08832323.2011.586006
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Capstone Teaching Models: Combining Simulation,
Analytical Intuitive Learning Processes, History
and Effectiveness
Maurice Reid , Steve Brown & Kambiz Tabibzadeh
To cite this article: Maurice Reid , Steve Brown & Kambiz Tabibzadeh (2012) Capstone
Teaching Models: Combining Simulation, Analytical Intuitive Learning Processes,
History and Effectiveness, Journal of Education for Business, 87:3, 178-184, DOI:
10.1080/08832323.2011.586006
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JOURNAL OF EDUCATION FOR BUSINESS, 87: 178–184, 2012
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2011.586006
Capstone Teaching Models: Combining Simulation,
Analytical Intuitive Learning Processes, History
and Effectiveness
Maurice Reid, Steve Brown, and Kambiz Tabibzadeh
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Eastern Kentucky University, Richmond, Kentucky, USA
For the past decade teaching models have been changing, reflecting the dynamics, complexities,
and uncertainties of today’s organizations. The traditional and the more current active models
of learning have disadvantages. Simulation provides a platform to combine the best aspects of
both types of teaching practices. This research explores the history of including simulation into
a teaching plan and outlines an empirical method to test the effect that including a simulation
can have in the classroom.
Keywords: business simulation, capstone course methodology, simulation effectiveness
Academic disciplines across the board are becoming increasingly aware that mastery of technical content is insufficient
preparation for today’s professional careers (Chung, Baker,
Harmon, & Burks, 2003; O’Reilly, 1994; Wheeler, 1998). Increasingly, capstone courses are being used to aid the student
in linking theory and the real world. Banios (1991) identified
five important attributes of capstone courses to meet changing needs: students should a) use significant and insightful
teamwork, b) focus and use much of the knowledge acquired
in the curriculum, c) solve problems representing real life,
d) acquire an understanding of professionalism, and e) learn
and practice planning, implementing, and controlling discipline related projects. This article adds to the discussion on
effective components of a capstone course by documenting
our experience with incorporating simulation into a capstone
course as a way to make the class more meaningful for the
student.
This article is organized into six parts; first we begin with
an explanation of the different types of learning processes
that are used to convey knowledge to students. Second, we
discuss what findings have been documented in the literature
about the value of using simulation as a teaching method,
followed by a description of the simulation model used in this
experiment. This section is followed by a description of the
Correspondence should be addressed to Maurice Reid, Eastern Kentucky University, Department of Marketing, Management and Administrative Communication, 521 Lancaster Avenue, BTC 011, Richmond, KY
40475-3102, USA. E-mail: maurice.reid@eku.edu
sample used in the present experiment and the findings. The
fifth section presents a discussion of the key findings followed
by our conclusions and suggestions for future research.
Types of Learning
Traditional learning models typically include in-depth study
of a particular discipline paying little attention to other areas of knowledge. Traditional models that include textbook
based lecture, rote memorization, static cases, role-playing,
and field study methods have been practiced for many generations and can be described as gaining knowledge at the
feet of the master within the silo of a discipline, such as an
apprenticeship. In complex problems, traditional models can
lead to quick problem identification but, with a higher incidence of misdiagnosis resulting in the implementation of
suboptimal solutions (Lohman, 2002; Raelin, 2000). Lohman
described an alternative method (double loop or action learning) of learning that includes reflection and problem reformulation and analysis of open-ended, ill-structured problems
that is proposed to be a more effective process of learning. In
double-loop learning the student must go through multiple iterations of problem framing, identification, analysis and solution development to achieve an acceptable problem solution,
using theory and personal experience to assist solution development. In other words, the emphasis is on experiential learning or learning by doing that is through the transformation of
experience (Farrell, 2005). Jonassen (1988) and Grabowski
(2004) supported the method as an effective way to improve student learning. Sharp, Knowlton, and Weiss (2005)
CAPSTONE TEACHING MODELS
demonstrated the applicability of generative (double loop)
learning in business settings and Zantow, Knowlton, and
Sharp (2005) used the method to demonstrate that business
simulation provided opportunities for generative learning.
Consistent with the suggestion of Herremans and Murch
(2003), business schools should use an array of methods
including the professional model or traditional model and
generative learning to comprehensively prepare students for
their early career as well as lifelong learning. In the present
research we explore if the inclusion of simulation facilitates
generative learning for the student enhancing their course
experience.
