Should Business Game Players Choose Their Teammates: A Study with Pedagogical Implications SHOULD BUSINESS GAME PLAYERS CHOOSE THEIR TEAMMATES: A STUDY WITH PEDAGOGICAL IMPLICATIONS Joseph Wolfe Experiential Adventures LLC Jwolfe8125@aol.com Robin McCoy University of San Diego rmurphy@sandiego.edu ABSTRACT Most top management business games have been designed to be team experiences. Despite the ramifications of this mandate, little research has been conducted on how the team’s members should be selected. The company staffing method may have severe ramifications as a major part of the learning anticipated by this experiential approach comes from the team’s interpersonal relations as they relate to the game’s model. An examination of two diametrically opposed methods for creating game teams was conducted. Randomly staffed versus self-staffed teams played a rela- tively complex computer-driven game for eight decision rounds. It was found self-selected teams were not more co- hesive than randomly staffed firms were and their ending- state cohesion levels were no better than those of the ran- domly staffed firms. They were, however, more profitable and less anxious about playing the game. Both groups were favorably disposed regarding this teaching technique after the simulation had ended. INTRODUCTION Schrieber (1958a; 1958b) announced the use of the first collegiate business game at the University of Washington in 1957. Since that time much and little has changed regarding their use, their structure and the environments within which they are played. At that time the game’s participants were “businessmen” and eight operating decisions per round in a five-firm industry was considered a challenging experience. The game was also hand-scored and the paradigm-setting business school report by Gordon and Howell (1959) had yet to be published. Today and fifty years later, much of this has changed. Female undergraduate business students are now in the majority (Digest, 2005). Many top management games require 40-200 or more decisions per round (Klein, Fleck and Wolfe, 1993) and most American business stu- dents will have played at least one computer-driven game during their college years (Faria, 1998). The reports by Pier- son (1959) and Porter and McKibbon (1988) have further broadened and liberalized business school curricula while most-recently the AACSB (2006) has reiterated its en- dorsement of the primacy of active learning methods in all coursework. More importantly many games are distributed, processed and played via the internet that has added an en- tirely new dynamic to the business gaming method. These have been monumental transformations. None- theless, many elements associated with a business game have remained the same and therefore continue to be prob- lematical. In a positive sense, this constancy can be attrib- uted to the wisdom and soundness of the design principles on which they were based. Their thorny aspects, however, are more associated with the demands of the teaching method they employ. It has been almost axiomatic that those who play a business game be grouped into teams. In this re- gard business gaming was at the vanguard as it anticipated the AACSB’s (2006) recommendations regarding active learning environments and the real-world’s needs for col- laborative decision-making skills due to flatter organiza- tional pyramids and operations within self-directed work groups (Antonioni, 1996; Blanchard, Carew and Parisi- Carew, 1996; Gordon, 1992; Lawler, Mohrman and Ledford, 1995). When the elements of group dynamics enter into the educational equation, a host of virtues and vices en- sue, just as is the case when working with real-world work groups as revealed in the management field’s classic litera- ture (Coch and French, 1948; Roethlisberger and Dickson, 1939). When a business game is used for instructional pur- poses the instructor can make many choices as to how the game’s teams will be formed (Connerly and Mael, 2002; Hamlyn-Harris, Hurst, von Baggo, and Bayley, 2006). Un- fortunately, there has been much anecdotal but relatively lit- tle empirical research on the pros and cons of alternative staffing methods (Bacon, Stewart and Anderson, 2001; Imel and Tisdell, 1996). There is much agreement that players should be put onto teams because of the benefits of group- based learning (Feichtner, and Davis, 1985; Hernandez, 2002; Johnson, Johnson and Smith, 1991; Michaelsen, 1994; Michaelsen, Bauman, Knight and Fink, 2004; Muller, 1989; Proll, 1972). There is also agreement that students should be put on teams for practice of the team decision- making skills required by employers in today’s workplace (Alexander and Stone, 1997; Antonioni, 1996; Blanchard, Carew and Parisi-Carew, 1996). The problem then is one of how to assign players to a group that possesses the range of attitudes and skills that allows its members to learn from Developments in Business Simulation and Experiential Learning, Volume 35, 2008 315 mailto:Jwolfe8125@aol.com mailto:rmurphy@sandiego.edu each other in a task-oriented situation while manifesting the cohesion required of an effective decision-making unit. The value of learning groups, and the need for group decision-making skills in the real world, has been firmly es- tablished. Accordingly, many recommend using self-guided groups as often as possible so students can learn how to work in a collaborative fashion (Ely and Thomas, 2001; Jehn, Northcraft and Neale, 1999; Katzenbach and Smith, 1993; Watson, Kumar and Michaelsen, 1993). Because a group’s cohesion has been found to be the most important mediator of its structure, morale and productivity (Chang and Bordia, 2001; Golembiewski, 1962; Katz and Kahn, 1978; Lott and Lott, 1965; O’Reilly, Caldwell and Barnett, 1989; Seashore, 1954) many instructors allow their players to self-select their teammates assuming that self-selection is the most-likely way to assemble learning teams that will be cohesive, highly socialized, motivated and in possession a high sense of ownership of the results produced (Bacon, Stewart and Stewart-Belle, 1998; De Vita, 1999; McCain, 1996; Mello, 1993; Payne and Monk-Turner, 2006). Other instructors randomly assign players to teams after observing that self-selection results in teams with high cohesion but low diversity (McCain, 1996; Tonn and Milledge, 2002). More importantly, the self-selection criteria players use may not be the criteria necessary for successful group work, op- timal learning and high game performance (Connerley and Mael, 2001; Muller, 1989). Given the lack of a sense of what is necessary for a group’s success in a business game from a personnel selection perspective, many