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Journal of Education for Business

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The Relationship Between Student Perceptions of
Team Dynamics and Simulation Game Outcomes:
An Individual-Level Analysis
Jonathan R. Anderson
To cite this article: Jonathan R. Anderson (2005) The Relationship Between Student
Perceptions of Team Dynamics and Simulation Game Outcomes: An Individual-Level Analysis,
Journal of Education for Business, 81:2, 85-90, DOI: 10.3200/JOEB.81.2.85-90
To link to this article: http://dx.doi.org/10.3200/JOEB.81.2.85-90

Published online: 07 Aug 2010.

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The Relationship Between Student
Perceptions of Team Dynamics and
Simulation Game Outcomes: An
Individual-Level Analysis
JONATHAN R. ANDERSON
UNIVERSITY OF WEST GEORGIA
CARROLLTON, GEORGIA

ABSTRACT. In many business courses, computer-based simulations are becoming a popular choice of pedagogical technique, yet research is only beginning to

consider how these simulation games
impact student outcomes. In this study, the
author investigated individual perceptions
of simulation team dynamics and their relationship to student affect regarding the simulation as well as simulation performance
in a sample of 172 responding students.
The results showed that a student’s affect
regarding the simulation game was influenced by student team cohesion and student
team independence. Alternatively, student
simulation performance was influenced by
team heterogeneity, opportunistic practices,
and hypothesis-driven thinking. These findings encourage instructors to consider
thoughtfully the outcomes they want students to experience when structuring student teams that will participate in simulation learning games.
Copyright © 2005 Heldref Publications

U

sing technology in the business
classroom may be one of the greatest challenges and opportunities afforded
to business educators today. One popular
method of integrating technology and

instruction is through simulation games
in business courses. Often business simulation games are used in capstone strategy courses as a tool to integrate information acquired through the business
curriculum and to provide a simulated,
hands-on business experience. From
stock market games (McMlatchey &
Kuhlemeyer, 2000) to business strategy
simulations (Doyle & Brown, 2000),
instructors are integrating traditional curricula with a computer simulation experience. Increased access to computers and
the World Wide Web have facilitated this
integration with more efficiency and
greater effectiveness than ever before.
Even so, we are just beginning to understand how these simulation teaching tools
impact student outcomes. In this study, I
explored two questions regarding outcomes of simulations for students: First,
which team dynamics produced a positive affect for the simulation exercise in
students and second, which team dynamics influenced simulation game performance. Research in both of these streams
has produced mixed results (Mitchell,
2004; Wolfe & Luethge, 2003).
Background Literature
For some time, research has recognized the positive potential of computer-

based simulations (Amini, 1995). In
fact, researchers have suggested that
business simulations have the ability to
create “microworlds” in which students
can gain a better understanding of not
only individual effects of decisions on a
company, but also the interactive effects
of environment, multiple competitors,
and employees all within a simulated
experience (Romme, 2003). Additionally, simulation activities have been
used to teach students business ethics
(Wolfe & Fritsche, 1998), the integration of business knowledge (Stephen,
Parente, & Brown, 2002), international
business communication (Doyle &
Brown, 2000), and differences in collectivist and individualistic cultures (Chatman & Barsade, 1995). Along with
teaching students a variety of management principles, business simulations
have been suggested to have the ability
to help students integrate computerbased communication skills (Amini).
Tompson and Tompson (1995) found

that computer-based simulations actually prepared students more thoroughly
for the real world of business than traditional group projects did.
Yet, despite the adoption of simulation exercises in the business school
classroom, in each classroom there are
students who do not fully enjoy or benefit from the simulation process. Walters and Coalter (1997) found that “individual locus of control, need for
achievement, and risk propensity were
associated with satisfaction with the
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game as a learning tool” (p.170). Additionally, one can point to a student’s
prior computer experience, ability to
use technology, and involvement in the
classroom as possible antecedents to a
positive experience with the simulation.
Although these issues are critical, simulation games are usually completed in
teams and little empirical research has

looked at the relationship between individual perceptions of team dynamics
and simulation game outcomes. Clearly
understanding the dynamics between
team characteristics and simulation
game outcomes is critical to improving
pedagogical technique when garnering
the greatest benefit from the simulation
activity as an instructional tool.
Researchers concentrating on simulation games generally consider performance as the dependent variable of
interest. For example, Schoenecker,
Martell, and Michlitsch (1997) found
that group dominance by one individual
negatively affected performance. Additionally, Hornaday and Curran (1996)
attempted to link formal planning by
students with their performance in the
business simulation game. Also, in a
series of five studies, Gosenpud and
Washbush (1996) found that students’
ratings on the Myers-Briggs personality
type inventory correlated with individual performance in a simulation game.

