ONLINE CUMULATIVE SIMULATION TEAM PERFORMANCE PACKAGE Developments in Business Simulations and Experiential Learning, Volume 32, 2005 ONLINE CUMULATIVE SIMULATION TEAM PERFORMANCE PACKAGE Aspy Palia University of Hawaii at Manoa aspy@hawaii.edu ABSTRACT LITERATURE REVIEW The Online Cumulative Simulation Team Performance Package (developed jointly with David Walton) enables competing participant teams in the marketing simulation COMPETE to assess their cumulative team performance each period on 18 performance criteria. Participants with Web-access can determine their cumulative team ranking each decision period on six profitability, three market share, three quality, three cost of production, and three efficiency criteria. In addition, a comparative ranking of all teams on each of the above criteria can be made accessible online to all teams either every decision period (quarter), every six months, or every year of operation, based on participant preference. Several studies have examined the relationship between grade point average (GPA) and simulation performance. Some studies have reported that higher GPA students performed better in simulation competitions (Hsu 1989; Wolfe and Keys, 1990; Wolfe and Channin, 1993). Other studies have reported no relationship between GPA and simulation performance (Faria, 1986; Gosenpud, 1987; Gosenpud and Washbush, 1991; Norris and Niebuhr, 1980; Wellington and Faria, 1995). Another group of studies have examined the relationship between learning (measured by final examination performance) and simulation performance. Again, some studies have reported a relationship between performance on mathematical problems and simulation performance (Faria and Whiteley, 1990; Whiteley and Faria, 1989). Other studies have reported no relationship between course final examination performance and simulation performance (Anderson and Lawton, 1992; Washbush and Gosenpud, 1993; Wellington and Faria, 1991; Whiteley, 1993). INTRODUCTION The comprehensive Online Cumulative Simulation Team Performance Package provides competing participant teams with feedback on their cumulative company profitability, market share by product, quality by product, cost of production by product, and efficiency with the Excel version of their simulation output for each decision period. In addition to the team ranking on each of 18 performance criteria, a graphic team profile reveals at a glance where the teams need to focus their attention in order to improve performance. Based on this periodic performance feedback, the competing teams are able to (a) identify areas of relative strength and/or weakness, (b) operationalize the 90-10 (Iceberg) Principle (McCarthy & Perrault, 1984), (c) analyze the reasons for good performance or lack thereof, and (d) use the insight derived to either exploit new opportunities or take corrective action (Kotler, 2003; Aaker, 2005). These results suggest that simulation game performance (generally measured at the team level) and learning (measured by final examination performance at the individual level) may measure different constructs. In addition, the unit of analysis in typical course settings may differ. Simulation performance measures reported in recent studies include earnings, profitability ratios and stock price (Hornaday and Ensley, 2000); profit, market share, return on sales, return on assets, return on equity, asset turnover, and stock price (Kickul, 2001); net sales revenue and net profits (Anderson and Lawton, 2002); 20 performance measures including market share, sales growth, sales, unit production costs, cash flow balance, current liquidity ratio, debt to asset ratio, return on equity and net profit margin (Bernard, 2004); and net income, return on assets and return on sales (Gosen and Washbush, 2001). The range of simulation performance measures currently in use may explain some of the differences in findings. First, the existing literature on the topic of simulation performance and its relationship to learning is reviewed. Next, the marketing simulation COMPETE and online performance-enhancing tools currently available to competing participant teams are indicated. Then, COMPETE