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Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
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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JOURNAL OF EDUCATION FOR BUSINESS, 85: 223–228, 2010
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323
DOI: 10.1080/08832320903449469
Student Learning in Business Simulation: An
Empirical Investigation
Yang Xu
Penn State University, New Kensington, Pennsylvania, USA
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Yi Yang
University of Massachusetts, Lowell, Massachusetts, USA
The authors explored the factors contributing to student learning in the context of business simulation. 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; Keeffe, Dyson, & Edwards, 1993), such as business strategy
(Stephen, Parente, & Brown, 2002), business ethics (Wolfe &
Fritzsche, 1998), and courses on cultural differences (Chatman & Barsade, 1995). In contrast to traditional teaching
methods, business simulations bridge the gap between the
classroom and the world of real-life business decision making through experiential learning experiences in which students 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 effects of environment, competitors, and employees (Romme,
2003).
In previous studies of business simulations, game performance is generally considered the dependent variable of
interest (Anderson, 2005; Hornaday & Curran, 1996; Schoenecker, Martell, & Michlitsch, 1997). Our research attempts
to explore the factors contributing to the formation of students’ mental models. A mental model represents an individual’s knowledge structure of a specific domain (Carley & 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 education (Dehler, 1996; Resnick & Klopfer, 1989). A critical
task of business education is helping students develop knowledge structures of specific domains. People digest information and transform it to structured knowledge (Weick, 1995).
However, few empirical studies have used mental models as
learning outcomes in the business education literature (Nadkarni, 2003). This study addresses this research gap. Specifically, 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 influence the complexity of students’ mental models? Next we
present the conceptual model and research hypotheses, followed 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, Bedell, & Johnson, 1997; Kasl, Marsick, & Dechant, 1997; Nonaka, 1994; Walsh, 1995), we developed a conceptual framework indicating that two factors—social interaction and psychological safety—are positively related to the development
of synergistic knowledge (Figure 1). Furthermore, the development of synergistic knowledge enhances the complexity
of the student’s mental model.
224
Y. XU AND Y. YANG
Team Psychological Safety
+
Synergistic
Knowledge
Development
Social Interaction
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FIGURE 1
+
Complexity of mental model
+
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 organizational learning and social cognition, collective knowledge
develops through the discussion and integration of the individual perspectives of a specific information domain (Nonaka; Senge, 1990; Walsh). A collective body of knowledge
consists of representation, development, and use of specific knowledge (Walsh). In business simulations, individual
members interpret tasks with their own knowledge structure.
Next, group members discuss and integrate their individual knowledge and use this collective body of knowledge to
manage the simulated company.
Business simulations focus on interactive problem solving and complex trade-offs. Teamwork is usually required
because of the complexity of the simulation. In this active learning process, students develop a collective body of
knowledge by synthesizing the unique perspectives of the individual members (Lang & Dittrich, 1982; Mu & Gnyawali,
2003). Building on previous studies, we hypothesized that
two factors would contribute to synergistic knowledge development in student groups—social interaction and team
psychological safety.
SOCIAL INTERACTION
Social interaction refers to the process of communication in
a group (Barker & Camarata, 1998). In business simulations,
students need to understand, inform, and persuade their teammates 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 dialogue 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 identify 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 (Johnson, Johnson, Stanne, & Garibaldi, 1990). In the feedback
sessions, students’ discussions may create a process of social discovery, clarifying individual members’ opinions and
centralizing their preferences (Eisenhardt, Kahwajy, & Bourgeois, 1997). Third, high social interaction enables people to
exchange tacit knowledge necessary for complex problem
solving (Nonaka, 1994). Learning is enhanced through extensive communication among the group members (Baldwin
et al., 1997); and knowledge is developed in this interactive
process (Barker & Camarata). Consequently, we hypothesized that social interaction would play a positive role in
synergistic knowledge development.