While performance on the simulation
exercise itself is interesting, a student’s
affect regarding the simulation game is
also critical to the game’s success as a
learning tool in the classroom. In this
study, I looked at a student’s perception
of five team dynamics and how those
perceptions related differentially to a
student’s affect regarding the game and
to simulation performance.
As instructors, if we better understand the team dynamics that serve as
antecedents to a student’s affect toward
the simulation game as well as to simulation performance, we can establish
better pedagogical practices that
increase both performance and student
affect. Student perceptions of team
dynamics that I explored were team
cohesiveness, team independence, team
heterogeneity, team opportunistic practices, and team hypothesis-driven thinking. Each of these team variables is
thought to have a unique relationship

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Journal of Education for Business

with student affect and simulation performance. The two objectives of this
study were to understand (a) which student team dynamics produced a positive
affect in students regarding the simulation exercise and (b) which team
dynamics influenced simulation game
performance. I surmised that each of the
variables listed above had a distinct
relationship with both student affect and
performance. These relationships were
developed in the following hypotheses.
Hypotheses
Each of the following hypotheses
pertains to one relationship between an
individual’s perception of a team
dynamic and both student affect regarding the simulation exercise and simulation performance. Below, I present each
independent variable and its hypothesized relationship with both dependent
variables. I selected this order for the

sake of brevity to refrain from discussing each independent variable
twice. In each pair of hypotheses,
hypothesis (H) A refers to the first study
objective (understanding antecedents to
student affect regarding the simulation
exercise) and hypothesis (H) B refers to
the second study objective (understanding factors that influenced simulation
exercise performance).
The first independent variable of
interest to me was cohesion. Cohesion
is considered a key element in determining team performance, yet in management decisions, if teams are too
cohesive, they may fall victim to thinking the same way (“groupthink”) and
restrict the number of options considered in decision making. Research has
generally supported this idea. For example, Carron et al. (2004) found that
groups that are too cohesive have lower
performance. Likewise, other research
has shown that low levels of team cohesiveness can benefit group performance
(Chansler, Swamidass, & Cammann,
2003). In a simulation game context, the
relationship between an individual team

member’s perception of team cohesion
and team performance is thought to
be negative. As in the business world,
highly cohesive teams can become too
friendly and comfortable, which can
negatively impact performance. If a

team becomes too cohesive, they are
likely to exhibit groupthink and consider a restricted set of options. A team
that is not as cohesive, however, may
consider options from a variety of perspectives and in turn produce better
decisions. While cohesion is thought to
impact performance negatively, a team
that is highly cohesive will produce
greater positive emotion and individual
friendship. Thus, cohesion should lead
to high individual affect and low simulation game performance. Thus, my
hypotheses about cohesion were
H1A: A student’s perception of team
cohesion will positively impact student

affect regarding the simulation game and
H1B: A student’s perception of team
cohesion will negatively impact simulation performance.

The next variable I considered was
team independence. A growing body of
literature recognizes the distinctions
between team independence and team
interdependence (Shaw, Duffy, &
Stark, 2000; Van Der Vegt, Emans, &
Van De Vliert, 2000). Team members
who are interdependent work better
with others than they do alone. Team
interdependence has been positively
correlated with team effectiveness
(Hertel, Konradt, & Orlikowski, 2004),
helping behaviors (Allen, Sargent, &
Bradley, 2003), and job and team satisfaction (Van Der Vegt, Emans, & Van
De Vliert, 2001); team interdependence has long been thought to be a
positive attribute of teams. Team independence is a team attribute distinguished by a perception that team
members work better alone than
together. Independent teams are characterized by a team member’s ability
to perform individually and a lack of
desire to work with others. Individuals
who are forced to work in teams but do
not work interdependently will miss
opportunities to capitalize on synergies
between team members’ ideas and
activities (Sprigg, Jackson, & Parker,
2000). I hypothesized that team independence would have the opposite
effect of team interdependence on simulation game performance. Indeed, I
believed that team independence
would negatively correlate with both
student affect regarding the simulation

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exercise and simulation game performance as stated below:
H2A: A student’s perception of team
independence will negatively impact student affect and
H2B: A student’s perception of team
independence will negatively impact simulation performance.