simulation performance output measures and the online cumulative simulation team performance package are described and illustrated. The concluding section acknowledges the limitations of this package and summarizes the benefits to participants. This paper presents an “Online Cumulative Simulation Team Performance Package” that can facilitate learning through simulation by improving the quality of the feedback that students receive. The intent here is to convey to simulation participants that team performance is multivariate rather than univariate in nature. Despite the complex multivariate nature of overall team performance, the competing participant teams are provided with a single 233 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 ONLINE PERFORMANCE-ENHANCING TOOLS (though simplistic) composite overall measure of performance. THE MARKETING SIMULATION COMPETE The competing participant teams are provided with several online strategic market planning, positioning, sales forecast model-building and performance analysis packages. They use the web-based Boston Consulting Group (BCG) Graphics Package (Palia, De Ryck & Mak, 2002) in Strategic Market Planning in order to decide the level of investment and individual strategies for each of the SBUs in their brand portfolio and to allocate resources among the nine SBUs (Aaker 2005). In addition, they use the web- based Product Positioning Map Graphics Package (Palia, De Ryck, & Mak, 2003) together with sample Values & Lifestyle Analysis (VALS) psychographic data in order to position their brands relative to competing brands. Later, they use the Multiple Regression Analysis Data Matrices Package (Palia, 2004) to build a linear, unrestricted, single- equation, multiple regression model to forecast the sales of each SBU. Finally, they use a wide array of web-based Excel worksheets to make shipment decisions, conduct scenario analysis, profit contribution analysis of each of their nine SBUs, sales, cost and margin analysis for each SBU, as well as cash flow, balance sheet ratio, breakeven, and time series analyses. COMPETE (Faria, Nulsen, & Roussos, 1994) is a widely used marketing simulation designed to provide students with marketing strategy development and decision- making experience. Competing student teams are placed in a complex, dynamic and uncertain environment. The participants experience the excitement and uncertainty of competitive events and are motivated to be active seekers of knowledge. They learn the need for and usefulness of mastering an underlying set of decision-making principles. Competing student teams plan, implement, and control a marketing program for three high-tech consumer electronic products in three regions within the United States. The features and benefits of each product and the characteristics of consumers in each region are described in the student manual. Based on a marketing opportunity analysis, a mission statement is generated. Next, specific, measurable, quantifiable, time-bound, challenging yet realistic and consistent goals are set. Then, marketing strategies are formulated to achieve these goals. Later, a tactical marketing plan is devised for each strategic business unit (SBU) and the individual SBU marketing plans are consolidated into a company-wide marketing program (McCarthy & Perreault, 1984). SIMULATION PERFORMANCE OUTPUT Constant monitoring and analysis of their own and competitive performance helps the teams better understand their markets and improve their decisions. In this regard the Iceberg (90-10) Principle reminds them that superficial analyses of the balance sheet and income statement are inadequate. Detailed analysis of each SBU’s contribution to the profitability of the company is necessary to determine if there are any underlying problems that need to be addressed before the company, like the Titanic, strikes an iceberg and sinks (McCarthy & Perrault, 1984). Each participant can access the COMPETE Online Decision Entry System (CODES) (Palia, Mak, & Roussos, 2000; Palia & Mak, 2001) and download (print) a substantive text printout of approximately nine to sixteen pages. This quarterly printout of