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Journal of Education for Business
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Student Learning in Business Simulation: An
Empirical Investigation
Yang Xu & Yi Yang
To cite this article: Yang Xu & Yi Yang (2010) Student Learning in Business Simulation: An Empirical Investigation, Journal of Education for Business, 85:4, 223-228, DOI: 10.1080/08832320903449469
To link to this article: http://dx.doi.org/10.1080/08832320903449469
Published online: 08 Jul 2010.
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CopyrightC Taylor & Francis Group, LLC ISSN: 0883-2323
DOI: 10.1080/08832320903449469
Student Learning in Business Simulation: An
Empirical Investigation
Yang Xu
Penn State University, New Kensington, Pennsylvania, USA
Yi Yang
University of Massachusetts, Lowell, Massachusetts, USA
The authors explored the factors contributing to student learning in the context of business simu-lation. Our results suggest that social interaction and psychological safety had a positive impact on knowledge development in student groups, and that this synergistic knowledge development enabled students to form complex mental models. Implications of the findings are discussed.
Keywords: business simulation, knowledge development, mental model, social interaction, student learning
Business simulations have become an increasingly popular teaching method in business courses (Faria, 1998, 2001; Ke-effe, Dyson, & Edwards, 1993), such as business strategy (Stephen, Parente, & Brown, 2002), business ethics (Wolfe & Fritzsche, 1998), and courses on cultural differences (Chat-man & Barsade, 1995). In contrast to traditional teaching methods, business simulations bridge the gap between the classroom and the world of real-life business decision mak-ing through experiential learnmak-ing experiences in which stu-dents design, implement, and control business strategies. In sophisticated simulations, students think in strategic ways, solve complex problems, and integrate knowledge across business functions. In the microworlds created by business simulations, students can better understand the interactive ef-fects of environment, competitors, and employees (Romme, 2003).
In previous studies of business simulations, game per-formance is generally considered the dependent variable of interest (Anderson, 2005; Hornaday & Curran, 1996; Schoe-necker, Martell, & Michlitsch, 1997). Our research attempts to explore the factors contributing to the formation of stu-dents’ mental models. A mental model represents an in-dividual’s knowledge structure of a specific domain (Car-ley & Palmquist, 1992; Lyles & Schwenk, 1992; Wilson & Rutherford, 1989). Scholars have recognized the importance
Correspondence should be addressed to Yang Xu, Penn State University, Department of Business and Economics, 3550 Seventh Street Road, New Kensington, PA 15068, USA. E-mail: yux4@psu.edu
of mental models for student learning in management edu-cation (Dehler, 1996; Resnick & Klopfer, 1989). A critical task of business education is helping students develop knowl-edge structures of specific domains. People digest informa-tion and transform it to structured knowledge (Weick, 1995). However, few empirical studies have used mental models as learning outcomes in the business education literature (Nad-karni, 2003). This study addresses this research gap. Specifi-cally, we examine two questions regarding learning outcomes of complex computer-based simulations: First, what factors influence knowledge development in student groups, and, second, to what extent does this knowledge development in-fluence the complexity of students’ mental models? Next we present the conceptual model and research hypotheses, fol-lowed by the methods and results. Finally, we discuss the limitations and implications of our findings.
HYPOTHESIS DEVELOPMENT
Drawing on theoretical perspectives in social cognition, group processes, and organizational learning (Baldwin, Be-dell, & Johnson, 1997; Kasl, Marsick, & Dechant, 1997; Non-aka, 1994; Walsh, 1995), we developed a conceptual frame-work indicating that two factors—social interaction and psy-chological safety—are positively related to the development of synergistic knowledge (Figure 1). Furthermore, the devel-opment of synergistic knowledge enhances the complexity of the student’s mental model.
224 Y. XU AND Y. YANG
FIGURE 1 A conceptual model of student learning in business simulation
FACTORS IN SYNERGISTIC KNOWLEDGE DEVELOPMENT
Synergistic knowledge development refers to the process by which a group integrates individual members’ perspectives (Mu & Gnyawali, 2003). According to theories of organi-zational learning and social cognition, collective knowledge develops through the discussion and integration of the indi-vidual perspectives of a specific information domain (Non-aka; Senge, 1990; Walsh). A collective body of knowledge consists of representation, development, and use of spe-cific knowledge (Walsh). In business simulations, individual members interpret tasks with their own knowledge structure. Next, group members discuss and integrate their individ-ual knowledge and use this collective body of knowledge to manage the simulated company.
