Dominance in Online Business Games Competitions DOMINANCE IN ONLINE BUSINESS GAMES COMPETITIONS Ricardo R. S. Bernard Universidade Federal de Santa Catarina bernard@cse.ufsc.br Moisés Pacheco de Souza Universidade Federal de Santa Catarina mpsouza1980@yahoo.com.br ABSTRACT Research concerning performance in business games has strongly demonstrated the persistence of the results as long as the simulations progress. The dominance of the leader companies starts in the earlier period and progress until at the end of the simulation. This paper extends previous studies by using business simulation competitions administrated on line. The goal is to verify if the dominance still remains in competition environments. Academic and general competitions were used involving manufacturing and retailing simulations. Results show that the dominance remains in all analyzed competitions. However, no different levels of dominance were found in terms of type of competition or simulators. Present results add external validity to previous studies about dominance in business games. Key-words: Management simulation; business game; performance; dominance, competition. INTRODUCTION Patz’s (1992, 1999, 2000, 2006) studies demonstrated that total enterprise (TE) simulations have a predictable performance pattern. According to Patz, teams that lead at the end of the simulation have led from the beginning and the lead grows as the period progresses. This predictable performance pattern was defined as dominance. Teach and Patel (2007) discuss dominance in business games from an economic theoretical point of view. According to this theory, a firm is considered dominant when it has 40 percent or more market share (Scherer and Roos, 1990 apud Teach and Patel, 2007). However, the definition of dominance from an economic theory it is not appropriate to business games. The starting conditions of simulated companies, and the evaluating criteria used in business games, prevent the adoption of dominance from an economic theory viewpoint. In real world companies can be arranged in monopoly, oligopoly or pure competition. In total enterprise simulations companies are usually designed to compete in oligopoly environments (Goosen et al., 2001). But, oligopolies in real and simulated worlds are formed in different contexts. In real world, companies are created in different years or decades. Thus, they are modeled in completely different economic and social conditions. In academic simulated environments, on the other hand, starting scenarios usually are composed by companies in identical situations. Although, this situation does not occur in real worlds, it is very important in simulated world for evaluation purposes. Otherwise, the instructor would have difficulties in knowing if the company, no matter which criterion used, was the winner because of the team performance or the different initial conditions given. Additionally, the economic theory assumes that the dominance in market share bring economic better results in short or long run to the dominant firm. In business games models, the initial equal conditions to the companies, and the limited simulated rounds, could not give them the necessary economic power and time to translate market share dominance in best economic results. These simulated characteristics can justify the reasons that most instructors of business games exercises prefer evaluate simulate companies based on economic results (Washbush and Gosen, 2001). Previous studies related to dominance in business games defined this construct as how earlier the winner company starts to lead (Patz, 1992, 1999, 2000, 2006). Dominance in present study was defined as the percentage of rounds led by the winner company. More specifically, the authors consider that the dominance occurs in a business game when the winner company led more than 50% of the rounds (not considering the first period because all companies are in equal situation). For instance, a winner company of eight simulated rounds led rounds 3, 4 5, 8 and 9; that is, it led 5 out 8 rounds. According to the present study the dominance occurred because the winner company led more than 50% of the rounds. However, according to Patz’s definition, dominance cannot be considered because the company skipped leadership in rounds 6 and 7. Thus, as a construct, dominance is a matter of degree. Consequently, dominance can exist, or not, depending on the definition considered to the construct. Previous studies related to dominance in business games were discussed using an academic orientation; that is, using BBA and MBA students from regular courses. Expected outcomes of such exercises were the learning achieved by the participants. Thus, the winner can be considered, at least from an instructor’s viewpoint, as a secondary outcome of the exercise. This study extends Patz’s (1999, 2000, 2001, 2006) previous research by considering simulations in on line competition environments. Two types of business games competitions were used: competitions encompassing two-year business school students and competitions open to all kind of participants. Eight competitions were analyzed. These competitions have used different simulators (manufacturing and retailing business simulators). In the total, 495 simulations were analyzed, 287 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 mailto:bernard@cse.ufsc.br mailto:mpsouza1980@yahoo.com.br involving 3,953 simulated companies. In such competitions the learning objectives are supposed to be less intensive, especially because each instructor was responsible for many simulations simultaneously. Moreover, the competitive simulated environment is supposed to be more intense because the competitions select a winner based on the performance of the simulated companies. Consequently, competitions involving business games can also have dominance of the leader, one of the possibilities found in competitive environments, not only in real world, but also in simulated world. Thus, the main research question to be addressed in this study is: Does it exist dominance in online business game competitions? HYPOTHESES The research question formulated in the previous item is converted into hypotheses to be tested, as follows: H1: There is no dominance of the winner manufacturing company in on line business game academic competitions. H2: There is no dominance of the winner retailing company in on line business game academic competitions. H3: There is no dominance of the winner manufacturing company in on line business game general competitions. H4: There is no dominance of the winner retailing company in on line business game general competitions. Considering the previous hypotheses are discarded, two additional hypotheses will be tested: H5: There is no significant difference between dominances in online academic competition and on line general competition. H6: There is no significant difference between dominances in manufacturing simulation and retailing simulation. THE METHOD Two different types of competitions were used to test the hypotheses about dominance in business games. First type was composed by competitions inside specific academic institutions. This type is called Academic Competition. Second type was composed by competitions realized annually, and open to all kind of participants. This type is called General Competition. Additionally, the two types of competitions had simulations using manufacturing and retailing simulations. All competitions were conducted toward one online website. Academic competitions are business simulation competitions composed by two-year business school students. These competitions are a mandatory exercise that all students have to follow in the related academic institutions. The competitions are similar to a regular course, but no formal classes are given. The students form their own teams (each 8 teams comprise one simulation), download the Player’s Manual and prepare themselves for a trial round. In the sequence, they have to run rounds on a weekly basis. Results of the simulation are available one day after the deadline to send the decisions. The final round is not known by the students until the end of the competition is declared. The instructor’s role is only clarifying doubts about the company and the simulated environment. Face to face interactions occur between the instructor and the students to solve doubts about the simulation. Data were extracted from academic competitions of the years 2006 (two editions), 2007 (one edition) and 2008 (two editions). In the total, 439 simulations from academic competitions were used. Some competitions had 7 rounds while other had 8 rounds (not considering the trial round). General competitions are business simulation competitions composed participants of all kind of background. However, the majority of participants are accounting and business undergraduate students from different universities. The teams fill out a subscription form, pay a fee to participate and receive a password to have access to the website of the competition. The competition is split in two phases: a qualifying and a final phase. In the qualifying phase, each 8 teams represent a simulation. They have a trial round, followed by 4 rounds on a weekly basis. Results of the simulation are available one day after the deadline to send decisions to the coordination. The final round is known at the beginning of the competition. No communications are made between the instructor and the participants, except by eventual technical computing problems. The winner of each simulation is classified to the final phase. All qualified companies compete again to define the winners. The third, second and first places receive money as prize. Data to this study were extracted only from the qualifying phase, representing 56 simulations from general competitions of the years 2002, 2005 and 2006. Manufacturing simulations were used in 2002 (general competition), and 2006 (academic and general competitions). A total enterprise simulator was used which companies had to produce and selling a single product to different regions. Production, marketing, human resource and finance were the main managerial functions to be managed (SIND, 2002, 2006). Retailing simulations were used in 2005 to the general competition and in 2006, 2007, and 2008 to the academic competitions. A total enterprise simulator was also used which