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Value of Simulation
The value of simulation has been extensively discussed in
the literature with the majority of the literature (Chapman
& Sorge, 1999; Farrell, 2005; Wolf & Luethge, 2003) supportive of the enhancement in learning that can occur. There
is a contrarian view as reported by Neuhauser (1976) and
others who did not identify benefits in the primary student
learning objectives of the class but did find other benefits
in using simulation. Examples of positive outcomes include
student teamwork and exam performance (Vaidyanathan &
Rochford, 1998). Washbush and Gosen (1998) attempted to
determine and evaluate the relationship between learning and
simulation performance. While simulation-related learning
did occur, there appeared to be no direct relationship between simulation performance and learning. Thavikulwat
(1999) indicated that simulations may be more effective
for assessment of business education. Stephen, Parente, and
Brown (2002) found large-scale simulations to be effective
in balancing functional and integrative knowledge and Farrell (2005) found that compared with various instructional
tools such as static case studies and textbook use, higher
student involvement and interest in simulation resulted in
greater perceived learning. Keys and Wolfe (1990) indicated
that simulation provides students with opportunities to experience hands-on decision making. Gopinath and Sawyer
(1999) claimed that the learning benefits from simulation
have not been fully understood and showed that simulation encourages strategic decision making and group behavior consistent with long-term strategy. Curland and Fawcett
(2001) examined the problems with numerical skills applied
in operations management and finance. They indicated that
business simulations can be of value in overcoming fear of
the use of numbers. Burke and Moore (2003) showed that because simulations go beyond lecture and other ordinary class
activities, they are likely to stimulate a better understanding
of the relevance of course content. Puto (2004) concluded
that the case method, when truly integrated with advanced
business simulations, can greatly enhance the effectiveness
of business education. Faria and Wellington (2004) surveyed
149,497 business faculty members to determine simulation
game usage and thoughts about these games. Of 1,085 re-
179
spondents, 30.6% were business game users, 17.1% were
former users, and 52.3% had never used such games. The
assumption here is that such widespread use implies benefits
gained by the student. This seems to support the claim of Zantow et al. (2005) that the real value of simulation continues
to be underestimated.
Simulation provides an ideal platform for merging professional and active learning. Chapman and Sorge (1999)
compared various instructional tools and found simulation to
be a valuable tool for developing greater student appreciation of real life business decision making. It can narrow the
gap between a complex reality and the classroom (Doyle &
Brown, 2000; Mizukami, 2002). When students make good,
well-planned decisions, they can clearly see the results and
rewards reflected in the simulation (Wolf & Luethge, 2003).
Simulation has also been found to be effective in the integration of the functional areas of business (Stephen et
al., 2002) as well as the integration of theory and practice
(Wolf & Luethge). Dynamic cases provide a structured environment for learning complex problems and empower students to act in a rational way and solve real world problems
(Brookfield, 1995; Senge & Fulmer, 1993; Shubik, 1975).
Gilgeous and D’Cruz (1996) offered the following advantages of using simulations: a) simulations support lecture
and theory validation through real life application, b) simulations add dynamics to cases where students can learn
about the quality of their decisions, c) simulations provide
much more personal interaction and team building, and d)
even the poorest simulation performers may be the most significant learners. Moratis, Hoff, and Reul (2006) indicated
that business schools face the dual challenges of relevance,
and development and implementation of innovative learning
methods. They reviewed the roles and functions of simulation and show that since simulations enable comprehensive
learning they contribute to creating effective business learning environments. Lynch and Tunstall (2008) indicated that
evidence suggests that relevant, well-designed simulations
can make a valuable contribution to students’ experience.
To understand factors that promote the effective use of
simulation in management education Adobor and Daneshfar (2006) were the first to separate learning and performance as outcomes and used data from 49 teams performing simulation. Their study showed a) simulation as reflective of real-life situations, and task conflict and ease of use
were positively associated with learning; b) emotional conflict in the team was negatively associated with learning; c)
ease of use and task conflict positively affected team performance; and (d) emotional conflict had a negative relationship
to team performance. User-friendliness, realism of simulation, and team dynamics were demonstrated to be the important criteria of effectiveness. Lainema and Nurmi (2006)
described and applied a dynamic computer-based business
learning environment and argued that to be effective, learning tools must be realistic, complex, authentic, facilitate
continuous problem solving, and embed learning in social
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180
M. REID ET AL.
experience. They concluded that dynamicity and interactivity are valuable characteristics of a business learning tool.
Robbins-Bell (2008) argued for the need for instructors in
higher education to improve student involvement in and outside of the classroom and break down the false separation between classroom learning and life learning. A virtual world
is offered as a means to turn passive, knowledge-receiving
students into active, knowledge-making students. Williams
(2008) noted that new economic realities dictate market
responsiveness in higher education and highly valued and
demanded skills include information analysis, collaborative
working, and just-in-time learning and realistic simulations
can facilitate collaborative exchange.