Hypothesis 1 (H 1 ): 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 safety refers to the group members’ beliefs 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, people in a psychologically safe environment display higher levels of self-efficacy and develop better mechanisms to deal
with conflicts (Campion, Medsker, & Higgs, 1993). Members need to be open to others’ ideas to create productive
group work (Kasl et al., 1997). The appreciation of others’ 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 constructive critique of different views. This group learning enriches the individual member’s understanding of the business 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.
H 2 : 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
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STUDENT LEARNING IN BUSINESS SIMULATION
capture an individual’s understanding of a specific domain
and reflect how the domain knowledge is arranged, connected, or situated in their minds (Carley & Palmquist, 1992;
Lyles & Schwenk, 1992; Nadkarni; Schneider & Schmitt,
1992; Wilson & Rutherford, 1989). In problem-solving situations, individuals make sense of complex problems and engage 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 development of synergistic knowledge has an impact on the
complexity of students’ mental models for the following reasons. 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 situation (Lyles & Schwenk, 1992). In addition, in diagnosing
an ambiguous and uncertain problem situation, the synergistic 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 communication 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.
H 3 : 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 enrolled in six sections of an undergraduate strategic management 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 during two semesters, using the same teaching approach. Participants 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 designed to emphasize integration across business functions,
such as research and development, marketing, production,
225
human resources, total quality management, and finance.
Each team developed a competitive strategy (e.g., cost or
differentiation) and used decision-support software to determine product positioning, price, sales, promotion, research
and development budgets, production levels, and financing
requirements. Team decisions were processed and then released 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 regarding their group processes and understanding of the Capstone simulation after they had completed a specific simulation 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 measured by use of a 7-point Likert-type scale ranging from 1
(strongly disagree) to 7 (strongly agree), with several reversecoded items. Table 1 presents the results of factor analysis, and questionnaire items for social interaction, psychological 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 focus on the concepts and the causal linkages between those
concepts in individuals’ belief systems (Finkelstein & Hambrick, 1996). To construct a student’s cognitive map on business simulation, we first developed a pool of constructs by
analyzing the functional areas in the Capstone business simulation. 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 identifying 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 software (Borgatti, Everett, & Freeman, 2002) to compute the
complexity measure. Complexity of the mental model is measured 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 complexity.
Ccomplexity =
links
constructs
The questionnaire asked the students to report their individual 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 decisions 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
development
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Item
1. The unique skills and talents of all the members of my group were fully valued
and utilized.
2. My group’s work integrated all the different opinions of the group members.
3. Compared with other teams, our team was better in terms of the way people got
along together.
4. Compared with other teams, our team was better in terms of the way people
helped each other on the job.
5. We regularly took time to figure out ways to improve our work processes and
performance.
6. My group had a feedback session to evaluate our group processes and discuss
how to improve our group work.
7. Members of our team asked each other for feedback on their work.
8. The members of my team sometimes rejected others for being different. (reverse
scored)
9. The members of my group had a hard time listening to an opposing point or
perspective. (reverse scored)
Eigenvalue
Percentage of variance explained by each factor
shown that gender plays a significant role in student learning (Clifton, Perry, Roberts, & Peter, 2008; Crombie, Pyke,
Silverthorn, Jones, & Piccinin, 2003; Kaenzig, Hyatt, & Anderson, 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.
Social interaction
Team psychological
safety
.869
.263
.072
.771
.832
.376
.136
.120
.219
.893
.203
.204
.436
.673
.150
.223
.859
.083
.306
.187
.730
.019
.302
.850
.249
.153
.685
3.493
26.900
2.261
17.400
1.899
14.600
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 dependent 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 development are presented in Table 3. Results for the dependent
variable mental model complexity are presented in Table 4.