companies had to purchase different products and selling them in a single region. Marketing, human resource, operations and finance were the main managerial functions to be managed (SIMCO, 2005, 2006, 2007, 2008). The main performance indicator from the two simulators was the stock value of the companies. This indicator is composed by the following indicators: Return on equity (the most important), market share, earnings margin, working capital, indebtedness and return on total assets. The stock value was used in all competitions to define the winner companies. Considering all competitions, the database to the study was composed by 495 simulations and 3,953 simulated companies. Table 1 presents the number of business game simulations separated by type of competition and simulator, while Table 2 shows the number of simulated companies, also separated by type of competition and simulator. 288 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 RESULTS AND ANALYSES The sample was formed by all companies that led the last round of its respective simulation. In the sequence, it was calculated how many rounds each company led. Such rounds were standardized in percentages because the simulations had different simulated rounds. Finally, these percentages, called dominance level, were diminished by the value 50%. Results higher than zero mean that the dominance has occurred in the given simulation. The maximum result value is 50%; that is, the company led all rounds. Negative results indicate that that winner company led less than 50% of the rounds and it is not, by definition, a dominant company. The minimum result value depends on the number of simulated rounds. However, it can never be -50% (or less) because this value indicates that the company did not lead any round (and all companies of the sample led at least the last round). Once the data were prepared, statistics procedures were performed to test the first four hypotheses. Basically, the ‘dominance levels’ were compared with ‘50% dominance’. Hypotheses H1, H2, H3 and H4 were discarded considering a 5% significance error. That is, the dominance is present in all competitions, no matter its type (academic or general competition) and simulator used (manufacturing or retailing simulator). In other words, in all analyzed competitions, the winner companies have significantly led more than 50% of rounds. Tables 3 through 6 show statistics results discarding tested hypotheses. Table 1 – Number of simulations separated by competition and simulation types Manufacturing simulation Retailing simulation Total Academic competition 51 388 439 General competition 32 24 56 Total 83 412 495 Table 2 – Number of simulated companies separated by competition and simulation types Manufacturing simulation Retailing simulation Total Academic competition 408 3,097 3,505 General competition 256 192 448 Total 664 3,289 3,953 The dominance is graphically represented through Figures 1 to 3. They present the number of rounds that the winner companies led the competitions. For example, in Figure 1 one can observe that nine companies led only the last round (frequency 9 to number of rounds led equal 1). Figure 1 shows the number of rounds that the winner companies led their competitions in the academic competitions with 7 rounds (not considering the trial round). Companies that led more than 3 rounds are considered dominant winners, while companies that led the 7 rounds led 100% of the rounds. Figure 2 shows the Table 3 – Statistics results rejecting Hypothesis 1 x' - x'' Sample size 51 Minimum -35.714 Maximum 50 Range 85.714 Mean 5.7423 Std. Deviation 24.1180 Standard error of mean 24.1180 d.f. 50 95% confidence for mean -1.041 to 12.528 t value testing mean=0 is 1.70 Significance level (p) is 0.0476 (4.76%) for 1 sided test One sided test assumes observed difference is the same sign as hypothesized Where: Mean Std. Dev. x' = Dominance level (%) 55.74 24.12 x'' = 50% of Dominance 50.00 0.00 289 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 number of rounds that the winner companies led their competitions in the academic competitions with 8 rounds (not considering the trial round). Companies that led more than 4 rounds are considered dominant winners, while companies that led the 8 rounds led 100% of the rounds. Finally, Figure 3 shows the number of rounds that the winner companies led their competitions in the general competitions. Companies that led more than 2 rounds are considered dominant winners, while Table 4 – Statistics results rejecting Hypothesis 2 x' - x'' Sample size 388 Minimum -37.50 Maximum 50 Range 87.50 Mean 10.673 Std. Deviation 25.378 Standard error of mean 1.2884 d.f. 387 95% confidence interval for mean 8.1398 to 13.206 t value testing mean=0 is 8.28 Significance level (p) is 0.000 (~0.00%) for 1 sided test One sided test assumes observed difference is the same sign as hypothesized Where: Mean Std. Dev. x' = Dominance level (%) 60.67 25.37 x'' = 50% of Dominance 50.00 0.00 Table 5 –Statistics results rejecting Hypothesis 3 x' - x'' Sample size 32 Minimum -25 Maximum 50 Range 75 Mean 7.8125 Std. Deviation 20.5150 Standard error of mean 3.6266 d.f. 31 95% confidence interval for mean 