students choose their teammates based on previous social relation- ships (Levine and Moreland, 1990). Even more importantly, from a class-conduct standpoint and the need to create teams with an equal ability to compete in the simulation, the result of the self-selection process can produce a leftover pool of marginalized individuals who are forced to create teams based on no prior affiliations or sentiments. Nonetheless using self-selection as a way to form busi- ness game teams seems to be a safe method for the instruc- tor. Most students like higher education’s trend towards group learning methods (Deeter-Schmeltz and Ramsey, 1998; Ford and Morice, 2003; McCorkle, et. al., 1999; McKinney and Graham-Buxton, 1993) and they are suspi- cious of instructor-controlled team assignment methods (Connerley and Mael, 2001). Those who have used learning groups have noted, however, that not all in the group learn equally, assigning individual grades to group results is diffi- cult to achieve and there can be “free riders” and social loafers who reap the benefits of the team’s results without expending any effort (Joyce, 1999; Latane, Williams and Harkins, 1979).The presence of non or low-participating team members can lead to a sense of inequity or a belief that it is inherently unfair for the instructor to use a team ap- proach to learning where the group as a whole is graded al- though not all members participated equally in earning or being responsible for the award given. Moreover, real- world managers do not get to pick their peers, which is what is done when using self-selection as a basis for creating a business game team. In the real world, peers are chosen more for their potential economic contributions to the group’s purpose rather than for purely social comfort. Because an instructor can greatly influence the amount of learning that comes from a group-oriented business game by how the group is initially formed (Bandura, 1986; Chapman and Van Auken, 2001) a study was undertaken of how two diametrically opposed team staffing methods af- fected (1) the firm’s cohesiveness, (2) an individual’s moti- vation to play and be involved in the learning experience, (3) player attitudes towards the group experience and (4) economic performance outcomes. LITERATURE REVIEW The literature relevant to this study is vast and deep given the pervasiveness of groups in all their guises and situations. The essential nature of many of the conundrums associated with groups, however, was captured by Spinoza in the mid-1600s in his observation that “Man is a social animal”. Because of this social nature, man on the one hand is sustained and given life by the group. On the other hand, man is a captive of the group, must obey its dictates, and therefore loses personal freedom. Therefore, man is not “free” but instead must balance personal needs for free ex- pression with the needs for sustenance and socially-derived self-worth. When Frederick Taylor conducted his pig- loading efficiency studies at Midvale Steel in 1899, the rate- busting “Schmidt” had to be protected from his fellow workers because he was a “rate buster” (Wrege and Perroni, 1974). Thirty years later the intricacies of the group’s power was discovered during the productivity studies conducted at Western Electric’s Hawthorne Works in the late-1920s (Mayo, 1933). When dealing with the group literature one must differentiate that which is concerned with purely social groups versus task group situations such as those found at Midvale and the Hawthorne Works. In the case of social groups, its members come together for self-pleasure. Task groups, however, are formed purposively to accomplish either immediate or long-term ends. This literature review will restrict itself to the group task side of the literature because business game players are put onto teams that exist in task oriented situations— they create or take over simulated companies and run them to achieve measurable, economic outcomes. In the process of doing this individual learning is supposed to occur. The review will deal first with the nature of the group formation process as it relates to the creation and maintenance of effective task-oriented learning groups. It then deals with the group’s cohesion as the major variable related to the group’s sustenance and the group’s ability to perform and learn within the experiential environment created by a business game. GROUP FORMATION, COHESION, TEAMWORK AND TASK ACCOMPLISHMENT In the now-classic presentation Tuckman (1965) outlined a typical group’s developmental stages. A Developments in Business Simulation and Experiential Learning, Volume 35, 2008 316 reasonable accomplishment of each stage’s needs is required if the group is to progress onto its next stage. 1. Forming—In this stage the group’s members first come together. They are more-or-less strangers to each other as applied to what the group will have to accomplish in both a social and task sense if it is to be successful. 2. Storming—All groups enter this stage but many never leave it. Here the group’s members seek to set an agenda that embraces their personal issues. This can impede real progress as these agenda may be contradic- tory or self-cancelling. To be successful the group needs to determine mutually agreeable ends as well as the means for accomplishing those ends. The healthy group addresses each member’s needs, brings to light hidden agenda and agrees upon an acceptable leader- ship model. 3. Norming—If successful at the group’s Storming stage the workgroup moves onto its Norming stage. Here the team’s members accommodate each other’s actual be- havior and routinize their work habits so that operations and decisions flow smoothly. Trust begins to build be- tween the group’s members and motivation increases as it becomes apparent this new-shared experience will be personally and professionally fulfilling. 4. Performing—Once the group has established its per- sonal and performance norms it can begin to accom- plish its agreed-upon tasks. High-performing teams ex- perience minimal conflict and operate in an almost automated fashion. Any dissent is handled routinely within the norms set by the group. 