Business simulations focus on interactive problem solv-ing and complex trade-offs. Teamwork is usually required because of the complexity of the simulation. In this ac-tive learning process, students develop a collecac-tive body of knowledge by synthesizing the unique perspectives of the in-dividual members (Lang & Dittrich, 1982; Mu & Gnyawali, 2003). Building on previous studies, we hypothesized that two factors would contribute to synergistic knowledge de-velopment in student groups—social interaction and team psychological safety.
SOCIAL INTERACTION
Social interactionrefers to the process of communication in a group (Barker & Camarata, 1998). In business simulations, students need to understand, inform, and persuade their team-mates concerning various issues. They frequently discuss and debate because of the complexity and interconnectedness of the various elements of decision making. This high level of social interaction enhances the extent of discussion and dia-logue among group members (Mu & Gnyawali, 2003). First, social interaction drives the creation of collective meaning (Thompson & Fine, 1999). As students communicate and collaborate repeatedly with their peers, they tend to develop a sophisticated understanding of the simulation and iden-tify effective strategies and tactics. Second, social
interac-tion facilitates a feedback process that helps group members understand their performance and specific responsibilities, examine member actions, and decide future actions (John-son, John(John-son, Stanne, & Garibaldi, 1990). In the feedback sessions, students’ discussions may create a process of so-cial discovery, clarifying individual members’ opinions and centralizing their preferences (Eisenhardt, Kahwajy, & Bour-geois, 1997). Third, high social interaction enables people to exchange tacit knowledge necessary for complex problem solving (Nonaka, 1994). Learning is enhanced through ex-tensive communication among the group members (Baldwin et al., 1997); and knowledge is developed in this interactive process (Barker & Camarata). Consequently, we hypothe-sized that social interaction would play a positive role in synergistic knowledge development.
Hypothesis 1 (H1): In business simulations, the level of social interaction among group members would be positively related to the development of synergistic knowledge.
TEAM PSYCHOLOGICAL SAFETY
Team psychological safetyrefers to the group members’ be-liefs that members of their group are open and receptive to different perspectives and that the other members would not reject or punish someone for bringing a different viewpoint (Edmondson, 1999). This mutual respect and trust provides psychosocial support (Ibarra, 1995). At the same time, peo-ple in a psychologically safe environment display higher lev-els of self-efficacy and develop better mechanisms to deal with conflicts (Campion, Medsker, & Higgs, 1993). Mem-bers need to be open to others’ ideas to create productive group work (Kasl et al., 1997). The appreciation of oth-ers’ views enables the group members to integrate multiple views and develop synergistic knowledge (Mu & Gnyawali, 2003). Consequently, learning behavior is enhanced in the psychologically safe environment. Further, silent members are more likely to contribute to the discussion when the group members encourage group learning behavior and con-structive critique of different views. This group learning en-riches the individual member’s understanding of the busi-ness simulation. The constructive critique of diverse views sharpens the individual member’s knowledge of this domain. Therefore, we hypothesized that team psychological safety would positively impact the development of synergistic knowledge.
H2: In business simulations, the team psychological safety among group members would be positively related to the development of synergistic knowledge.
COMPLEXITY OF MENTAL MODELS AS LEARNING OUTCOMES
Mental models represent the stock of knowledge developed by students in a knowledge domain (Nadkarni, 2003). They
capture an individual’s understanding of a specific domain and reflect how the domain knowledge is arranged, con-nected, or situated in their minds (Carley & Palmquist, 1992; Lyles & Schwenk, 1992; Nadkarni; Schneider & Schmitt, 1992; Wilson & Rutherford, 1989). In problem-solving situ-ations, individuals make sense of complex problems and en-gage in intensive mental processing (Hong & O’Neil, 1992). The complexity of a mental model reflects the breadth of a student’s understanding of the specific knowledge domain (Nadkarni; Wilson & Rutherford). Complexity is measured by the number of concepts and linkages between concepts in a mental model (Carley & Palmquist; Eden, Ackermann, & Cropper, 1992). The student with more complex mental models is more likely to identify key concepts and link these concepts in solving problems (Nadkarni).
In a business simulation, we would expect that the de-velopment of synergistic knowledge has an impact on the complexity of students’ mental models for the following rea-sons. First, when students analyze a problem from different perspectives and identify multiple alternatives, they are less likely to miss important variables relating to the problem sit-uation (Lyles & Schwenk, 1992). In addition, in diagnosing an ambiguous and uncertain problem situation, the syner-gistic knowledge development enables students to establish more cause–effect relations between these variables. Finally, communication and leadership skills are enhanced during the process of integrating different perspectives (Colbeck, Campbell, & Bjorklund, 2000). These improved communi-cation and leadership skills help students understand their peers’ opinions and enrich their own domain knowledge. To conclude, we proposed that the development of synergistic knowledge would have a positive impact on the complexity of students’ mental models.
