term and played no significant importance in the overall purpose of the paper. The only paper that signifi- cantly discussed the nature of “potential customers” was by Goosen (1995). The concept of “potential customers” does not appear to be an important decision-making number in business simulations. No evidence was found that the concept of “potential customers” is employed in business enterprise simulations or that information on potential customers is provided. Neither is information on potential customers provided in the output results of business enterprise simula- tion. It seems logical to the author of this paper, that knowl- edge of potential customers would be helpful in making decision concerning the following: 1. Helpful in determining the dollar size of the ad- vertising budget 2. Helpful in determining how many sales reps to hire 3. Providing useful information on the need for fu- ture production capacity 4. Helpful in developing a strategic plan. A knowledge of potential customers at the industry level and also at the firm level should provide a better data base foundation for decision-making. PROBLEMS OF DEFINING FIRM DEMAND WEIGHTS AS POTENTIAL CUSTOMERS Consider the industry demand schedule in example 1. Price ranges from $110 to $10. At a price of $10, industry demand is 1,000. Let us assume that this represents maxi- mum sales. At a price of $10 and assuming each firm charges a price of $10, then the total weights (label used in method 1) would be 2,800 (assuming only two firms). Does the 2,800 value mean that there are 2,800 individual poten- tial customers. The answer is no because of overlap. An individual can be a potential customer of more than one firm. Another problem involved in the term potential cus- tomer as used in this paper is that a customer that has pur- chased is still considered a potential customer and included in the count. Determining the number of potential custom- ers that have never purchased is not necessarily easy. However, the 2,800 value used above does tell us what value that potential customers can not exceed. In current simulation development and design, the de- mand weights by each firm are never communicated to the simulation participations. How firm demand is actually computed is never explicitly revealed. If the interpretation of the values generated by equation 2 as potential custom- ers is of value, then how can the knowledge of potential customers be of value to simulation participants. If knowl- edge of this value does not enhance decision making, then method two is not any better than the conventional method. One of the problems of current simulations is that how much to budget for advertising is never clear. There are never any clues as to how much advertising is too much and how much is not enough. If a student were told that total potential customers based on price alone is 2,800 and the cost of reaching each potential customer through adver- tising is $2.00, then it is apparent that at a minimum adver- tising should be approximately equal to $5,600. Some knowledge of potential customers should provide bounda- ries for advertising. Also, most simulations allow sales people to be an important marketing decision. If sales people make calls, then how many sales people are needed to reach all poten- tial customers. Again, some knowledge of total potential customers should be of value in making the sales people decision. The theory proposed in this paper needs further devel- opment and analysis. It may eventually turn out that the suggested method involves too many difficulties to allow it to be implemented. However, in the process of critical analysis, some useful refinements in simulation design in the demand algorithm may result. SUMMARY AND CONCLUSIONS 1. The interpretation of the results of the equation 2, QF i = (Po - Pi)/K, as simply providing weights for computing market share seems inadequate. 2. The idea of computing market share percentages be- fore computing firm demand seems illogical. It seems rather odd that the demand equation, equation 2, determines market share rather than firm demand of some type. 3. It has been proposed that a more logical interpreta- tion of the results of this equation is that it generates potential customer values. Page 381 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 4. If the values generated by equation 2 are interpreted as potential customers, then it is not necessary to compute market share to first determine firm de- mand. 5. It is logical and quite simple to compute the average purchase rate per potential customer? The average sales (or purchase) per potential customer, QI/TPC, may be used directly to convert potential customers to firm demand. QFi = (QI/TPC ) x PCi 6. While the traditional approach of first computing market share percentages and the new approach pre- sented in this paper give the same results, the new approach advocated in this paper seems more logical to the author of this paper. 