5. Adjourning—Tuckman (1977) later added this stage to the process, which is especially appropriate to class- room associated business games as they have been de- signed to end at a particular time and the learning group’s existence is no longer necessary. Some have called this stage as being one calling for Mourning as with the group’s expiration its members will no longer receive the rewards it has grown accustomed to receiv- ing. How the above steps play out, however, is necessarily circumscribed by the school’s geographic setting because the instructor can only deal with the students available at the time the groups are formed. These students may be diverse or relatively homogenous regarding their ages, ethnicity, work histories and gender. It has been noted, however, that diversity based on such typical criteria as age, ethnicity and gender is merely a superficial or surface-level diversity that ignores the deep-level diversity present in the group (Harri- son, Price and Bell, 1998). This deep-level diversity em- braces the true divisions and value orientations possessed by each person. For educational purposes, however, it has been found in real-world business situations deep-level diversity can be modified within systems possessing strong values and meaningful reward systems (Westphal and Milton, 2000). The value of diversity for both educational purposes and real-world group productivity has been firmly established because diversity brings alternative perspectives and talents to the workgroup (Ely and Thomas, 2001; Jehn, Northcraft and Neale, 1999; Watson, Kumar and Michaelsen, 1993; Williams and O’Reilly, 1998). Diversity, however, is a two-edged sword. It necessity is actually a function of the nature of the group’s tasks. If its tasks are routine, do not require creative responses and have low interdependencies between its members one mind-set is all that is needed (Barrick, Bradley and Colbert, 2007; Van de Ven, Delbecq and Koenig, 1976). Thus for a relatively simple game such as The Executive Game (Henshaw and Jackson, 1989) which has only one, easily-calculated decision for each of the simulated company’s functions, the presence of number of different perspectives could be self- defeating. In a typical classroom situation the instructor has a group of students who are placed by some method onto teams. It is then up to these newly formed individuals to make themselves into an effective, task-oriented work group. In many cases a good workgroup comes about through the natural social psychological factors found in the situation. If all comes together well the group becomes en- ergized, individual needs become subordinate to the group’s needs and all contribute equally to the team’s results. While this happy result often happens, just the opposite can occur to the detriment of a valuable learning experience. If the chemistry is wrong a malaise falls across the group. Some individuals mentally and physically dropout and others try to take up the slack in the pursuit of their own self-interests. More importantly, little of the exercise’s intended learning results do not occur and those results are unequally and in- consistently spread across the group’s members. For better or worse Exhibit 1 indicates the business gaming process involved as it is related to the chemistry and results that ac- company staffing teams for learning. TEAM STAFFING AND COHESION-RELATED STUDIES EXHIBIT 1 THE BUSINESS GAMING PROCESS Developments in Business Simulation and Experiential Learning, Volume 35, 2008 317 This review will necessarily be brief although it will emphasize the designs and instruments used to examine how an instructor’s company staffing method affects player atti- tudes towards their group and business games as teaching devices, the team’s cohesiveness and player participation rates, the degree of motivation demonstrated and company performance outcomes. Because the business gaming movement’s founders always placed their players on teams the group effect was noted early. Using data collected in 1961 Dill and Doppelt (1963) reported that within their groups playing The Carnegie Tech Management Game (Cohen, Dill, Kuehn and Winters, 1964) the amount of re- ported satisfaction with the gaming experience was associ- ated with the player’s position in their simulated companies. As they reported (Cohen, Dill, Kuehn and Winters, 1964, p. 41) After the game, presidents and marketing managers— the men with the most difficult and time-consuming jobs—have consistently been happiest about their ex- periences. The finance, production, and research and development managers were next most satisfied. The controllers, operations research specialists, and execu- tive vice-presidents were least satisfied. The least- satisfying jobs are so in part because within the context of the game, they are regarded as routine and unchal- lenging—they offer the fewest opportunities to learn or to take actions that will affect the fortunes of the team. It was not divulged as to how the study’s teams were staffed or how individuals were placed within their companies even though these choices had a great effect on what was learned and satisfaction with their company’s efforts. This satisfaction, which varied with the player’s centrality to the firm’s vital processes, was also associated with the reported learning sources where 76.0% of the learning was attributed to interactions within the team itself rather than from the game’s model. Over the next three decades the business gaming field saw a number of studies on the role team cohesion plays on learning, satisfaction with or attitudes about the game experience and player motivation. It has been found that high cohesion is in most cases associated with the team’s productivity (Gentry, 1980; Gosenpud and Miesing, 1992; Gosenpud, Miesing, and Milton, 1984; Gosenpud, Milton and Larson, 1985; Hornaday and Ensley, 2000; Hsu, 1984; Miesing and Prebel, 1985; Neal, 1997; Norris and Niebuhr, 1980; Wolfe and Box, 1988), it can be created or manipulated (Deep, Bass and Vaughan, 1967; Wolfe, Bowen and Roberts, 1989) and may change (Neal, 1997; Wolfe and Box, 1988; Wolfe, Bowen and Roberts, 1989) or remain relatively constant regardless over the game’s duration (Wellington and Faria, 1996). Cohesion has been measured both via observation, interviews, essays, and most-often when an instrument is used, by some version of the scales created by Seashore (1954). Along the way the role of a strong but integrative leader (Wolfe and Box, 1988) has been noted as well as there being a need for teamwork or consensus amongst the players (Gosenpud, Miesing, and Milton, 1984; Hornaday and Ensley, 2000). Few studies have been conducted on the effects of op- posing team-staffing methods in business course work al- though it has been confidentially assumed that self-selected teams are more cohesive and therefore have the best chance of being productive (Hergert and Hergert, 1990). Unfortu- nately, what literature that has been produced suffers from the lack of true experimental designs and is often anecdotal in nature (Bacon, Stewart and Silver, 1999; Muller, 1989). Most-often, one team staffing method is examined