Another variable that I studied was
team heterogeneity. Team heterogeneity has a mixed relationship with performance (Michie, Dooley, & Fryxell,
2002). Some have considered heterogeneity to be synonymous with diversity in race, background, or culture
(Mohammed & Angell, 2003), while
others consider heterogeneity to exist
only in the realm of ideas and ideals
(Harrison, Price, Gavin, & Florey,
2002). Team heterogeneity of ideas
seemingly would have a larger impact
on simulation outcomes than heterogeneity of race, background, or culture. As team members develop relationships, they are likely to understand
the level of heterogeneity that exists
between team members. If the team is
high in idea heterogeneity, the team
will experience conflict in decision
making. The team will likely disagree
on direction and team members may
begin to harbor feelings of discomfort
concerning group processes. Indeed, a
team member’s perception of team heterogeneity will negatively influence
student affect. Team heterogeneity will
force the group to consider a variety of
ideas, which in turn will encourage
more thorough decision making and
improve team performance. Therefore,
my hypotheses about team heterogeneity of ideas were
H3A: A student’s perception of team heterogeneity will negatively impact a student’s affect for the simulation game and
H3B: A student’s perception of team heterogeneity will positively impact simulation performance.

Yet another variable I studied was
team opportunistic practices. Team
opportunistic practices stem from
aggressive and opportunistic personalities within the team. As team members
develop strategies and make decisions,
individual tolerance for risk taking and
opportunistic practices will influence

individual input into team discussions as
well as the decisions themselves. Opportunistic practices are defined as the
ability to identify opportunities coupled
with the fortitude to exploit them. It must
be noted that opportunistic practices are
not reckless, but a calculated ability to
identify and exploit potential profit
prospects (Stanton, Ashleigh, Roberts, &
Xu, 2001). Opportunistic practices
should be rewarded in simulation games
as they are in the business practice. One
would expect that team opportunistic
practices will lead to higher team performance. Likewise, one would expect that
team opportunistic practices will rally
the team around team decisions (as they
identify opportunities to exploit) and
increase student affect regarding the simulation exercise. I developed the following hypotheses about team opportunistic
practices:
H4A: A student’s perception of team
opportunistic practices will positively
impact student affect and
H4B: A student’s perception of team
opportunistic practices will positively
impact simulation performance.

Still another variable that I studied
was team hypothesis-driven thinking.
Team hypothesis-driven thinking is
defined as a student’s perception of a
team’s ability to think in terms of
“what if,” or, the team’s ability to
direct thought to potential future
actions, and make decisions based on
potential future outcomes. If a team
participates in hypothesis-driven
thinking, individual members will
develop an ability to work together to
complete situational analyses. Team
members will spend time analyzing
potential outcomes of decisions and
work to provide proper evaluation for
decision making. One would expect
that hypothesis-driven thinking would
be associated with both performance
and student affect; performance as
hypothesis-driven thinking should
improve decisions and strategic actions
as well as affect because the hypothesis-driven thinking process will
increase team member interaction.
Thus, I hypothesized that
H5A: A student’s perception of team
hypothesis-driven thinking will positively
impact student affect and

H5B: A student’s perception of team
hypothesis-driven thinking will positively
impact simulation performance.

METHOD
I assigned students (N = 220)
enrolled in a large section of an introductory management class at a large
southeastern research university to
three-person teams through random
assignment stratified by major. I
assigned each team to participate in a 4week simulation exercise that included
both written and simulation requirements. Through simulation, teams managed a $40 million dollar electronics
company through 8 years of performance. The simulation used for this
study was Capsim Foundation (Management Simulations, 2004). In this
simulation, students are required to
input Research and Development
(R & D), marketing, production, financial, human resources (HR), and total
quality management (TQM) initiatives
in rounds that represent 1 complete year
of operations. This simulation game is
referred to as Capsim (the official title
of the Web page the students use)
throughout the rest of the article. I also
required students to turn in written
assignments that reflected how they
intended to manage the organization
throughout the exercise. I gave students
the opportunity to complete a survey for
extra credit on their final exam in the
course. There were 172 students who
provided useable responses (a total
response rate of 78%).
Measures
I measured each variable on the individual level. Thus, each case represented a student’s perception of the team
dynamic. I did not aggregate data to the
team level because the individual’s perception was the variable of interest. I
used a one-item scale to measure the
dependent variable of a student’s affect
toward the simulation exercise (“I liked
the Capsim simulation.”). This item had
a 5-point Likert-type scale for responses
(strongly disagree to strongly agree).
I measured overall performance in
the simulation exercise on a 10-point
scale based on criteria included in the
game itself. The weighted scale includNovember/December 2005