H 1 and H 2 referred to the relationship between both social interaction and team psychological safety and synergistic knowledge development. As shown in Table 3, social
RESULTS
Table 2 presents the descriptive statistics and correlation matrix of all variables. We performed hierarchical regression
TABLE 2
Descriptive Statistics and Correlations (N = 140)
Variable
1. Instructor
2. Section
3. Major
4. Complexity
5. Year
6. Individual
Effort
7. Time
8. Gender
9. Synergy
10. Social
interaction
11. Psychological
safety
∗p
1
2
.01
−.04
.20∗
.10
−.23∗∗
—
−.50∗∗
−.09
−.80∗∗
−.11
−.25∗∗
−.18∗
.41∗∗
.46∗∗
.10
.01
−.11
−.20∗
−.05
−.10
3
4
5
6
7
8
9
10
11
—
< .05. ∗∗ p < .01.
—
.14
.08
.03
—
.04
−.16
—
−.06
−.03
−.08
−.09
−.13
−.26∗∗
.23∗∗
.17∗
−.09
.04
.11
.26∗∗
.04
.22∗∗
.06
.04
—
.15
.12
.04
−.06
.17∗
—
.09
−.19∗
−.27∗∗
−.03
—
−.12
−.08
—
.62∗∗
−.14
.42∗∗
—
.40∗∗
—
M
SD
0.79
0.36
0.31
0.24
3.48
2.88
0.41
0.48
0.46
0.14
1.72
0.58
2.91
0.41
5.96
5.56
0.55
0.49
1.16
1.27
6.51
0.82
STUDENT LEARNING IN BUSINESS SIMULATION
TABLE 3
Results of Hierarchical Regression: Synergistic
Knowledge Development as Dependent Variable
(N = 140)
Model 1
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Variable
Instructor
Section
Major
Gender
Social
interaction
Psychological
safety
R2
R2
Model 2
Model 3
β
p
β
p
β
p
.398
−.192
−.165
−.049
.000
.032
.066
.532
−160
−.024
−.045
−.047
.532
.040
.764
.574
.489
.000
.251
−.040
−.073
−.002
.377
.001
.608
.338
.981
.000
.280
.000
.464
.058
.000
.000
.204
.000
.406
.201
.000
.000
interaction (β = .377, p = .000) and team psychological
safety (β = .280, p = .000) positively correlated with synergistic knowledge development. The entire regression equation explained 46.4% of the variance in synergistic knowledge development (p < .001). The results supported H 1 and
H2.
H 3 referred to the relationship between synergistic knowledge 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 supported H 3 .
DISCUSSION AND CONCLUSION
This research extends the literature on the factors that enhance student learning in business simulations. The results
TABLE 4
Results of Hierarchical Regression: Mental Model
Complexity as Dependent Variable (N = 140)
Model 1
Variable
Instructor
Section
Major
Gender
Year
Individual effort
Time
Synergistic
knowledge
development
R2
R2
Model 2
β
p
β
p
.141
−.042
.122
−.226
−.029
−.080
−.040
.131
.831
.304
.010
.869
.362
.650
.052
.060
.177
−.212
.037
−.113
−.023
.213
.604
.764
.140
.015
.830
.197
.794
.027
.162
.034
.004
.027
.128
.016
227
of the analysis suggest that social interaction and a psychologically safe team environment help students to develop synergistic knowledge, which enriches students’ mental models
of business simulation. Students develop high-order knowledge and problem-solving skills by synthesizing diverse perspectives. 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 developing 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 evaluate student learning in a specific knowledge domain so that
students are aware of what they know and consequently improve their knowledge or skills. This might have resulted in
a higher level of student learning.
For further research, researchers should examine the relationship 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 business simulations. Bigger groups experience intensified cognitive conflict (Amason & Sapienza, 1997); however, group
members are more likely to bring diverse perspectives to discussion (Bantel & Jackson, 1989). Fourth, because various
instructional methods contribute to student learning differently, 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 examine the level of learning of MBA students with higher learning 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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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
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
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Published online: 08 Jul 2010.