H3: In business simulation, the development of synergistic knowledge in student groups would be positively related to the complexity of students’ mental models.
METHOD
Research Setting
Data were collected from 140 senior business students en-rolled in six sections of an undergraduate strategic manage-ment course at two large northeastern public universities. The Capstone (http://www.capsim.com) business simulation was used as an ongoing hands-on experience for these students. The two coauthors taught all six sections of the course dur-ing two semesters, usdur-ing the same teachdur-ing approach. Partici-pants were randomly assigned to four- or five-member teams. Each team acted as an executive committee responsible for running a company that manufactured an electronic sensor device in a competitive environment. The simulation was de-signed to emphasize integration across business functions, such as research and development, marketing, production,
human resources, total quality management, and finance. Each team developed a competitive strategy (e.g., cost or differentiation) and used decision-support software to deter-mine product positioning, price, sales, promotion, research and development budgets, production levels, and financing requirements. Team decisions were processed and then re-leased to teams in the form of a report containing information about the industry and the competitors’ performance.
Measures
We requested students to complete a three-page survey re-garding their group processes and understanding of the Cap-stone simulation after they had completed a specific simu-lation year. Out of 180 questionnaires sent to the students, 140 were completed for a response rate of 78%. On the basis of previous research literature, the survey items were mea-sured by use of a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree), with several reverse-coded items. Table 1 presents the results of factor analy-sis, and questionnaire items for social interaction, psycho-logical safety, and synergistic knowledge development. The exploratory factor analysis with varimax rotation generated three factors.
Mental models are typically represented as cognitive maps (Carley & Palmquist, 1992; Ford & Hegarty, 1984). They fo-cus on the concepts and the causal linkages between those concepts in individuals’ belief systems (Finkelstein & Ham-brick, 1996). To construct a student’s cognitive map on busi-ness simulation, we first developed a pool of constructs by analyzing the functional areas in the Capstone business sim-ulation. The questionnaire items on cognitions were finalized based on the analysis and a pilot test. In the second step, we had each student select a fixed number of constructs by iden-tifying items from a constant pool of constructs. Finally, we constructed the causal map of each student by having each one assess the influence of each selected construct on the other selected constructs.
We input each causal map matrix into the UCINET soft-ware (Borgatti, Everett, & Freeman, 2002) to compute the complexity measure. Complexity of the mental model is mea-sured by the density of a cognitive map. The density of a cognitive map refers to the ratio of causal links to the total number of constructs in the causal map (Eden et al., 1992). A higher ratio indicates that the student’s cognitive map is densely connected and presumably higher in cognitive com-plexity.
Ccomplexity=
links
const ruct s
The questionnaire asked the students to report their in-dividual effort (the average weekly hours the student spent individually on the decisions for the past two years), time (the average time the student group spent on making deci-sions for the present year), and the simulation year the group has finished the decisions. Because numerous studies have
226 Y. XU AND Y. YANG
TABLE 1
Results of Exploratory Factor Analysis (Principal Component Analysis)
Synergistic
knowledge Team psychological
Item development Social interaction safety
1. The unique skills and talents of all the members of my group were fully valued and utilized.
.869 .263 .072
2. My group’s work integrated all the different opinions of the group members. .771 .376 .120 3. Compared with other teams, our team was better in terms of the way people got
along together.
.832 .136 .219
4. Compared with other teams, our team was better in terms of the way people helped each other on the job.
.893 .203 .204
5. We regularly took time to figure out ways to improve our work processes and performance.
.436 .673 .150
6. My group had a feedback session to evaluate our group processes and discuss how to improve our group work.
.223 .859 .083
7. Members of our team asked each other for feedback on their work. .306 .730 .302 8. The members of my team sometimes rejected others for being different. (reverse
scored)
.187 .019 .850
9. The members of my group had a hard time listening to an opposing point or perspective. (reverse scored)
.249 .153 .685
Eigenvalue 3.493 2.261 1.899
Percentage of variance explained by each factor 26.900 17.400 14.600
shown that gender plays a significant role in student learn-ing (Clifton, Perry, Roberts, & Peter, 2008; Crombie, Pyke, Silverthorn, Jones, & Piccinin, 2003; Kaenzig, Hyatt, & An-derson, 2007), gender was a control variable. In addition, we added three dummy variables to control for the differences in terms of instructor, section, and major.