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ed cumulative profit, market share,
return on sales, asset turnover, return on
assets, return on equity, stock price, and
ending market cap. Each team received
a computer-generated score based on
the weighting of each of the listed categories and the team’s performance on
those categories.
I measured independent variables
with 5-point scale response options.
Responses ranged from strongly agree
to strongly disagree. I measured a student’s perception of group cohesiveness
using a 2-item scale (Cronbach’s α =
.64) developed by Cammann, Fichman,
Jenkins, and Klesh (1983). To measure
team independence, I developed a 5item scale (α = .90). The literature base
for this scale was the work on team
interdependence (see Allen et al., 2003;
Campion & Papper, 1996; Hertel et al.,
2004; Shaw et al., 2000; Van Der Vegt et
al., 2000). These items reflected the
opposite meaning of similar items that
measured interdependence. Items
included the following: “Our team
members work better alone than together,” “Our team would do better working
individ-ually,” “I make better decisions
when working alone rather than with
my team,” “Team members are more of
a hindrance than a benefit,” and “I do a
better job alone than with my team.” I
measured team heterogeneity using a 3item scale (α = .74) developed by Campion and Medsker (1993). To measure
team opportunistic thinking, I developed a 5-item scale (α = .86). Items

included the following: “Our team identifies and recognizes opportunities,”
“Our team understands the difference
between good and bad opportunities,”
“Our team is not afraid of pursuing new
opportunities,” “Our team generally
makes good decisions when opportunities are presented,” and “Our team fails
to capitalize on opportunities.” This
scale was loosely based on the work of
Stanton et al. (2001). I developed another 5-item scale (α = .89) to measure
team hypothesis-driven thinking. Items
included the following: “Our team often
considers ‘if/then’ situations,” “Our
team discusses the possibility of ‘what
if,’” “Our team explores possible future
scenarios,” “Our team looks at possible
future outcomes of current decisions,”
and “Our team often has hypotheses
about what could happen.” This scale
was also loosely based on the work of
Stanton et al.
I measured the control variables as follows. I gathered a student’s prior work
experience using a self-report 5-point
scale (1 = no experience; 2 = 1 year; 3 =
2 years; 4 = 3 years; and 5 = 4 or more
years). I collected the students’ majors by
self-report as well (coded as either 1 =
business or 0 = nonbusiness; business
majors included those within the college
of business and agricultural economics). I
did not use these controls in the research
design. However, I included them in the
regression analysis to control for their
effects while testing the hypotheses
(Cohen, Cohen, West, & Aiken, 2003).

RESULTS
For the sake of brevity, I have presented the means (M), standard deviations (SD), and zero-order correlations
for each of the control, independent, and
dependent variables in Table 1. In analyzing the data, I used linear regression.
First, I regressed the control variables
on each dependent variable. Then I
regressed the control variables and the
independent variables on each dependent variable using two-step hierarchical regression. This process allows the
effects of each independent variable to
account for variance explained beyond
that of the control variables (Cohen et
al., 2003). Results for the dependent
variable student affect are presented in
Table 2. Results for the dependent variable simulation performance are presented in Table 3.
Hypotheses 1A and 1B referred to the
relationship between team cohesion and
both student affect and simulation performance. As expected (H1A), a student’s
perception of team cohesiveness positively correlated with a student’s affect
regarding the simulation game, (β = .391,
p = .000) and negatively correlated with
team performance (β = −.227, p = .024).
The results supported both hypotheses
1A and 1B (see Tables 2 and 3).
Hypotheses 2A and 2B referred to
the level of team interdependence
observed by the student. As shown in
Table 2, team interdependence did correlate with student affect (β = .225, p =

TABLE 1. Means, Standard Deviations (SD), and Zero-Order Correlations for All Variables
Variable
1. Work experience
2. Major
3. Student affect
4. Performance
5. Cohesion
6. Independence
7. Heterogeneity
8. Opportunistic
practices
9. Hypothesis-driven
thinking

M

SD

1

2

3

4

5

6

2.76
0.43
3.10
5.96
3.72
2.26
3.59

1.48
0.50
1.35
2.40
0.92
0.95
0.81

–0.022
–0.024
–0.111
–0.001
–0.047
–0.116

–0.054
–0.004
0.039
–0.022
–0.025

0.446*
–0.463*
0.633*
0.512*

–0.296*
0.506*
0.495*

–0.421*
–0.454*

0.643*

3.95

0.75

0.150

0.118

0.344*

0.205*

–0.014

0.248*

0.228*

3.87

0.72

0.031

0.118

0.096

0.016

–0.107

0.285*

0.246*

Note. N = 172.
*p < .01.