and then an opposing staffing method’s effects are subjected to a speculative interpretation (Chapman, et al., 2006). Regarding direct, true experimental alternative staffing method studies only one controlled research design has been used in the business gaming field. Hsu (1984) split 63 stu- dents between self-selected and instructor-assigned groups for a computer-based business game in a senior-level busi- ness policy course. It was presumed the self-selected com- panies would be more cohesive and therefore would obtain for themselves the many benefits associated with self- selection such as openness, flexibility, participation and mo- tivation. In reality, neither staffing method had an effect on player attitudes or behavior. One other study that is rigorous and experimental in nature compared the attitudes and group dynamics associated with self-assigned teams versus ran- domly assigned teams. In this case, however, the students were engaged in various projects rather than a business game. Chapman, et al. (2006) collected survey instruments from 583 mostly-senior students enrolled in four different marketing courses spread across 16 sections. A semester long group project was part of each course’s grade require- ment. Groups ranged in size from 2-6 with a modal size of four. Regarding the study’s outcome measures, no differ- ences in self-assessed goal achievement, the effectiveness of using groups as learning environments, the amount of within-group conflict and self-assessed grades were found. On the affirmative side, the self-selected groups expressed more-positive group dynamics and attitudes towards the group experience. HYPOTHESES TESTED Based on the literature’s classic, unanswered questions, the following hypotheses were tested. They have been pos- ited using self-selection as the basis of comparison as this study’s literature review has indicated this assignment method generally produced the most-positive results regard- ing motivation and attitude levels. It is the assignment method preferred by players. 1. Self-assigned teams will manifest higher pre-game co- hesion levels than random-assigned teams. 2. Self-assigned teams will manifest higher post-game co- hesion levels than random-assigned teams. 3. Self-assigned teams will express higher pre-game teamwork expectations than random-assigned teams. 4. Self-assigned teams will express higher post-game teamwork beliefs than random-assigned teams. Developments in Business Simulation and Experiential Learning, Volume 35, 2008 318 5. Self-assigned teams will express higher pre-game fair- ness expectations than random-assigned teams. 6. Self-assigned teams will express higher post-game fair- ness beliefs than random-assigned teams. 7. Self-assigned teams will express higher pre-game per- formance expectations than random-assigned teams. 8. Self-assigned teams will express higher post-game ap- praisals of their performance than random-assigned teams. 9. Self-assigned individuals and teams will be more moti- vated than will random-assigned individuals and teams during the game’s entire course. 10. Members of self-assigned teams will be more-equally participative than will be members of random-assigned teams. 11. Self-assigned companies will be associated with higher economic outcomes than will be random-assigned com- panies. The first two hypotheses test the degree that self- assignment creates highly cohesive teams and the degree to which this cohesion is enduring over the game’s course. The next two hypotheses test for the existence of good teamwork that accompanies highly cohesive workgroups. Hypotheses 5-6 test whether a sense of fairness is associated with a team’s staffing method. The next two hypotheses deal with each player’s performance expectations both before and af- ter the fact. Hypothesis 9 determines whether self-selection results in more-highly motivated players while hypothesis 10 looks to see whether the team’s actual workload was evenly distributed and there was a minimum amount of “free riding”. Finally, the last hypothesis tests whether self- assigned teams are more productive in an economic sense. METHOD The study’s subjects (n=30) were seniors enrolled at a large mid-western state university. The subjects came from two separate back-to-back semester-long sections of a cap- stone-type strategic management course. Table 1 describes the characteristics of the subjects where statistical tests indi- cated there were no significant between-section differences in their composition. The same instructor taught both sec- tions, was an experienced user of the game and pursued the same learning objectives for both sections. Performance in the simulation directly counted for 10.0% of the course’s fi- nal grade with another 20.0% associated with team write- ups and presentations related to its economic performance. The participants played eight weekly rounds, or two simu- lated business years of The Global Business Game (www.onlinegbg.com) at the rate of two decision sets per week. The game itself is a flexible and relatively complex computer-based online game. It allows players, at the in- structor’s discretion, to implement a wide variety of strate- gic and tactical decisions in the television set industry for the world’s three major trading communities. In this case, the instructor chose the game’s stable NAFTA version. In this form, players could make up to 130 individual decisions per round with business conducted in Mexico and the United States in a nonfluctuating economic environment. As an indication of how team diversity could be an asset in the game Table 2 indicates the decision area coverage a suc- cessful team must accomplish by functional area and coun- try. Game play began in the course’s 7th week after the stu- dents had been assigned to learning groups and had been presented the course-related concepts believed to be neces- sary for successful game play. As determined by a coin-flip before class time one section’s students were randomly as- signed (RAND) to companies. The other section’s students were allowed to self-select (SELF) their teammates. Class time was devoted to assembling these groups. Two indus- tries with identical economic parameters and playing condi- tions were then created with each having an approximately equal number of companies. Table 3 shows the two indus- tries created and the sizes of their firms. Each player’s expectations and realizations regarding the elements that lead to the fulfillment of a successful workgroup’s task and social elements, attitudes towards the gaming experience and performance outcomes were col- lected. A “Before” version of an instrument based on that created by Chapman, et