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Date: 11 January 2016, At: 22:33
JOURNAL OF EDUCATION FOR BUSINESS, 85: 223–228, 2010
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323
DOI: 10.1080/08832320903449469
Student Learning in Business Simulation: An
Empirical Investigation
Yang Xu
Penn State University, New Kensington, Pennsylvania, USA
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Yi Yang
University of Massachusetts, Lowell, Massachusetts, USA
The authors explored the factors contributing to student learning in the context of business simulation. 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; Keeffe, Dyson, & Edwards, 1993), such as business strategy
(Stephen, Parente, & Brown, 2002), business ethics (Wolfe &
Fritzsche, 1998), and courses on cultural differences (Chatman & Barsade, 1995). In contrast to traditional teaching
methods, business simulations bridge the gap between the
classroom and the world of real-life business decision making through experiential learning experiences in which students 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 effects of environment, competitors, and employees (Romme,
2003).
In previous studies of business simulations, game performance is generally considered the dependent variable of
interest (Anderson, 2005; Hornaday & Curran, 1996; Schoenecker, Martell, & Michlitsch, 1997). Our research attempts
to explore the factors contributing to the formation of students’ mental models. A mental model represents an individual’s knowledge structure of a specific domain (Carley & 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 education (Dehler, 1996; Resnick & Klopfer, 1989). A critical
task of business education is helping students develop knowledge structures of specific domains. People digest information and transform it to structured knowledge (Weick, 1995).
However, few empirical studies have used mental models as
learning outcomes in the business education literature (Nadkarni, 2003). This study addresses this research gap. Specifically, 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 influence the complexity of students’ mental models? Next we
present the conceptual model and research hypotheses, followed 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, Bedell, & Johnson, 1997; Kasl, Marsick, & Dechant, 1997; Nonaka, 1994; Walsh, 1995), we developed a conceptual framework indicating that two factors—social interaction and psychological safety—are positively related to the development
of synergistic knowledge (Figure 1). Furthermore, the development of synergistic knowledge enhances the complexity
of the student’s mental model.
224
Y. XU AND Y. YANG
Team Psychological Safety
+
Synergistic
Knowledge
Development
Social Interaction
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FIGURE 1
+
Complexity of mental model
+
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 organizational learning and social cognition, collective knowledge
develops through the discussion and integration of the individual perspectives of a specific information domain (Nonaka; Senge, 1990; Walsh). A collective body of knowledge
consists of representation, development, and use of specific knowledge (Walsh). In business simulations, individual
members interpret tasks with their own knowledge structure.
Next, group members discuss and integrate their individual knowledge and use this collective body of knowledge to
manage the simulated company.
Business simulations focus on interactive problem solving and complex trade-offs. Teamwork is usually required
because of the complexity of the simulation. In this active learning process, students develop a collective body of
knowledge by synthesizing the unique perspectives of the individual members (Lang & Dittrich, 1982; Mu & Gnyawali,
2003). Building on previous studies, we hypothesized that
two factors would contribute to synergistic knowledge development in student groups—social interaction and team
psychological safety.
SOCIAL INTERACTION
Social interaction refers to the process of communication in
a group (Barker & Camarata, 1998). In business simulations,
students need to understand, inform, and persuade their teammates 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 dialogue 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 identify 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 (Johnson, Johnson, Stanne, & Garibaldi, 1990). In the feedback
sessions, students’ discussions may create a process of social discovery, clarifying individual members’ opinions and
centralizing their preferences (Eisenhardt, Kahwajy, & Bourgeois, 1997). Third, high social interaction enables people to
exchange tacit knowledge necessary for complex problem
solving (Nonaka, 1994). Learning is enhanced through extensive communication among the group members (Baldwin
et al., 1997); and knowledge is developed in this interactive
process (Barker & Camarata). Consequently, we hypothesized that social interaction would play a positive role in
synergistic knowledge development.