RESULTS
Table 2 presents the descriptive statistics and correlation ma-trix of all variables. We performed hierarchical regression
analysis to test the hypotheses. First, we regressed the control variables on each dependent variable. Next we regressed the control variables and independent variables on each depen-dent variable. This two-step hierarchical regression analysis allows the effects of each independent variable to account for variance explained beyond that of the control variables. Results for the dependent variable synergistic knowledge de-velopment are presented in Table 3. Results for the dependent variable mental model complexity are presented in Table 4.
H1 andH2referred to the relationship between both so-cial interaction and team psychological safety and syner-gistic knowledge development. As shown in Table 3, social
TABLE 2
Descriptive Statistics and Correlations (N=140)
Variable 1 2 3 4 5 6 7 8 9 10 11 M SD
1. Instructor — 0.79 0.41
2. Section .01 — 0.36 0.48
3. Major −.04 −.50∗∗ — 0.31 0.46
4. Complexity .20∗ −.09 .14 — 0.24 0.14
5. Year .10 −.80∗∗ .08 .04 — 3.48 1.72
6. Individual Effort
−.23∗∗ −.11 .03 −.16 .06 — 2.88 0.58
7. Time −.25∗∗ .10 −.06 −.13 −.09 .15 — 2.91 0.55
8. Gender −.18∗ .01 −.03 −.26∗∗ .04 .12 .09 — 0.41 0.49
9. Synergy .41∗∗ −.11 −.08 .23∗∗ .11 .04 −.19∗ −.12 — 5.96 1.16
10. Social interaction
.46∗∗ −.20∗ −.09 .17∗ .26∗∗ −.06 −.27∗∗ −.08 .62∗∗ — 5.56 1.27
11. Psycholog-ical safety
−.05 −.10 .04 .22∗∗ .04 .17∗ −.03 −.14 .42∗∗ .40∗∗ — 6.51 0.82
∗p<.05.∗∗p<.01.
TABLE 3
Results of Hierarchical Regression: Synergistic Knowledge Development as Dependent Variable
(N=140)
Model 1 Model 2 Model 3
Variable β p β p β p
interaction (β = .377, p = .000) and team psychological safety (β=.280, p=.000) positively correlated with syner-gistic knowledge development. The entire regression equa-tion explained 46.4% of the variance in synergistic knowl-edge development(p<.001). The results supportedH1and
H2.
H3referred to the relationship between synergistic knowl-edge development and mental model complexity. As shown in Table 4, synergistic knowledge development positively correlated with mental model complexity (β = .213, p= .027). The entire regression equation explained 16.2% of the variance in synergistic knowledge development(p<.005). The results supportedH3.
DISCUSSION AND CONCLUSION
This research extends the literature on the factors that en-hance student learning in business simulations. The results
TABLE 4
Results of Hierarchical Regression: Mental Model Complexity as Dependent Variable (N=140)
Model 1 Model 2
of the analysis suggest that social interaction and a psycho-logically safe team environment help students to develop syn-ergistic knowledge, which enriches students’ mental models of business simulation. Students develop high-order knowl-edge and problem-solving skills by synthesizing diverse per-spectives. Our findings have the following implications for teaching and research.
For teaching, instructors need to provide students with systematic guidance of team-based business simulations in order to foster a psychologically safe group environment. Early in the semester, instructors should help students to develop a set of group norms that promote open exchange of ideas (Bolton, 1999) and emphasize group processes to facilitate interactions among students. During the semester, instructors need to continuously monitor the groups, remind them of their group norms, and emphasize various ways of de-veloping synergistic knowledge. Adequate class time needs to be allocated to help students to understand the mechanisms necessary for constructive discussion. In addition, instructors should represent learning outcomes as mental models to eval-uate student learning in a specific knowledge domain so that students are aware of what they know and consequently im-prove their knowledge or skills. This might have resulted in a higher level of student learning.
For further research, researchers should examine the re-lationship between synergistic knowledge development and the objective simulation performance. Second, an interesting research topic would be an examination of the student group’s mental model by having the group as a whole construct the cognitive map, so as to study the effects of individual- and group-level variables on synergistic knowledge development and mental models. Third, a related issue to study is the effects of varying group sizes on student learning in busi-ness simulations. Bigger groups experience intensified cog-nitive conflict (Amason & Sapienza, 1997); however, group members are more likely to bring diverse perspectives to dis-cussion (Bantel & Jackson, 1989). Fourth, because various instructional methods contribute to student learning differ-ently, scholars should also use mental models to assess the level of student learning in various instructional contexts. Fifth, the present study focused on undergraduate students with low learning maturity; future researchers should exam-ine the level of learning of MBA students with higher learn-ing maturity. Finally, a limitation of the present study was that all the measures were based on students’ self-reports. Researchers should develop and test objective measures of student learning in business simulations and other knowledge domains.
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