al. (2006) was applied immediately after the teams had been formed. The subjects’ post-game cohesion levels, attitudes and beliefs were collected via an “After” version of the same instrument one day before the competition had ended. The instrument itself is a collection of measures used in past group dynamics research. This study’s Teamwork scales came from work originally done by Berry (1995) as adapted for the classroom by Deeter- Schmelz and Ramsey (1998). The Price and Mueller (1986) scales were used to measure Cohesion as adapted for class- room use by Chapman, et al. (2006) following the work of Seashore (1954). The Fairness scale, or a team member’s sense of equity, was taken directly from the Chapman, et al. (2006) instrument. Cronbach alpha values indicate instru- ment reliability (Teamwork alpha = .96, Cohesion alpha = .90, Fairness alpha = .93; Nunnally, 1978; Peterson, 1994). Motivation levels were measured by how often the game’s interface was used within and across all members of each management team. The measuring technique used here is considered to be superior to either student self-reports or visits to the instructor’s office (Schriesheim and Yaney, 1975) as have been used in the past. The Global Business Game, as an online simulation, keeps a record of each player’s activities for the instructor’s use. Over the game’s course players can access its interface to view and print-out past results, seek online help, make new decisions and sub- mit their company’s quarterly decision set. They can also in- teract online within their teams as they make their decisions and they can negotiate between companies in the attempt to strike strategic alliances. This game feature was used to re- cord each action taken by a player as an episode that indi- cates an interest in the company’s progress and being able to actively contribute to any team decision-making session. Al- though each episode’s duration could be determined, the de- gree to which each player actually used the interface during the episode could not be established. Therefore, only the na- ture of each episode was noted and categorized as follows as Developments in Business Simulation and Experiential Learning, Volume 35, 2008 319 http://www.onlinegbg.com/ it might be related to individual motivation, teamwork and decision-making centrality: TABLE 1 SUBJECT DEMOGRAPHIC CHARACTERISTICS Demographic Characteristic Proportion/ Average Female 48.3% Male 51.7% Age 22.4 Major: Accounting 40.0% Finance 20.0% HR/Management 13.3% Marketing 13.3% Business Administration 3.3% Not reported 10.0% Grade-point-average 3.2 TABLE 2 POSSIBLE DECISIONS BY FUNCTION AND COUNTRY Country Functional Area U.S. Mexico Channel Management 8 8 Construction 11 12 Finance 8 5 Logistics 4 4 Marketing 10 6 Market Research 12 0 Personnel Administration 14 14 Production 7 7 Strategic Alliances 7 6 Supply Chain Management 6 6 Total 87 68 TABLE 3 INDUSTRIES, COMPANIES AND TEAM SIZES Assignment Method RAND SELF Industry A Industry B Firm 1— 4 players Firm 1— 3 players Firm 2— 3 players Firm 2— 3 players Firm 3— 4 Players Firm 3— 3 players Firm 4— 3 Players Firm 4— 4 players Firm 5— 3 players 1. The number of episodes engaged in by player—A be- havioral measure of the individual player’s motivation, enthusiasm or willingness to engage in the firm’s activi- ties. 2. Individual Participation Index—A measure of the de- gree to which the player engaged in a fair-share of the total number of episodes engaged in by all the team’s members. 3. Company Participation Index—The average of all indi- vidual within-company Participation Indexes. 4. Company Activity—The per-player average number of episodes by company. 5. Centrality—The proportion of all episodes recorded by the firm’s dominant participant. Regarding the calculation of the Participation Index if a player engaged in one-third of the episodes on a three- member team, that player would generate a Participation In- dex value of 1.00. Engaging in either more or fewer epi- sodes of the total episodes engaged in would result in index values proportionally lower than the ideal index value of 1.00. Regarding the Centrality Index, the number of epi- sodes engaged in by each quarter’s dominant player was noted over the game’s entire run and averaged by team staffing method. As a result, a high value indicates that one player consistently engaged in the most episodes for his/her company and simultaneously that the team’s between-player participation rate was low. The firm’s economic performance was based on the firm’s profitability. The simulation itself generates five in- dividual penultimate performance measures that produce a final within-industry ranked index that is a weighted combi- nation of the five previous measures. Profits were chosen as the firm’s only success indicator in this study, as opposed to the simulation’s other available measures of earnings per share, stock price and rates-of-return on assets and equity. This measure was chosen because the firm’s profits are the least susceptible to financial reporting manipulation as well as being the engine for calculating the game’s other eco- nomic performance measures. RESULTS Table 4 indicates the results associated with the tests of Hypotheses 1-2. It can be seen these hypotheses were re- jected as there were no significant differences in the players’ cohesion scores both before and after the game. Thus it ap- pears that allowing players to choose their teammates cre- ates a relatively high cohesion level on a scale of 1-7 but these cohesion levels are not superior to those associated with random assignment. The table shows that at the game’s end both assignment methods were associated with statisti- cally equal cohesion scores. Table 5, which tracks within team-assignment method cohesion, indicates each group’s cohesion scores did not change from their initial levels. More importantly, based on their experience with the game, the randomly assigned players found their teammates were more trustworthy and the group was more enjoyable to work with than originally thought. The self-selected teams found their teammates were less personally interested in them than they originally believed would be the case given they had chosen them as partners. Developments in Business Simulation and Experiential Learning, Volume 35, 2008 320 TABLE 4 PRE-GAME AND POST-GAME MEASURES BY TEAM ASSIGNMENT METHOD Measures and Scales Pre-Game Post-Game Cohesion RAND SELF RAND SELF Friendly 6.07 6.13 6.36 6.44 Helpful 5.93 6.27 6.21 6.25 Personally interested 4.57 4.93 3.86 4.13 Trustworthy 5.29 5.93 6.14 5.94 Enjoyable group 5.36 5.80 6.00 6.13 Teamwork RAND SELF RAND SELF Open lines of communication 