There is a substantial record of simulation methodology
applied to business school instructional practices, the 32 articles noted in this section attest to the attention that the simulation process has generated over the past 20 years. Simulation
applications can be targeted to specific learning objectives
or be very generic with ambiguous ill-defined problems requiring multiple solution identification and implementation
iterations. Researchers recognize that the complexity of simulations are more realistic approaches to convey practical
theoretical knowledge, but the majority of the literature surveyed did not provide statistically significant evidence that a
student’s learning was more effective using simulation versus alternative learning methods. The history of simulation
is rich and robust; the evidence of its effectiveness presented
to date in business settings is more circumstantial.
THE CAPSTONE SIMULATION
The Capstone Simulation was originally developed for management training seminars in large corporations, and has been
adopted for use by over 500 colleges and universities to help
teach business strategy.1 For a simulated eight-year span,
competing management teams run $100 million electronic
sensor manufacturing companies. A series of decisions are
made each year of the simulation in the functional areas
of research and development, marketing, production, human
resources, finance, process management, and total quality.
Furthermore, there are five market segments, each having
different customer requirements and the teams are provided
with periodic financial reports. Once the decisions are uploaded for each team, they are processed. The results are
automatically entered into the matrices for the beginning of
the next period’s decisions. The results are also made available to each team through the Capstone Courier detailing
each team’s decisions and performance. The teams take the
data from the updated spreadsheets and the Capstone Courier
reports and use the data to develop the next period’s decisions.
Sample
One objective of the present research was to identify the differences in student performance resulting from a simulation
exercise in the classroom from a traditional group paper as
has been the practice in our school for more than a decade.
To accomplish this objective an experiment was designed in
which two sections of the same course, taught by one instructor, comprised the control and treatment groups of the
experiment. Each class was divided into teams of four or five
members: six teams in the larger class (the control group)
and five teams in the smaller class (the treatment group). The
five teams from the treatment group competed against each
other in the Capstone Simulation (Management Simulations,
2008).
Each class was taught in the traditional lecture–
paper–exam manner and during the last two weeks of the
course the control group was assigned a project where each
team was assigned to analyze the strategy, performance, and
potential of a competitor in the golf equipment industry.
The analysis and recommendations were documented in a
short paper and presented in class. The competitor teams
questioned the work of the presenters and were asked how
their company or client would or should respond to their
competitor’s plans. The treatment class had the same lectures and assignments up until the golf equipment company
assignment, which was replaced with the simulation. Each
section was given identical exams throughout the course,
including the final exam, which was administered in the
week following the last presentation by the teams in each
class.
The simulation grades were based on market share, stock
price, and return on assets over the life of the simulation,
and the explanations given by the management team of what
processes they would use to improve their company’s performance in these measures. The grading for the strategic review
case was based on the team’s ability to identify problems and
recommend solutions that were available to their assigned
client company. In the design of the experiment the grade for
the simulation component and the industry strategic review
were not considered as part of the experiment. The hypothesis was that the treatment section would perform better on
the final exam than the control group.
Null Hypothesis: There would be a difference in student
performance on the final exam in the capstone business strategy course between the treatment and control
groups.
The final exam consisted of a case analysis; the case was
given to the students one week prior to the final exam period
in which a series of questions about the strategy used in the
case were administered.
The quantitative data collected consists of the exam
scores, which included the grade received from responding to questions about the case analyzed for the final, which
was identical for both class sections. In addition to the quantitative data, qualitative data were collected in the form of
course evaluations and comments solicited from the students
regarding how they felt about their experience.
CAPSTONE TEACHING MODELS
TABLE 1
Test Results
The data generated in this experiment consisted of the means
of the exam scores given at three times during the course.
The scores are summarized in Table 1. The KolmogorovSmirnov (K-S) test was used to validate the assumption of
a normally distributed data set, which allowed the use of
parametric statistical tests of the means.
The K-S test indicates that Exam 3 should not be assumed
to have a normal distribution based on the data obtained, and
in fact is a skewed distribution with six scores of 95% or
better. In Class 2 both the second and third exams had clusters of scores that appeared to make the distribution bimodal.
Although these deviations are worth noting, it was not perceived as significant enough to alter the conclusions of the
test. As explained by Hays (1994), “so long as the sample
size is even moderate for each group, quite severe departures
from normality seem to make little practical difference in the
conclusions reached” (p. 327).
Following this check of the data, t tests were conducted
to determine if there was a significant difference in the mean
exam scores obtained and this calculation is summarized in
Table 2.