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Journal of Education for Business

7

8

0.294*

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TABLE 2. Hierarchical Regression Analysis Results for Variables
Predicting Student Affect
Variable

B

SE B

β

.066
.199
.041

0.143+
0.092
0.278*

.062
.184
.041
.124
.112
.108
.140
.129

0.172*
0.108
0.263*
0.391*
0.225*
0.111
–0.079
0.085

Step 1a
Work experience
Major
Performance

.131
.251
.157
Step 2 b

Work experience
Major
Performance
Cohesion
Heterogeneity
Independence
Opportunistic practices
Hypothesis-driven thinking

.157
.294
.149
.527
.149
.307
–.107
.115

Note. N = 172.
a 2
R = .336, p < .05. bR 2 = .155, p < .05.
+p < .10. *p < .05.

Hypotheses 4A and 4B referred to a
student’s perception of team opportunistic practices. Team opportunistic
practices did not correlate with a student’s affect (β = .079, p = .446).
Therefore, the results did not support
hypothesis 4A (Table 2). However, team
opportunistic practices did correlate
with simulation game performance (β =
3.171, p = .002), as shown in Table 3,
supporting hypothesis 4B.
Finally, hypotheses 5A and 5B
referred to a team’s hypothesis-driven
thinking. Hypothesis-driven thinking
did not correlate with a student’s affect
(β = .085, p = .371), failing to support
hypothesis 5A (see Table 2). However,
team hypothesis-driven thinking did
correlate with simulation performance
(β = 1.683, p = .094), lending support
for hypothesis 5B (see Table 3).
DISCUSSION

TABLE 3. Hierarchical Regression Analysis Results for Variables
Predicting Simulation Performance
Variable

B

SE B

β

.120
.357
.133

–.011
.060
.286*

.116
.340
.138
.236
.202
.202
.249
.234

–.005
.061
.284*
–.227*
–.215*
–.075
.333*
.165+

Step 1a
Work experience
Major
Student affect

–.017
.291
.504
Step 2 b

Work experience
Major
Student affect
Cohesion
Heterogeneity
Independence
Opportunistic practices
Hypothesis-driven thinking

–.008
.292
.502
–.540
–.510
–.180
.790
.395

Note. N = 172.
a 2
R = .297, p < .05. bR 2 = .121, p < .05.
+p < .10. *p < .05.

.005); thus, the results supported
hypothesis 2A. However, team independence did not correlate with simulation performance (Table 3). Therefore, the results did not support
hypothesis 2B (β = −.889, p = .375).
Hypotheses 3A and 3B referred to a
student’s perception of team heterogeneity. As shown in Table 2, team het-

erogeneity did not correlate with student affect (β =.111, p =.184), meaning
that the results did not support hypothesis 3A . However, team heterogeneity
did correlate with simulation performance (β = −.215, p = .013), but the
relationship was in the opposite direction of that expected (Table 3). Thus, the
results did not support H3B.

This project supports the general argument that performance and student affect
have different antecedents in a simulation game setting. The findings suggest
that students who perceive their teams to
be cohesive and independent have strong
affect for the exercise, but this does not
translate into strong performance. However, students who perceive their teams
to have low cohesiveness, low heterogeneity, high opportunistic practices, and
high hypothesis-driven thinking experience higher team performance.
This research suggests that teachers
must be aware of the desired outcomes of
the simulation exercise. If an instructor is
interested in building a sense of unity and
positive affect within each team, the
instructor should create cohesive and
independent teams. This can be accomplished through team-building exercises
and selecting teams whose members are
generally similar to each other. As an
alternative, if instructors are interested in
building teams for performance, teams
consisting of members who are similar
(low heterogeneity) and can think and act
opportunistically and grasp hypothesisdriven thinking should be created and
fostered. Indeed, simulation activities in
classroom instruction seem to be increasing in use and functionality, and it is the
instructor’s opportunity to capitalize on
the potential benefits for students
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through understanding individual and
team dynamics that are associated with
simulation game outcomes. This article
is a step in that direction.
NOTE
An earlier version of this article entitled, “To
simulate or not to simulate: Antecedents to positive student affect toward a strategic management
simulation exercise,” was presented at the Mountain Plains Management Conference, Moscow,
Idaho in October 2003 and summarized in its
unpublished proceedings.
Correspondence concerning this article should
be addressed to Dr. Jonathan R. Anderson, Assistant Professor, Department of Management &
Business Systems, Richards College of Business,
State University of West Georgia, Carrollton, Georgia 30118-3030. E-mail: janderso@westga.edu.
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