5.71 5.80 6.00 6.25 Enthusiastic about working together 4.86 5.40 6.00 5.56 Follow through on commitments 5.79 6.07 5.79 6.25 Pride in work 5.79 5.87 6.57b 5.88 Stay focused on tasks 5.71 6.00 6.21 5.87 Resolve conflict effectively 5.57 6.00 6.36 6.13 Fairness RAND SELF RAND SELF A group leader 5.36 5.07 5.36 4.00a Fair share of the work 6.07 6.07 5.64 5.88 Not worry about grade 3.29 4.14 4.50 4.38 Work divided evenly 5.57 6.47b 5.36 5.56 Ratings based on a 7-point scale with 1 = Strongly disagree and 7 = Strongly agree. aSignificance p < .01. bSignificance p < .02. A return to Table 4 shows self-selection results in higher teamwork feelings than for those who were randomly assigned to their companies. The table also indicates Hy- pothesis 4, which stated the self-selected teams would mani- fest more expressions of teamwork, must be rejected. In fact, the randomly assigned group’s feelings about pride in their company’s work were superior to those of the self- selected teams. After playing the game, it turned out in Ta- ble 5 the RAND-group’s pessimism about working together, being proud of their work and being able to resolve conflict was not warranted. Hypotheses 5-6 stated there would be a superior level of fairness and equitable effort associated with being on a self-selected team both before and after the game. Neither of these hypotheses were supported. There were no superior feelings before the game and in fact, Table 4 indicates there were superior feelings for the RAND-group on the workload being evenly divided and inferior feelings for the SELF- group on there being a group leader. The next two hypotheses stated the SELF-group would have higher expected outcomes and that after the game they would state higher than actual performance results for them- selves (Neal, 1997; Wellington and Faria, 1996). Table 6 shows the expected outcomes hypothesis was rejected. Be- fore the game began both groups were the same regarding the belief they would achieve their company’s goals and would be proud of the results they would obtain. They also assigned themselves the same grade for their work. After the game, the hypothesis operated in the opposite fashion for the RAND-group as its companies were more proud of their results than were the SELF-companies. Regarding the self- assessed post-game grade, both groups were somewhat de- lusional. Table 7 presents the actual cumulative profit re- sults posted by each company by team assignment method along with the grades each company would receive assum- ing the first-place company received a grade of 100.00 and the base line for a grade was a possible 65.00 if a negative profit condition was found. On this basis, RAND’s average game grade might have been estimated to be 75.5 rather than its 90.86 and SELF’s grade could have been estimated to be 82.8 rather than its estimated 88.07. Certainly wishful thinking is involved in these estimates but the players were well-aware that their respective industries featured a leading company that far outpaced the others which would necessar- ily place the remaining firms far behind in their grades. It was hypothesized self-selection would result in more- highly motivated teams as indicated by the number of epi- sodes engaged in by each player. The long-term average number of episodes by quarter in the game by each assign- ment group shown in Table 8 was nonsignificantly different. Thus Hypothesis 9 is rejected. Hypothesis 10 stated self-selected players would more- equally participate within the teams they created. Table 9 indicates this was not the case as the average participation rates were statistically equal on both a quarterly and total game basis. It should be noted the average Participation In Developments in Business Simulation and Experiential Learning, Volume 35, 2008 321 TABLE 5 PRE-GAME VS. POST-GAME MEASURES BY TEAM ASSIGNMENT METHOD Measures and Scales RAND SELF Cohesion Pre-game Post-game Pre-game Post-game Friendly 6.07 6.36 6.13 6.44 Helpful 5.93 6.21 6.27 6.25 Personally interested 4.57 3.86 4.93 4.13b Trustworthy 5.29 6.14b 5.93 5.94 Enjoyable group 5.36 6.00c 5.80 6.13 Teamwork Pre-game Post-game Pre-game Post-game Open lines of communication 5.71 6.00 5.80 6.25 Enthusiastic about working together 4.86 6.00a 5.40 5.56 Follow through on commitments 5.79 5.79 6.07 6.25 Pride in work 5.79 6.57b 5.87 5.88 Stay focused on tasks 5.71 6.21 6.00 5.87 Resolve conflict effectively 5.57 6.36a 6.00 6.13 Fairness Pre-game Post-game Pre-game Post-game A group leader 5.36 5.36 5.07 4.00b Fair share of the work 6.07 5.64 6.07 5.88 Not worry about grade 3.29 4.50 4.14 4.38 Work divided evenly 5.57 5.36 6.47 5.56a Ratings based on a 7-point scale with 1 = Strongly disagree and 7 = Strongly agree. aSignificance p < .01. bSignificance p < .02. cSignificance p < .05. TABLE 6 PRE-GAME AND POST-GAME PERFORMANCE EXPECTATIONS AND RESULTS BY TEAM ASSIGN- MENT METHOD Pre-Game Post-Game Performance Measures RAND SELF RAND SELF Achieve goals 5.93 6.00 5.43 5.50 Proud of results 6.00 6.07 6.15a 5.31 Game grade 91.64 90.84 90.86 88.07 a Significance p<.03. TABLE 7 PROFIT PERFORMANCE-BASED GRADES RAND Profit Grade Firm 1 $22,228,278 100.00 Firm 2 -$3,002,735 65.00 Firm 3 -$336,255 68.00 Firm 4 -$89,155 69.00 SELF Profit Grade Firm 1 $3,204,289 75.00 Firm 2 $5,733,926 77.30 Firm 3 $5,400,039 77.30 Firm 4 $28,847,642 100.00 Firm 5 $12,992,993 84.40 Developments in Business Simulation and Experiential Learning, Volume 35, 2008 322 TABLE 8 AVERAGE NUMBER OF EPISODES PER PLAYER BY QUARTER Decision Quarter Assignment Method 1 2 3 4 5 6 7 8 Average RAND 104.9 84.8 112.0 119.2 104.2 95.6 89.0 66.9 97.1 SELF 150.9 82.3 59.4 144.9 111.1 92.9 108.7 114.1 114.1 TABLE 9 AVERAGE COMPANY PARTICIPATION INDICES BY QUARTER Decision Quarter Assignment Method 1 2 3 4 5 6 7 8 Average RAND 0.50 0.37 0.49 0.41 0.41 0.40 0.48 0.43 0.44 SELF 0.65 0.54 0.32 0.46 0.44 0.39 0.40 0.40 0.45 EXHIBIT 2 AVERAGE COMPANY EARNINGS BY STAFFING METHOD -$1,000,000 $0 $1,000,000 $2,000,000 $3,000,000 $4,000,000 $5,000,000 $6,000,000 1 2 3 4 5 6 7 8 Quarter Ea rn in gs RAND SELF dices of 0.44 and 0.45 indicates there was much “free rid- ing” or low participation in many teams with one individu- aloften-recording most of each quarter’s episodes (Strong and Anderson, 1990). A review of each firm’s episode logs indicated that 10 of 30 players rarely or never accessed the game’s website or otherwise prepared themselves for the quarter’s decision-making session as the game’s interface records whether the screen’s output was printed, or was oth- erwise made available for viewing. The teaching/learning implications of this phenomenon will be discussed later as a decision-maker centrality