These results do not detect a difference in the mean exam
scores at any point during the semester between the control
group and the treatment group. Although not shown here,
similar results are obtained if the assumption of equal variances between the two classes is relaxed.
In the test results table, the Shapiro-Wilk test result was
given in addition to the K-S test result, which indicates that
the exam results for Class 2 Exams 1 and 2 also fail the
assumption of normality, even though they pass the K-S test.
According to Field (2000, 2009), the Shapiro-Wilk test is
more accurate.2 Because of this result, two nonparametric
tests of the data were conducted to identify differences in the
mean scores of the final exam, the Mann-Whitney test and
the two-sample K-S test, which both resulted in rejecting
the hypothesis that there would be a significant difference
between the means of the final exam scores.
This segment of the results section is on statistical power,
as our sample size was limited to the number of students
registered in the class, and confined to two sections; it is important to determine the ability of our experiment to reject the
null hypothesis when it is false. The observed power of this
analysis is .150—a dismally low number. To raise the power
to .8, the value typically accepted as a minimum; a sample
size of approximately 122 (61 per treatment group) would be
required to detect a 10% grade shift with a 95% confidence
level (Hays, 1994). This target is not possible at this institution as a one-time experiment, as at peak enrollment there
were only 100 students registered for the course. An administrative constraint is that present faculty teaching load does
not typically exceed three classes equivalent to a maximum
of 75 students. The result is that the design of the experiment
would have to be significantly modified, affecting variables
in the environment that are of no interest in the analysis.
To supplement the statistical analysis, each group was
given the opportunity to evaluate the course, and the treatment groups were asked specifically to comment on their
experience with the simulation. A summary of the comments
appears in Appendix A, and the comments and observations
of the instructor suggest that the students treated to the simulation were significantly more engaged with their assignment.
They were much more emotional about the periodic feedback
and protective of their strategic plans. They had complete discretion of when their team met and how they made decisions,
with the only requirements being to have their decisions input
by the announced deadline and to respond to any correspondence received from the instructor (in the form of a major
stockholder, government regulator or other stakeholder). The
most common complaint was there was insufficient time to
meet (as a group) to review and analyze the available information before the next set of decisions was due. This was not
an issue in the class with the case assignment.
TABLE 2
Results of t Test
DISCUSSION
Class
Exam 1 Exam 2
Class 1
M
SD
Sample size
Kolmogorov-Smirnov test
Shapiro-Wilk
Class 2
M
SD
Sample size
Kolmogorov-Smirnov test
Shapiro-Wilk
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181
aSignificant
Exam 3
69
10
26
.200
.140
68
12
26
.200
.755
82
12
26
.043a
.030a
75
12
21
.200
.334
70
13
21
.083
.028a
79
07
21
.101
.020a
departure from normal distribution.
RESULTS
Exam
1
2
3
t
df
−1.646 45
−0.614 45
0.933 45
Significance M difference Confidence interval
.107
.543
.356
−.0511
−.02315
.02767
[−.1136, .0014]
[−.0991, .0528]
[−.0320, .0874]
The net result of this research is that we are unable to objectively identify a difference between using a simulation
method versus the traditional term paper or written assignment in a controlled experiment. Using the subjective evidence, there is heightened sense of engagement among the
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182
M. REID ET AL.
students that seems to be fueled by the competitive atmosphere created in the simulated environment. This is consistent with other findings in the literature (Anderson & Lawton, 1992; Chapman & Sorge, 1999; Faria, 2001; Gopinath
& Sawyer, 1999; Hornaday & Curran, 1996; Knotts & Keys,
1997; Washbush & Gosen, 2001) in which objective statistical evidence of the effect of including simulation in a
management strategy course was inconsistent.
Our approach in which the variation in all the environmental variables, textbook, instructor, and lectures were minimized is a rigorous attempt to isolate the effect of the simulation, but still requires more students, more sections of the
course, and more instructors than our student and faculty population on campus have to generate results sufficient to generalize the findings to a larger population. To achieve this level
of statistical accuracy it is necessary to introduce more variation in the number of instructors, lecture style, and course
content emphasis. An alternative way to achieve a larger
sample would be to replicate the experiment over multiple
terms with one instructor, which could introduce variation in
lecture content and emphasis plus introduce bias in student
selection of the course, where students select the style of
course in which they feel more comfortable or successful.
Another aspect of this research that bears inspection is the
type of assessment used to gauge student comprehension of
the model taught in class. The posttreatment exam consisted
of a short case analysis designed to demonstrate the students’
understanding of the application of the strategy model taught
in the class. The questions included critiquing the external
environment, the internal environment and making recommendations that would support their understanding of the
mission of the organization in the case.