0.41604 to 15.209 t value testing mean=0 is 2.15 Significance level (p) is 0.0196 (1.96%) for 1 sided test One sided test assumes observed difference is the same sign as hypothesized Where: Mean Std. Dev. x' = Dominance level (%) 57.81 20.52 x'' = 50% of Dominance 50.00 0.00 290 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 companies that led the 4 rounds led 100% of the rounds. Analyzing the figures, one can observe that the highest dominance occurs in the competitions with 8 rounds (Figure 2), while the lowest dominance occurs in the competitions with 4 rounds (Figure 3). These visual analyses indicate that the dominance grows as the period progresses, similar to the findings presented by Patz (2006). Considering the first four hypotheses were discarded, the next step is testing if the dominance has significant difference in terms of type of competition or type of simulator used. Analyses of variance were used to test H5 and H6. Results indicate that there is no significant difference when comparing the dominance found in academic competitions and general competitions. Thus, H5 is supported. When comparing the dominance between manufacturing and retailing simulations, again no significant differences were found. Consequently, H6 is also supported. Tables 7 and 8 show results of the analysis of variance performed to compare dominance in different competitions and simulators, respectively. Table 6 –Statistics results rejecting Hypothesis 4 x' - x'' Sample size 24 Minimum -25 Maximum 50 Range 75 Mean 14.5830 Std. Deviation 23.2150 Standard error of mean 4.7388 d.f. 23 95% confidence interval for mean 4.7804 to 24.386 t value testing mean=0 is 3.08 Significance level (p) is 0.0027 (0.27%) for 1 sided test One sided test assumes observed difference is the same sign as hypothesized Where: Mean Std. Dev. x' = Dominance level (%) 64.58 23.22 x'' = 50% of Dominance 50.00 0.00 Figure 1 – Number of rounds that the winners led in the academic competitions with 7 rounds 291 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 Figure 2 – Number of rounds that the winner led in the academic competitions with 8 rounds Figure 3 – Number of rounds that the winners led in the general competitions with 4 rounds 292 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 CONCLUSIONS The dominance is present in all competitions analyzed; that is, academic competitions, general competitions, both using manufacturing and retailing simulations. Further, no significant differences were found in terms of dominance in academic competitions versus general competitions and in manufacturing competitions versus retailing competitions. Combined results indicate that dominance does exist in the business games competitions analyzed. However their differences are not significant in terms of competition type or simulator used. Table 7 – Analysis of Variance – Academic competition dominance versus general competition dominance x' x'' Sample size 439 56 Minimum 12.5 25 Maximum 100 100 Range 87.5 75 Mean 60.1650 57.143 Variance 638.5085 379.87 d.f. 438 55 d.f. adjusted 493 Variance adjusted (Sa²) 573.6503 t value testing mean=0 is 0.0803 Significance level (p) is 0.9360 (93.6%) for 2 sided test Where: x' = Dominance level of academic competition x'' = Dominance level of general competition Table 8 – Analysis of Variance – Retailing simulation dominance versus manufacturing simulation dominance x' x'' Sample size 412 83 Minimum 12.5 14.29 Maximum 100 100 Range 87.5 85.71 Mean 60.7010 55.636 Variance 627.2731 503.0863 d.f. 511 82 d.f. adjusted 493 Variance adjusted (Sa²) 532.1936 t value testing mean=0 is 0.155202 Significance level (p) is 0.8767 (87.7%) for 2 sided test Where: x' = Dominance level of retailing simulation x'' = Dominance level of manufacturing simulation Patz’s (1992, 1999, 2000, 2006) studies indicate that dominance exists to different students (BBAs and MBAs), simulators (MICROMATIC, CORPORATION, BSG) and settings (face-to-face and online). Although the definition of dominance had been different than the definition used in Patz’s studies, the results found in this paper add external validity to dominance in business games. Replications were conducted in 293 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 two more simulators (SIMCO and SIND) and two online types of business games administrations. Most important, dominance is present not only in academic regular courses, but also in competitions as well. As business games competitions have a high abandon rate, one important issue raise: Is the dominance one determinant factor to the abandon of teams in business game competitions? If so, how can the coordination manage this issue to decrease the abandon rate in business game competitions? These issues could be addressed in future research. REFERENCES Goosen, K. R., Jesen, R., Wells, R. (2001) “Purpose and learning benefits of simulations: A design and development perspective”. Simulation & Gaming. Vol. 32, 1, 21 - 39 Patz, Alan L. (1992), “Confidence extremes diminish quality performance in a total enterprise simulation,” Develops in Business Simulation and Experiential Learning Vol. 19, 136 – 140. Reprinted in the Bernie Keys Library, 9th