JOURNAL OF EDUCATION FOR BUSINESS, 85: 223–228, 2010
Taylor & Francis Group, LLC
Student Learning in Business Simulation: An
Penn State University, New Kensington, Pennsylvania, USA
University of Massachusetts, Lowell, Massachusetts, USA
The authors explored the factors contributing to student learning in the context of businesssimu-
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,
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 learning 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,
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: firstname.lastname@example.org
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.
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
Team Psychological Safety
Social Interaction +
Complexity of mental model
FIGURE 1 A conceptual model of student learning in business simulation
FACTORS IN SYNERGISTIC KNOWLEDGE
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 collective 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
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 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, Johnson, 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 safety refers 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
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
Mental models represent the stock of knowledge developed
by students in a knowledge domain (Nadkarni, 2003). They
STUDENT LEARNING IN BUSINESS SIMULATION 225
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.
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, using the same teaching 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.
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)to7(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
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-
const r uct 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
Results of Exploratory Factor Analysis (Principal Component Analysis)
knowledge Team psychological
Item development Social interaction safety
1. The unique skills and talents of all the members of my group were fully valued
.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
.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
.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
.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.
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.
H1and H2referred to the relationship between both so-
cial interaction and team psychological safety and syner-
gistic knowledge development. As shown in Table 3, social
Descriptive Statistics and Correlations (
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
−.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
.46∗∗ −.20∗−.09 .17∗.26∗∗ −.06 −.27∗∗ −.08 .62∗∗ —5.56 1.27
−.05 −.10 .04 .22∗∗ .04 .17∗−.03 −.14 .42∗∗ .40∗∗ —6.51 0.82
STUDENT LEARNING IN BUSINESS SIMULATION 227
Results of Hierarchical Regression: Synergistic
Knowledge Development as Dependent Variable
Model 1 Model 2 Model 3
Instructor .398 .000 −160 .040 .251 .001
Section −.192 .032 −.024 .764 −.040 .608
Major −.165 .066 −.045 .574 −.073 .338
Gender −.049 .532 −.047 .489 −.002 .981
.532 .000 .377 .000
R2.204 .000 .406 .000 .464 .000
R2.201 .000 .058 .000
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 supported H1and
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 supported H3.
DISCUSSION AND CONCLUSION
This research extends the literature on the factors that en-
hance student learning in business simulations. The results
Results of Hierarchical Regression: Mental Model
Complexity as Dependent Variable (
Model 1 Model 2
Instructor .141 .131 .052 .604
Section −.042 .831 .060 .764
Major .122 .304 .177 .140
Gender −.226 .010 −.212 .015
Year −.029 .869 .037 .830
Individual effort −.080 .362 −.113 .197
Time −.040 .650 −.023 .794
R2.128 .016 .162 .004
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
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