The application of the knowledge gained during the simulation is different than the way knowledge is gained in a case
study, which could bias the control group to perform better
at recognizing the issues in the case environment, whereas
the treatment group (those in the simulation) had more emphasis on implementing strategy. Students are taught that
implementation is at least as important as devising a good
strategy and the thought that a mediocre strategy well executed can beat an excellent strategy poorly executed was
mentioned in lecture. Given this bias, is the posttreatment
exam of a case analysis the best way to assess a student’s
understanding of the material? More emphasis on the implementation of strategy could have been included in the final
exam, although having feedback loops that are important in
implementation, are impractical for a simple written exam.
Although the identification and implementation of strategic
issues and initiatives are taught, testing and lecture topics are
biased toward identification.
A second concern in this experiment is that this research
used teams of students to prepare all the papers written in
the course yet tested students on their individual knowledge
of the theoretical concepts. In the posttreatment exam the
students were individually tested on their ability to identify
problems and suggest recommendations. The group versus
individual dynamic has been identified and recognized as
a factor (Thorelli, 2001) in the performance difference between groups and the individuals that comprise the group.
The method chosen for this research was to base the performance metric on individual performance and the group
performance was not examined.
The subjective comments about the simulation were generally very positive and during the sessions that were observed by the instructor the involvement of the entire team
was a positive experience (in terms of course objectives,
listed in the Appendix B). Some teams organized functionally (with each member assigned a discipline) for the decisions to be made and others used a more democratic process
(each member contributed to all the decisions to be made).
The organization of the team required the use of management techniques learned in other classes, as did understanding the periodic reports generated as a result of the decisions
they input; using these skills were additional benefits of the
simulation. In reviewing the strategies the student teams employed it was interesting to see that some teams consciously
chose strategies that were discussed in class as being nonoptimal choices: for example, choosing to compete in the most
sophisticated product market with minimal research and development spending. This presented a learning opportunity
when they lost market share, revenue, and shareholder value
until they realized their error. This experience was significantly more meaningful to the team than missing a question
on a test. This type of experience also emphasized to the
instructor the value in more extensive debriefing of the simulation to insure that the teams recognize the errors they made
and how to correct their errors.
CONCLUSION
This experiment had a positive impact on the student participants and on the instructor. The theory that was assumed
to be understood by the instructor clearly was not understood by the class, and the simulation highlighted many of
these areas. The design of the assessment measures used in
the course has limitations that do not sufficiently incorporate
strategic implementation and the use of feedback systems
to insure a strategy is moving toward the desired objectives.
Communication within the teams works differently when
there is additional stress put on the organization, and the
stress created on the term paper group was a different type
of stress than the stress created in the simulation. The stress
was different because of the multiple deadline periods, the
unstructured and variable problems that the teams had to
identify and resolve that appeared erratically within the simulation, and the required response to competitive teams that
were out to satisfy the same universe of customers. These
characteristics make the two approaches very different, and
the simulation has value just because it is a different way of
creating a learning environment that incorporates the same
learning goals through a different delivery method.
CAPSTONE TEACHING MODELS
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The initial objective of this study was to review the use of
simulation in a business classroom setting and compare the
method to the case and lecture method. Although our findings
are consistent with the majority of the existing literature, we
note that there is a lack of research that explores the student
performance effect of simulation use in a statistically reliable
and valid way. In the preface to the 17th edition of Crafting
and Executing Strategy, Thompson, Strickland, and Gamble (2010) stated that “a three-pronged text-case-simulation
course model has significantly more teaching/learning power
than the traditional text-case model” (p. xvii) and the way this
power is reflected in student performance needs to be more
effectively documented.
NOTES
1. Capsim Management Simulations Inc., http://www.
capsim.com
2. “The Shapiro-Wilk Statistic yields exact significance
values where as the K-S Test sometimes gives an approximation of .2 for the significance because SPSS
cannot calculate exact significances. This finding highlights an important difference between the K-S Test and
the Shapiro-Wilk Test: in general the Shapiro-Wilk Test
is more accurate” (Field, 2009, p. 546).
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O’Reilly, B. (1994). Reengineering the MBA. Fortune, 129(2), 38–44.
Puto, C. (2004, May/June). The next best thing. BizEd, 44–49.
Raelin, J. A. (2000). Work-based learning: The new frontier of management
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Robbins-Bell, S. (2008). Higher education as virtual conversation. Educause
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Senge, P. M., & Fulmer, R. H. (1993). Simulations systems thinking and
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Sharp, D. C., Knowlton, D. S., & Weiss, R. E. (2005). Applications of
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Shubik, M. (1975). The uses and methods of games. New York, NY: Elsevier.