the simulation results for each decision period consists of a message center, balance sheet, income statement, three regional income contribution statements, potential and actual market share (by SBU) report, quality report, cost report, overtime report, shipment and inventory report, earnings per share report, market share by product report, sales force activity report, and selected optional market research reports including an informative Trade Association Report). Each decision period (quarter), the competing teams make a total of 74 marketing decisions with regard to marketing their three brands in the three regional markets. These decisions include nine pricing decisions, nine shipment decisions, three sales force size decisions, nine sales force time allocation decisions, one sales force salary decision, one sales force commission decision, twenty-seven advertising media decisions, nine advertising content decisions, three quality-improvement research and development (R&D) decisions, and three cost-reduction R&D decisions. Successful planning, implementation, and control of their respective marketing programs require that each company constantly monitor trends in its own and competitive decision variables and resulting performance. The comprehensive simulation output provides each participant with sufficient detail to analyze the performance of their area of responsibility within the organization. Based on the organizational structure (functional, geographic, divisional, market, committee, matrix, or hybrid) selected, the individual managers can use one or more of the analytical tools and graphics packages provided to assess their own performance within the firm. Yet, the approximately 16-page performance output does not provide an overall measure of team performance. In order to provide the competing participant teams with feedback on their overall performance, a simplistic schema based on rankings was devised. First, nine Excel worksheets were developed to keep track of team performance on balance sheet accounts, cost of production, earnings per share, efficiency, industry effort index, market 234 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 share, profitability, and quality over the course of the simulation competition. Conversion of the DOS-text simulation output into an Excel workbook yields additional benefits. First, internal and external links between worksheets in the same Excel workbook can be used to access the required data in order to calculate each teams ROTA, NPM, SATO, ROE, Sales-to- Advertising, Sales-to-Sales force Expense, and Sales-to- Promotional Expense ratios. Next, a Visual Basic program is used to provide each team with their rank on each performance measure, the graphic team performance profile, a cumulative team score, and an overall ranking on all 18 performance measures. Then, a liquidity check is made of the Short Term Notes Payable (STNP is the amount of debt incurred based on a Sources and Uses of Cash statement) account in the balance sheet. If a team’s STNP exceeds $1 million at the end of comp-etition, the team is deemed to be bankrupt and the overall ranking is adjusted downward (see Figure 1). Next, eighteen measures of performance are selected to provide teams with feedback on overall performance. These include six profitability, three market share, three quality, three cost of production, and three efficiency measures. Six measures of profitability are selected since one of the tenets of marketing is long-term profitability (Kotler, 2003; McCarthy & Perreault, 1984; Perreault & McCarthy, 1996). These measures are Earnings per Share (EPS is an overall measure of company profitability), Return on Total Assets (ROTA measures how well the assets are used to generate profits), Net Profit Margin (NPM measures how profitable the sales are), Sales to Asset Turnover (SATO measures how well the assets are used to generate sales), Return on Equity (ROE measures how well the equity is used to generate profits), and Retained Income (a cumulative measure of profits). The three market share, quality, and cost-of production measures of performance are by product. Finally, the