issue. The profit results by companies within their respective industries have been graphed in Exhibit 2. As indicated, there were no significant performance differences in an in- dustry’s firms for the game’s first three quarters. Thereafter the self-selected companies outperformed the randomly as- signed companies as measured by a Mann-Whitney test of ranked performance differences (z = 2.71, p < 0.01). This latter-game superiority led to significantly higher total profit outcomes for the self-selected companies (z = 1.92, p = 0.028). Thus Hypothesis 11 is supported. Self-selection in a business game is associated with higher economic perform- ance. DISCUSSION This study’s results give rise to a number of issues that deal with the teaching power of a self-guided and group-run business game team, attitudinal predispositions towards group learning experiences, and the instructor’s role in cre- ating a viable learning environment. The results also indi- cate the instructor would be justified in using either method for assigning players to a game. This is because different af- fects were associated with each method. It was found for those who were randomly assigned to their groups, attitudes and beliefs about team cohesion and teamwork improved significantly. No changes occurred in the self-assigned groups. Thus the random assignment method should be cho- sen if the instructor wants to emphasize the nature of pre and post perceptions on a group’s attitudes and beliefs. This same lesson, however, could be taught via self-selection but in a negative fashion. The self-assigned players found a number of their pre-game beliefs were not true as their teammates were less personally interested in them, the pres- ence of a group leader often did not occur and the workload was often not divided evenly. A striking feature of this study’s results is how similar each group actually was despite the diametrically opposed team staffing method employed. Part of this lack of Developments in Business Simulation and Experiential Learning, Volume 35, 2008 323 TABLE 10 PRE-GAME KNOWLEDGE OF COMPANY MEMBERS Staffing Method Knowledge Score Random assignment 2.10 Self selection 2.59 Knowledge based on a 7-point scale with 1 = Knew Nothing and 7 = Knew a Lot TABLE 11 ATTITUDE TOWARD GROUP LEARNING METHOD BY TEAM ASSIGNMENT METHOD RAND SELF Group Learning Attitude Measure* Pre-Game Post-Game Pre-Game Post-Game Bad or good experience 4.29 5.08c 4.87 4.81 Waste or good use of time 3.79 4.38 4.67 4.44 Valueless vs. valuable 4.64 5.92a 5.87 5.38 Unsatisfactory or satisfactory 4.14 4.54 4.80 4.44 Unenjoyable vs. enjoyable 4.64 5.31 5.20 5.25 Useless or useful 5.14 5.62 5.80 5.19 Undesireable vs. desirable 3.36 4.23 4.33 4.06 Ineffective vs. effective 5.21 5.77 5.67 5.31 *Descriptors and scores cited from negative to positive for reporting purposes. aSignificance p < .01. cSignificance p < .05. difference could be traced to the homogeneity of the univer- sity’s student population. This fact could also be highlighted by viewing Table 10. It shows there was a non-significant difference in the SELF-teams’ knowledge about each other than the RAND players. Because of this, something other than knowing about the person was the criterion for choos- ing teammates but also that the pool of players to choose from was limited in breadth. It has been stated earlier that group work is both useful and necessary if today’s business student is to be adequately prepared for the workplace. Their attitudes toward group projects, however, are often negative (Pfaff and Huddleston, 2003). Table 11 indicates this study’s players had mixed feelings about the value of business game they were about to play. Both groups were somewhat neutral rather than positive regarding all aspects of the experience. The RAND- group, however, expressed a higher opinion of the experi- ence in Table 12 while there was no change in the opinions of the SELF-students. Interestingly, despite somewhat neutral pre-game opin- ions about the value of group learning experiences, the SELF-group was more optimistic on four out of eight meas- ures. Afterwards both groups were equal in their assess- ments of the experience. Based on this table there seems to be a “halo” attached to having players choose their own teammates as it appears self-choice enables them to feel more positive, or more in control of the situation. This pref- erence for self-selection can be seen in Table 13 both be- tween and within groups by staffing method. Thus, this evi- dence bodes well for using games as group learning experi- ences and that having players select their own teammates goes a long way towards lessening any initial fears about the technique. Based on this study’s findings it appears neither staffing method generates distinctly different groups. In their initial state they were equally cohesive, the teams were no more or less familiar with their partners, there were no differences in their behaviorally-demonstrated motivation and participa- tion levels, equal in their performance expectations, sensing fair play from their partners and feelings of teamwork on three of four measures. Despite these before-game similari- ties, something was different. As Exhibit 2 demonstrated, the groups experienced intermingled profits during the game’s first three quarters. After this important, strategy- implementing period, the groups diverged dramatically in their earnings. For the entire game the RAND-group’s aver- age quarterly earnings were $587,505 per quarter while they were $1,404,492 for the SELF group. Insight into why this divergence occurred might be gleaned by examining each team’s degree of decision- making centrality. As noted before by Dill and Doppelt (1963) learning and satisfaction with the gaming experience was tied to the individual decision-maker’s centrality or having a key role in the firm’s success. An examination of each firm’s centrality, or the degree to which one player dominated the number of episodes engaged in on a quarterly basis, was undertaken. A highly centralized firm would be one where the same person made the majority of the firm’s decisions. Under this condition it could be presumed the team’s other members played marginal roles and had low power within their company. Firms with low Centralization Indexes were more-or-less decentralized with different Developments in Business Simulation and Experiential Learning, Volume 35, 2008 324 TABLE 12 ATTITUDE CHANGES TOWARD GROUP LEARNING METHOD BY TEAM ASSIGNMENT METHOD Pre-Game Post-Game Group Learning Attitude Measure* RAND SELF RAND SELF Bad or good experience 4.29 4.87 5.08 4.81 Waste or good use of time 3.79 4.67 4.38 4.44 Valueless