Hypothesis 1 (H 1 ): 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 safety refers to the group members’ beliefs 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, people in a psychologically safe environment display higher levels of self-efficacy and develop better mechanisms to deal
with conflicts (Campion, Medsker, & Higgs, 1993). Members need to be open to others’ ideas to create productive
group work (Kasl et al., 1997). The appreciation of others’ 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 constructive critique of different views. This group learning enriches the individual member’s understanding of the business 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.
H 2 : 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
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STUDENT LEARNING IN BUSINESS SIMULATION
capture an individual’s understanding of a specific domain
and reflect how the domain knowledge is arranged, connected, or situated in their minds (Carley & Palmquist, 1992;
Lyles & Schwenk, 1992; Nadkarni; Schneider & Schmitt,
1992; Wilson & Rutherford, 1989). In problem-solving situations, individuals make sense of complex problems and engage 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 development of synergistic knowledge has an impact on the
complexity of students’ mental models for the following reasons. 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 situation (Lyles & Schwenk, 1992). In addition, in diagnosing
an ambiguous and uncertain problem situation, the synergistic 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 communication 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.
H 3 : 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 enrolled in six sections of an undergraduate strategic management 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 during two semesters, using the same teaching approach. Participants 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 designed to emphasize integration across business functions,
such as research and development, marketing, production,
225
human resources, total quality management, and finance.
Each team developed a competitive strategy (e.g., cost or
differentiation) and used decision-support software to determine product positioning, price, sales, promotion, research
and development budgets, production levels, and financing
requirements. Team decisions were processed and then released 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 regarding their group processes and understanding of the Capstone simulation after they had completed a specific simulation 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 measured by use of a 7-point Likert-type scale ranging from 1
(strongly disagree) to 7 (strongly agree), with several reversecoded items. Table 1 presents the results of factor analysis, and questionnaire items for social interaction, psychological 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 focus on the concepts and the causal linkages between those
concepts in individuals’ belief systems (Finkelstein & Hambrick, 1996). To construct a student’s cognitive map on business simulation, we first developed a pool of constructs by
analyzing the functional areas in the Capstone business simulation. 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 identifying 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 software (Borgatti, Everett, & Freeman, 2002) to compute the
complexity measure. Complexity of the mental model is measured 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 complexity.
Ccomplexity =
links
constructs
The questionnaire asked the students to report their individual 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 decisions 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
development
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Item
1. The unique skills and talents of all the members of my group were fully valued
and utilized.
2. My group’s work integrated all the different opinions of the group members.
3. Compared with other teams, our team was better in terms of the way people got
along together.
4. Compared with other teams, our team was better in terms of the way people
helped each other on the job.
5. We regularly took time to figure out ways to improve our work processes and
performance.
6. My group had a feedback session to evaluate our group processes and discuss
how to improve our group work.
7. Members of our team asked each other for feedback on their work.
8. The members of my team sometimes rejected others for being different. (reverse
scored)
9. The members of my group had a hard time listening to an opposing point or
perspective. (reverse scored)
Eigenvalue
Percentage of variance explained by each factor
shown that gender plays a significant role in student learning (Clifton, Perry, Roberts, & Peter, 2008; Crombie, Pyke,
Silverthorn, Jones, & Piccinin, 2003; Kaenzig, Hyatt, & Anderson, 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.
Social interaction
Team psychological
safety
.869
.263
.072
.771
.832
.376
.136
.120
.219
.893
.203
.204
.436
.673
.150
.223
.859
.083
.306
.187
.730
.019
.302
.850
.249
.153
.685
3.493
26.900
2.261
17.400
1.899
14.600
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 dependent 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 development are presented in Table 3. Results for the dependent
variable mental model complexity are presented in Table 4.