7. If the interpretation of equation 2 as either generated “weights” or potential customers, and the use of ei- ther method 1 or method 2 results in the same an- swer, (which is the case) the argument could be made that that the interpretation of the results of equation 2 as potential customers is unnecessary. However, the introduction of the concept of potential customers could stimulate new research and some new ideas in the creation of business simulation demand algo- rithms. The introduction of the concept of potential customers gives a simulation more realism and makes the concept of advertising and marketing strategy more relevant. Market- ing is often defined in terms of potential customers. For example, “Marketing is the process of interesting potential customers and clients in your products and/or services. “ (http://www.yournorthhills. com/blog/rev-marketing/ many-hats-marketing). Furthermore, the purpose of adver- tising is to cause potential customers to become actual cus- tomers. However, how to introduce advertising in terms of potential customers must be the focus of a separate paper. REFERENCES Carvalho, Gerald(1991). “Theoretical Derivation of a Demand Function for Business Simulators, De- velopments in Business Simulations and Experi- ential Learning, Vol. 18 Gold, Steven C. and Thomas F. Pray (1983). “Simulating Market and Firm Level Demand: A Robust Demand System”, Developments in Busi- ness Simulations and Experiential Learning, Vol. 10 Goosen, Kenneth R(1986). “An Interpolation Ap- proach to Developing Mathematical Functions in Business Simulations”, Simulation and Gaming, Vol. 13 Goosen, Kenneth R., 1995.”An Analytical Advertis- ing Approach to Determination of Market De- mand”. Developments in Business Simulations and Experien- tial Learning Goosen, Kenneth R.(2007). “An Analysis of the Inter- action of Firm Demand and Industry Demand in Business Simulations”, Developments in Business Simulations and Experiential Learning Lambert, Nancy E. and David R. Lambert (1988). ”Advertising Response in the Gold and Pray Al- gorithm: A Critical Assessment”, Developments in Business Simulations and Experiential Learn- ing Liebhafsky, H. H.(1963). The Nature of Price Theory, The Dorsey Press, Inc. Teach, Richard D., 1990, “Demand Equations Which Include Product Attributes, Developments in Business Simulations and Experiential Learning, Vol. 17 Thavikulwat, Precha (1988) “Simulating Demand in an Independent-Across-Firms Management Game”, Developments in Business Simulations and Experiential Learning, Vol. 15 Table of Contents Volume 38, 2011 Simulated Tabletop Exercise for Risk Management - Anti Bio-terrorism Scenario Simulated Tabletop Exercise From Business Games to Simulations - Simuworlds & Microworlds Demand Equation Redux: The Design and Functionality of the Gold/Pray Model in Computerized Business Simulations Managing Client-Based Learning: Insights from Successful Teaching Project Courses in Marketing Tracking Forecast Error Type, Frequency and Magnitude with the Forecast Error Package Responding to Facilitate Collaboration Simulating Sudden Change and the Value of Timely Information Managing Human Resources Simulation A Study on Collectivism and Group Decision-Making: An International Comparison of Japan, China, and Russia Using a Gaming Simulation The Use of Management Games in the Management Research Agenda Gaming On-Line: A Simulation Application Positioning and Performance in Simulated Networks Supply Chain Management: A Simulation Application Simulation as a Teaching Method in Strategic Management Distance Studies Entrepreneurship: A Game of Risk and Reward Phase II: The Start-Up Return to the Paradise Islands: From Confrontation to Cooperation Effect on Market Performance of Displaying Supply and Demand Curves in a Business Simulation Appreciating Complexity: The Chief of Staff of the Army Game Managing Organizations: Experiential MBA Course Teaches Alternatives to the Machine Model A Simulation Model for Analyzing the Night-Time Emergency Health Care System in Japan The Continuing Evoluation of Assessing Project Management as an Academic Learning Outcome (ALO) Should College Instructors Change Their Teaching Styles to Meet the Millenial Student? The Mouse Game and its Effects on Team Interdependence Learning from the Gulf Oil Spill to Prepare for a Brighter Future: A New Game Engaging Stake Holders in Triple Bottom Line Accounting & Strategic Planning Video Killed the Biblio Star: The Impact of Digital Media on Student Learning Outcomes Exploring Motivation: Using Emoticons to Map Student Motivation in a Business Game Exercise An Alternative to PC and Internet Based Simulations: The Internet Integrated Mode MiddleState University -- A Crisis in Education Complexity Avoidance, Narcissism and Experiential Learning Examining the Cognitive, Affective, and Psychomotor Dimensions in Management Skill Development Through Experiential Learning: Developing a Framework A Situational Leadership Exercise Based on the Biology of a Starfish JOGAI CEFET -- The Industrial Administration Undergraduate Game Would You Take a Marketing Man to a Quick Service Restaurant? Modeling Corporate Social Responsibility In A Food Service Menu-Management Simulation Use of a Simulation in a Large Class Environment for a Marketing Principles Class: A Qualitative Analysis of Whether Learning Objectives were Met A Team Based Information Literacy Exercise ABSEL Marketing Communications Plan An Interdisciplinary Study of the Impact of Playing a Marketing Simulation Game on Student Knowledge of Management Accounting/Finance Principles Analyzing Construction Planning of Interiro Finish Work of Apartment Building by Simulation Doing Murder One Again The Simple Business Game and Simulation Transfering the Knowledge of Middle Management to Novices Infectious Disease Simulation Model for Estimation of Spreading Understanding the Relative Influence of Several Factors in ERP Simulation Performance: An Exploration of Ecological Validity Tragedy of the Commons: An Exercise Using Clickers to Illustrate and Teach a Key Concept in Negotiations The Meaning of Firm Demand in Business Simulations If the Games Work, Why Aren't More Faculty Willing to Play? Those Who Do and Those That Don't: A Study of Engaged and Disengaged Business Game Players