Stephen, J., Parente, D. H., & Brown, R. C. (2002). Seeing the forest and the
trees: Balancing functional and integrative knowledge using large scale
simulation in capstone business strategy classes. Journal of Management
Education, 26, 164–193.
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Thompson, A., Strickland, A. J., & Gamble, J. E. (2010). Crafting and
executing strategy, text and readings (17th ed.). New York, NY: McGrawHill Irwin.
Thorelli, H. B. (2001). Ecology of international business simulation games.
Simulation & Gaming, 32, 492–513.
Vaidyanathan, R., & Rochford, L. (1998). An exploratory investigation of
computer simulations, student preferences, and performance. Journal of
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Washbush, J. A., & Gosen, J. (1998). Total enterprise simulation performance and participant learning. Journal of Workplace Learning, 10,
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Washbush, J. A., & Gosen, J. (2001). An exploration of game-derived learning in total enterprise simulations. Simulation Gaming, 32, 281–296.
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Wolf, J., & Luethge, D. J. (2003). The impact of involvement on performance
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Zantow, K., Knowlton, D. S., & Sharp, D. C. (2005). More than fun and
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Academy of Management Learning & Education, 4, 451–458.
APPENDIX A—SUMMARY COMMENTS MADE
BY THE STUDENT TEAMS AT THE END OF
THE SIMULATION
Team A
• We tried to focus on two markets with insufficient resources to satisfy market demand and other customer requirements
• We consistently priced our product too high relative to the
competition
• We had poor communication processes among the management team
• There wasn’t enough time to fully analyze the environment
prior to making decisions
Team B
• Focused on one market
• Extremely satisfied with company performance; ROS of
14.9% over the life of the simulation, ROA of 24.5% and
shareholder value quadrupled.
• Developed highly automated production and a low tech
product for cost leadership in the market sector
• Emphasized use of research and development (high spending level each period)
• Did not use price as a competitive weapon
• Committed to human resource development
• Issued stock to fund R&D and manufacturing plant capacity growth
• Invested in Total Quality Management to keep production
costs low
Team C
• Identified strategic choice as maintaining competitiveness
without dominating any one market segment
• Communication problems at the start of the simulation
resulted in a more autocratic/hierarchical management
structure
• Choose to participate in two markets
• Made poor forecasts one half way through the simulation
that put the team behind the competition (firm performance)
• Enjoyed the financial modeling aspect of the simulation.
Team D
• Strategy: Low research and development investment,
while competing in the high tech segment
• Use competitive intelligence to maintain competitiveness
◦ Note: This team had strategies for R&D, Marketing,
Production and Finance that were not complementary
• Group decisions had better outcomes than individual decisions; but the time constraints detracted from the experience
Team E
• Identified the mission as market leader in price and sales
• Focus investment in production, and marketing
• Inflexibility of automated production became a problem
as market technology improved making the manufacturing
facilities obsolete
• Used a democratic management style, team vote on all
decisions
• Kept team member participation high
• First five periods reinvested all earnings then declared a
dividend in the remaining periods.
APPENDIX B—CAPSTONE COURSE
OBJECTIVES
• To develop your capacity to think strategically about a
company, its environment, objectives and direction.
• To integrate the skills you have acquired in various business disciplines to solve real world complex
problems. To demonstrate the advantages of managing
the different parts of a business with complimentary
strategies.
• To sensitize you to the importance of ethics and values in
management practices.
• Provide students with an ability to:
◦ Approach and analyze problems in a systematic manner
◦ Communicate with analysts in other disciplines
◦ Understand the solutions obtained
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Capstone Teaching Models: Combining Simulation,
Analytical Intuitive Learning Processes, History
and Effectiveness
Maurice Reid , Steve Brown & Kambiz Tabibzadeh
To cite this article: Maurice Reid , Steve Brown & Kambiz Tabibzadeh (2012) Capstone
Teaching Models: Combining Simulation, Analytical Intuitive Learning Processes,
History and Effectiveness, Journal of Education for Business, 87:3, 178-184, DOI:
10.1080/08832323.2011.586006
To link to this article: http://dx.doi.org/10.1080/08832323.2011.586006
Published online: 01 Feb 2012.