edition [Available from http://ABSEL.org] Patz, Alan, L. (1999), “Overall dominance in total enterprise simulation performance,” Develops in Business Simulation and Experiential Learning Vol. 26, 115 - 116. Reprinted in the Bernie Keys Library, 9th edition [Available from http://ABSEL.org] Patz, Alan, L. (2000), “One more time: Overall dominance in total enterprise simulation performance,” Develops in Business Simulation and Experiential Learning Vol. 27, 254 – 258. Reprinted in the Bernie Keys Library, 9th edition [Available from http://ABSEL.org] Patz, Alan, I. (2006), “The BUSINESS STRATEGY GAME: A performance review on the on-line edition” Develops in Business Simulation and Experiential Learning Vol. 33, 58-64. Reprinted in the Bernie Keys Library, 9th edition [Available from http://ABSEL.org] SIMCO – Retailing Simulation. Bernard Sistemas (Versions 2005, 2006, 2007, and 2008). Florianópolis, Brazil: www.bernard.com.br SIND – Manufacturing Simulation. Bernard Sistemas (Versions 2002 and 2006). Florianópolis, Brazil: www.bernard.com.br Teach, R.; Patel, V. (2007). “Assessing Learning in a Business Simulation.” Developments in Business Simulation and Experimental Learning, Vol. 34, 76-8. Reprinted in the Bernie Keys Library, 9th edition [Available from http://ABSEL.org] Washbush, J., Gosen, J. (2001). An Exploration of Game- derived Learning in Total Enterprise Simulations. Simulation & Gaming. vol. 32 Nº 3, 281-296. 294 | Developments in Business Simulation and Experiential Learning, Volume 36, 2009 http://www.bernard.com.br/ http://www.bernard.com.br/ Table of Contents Volume 36, 2009 Protecting Academic Integrity: Student Assessment In The Online Environment Pedagogical Shift: Teaching Report Writing For Accountants Online Increasing Student Learning In An Investment Management Course Through The Innovative Use Of Experiential Learning Pedagogy A Moral Development Unit For Business Courses Dividing Up Grandma’s Things: A Multifaceted Exercise In Critical Thinking Application Of Haekel’s Thesis To ABSEL Development Linking Stories With On-Line Threaded Discussions For Critical Thinking In Management Curriculum Why Have We Neglected Vicarious Experiential Learning? A Triadic Multi-Disciplinary Approach To Enhancing The Efficacy Of Experiential Learning Movement Toward Increased Student Roles In The Design Of Experiential Exercises Ready-Mix Concrete Company: An Experiential Exercise In Management Theory Using Portfolio Theory In A General Management Simulation An Experimental Analysis Of Advertising Strategies And Advertising The Relationship Between Goal Orientation And Simulation Performance With Attitude Change And Perceived Learning Beyond The Profitable-Product Death Spiral: Managing Product Mix In An Environment Of Constrained Resources Online Marketing Control With The Strategic Business Unit Analysis Package Beat The Market Game Creating Decision Support Systems In Business Simulation Games The Use Of Computer-Assisted, Interactive Role-Play Simulation In China Asia Marketing: An International Business Game An Empirical Test Of “Behavioral Immersion” In Experiential Learning Existing And Emerging Business Simulation-Game Design Movements Computerized Business Simulations: A Systems Dynamics Process Service Launch Evaluating Business Plans In A Simulation Environment Teaching Software Development By Means Of A Classroom Game: The Software Development Game Group Decision Experiments Using Business Game - Problem Solving With Conflict Luna: A Role Play Game For Learning Incoterms 2000 Entrepreneurship: A Game Of Risk And Reward Phase I -- The Search For Opportunity The Wee Game: A Pre-Game Learning Inhibitors In Business Simulations And Games New Version Of An Old Simulation Helps Build A Perfect Capstone Course Project Competitor: A Simulation Game For Project Management With 2 Models And 2 Modes Mitigating The Winner’s Curse In The Auction Market Of A Computer-Assisted Business Gaming Simulation Evaluation Of Collaborative Filtering By Agent-Based Simulation Considering Market Environment Marketing Simulation Game Decision Making Experience And Its Impact On Indecisiveness Among Introductory Marketing Students Individual And Organizational Learning In A Top Management Game In Pursuit Of Stockholder Value: Reinforcing Core Concepts In A Business Strategy Simulation With A “Shadow” Stock Market Competition The Simplicity Paradox: Another Look At Complexity In Design Of Simulations And Experiential Exercises In Search Of The Ethnocentric Consumer: Experiencing “Laddering” Research In International Advertising The Paradise Islands Revisited: Trouble In Paradise Developing And Assessing Student Information Literacy Competency Do First Mover Advantages Exist In Competitive Board Games: The Importance Of Zugzwang The Natural Debriefing Approach: A Case In A Simple Business Game Pursued For Perfect Communications New Product Development Simulation Dominance In On Line Business Games Competitions Cooperative Business Game With Framing Effect Experiential Exercises In The Online Environment Enhancing Web-Based Simulations With Game Elements For Increased Engagement The Ginseng Game Experience Business Using a Simple Business Simulation