three Efficiency measures include Sales-to- Advertising Ratio, Sales-to-Sales force Expense Ratio, and Sales-to-Promotional Expense Ratio. Based on participant preference, a comparative ranking of all teams on each of the 18 performance criteria as well as their overall ranking can be made accessible online to all teams every decision period (quarter), every six months, every year of operation, midway through the competition, or only at the end of competition (see Figure 2). While the teams derive additional insights about their competitors (similar to Company Annual Reports), they yield valuable information about their own relative strengths and weaknesses to their competitors. Accordingly, a conscious (consensus) choice in this regard is made by all teams at the beginning of competition. Then, measures of overall simulation performance are derived for each measure. For example the EPS and market share by product performance is reported in and taken directly from the simulation printout. ROTA, NPM, SATO, and ROE are calculated over the duration of the simulation competition. Retained Income is taken directly from the balance sheet for the final period of competition. Quality and cost of production are taken for the period subsequent to the final period of competition in order to preclude end-of- game strategy. LIMITATIONS Later, the teams are ranked on their overall simulation performance on each of the 18 performance criteria. Assuming a five-team industry, the number of first ranks are multiplied by the smallest weight of 1, the number of second ranks by a weight of 2, the number of third ranks by 3, the number of fourth ranks by 4, and the number of fifth ranks by 5. A dominant team with 1st ranks on all 18 performance attributes would earn a cumulative score of 18x1=18. Consequently, the teams with the lowest overall weighted rank score are deemed to be the best overall performers on all 18 performance criteria. While profitability (6 measures) is weighted more heavily than market share, quality, cost, and efficiency (3 measures each) in order to satisfy the marketing tenet of long-term profitability, the weightings are arbitrary. An improved performance evaluation paradigm may permit teams to assign relative weights to each of the performance criteria, based on the strategy selected. For instance, a team that uses a brand differentiation strategy may assign heavier weights to profitability and quality criteria. Yet, such a schema is likely to increase the difficulty of administering the simulation and evaluating inter-team performance. Further, the overall team performance scores are calculated based on relative rankings (not absolute measures) on each performance attribute. Consequently, the Online Simulation Team Performance Package does not discriminate between a team in one industry that ranks second by a 0.1% market share difference from another team that ranks second by a 20% market share difference. As in simulation design, a conscious trade-off has been made on the ‘schema simplicity’-‘performance realism’ continuum. CUMULATIVE SIMULATION TEAM PERFORMANCE An Excel-based macro program is used to convert the entire original DOS-text COMPETE simulation output into an Excel workbook. This facilitates direct analysis of the simulation results without having to re-enter the relevant DOS-text data into Excel worksheets for subsequent analysis. Substantial time savings are realized by (a) the competing teams during analysis of team performance, and (b) the administrator when tracking team performance. In addition, potential keystroke error is precluded. 