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Date: 11 January 2016, At: 22:00
JOURNAL OF EDUCATION FOR BUSINESS, 87: 178–184, 2012
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2011.586006
Capstone Teaching Models: Combining Simulation,
Analytical Intuitive Learning Processes, History
and Effectiveness
Maurice Reid, Steve Brown, and Kambiz Tabibzadeh
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Eastern Kentucky University, Richmond, Kentucky, USA
For the past decade teaching models have been changing, reflecting the dynamics, complexities,
and uncertainties of today’s organizations. The traditional and the more current active models
of learning have disadvantages. Simulation provides a platform to combine the best aspects of
both types of teaching practices. This research explores the history of including simulation into
a teaching plan and outlines an empirical method to test the effect that including a simulation
can have in the classroom.
Keywords: business simulation, capstone course methodology, simulation effectiveness
Academic disciplines across the board are becoming increasingly aware that mastery of technical content is insufficient
preparation for today’s professional careers (Chung, Baker,
Harmon, & Burks, 2003; O’Reilly, 1994; Wheeler, 1998). Increasingly, capstone courses are being used to aid the student
in linking theory and the real world. Banios (1991) identified
five important attributes of capstone courses to meet changing needs: students should a) use significant and insightful
teamwork, b) focus and use much of the knowledge acquired
in the curriculum, c) solve problems representing real life,
d) acquire an understanding of professionalism, and e) learn
and practice planning, implementing, and controlling discipline related projects. This article adds to the discussion on
effective components of a capstone course by documenting
our experience with incorporating simulation into a capstone
course as a way to make the class more meaningful for the
student.
This article is organized into six parts; first we begin with
an explanation of the different types of learning processes
that are used to convey knowledge to students. Second, we
discuss what findings have been documented in the literature
about the value of using simulation as a teaching method,
followed by a description of the simulation model used in this
experiment. This section is followed by a description of the
Correspondence should be addressed to Maurice Reid, Eastern Kentucky University, Department of Marketing, Management and Administrative Communication, 521 Lancaster Avenue, BTC 011, Richmond, KY
40475-3102, USA. E-mail: maurice.reid@eku.edu
sample used in the present experiment and the findings. The
fifth section presents a discussion of the key findings followed
by our conclusions and suggestions for future research.
Types of Learning
Traditional learning models typically include in-depth study
of a particular discipline paying little attention to other areas of knowledge. Traditional models that include textbook
based lecture, rote memorization, static cases, role-playing,
and field study methods have been practiced for many generations and can be described as gaining knowledge at the
feet of the master within the silo of a discipline, such as an
apprenticeship. In complex problems, traditional models can
lead to quick problem identification but, with a higher incidence of misdiagnosis resulting in the implementation of
suboptimal solutions (Lohman, 2002; Raelin, 2000). Lohman
described an alternative method (double loop or action learning) of learning that includes reflection and problem reformulation and analysis of open-ended, ill-structured problems
that is proposed to be a more effective process of learning. In
double-loop learning the student must go through multiple iterations of problem framing, identification, analysis and solution development to achieve an acceptable problem solution,
using theory and personal experience to assist solution development. In other words, the emphasis is on experiential learning or learning by doing that is through the transformation of
experience (Farrell, 2005). Jonassen (1988) and Grabowski
(2004) supported the method as an effective way to improve student learning. Sharp, Knowlton, and Weiss (2005)
CAPSTONE TEACHING MODELS
demonstrated the applicability of generative (double loop)
learning in business settings and Zantow, Knowlton, and
Sharp (2005) used the method to demonstrate that business
simulation provided opportunities for generative learning.
Consistent with the suggestion of Herremans and Murch
(2003), business schools should use an array of methods
including the professional model or traditional model and
generative learning to comprehensively prepare students for
their early career as well as lifelong learning. In the present
research we explore if the inclusion of simulation facilitates
generative learning for the student enhancing their course
experience.