H 1 and H 2 referred to the relationship between both social interaction and team psychological safety and synergistic knowledge development. As shown in Table 3, social
RESULTS
Table 2 presents the descriptive statistics and correlation matrix of all variables. We performed hierarchical regression
TABLE 2
Descriptive Statistics and Correlations (N = 140)
Variable
1. Instructor
2. Section
3. Major
4. Complexity
5. Year
6. Individual
Effort
7. Time
8. Gender
9. Synergy
10. Social
interaction
11. Psychological
safety
∗p
1
2
.01
−.04
.20∗
.10
−.23∗∗
—
−.50∗∗
−.09
−.80∗∗
−.11
−.25∗∗
−.18∗
.41∗∗
.46∗∗
.10
.01
−.11
−.20∗
−.05
−.10
3
4
5
6
7
8
9
10
11
—
< .05. ∗∗ p < .01.
—
.14
.08
.03
—
.04
−.16
—
−.06
−.03
−.08
−.09
−.13
−.26∗∗
.23∗∗
.17∗
−.09
.04
.11
.26∗∗
.04
.22∗∗
.06
.04
—
.15
.12
.04
−.06
.17∗
—
.09
−.19∗
−.27∗∗
−.03
—
−.12
−.08
—
.62∗∗
−.14
.42∗∗
—
.40∗∗
—
M
SD
0.79
0.36
0.31
0.24
3.48
2.88
0.41
0.48
0.46
0.14
1.72
0.58
2.91
0.41
5.96
5.56
0.55
0.49
1.16
1.27
6.51
0.82
STUDENT LEARNING IN BUSINESS SIMULATION
TABLE 3
Results of Hierarchical Regression: Synergistic
Knowledge Development as Dependent Variable
(N = 140)
Model 1
Downloaded by [Universitas Maritim Raja Ali Haji] at 22:33 11 January 2016
Variable
Instructor
Section
Major
Gender
Social
interaction
Psychological
safety
R2
R2
Model 2
Model 3
β
p
β
p
β
p
.398
−.192
−.165
−.049
.000
.032
.066
.532
−160
−.024
−.045
−.047
.532
.040
.764
.574
.489
.000
.251
−.040
−.073
−.002
.377
.001
.608
.338
.981
.000
.280
.000
.464
.058
.000
.000
.204
.000
.406
.201
.000
.000
interaction (β = .377, p = .000) and team psychological
safety (β = .280, p = .000) positively correlated with synergistic knowledge development. The entire regression equation explained 46.4% of the variance in synergistic knowledge development (p < .001). The results supported H 1 and
H2.
H 3 referred to the relationship between synergistic knowledge 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 supported H 3 .
DISCUSSION AND CONCLUSION
This research extends the literature on the factors that enhance student learning in business simulations. The results
TABLE 4
Results of Hierarchical Regression: Mental Model
Complexity as Dependent Variable (N = 140)
Model 1
Variable
Instructor
Section
Major
Gender
Year
Individual effort
Time
Synergistic
knowledge
development
R2
R2
Model 2
β
p
β
p
.141
−.042
.122
−.226
−.029
−.080
−.040
.131
.831
.304
.010
.869
.362
.650
.052
.060
.177
−.212
.037
−.113
−.023
.213
.604
.764
.140
.015
.830
.197
.794
.027
.162
.034
.004
.027
.128
.016
227
of the analysis suggest that social interaction and a psychologically safe team environment help students to develop synergistic knowledge, which enriches students’ mental models
of business simulation. Students develop high-order knowledge and problem-solving skills by synthesizing diverse perspectives. 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 developing 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 evaluate student learning in a specific knowledge domain so that
students are aware of what they know and consequently improve their knowledge or skills. This might have resulted in
a higher level of student learning.
For further research, researchers should examine the relationship 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 business simulations. Bigger groups experience intensified cognitive conflict (Amason & Sapienza, 1997); however, group
members are more likely to bring diverse perspectives to discussion (Bantel & Jackson, 1989). Fourth, because various
instructional methods contribute to student learning differently, 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 examine the level of learning of MBA students with higher learning 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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