235 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 CONCLUSION Gosenpud, J.J. and J.B. Washbush (2001). An Initial Validity Investigation of A Test Assessing Total Enterprise Simulation Learning. Developments in Business Simulation and Experiential Exercises, 28: 92- 5. The Online Cumulative Simulation Team Performance Package enables competing participant teams in the COMPETE simulation to assess their cumulative team performance each period on 18 performance criteria. Participants with Web-access can determine their cumulative team ranking each decision period on six profitability, three market share, three quality, three cost of production, and three efficiency criteria. Based on participant preference, a comparative ranking of all teams on each of the above criteria can be made accessible online to all teams either every decision period (quarter), every six months, or every year of operation. This package facilitates the operationalization of the Iceberg Principle and the integration of computers, the Internet and the World Wide Web into the marketing curriculum. __________ & __________ (1991). Predicting Simulation Performance: Differences between Groups and Individuals. Developments in Business Simulation and Experiential Exercises, 18: 44-8. Hornaday, R.W., and Ensley, M. (2000). Teamwork Attributes in a Classroom Simulation. Developments in Business Simulation and Experiential Learning, 27: 195-200. Hsu, E. (1989). Role-Even Gaming-Simulation in Management Education: A Conceptual Framework and Review. Simulation and Games, 20 (4): 409-38. Kickul, G. (2001). Antecedents of Work Team Performance in a Business Simulation: Personality and Group Interaction. Developments in Business Simulation and Experiential Learning, 28: 128-36. REFERENCES Kotler, P. (2003), Marketing Management, 11th ed. Upper Saddle River, NJ: Prentice-Hall. Aaker, D. (2005), Strategic Market Management, 7th ed. New York: Wiley. McCarthy, E.J. & Perreault, Jr., W.D. (1984), Basic Marketing, 8th ed. Homewood, IL: Irwin. Anderson, P.H., & Lawton. L. (1992). The Relationship between Financial Performance and Other Measures of Learning on a Simulation Exercise. Simulation and Gaming, 23 (3): 326-40. Norris, D.R., & Niebuhr, R.E. (1980). Group Variables and Gaming Success. Simulation and Games, 11 (3): 301- 12. __________ & __________ (2002). Is Simulation Performance Related to Application? An Exploratory Study. Developments in Business Simulation and Experiential Learning, 29: 108-12. Palia, A.P. (2004). Online Sales Forecasting With the Multiple Regression Analysis Data Matrices Package. Developments in Business Simulation and Experiential Learning, 31: 53-7. Bernard, R.R.S. (2004). Assessing Individual Performance in a Total Enterprise Simulation. Developments in Business Simulation and Experiential Learning, 31: 197-203. Faria, A.J. (1986). A Test of Student Performance and Attitudes under Varying Game Conditions. Developments in Business Simulation and Experiential Exercises, 13: 70-5. __________, De Ryck, J., & Mak, W.K. (2002). Interactive Online Strategic Market Planning With the Web-based Boston Consulting Group (BCG) Matrix Graphics Package. Developments in Business Simulation and Experiential Learning, 29: 140-2. __________, __________, & __________ (2003). Interactive Online Positioning With the Web-based Product Positioning Map Graphics Package. Developments in Business Simulation and Experiential Learning, 30: 202-6. __________, Nulsen, Jr., R.O., & Roussos, D.S. (1994), COMPETE: A Dynamic Marketing Simulation, 4th ed. Burr Ridge, IL: Irwin. __________ & Mak, W.K. (2001). An Online Evaluation of The COMPETE Online Decision Entry System (CODES). Developments in Business Simulation and Experiential Learning, 28: 188-90. __________ & Whiteley, T.R. (1990). An Empirical Evaluation of the Pedagogical Value of Playing a Simulation Game in a Principles of Marketing Course. Developments in Business Simulation and Experiential Learning, 17: 53-7. __________, __________, & Roussos, D.S. (2000). Facilitating Learning in the New Millennium With The COMPETE Online Decision Entry System (CODES). Developments in Business Simulation and Experiential Learning, 27: 248-9. Gosen, J. (2001). An Initial Validity Investigation of a Test Assessing Total Enterprise Simulation Learning. Developments in Business Simulation and Experiential Learning, 28: 92-95. Perreault, W.D., Jr., & McCarthy, E.J. (1996), Basic Marketing: A Global-Managerial Approach, Chicago, IL: Irwin. Gosenpud, J.J. (1987). Research on Predicting Performance in the Simulation. Developments in Business Simulation and Experiential Exercises, 14: 75-9. Washbush, J.B., & Gosenpud, J.J. (1993). The Relationship between Total Enterprise Simulation Game Performance and Learning. Developments in Business Simulation and Experiential Exercises, 20: 141. 