vs. valuable 4.64 5.87 5.92 5.38 Unsatisfactory or satisfactory 4.14 4.80 4.54 4.44 Unenjoyable vs. enjoyable 4.64 5.20 5.31 5.25 Useless or useful 5.14 5.80 5.62 5.19 Undesireable vs. desirable 3.36 4.33 4.23 4.06 Ineffective vs. effective 5.21 5.67 5.77 5.31 *Descriptors and scores cited from negative to positive for reporting purposes. aSignificance p < .01. cSignificance p < .05. EXHIBIT 3 COMPANY CENTRALITY BY STAFFING METHOD 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 RAND SELF C en tra liz at io n In de x individuals engaged in the majority of the firm’s decisions and episodes on a quarterly basis. Under this condition the knowledge of the firm’s strategy and its implementation was shared. Exhibit 3 shows the range of each group’s centrali- zation indexes. The RAND-group companies were more centralized than the SELF-group which indicates they were less flexible and more autocratic in their decision-making operations. The mean centralization index for the RAND- group was 0.80 with one firm having one player consistently engaged in 78.0% of each quarter’s episodes while also submitting and retrieving all its decisions and outputs. The Centralization Index was 0.62 for the SELF-group with no single player dominating all decision quarters. This observa- tion regarding the company’s degree of centralization merits further research. CONCLUSIONS AND RECOMMENDATIONS This study’s results suggest numerous areas of further research and improvements in its methodology. Research should be conducted using other games of greater and lesser complexity. A more-complex game, or a more-complex ver- sion of the study’s game that would require higher levels of coordination and interpersonal relationships which would require higher levels of cohesion , teamwork may result in different appraisals of the value of a business game experi- ence. Additionally this study’s instructor used the game in its static mode where economic conditions were frozen. Players facing the game’s dynamic environment would be facing a turbulent situation and this situation may have an effect on the firm’s needs for cohesion and decentralized decision-makers. This study should also be repeated at sites where the student population is more diverse. Drawing on this more-diverse population might place greater strains on each team’s ability to create an effective workgroup. Rela- tively homogenous groups may have naturally occurred due to a lack of a natural diversity in the pool of student avail- able for company staffing purposes. Developments in Business Simulation and Experiential Learning, Volume 35, 2008 325 This study also made extensive use of player online logs to determine player motivation, centralization and par- ticipation. These logs are only a partial measure of the group-centered activities player can engage in. The logs only indicated that a player had logged onto the game’s website but not how the website was used or how many players might have been viewing the monitor being used. The study also did not determine if players worked together face-to-face although it did know that teams did not printout their results so they could be used during joint decision- making sessions. 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Developments in Business Simulation and Experiential Learning, Volume 35, 2008 328 http://goliath.ecnext.com/coms2/browse_R_C100 Table of Contents Volume 35, 2008 Linking Team Covenants To Peer Assessment Of Simulation And Experiential Performance Using the Balance Scorecard Approach: A Group Exercise IndoAmerican Enterprises Class Size and Game Design Implementation Of Effective Experiential Learning Environments Simulation Sensemaking: The BusinessWeek Approach To Effective Debriefing A Lesson in Hide-And-Go-Seek: A Team Building Game Thoughts On How To Motivate Students Experientially Experiential Learning Is Not Just Experiential Teaching: Measurement of Student Skill Acquisition via Assessment Centers ABSEL Redux: Reflections after a 25 Year Hiatus College Student's Expectations of Technology- Enhanced Classrooms: Comparing 1996 and 2006 The Business Student Satisfaction Inventory (BSSI): Development and Validation of a Global Measure of Student Satisfaction The "Big Picture Question" Project: Explorations in Teaching Creativity within a Force-Field Research Framework "Viva Voce": Oral Exams as a Teaching & Learning Experience Internships And Occupational Socialization: What Are Students Learning? Does Learning Occur in One-Shot, Non-Cooperative Games? Beliefs and Behavior, an Ancient Perspective and Modern Application Developing Enterprise Culture Among the Students Through Intercollegiate Competitions: A Case of Student Enterprise Competition (SEC) 2007 Student Views of Management Skills and Their Future Careers after Using Business Simulations Back to the Future: Gender Differences In Self-Ratings of Team Performance Criteria A Case for Experiential Learning: Using Central Europe as a Classroom Modeling Strategic Opportunities in Product-Mix Strategy: A Customer- Versus Product-Oriented Perspective Assessment in the Modern Large High-Tech Classroom Target Profit Pricing With the Web-Based Breakeven Analysis Package Are the Business Simulations We Play Too Complex? Shared Experience as Incentive for Horizontal Integration in Business Simulations Affinity Propagation: A Clustering Algorithm for Computer-Assisted Business Simulations and Experiential Exercises Marketing Simulation Results as Embedded Forms of Program Assessment Human and Agent Playing the "Beer Game" Issues in Porting a LAN-based Total Enterprise Simulation Game to a Web-based Environment Partners or Competitors? A B2B Simulation Evaluation Model of the Global Performance of a Management Simulation for the Academic Environment Applying Bloom's Revised Taxonomy In Business Games Do Price Strategies Work in Business Simulations? Early Japanese Gaming Simulation Efforts Using Par Players to Enhance Learning in Business Simulations Corporate Cartooning: The Art of Computerized Business Simulation Design Should Business Game Players Choose Their Teammates: A Study with Pedagogical Implications Goal Orientation and Simulation Performance Design and Demonstration of an Online Managerial Economics Game with Automated Coaching For Learning and Graded Exercises for Assessment Are Good Strategy Decisions Consistently Good? A Real-Time Investigation Active Learning 2.0 or Wiki is not a 4-letter Word Comparing Student Learning in Online and Classroom Formats of the Same Course How Do We Get To Tomorrow? The Path to Online Learning Similar Media Attributes Lead to Similar Learning Outcomes Facilitating Business Gaming Simulation Modeling