236 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 Wellington, W.J., & Faria, A.J. (1991). An Investigation of the Relationship between Simulation Play, Performance Level, and Recency of Play on Exam Scores. Developments in Business Simulation and Experiential Exercises, 18: 111-7. __________ (1995). A Repeated Measures Examination of the Effect of Team Cohesion, Player Attitude, and Performance Expectations on Simulation Performance Results. Simulation and Gaming, 25 (1): 41-65. Whiteley, T.R. (1993). An Empirical Investigation of Cognitive and Performance Consistency in a Marketing Simulation Game Environment. Developments in Business Simulation and Experiential Exercises, 20: 144. __________ & Faria, A.J. (1989). A Study of the Relationship between Student Final Exam Performance and Simulation Game Performance. Simulation and Games, 20 (1): 44-64. Wolfe, J., & Chanin, M. (1983). The Integration of Functional and Strategic Management Skills in a Business Game Learning Environment. Simulation and Games, 24 (1): 34-46. __________ & Keys. J.B. (1990). The Role of Management Games and Simulations in Education and Research. Yearly Review of Management, 16: 307-36. 237 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 FIGURE 1 Industry M - Company 2 - Period 1 Definity Cumulative Performance Ranking & Profile Rank 1 2 3 4 5 1 Earnings per Share (EPS) X 2 Return on Total Assets (ROTA) X 3 Return on Equity (ROE) X 4 Return on Sales (NPM) X 5 Asset Turnover (SATO) X 6 Retained Earnings X 7 Market Share – TST X 8 Market Share – CVE X 9 Market Share – SSL X 19 Quality Index – TST X 11 Quality Index – CVE X 12 Quality Index – SSL X 13 Cost of Production - TST X 14 Cost of Production - CVE X 15 Cost of Production - SSL X 16 Sales / Advertising Expense Ratio X 17 Sales / Salesforce Expense Ratio X 18 Sales / Promotional Expense Ratio X Frequency 7 10 1 0 0 × × × × × Weight 1 2 3 4 5 = = = = = Score 7 20 3 0 0 Cumulative Score 30 Performance Rank 1 Bankrupt? No Adjusted Rank 1 238 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 239 FIGURE 2 EMBA XIV - BUS 615b Industry M - Period 12 Cumulative Team Performance Ranking Team Comparison Team 1 Team 2 Team 3 Team 4 EPS 4 1 2 3 ROTA 4 1 2 3 ROE 4 1 2 3 NPM 4 1 3 2 SATO 1 4 2 3 Retained Earnings 4 1 2 3 Market Share – TST 3 2 1 4 Market Share – CVE 4 3 1 2 Market Share – SSL 2 1 3 4 Quality Index – TST 4 1 1 1 Quality Index – CVE 1 1 1 4 Quality Index – SSL 2 2 4 1 Cost of Production – TST 2 4 1 3 Cost of Production – CVE 3 4 1 2 Cost of Production – SSL 3 1 4 2 Sales / Advertising Expense Ratio 2 3 1 4 Sales / Salesforce Expense Ratio 4 1 2 3 Sales / Promotional Expense Ratio 3 1 2 4 Score 54 33 35 51 Performance Rank 4 1 2 3 Bankrupt? No No No No Adjusted Rank 4 1 2 3 Table of Contents Volume 32, 2005 LEARNER BEHAVIOR IN THE ONLINE CLASSROOM EXPERIENCE THE EFFECTIVENESS OF A SIMULATION EXERCISE FOR INTEGRATING PROBLEM-BASED LEARNING IN MANAGEMENT EDUCATION DEMONSTRATION OF FOUR WEB-BASED SIMULATIONS THRESHOLD COMPETITOR: A MANAGEMENT SIMULATION ENTREPRENEUR: A NEW VENTURE SIMULATION MERLIN: A MARKETING SIMULATION MICROMATIX: A STRAGETIC MANAGEMENT SIMULATION LEARNING STYLES INFLUENCES ON SATISFACTION AND PERCEIVED LEARNING: ANALYSIS OF AN ONLINE BUSINESS GAME INTERNATIONAL INTERNSHIPS: DESIGN AND EXPERIENCES SIM MAP: TURNING ACTION-BASED LEARNING INTO SIMULATED CONSULTING PROJECTS NOTEL HEALTH SERVICES: A ROLE-PLAYING SIMULATION TEACHING EXPERIENTIALLY WITH THE MADELINE HUNTER METHOD: AN APPLICATION IN A MARKETING RESEARCH COURSE SIMULATING CUSTOMER LIFETIME VALUE: IMPLICATIONS FOR GAME DESIGN AND STUDENT PERFORMANCE VIRTUAL PROGRESS: SIMULATING ECONOMIC DEVELOPMENT ONLINE STRATEGIC MANAGEMENT: AN EVALUATION OF THE USE OF THREE LEARNING METHODS IN CHINA ADOPTION OF DISCUSSION-BASED TEACHING AND ASSESSMENT IN TEACHING STRATEGIC MANAGEMENT IN CHINA STUDENTS' VIEW ON THE USE OF CASE METHOD IN CHINA CHINESE STUDENTS' PERCEPTIONS OF BUSINESS GAMING EXPERIENCECSR - A CORPORATE SOCIAL RESPONSIBILITY SIMULATION CREATING DYNAMIC INTERACTION IN A VIRTUAL WORLD: ADD VALUE TO ONLINE CLASSROOMS THROUGH LIVE ELEARNING AND COLLABORATION: A DEMONSTRATION CAPABILITIES OF EXPERIMENTAL BUSINESS GAMING A COMPARISON BETWEEN SOLUTIONS AND DECISIONS IN A BUSINESS GAME VALIDATING BUSINESS SIMULATIONS: DOES HIGH PRODUCT QUALITY LEAD TO HIGH PROFITABILITY? ALIGNING ART AND EPISTEMOLOGY: ILLUSTRATIONS TO DISTINGUISH DISCOVERY FROM KNOWLEDGE BUILDING TUTORIALS USING WINK STUDENTS AS LAB RATS: THE ETHICS OF CONDUCTING NON-PEDAGOGICAL RESEARCH IN THE CONTEXT OF CLASSROOM SIMULATIONS AND EXPERIENTIAL LEARNING THE EFFECT ON GAME PERFORMANCE OF DIFFERENT MEASURES AND UNITS OF ANALYSIS IN QUANTITATIVE ANALYSIS ANALYZING AND THINKING WHILE PLAYING A SIMULATION COMPUTER BUSINESS SIMULATION DESIGN: THE ROCK POOL METHOD EXPANDING THE ROLE OF E-ROOMS IN DISTANCE LEARNING APPLICATIONS TO MANAGEMENT EDUCATION APPLICATION OF TRADITIONAL AND ONLINE JOURNALING AS PEDAGOGY AND MEANS FOR ASSESSING LEARNING IN AN ENTREPRENEURIAL SEMINAR DEVELOPING MANAGERIAL EFFECTIVENESS: ASSESSING AND COMPARING THE IMPACT OF DEVELOPMENT PROGRAMMES USING A MANAGEMENT SIMULATION OR A MANAGEMENT GAME INTERNATIONAL MANAGEMENT GAME Œ AN INTEGRATED TOOL FOR TEACHING STRATEGIC MANAGEMENT INTERNATIONALLY STUDENT EXPECTATIONS OF SIMULATIONS DISTANCE EDUCATION DELIVERY OF AN INTENSIVE SIMULATION BASED COURSE TEACHING SERVICE LEARNING USING A BUSINESS GAME ROLE-PLAY SIMULATION EDUCATIONAL PERSPECTIVE OF COLLABORATIVE VIRTUAL COMMUNICATION AND MULTI-USER VIRTUAL ENVIRONMENTS FOR BUSINESS SIMULATIONS SIMULATION PERFORMANCE & PREDICTOR VARIABLES: ARE WE LOOKING IN THE WRONG PLACES TO MEASURE THE RIGHT LEARNING? VIDEO CASE: JET-A-WAY INC. Œ FOCUSING ON DIVERSITY AND ENTREPRENEURIAL LEADERSHIP ACTIVE LEARNING: WHAT IS IT AND WHY SHOULD I USE IT? FACILITATING THROUGH COLLABORATIVE REFLECTIONS TO ACCOMMODATE DIVERSE LEARNING STYLES FOR LONG-TERM RETENTION ONLINE CUMULATIVE SIMULATION TEAM PERFORMANCE PACKAGE WHEN PROPHECY FAILS: A SMALL SAMPLE, PRELIMINARY STUDY USING EXPERIENTIAL LEARNING TO INTEGRATE THE BUSINESS CURRICULUM FORECASTING STOCK VALUE EMPLOYING PROGRESSIVE PRACTICES AND PRINCIPLES TO FACILITATE SEMINAR ROOM LEADERSHIP AMONG LEARNERS: SHARED POWER AND COLLECTIVE ACCOUNTABILITY INDIVIDUAL ACHIEVEMENT DOES NOT GUARANTEE TEAM PERFORMANCE: AN EVIDENCE OF ORGANIZATIONAL LEARNING WITH BUSINESS GAMES DECISION MAKING IN BUSINESS SIMULTION DESIGN ZUG UM ZUG 2015: COLLECTIVE BARGAINING AS A TWO-LEVEL GAME A NEW METHOD FOR MODELING INNOVATION AND R&D IN BUSINESS SIMULATIONS: ILLUSTRATED WITH A SIMULATION OF A NEW PRODUCT DEVELOPMENT PORTFOLIO DEVELOPING A MICRO SIMULATION EFFECT OF MARKET SHARE AND PRODUCTION EXPERIENCE ON COMPANY PROFITABILITY RE-DESIGNING A CURRICULUM THAT VALUES A WORK-INTEGRATED APPROACH TO STUDENT LEARNING HOW SIMULATIONS AND EXPERIENTIAL LEARNING FIT AS WE COMPLY WITH LEGISLATIVE AND AACSB ASSESSMENT GUIDELINES: HOW TO DEVELOP ACADEMICALLY SOUND COURSES THAT ALSO MEET STAKEHOLDER NEEDS EVALUATING SERVICE LEARNING: REFLECTION AND ASSESSMENT FROM THE STUDENT POINT OF VIEW SIMPLIFYING AND ENHANCING FINANCIAL ANALYSIS IN CASES AND SIMULATIONS OVERCOMING THE BUSINESS GAME COMPLEXITY PARADOX EXPLORING THE PREFERENCE IN LEARNING APPROACH AMONG THE HONG KONG UNIVERSITY STUDENTS: CASE STUDY, PROBLEM-BASED OR TRADITIONAL TEXTBOOK QUESTION AN EXERCISE FOR EXPLORING THE RELATIONSHIP BETWEEN JUNGIAN PSYCHOLOGICAL TYPES AND POLITICAL STYLE IN THE WORKPLACE EVALUATING THE DIRECTION OF RESEARCH IN ONLINE EDUCATION: ARE WE GOING ANYWHERE? AIS RAIL SYSTEM: A COMPUTER-BASED JOB-ORDER COST SIMULATION BLOOM BEYOND BLOOM: USING THE REVISED TAXONOMY TO DEVELOP EXPERIENTIAL LEARNING STRATEGIES DELIVERING A TECHNOLOGY-BASED CASE IN A TECHNOLOGICAL WAY: THE SMARTCART CASE USING THE INTERNET TO ENHANCE COURSE PRESENTATION: A HELP OR HINDRANCE TO STUDENT LEARNING TEACHING PRACTICES: A CLUSTER ANALYSIS OF STUDENTS IN HONG KONG TEACHING PRACTICES: A CLUSTER ANALYSIS OF TEACHING STAFF IN HONG KONG EVALUATING A SIMULATION WITH A STRATEGIC EXPLORATION TOOL A SYMBOLIC MODEL OF THE SIMULTANEOUS ACHIEVEMENT OF CONCRETE BENEFITS AND LEARNING BY PARTICIPATING GROUPS IN EXPERIENTIAL ACTIVITIES: ‚THE SPHERE OF EXPERIENTIAL LEARNING