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Value of Simulation
The value of simulation has been extensively discussed in
the literature with the majority of the literature (Chapman
& Sorge, 1999; Farrell, 2005; Wolf & Luethge, 2003) supportive of the enhancement in learning that can occur. There
is a contrarian view as reported by Neuhauser (1976) and
others who did not identify benefits in the primary student
learning objectives of the class but did find other benefits
in using simulation. Examples of positive outcomes include
student teamwork and exam performance (Vaidyanathan &
Rochford, 1998). Washbush and Gosen (1998) attempted to
determine and evaluate the relationship between learning and
simulation performance. While simulation-related learning
did occur, there appeared to be no direct relationship between simulation performance and learning. Thavikulwat
(1999) indicated that simulations may be more effective
for assessment of business education. Stephen, Parente, and
Brown (2002) found large-scale simulations to be effective
in balancing functional and integrative knowledge and Farrell (2005) found that compared with various instructional
tools such as static case studies and textbook use, higher
student involvement and interest in simulation resulted in
greater perceived learning. Keys and Wolfe (1990) indicated
that simulation provides students with opportunities to experience hands-on decision making. Gopinath and Sawyer
(1999) claimed that the learning benefits from simulation
have not been fully understood and showed that simulation encourages strategic decision making and group behavior consistent with long-term strategy. Curland and Fawcett
(2001) examined the problems with numerical skills applied
in operations management and finance. They indicated that
business simulations can be of value in overcoming fear of
the use of numbers. Burke and Moore (2003) showed that because simulations go beyond lecture and other ordinary class
activities, they are likely to stimulate a better understanding
of the relevance of course content. Puto (2004) concluded
that the case method, when truly integrated with advanced
business simulations, can greatly enhance the effectiveness
of business education. Faria and Wellington (2004) surveyed
149,497 business faculty members to determine simulation
game usage and thoughts about these games. Of 1,085 re-
179
spondents, 30.6% were business game users, 17.1% were
former users, and 52.3% had never used such games. The
assumption here is that such widespread use implies benefits
gained by the student. This seems to support the claim of Zantow et al. (2005) that the real value of simulation continues
to be underestimated.
Simulation provides an ideal platform for merging professional and active learning. Chapman and Sorge (1999)
compared various instructional tools and found simulation to
be a valuable tool for developing greater student appreciation of real life business decision making. It can narrow the
gap between a complex reality and the classroom (Doyle &
Brown, 2000; Mizukami, 2002). When students make good,
well-planned decisions, they can clearly see the results and
rewards reflected in the simulation (Wolf & Luethge, 2003).
Simulation has also been found to be effective in the integration of the functional areas of business (Stephen et
al., 2002) as well as the integration of theory and practice
(Wolf & Luethge). Dynamic cases provide a structured environment for learning complex problems and empower students to act in a rational way and solve real world problems
(Brookfield, 1995; Senge & Fulmer, 1993; Shubik, 1975).
Gilgeous and D’Cruz (1996) offered the following advantages of using simulations: a) simulations support lecture
and theory validation through real life application, b) simulations add dynamics to cases where students can learn
about the quality of their decisions, c) simulations provide
much more personal interaction and team building, and d)
even the poorest simulation performers may be the most significant learners. Moratis, Hoff, and Reul (2006) indicated
that business schools face the dual challenges of relevance,
and development and implementation of innovative learning
methods. They reviewed the roles and functions of simulation and show that since simulations enable comprehensive
learning they contribute to creating effective business learning environments. Lynch and Tunstall (2008) indicated that
evidence suggests that relevant, well-designed simulations
can make a valuable contribution to students’ experience.
To understand factors that promote the effective use of
simulation in management education Adobor and Daneshfar (2006) were the first to separate learning and performance as outcomes and used data from 49 teams performing simulation. Their study showed a) simulation as reflective of real-life situations, and task conflict and ease of use
were positively associated with learning; b) emotional conflict in the team was negatively associated with learning; c)
ease of use and task conflict positively affected team performance; and (d) emotional conflict had a negative relationship
to team performance. User-friendliness, realism of simulation, and team dynamics were demonstrated to be the important criteria of effectiveness. Lainema and Nurmi (2006)
described and applied a dynamic computer-based business
learning environment and argued that to be effective, learning tools must be realistic, complex, authentic, facilitate
continuous problem solving, and embed learning in social
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M. REID ET AL.
experience. They concluded that dynamicity and interactivity are valuable characteristics of a business learning tool.
Robbins-Bell (2008) argued for the need for instructors in
higher education to improve student involvement in and outside of the classroom and break down the false separation between classroom learning and life learning. A virtual world
is offered as a means to turn passive, knowledge-receiving
students into active, knowledge-making students. Williams
(2008) noted that new economic realities dictate market
responsiveness in higher education and highly valued and
demanded skills include information analysis, collaborative
working, and just-in-time learning and realistic simulations
can facilitate collaborative exchange.
There is a substantial record of simulation methodology
applied to business school instructional practices, the 32 articles noted in this section attest to the attention that the simulation process has generated over the past 20 years. Simulation
applications can be targeted to specific learning objectives
or be very generic with ambiguous ill-defined problems requiring multiple solution identification and implementation
iterations. Researchers recognize that the complexity of simulations are more realistic approaches to convey practical
theoretical knowledge, but the majority of the literature surveyed did not provide statistically significant evidence that a
student’s learning was more effective using simulation versus alternative learning methods. The history of simulation
is rich and robust; the evidence of its effectiveness presented
to date in business settings is more circumstantial.
THE CAPSTONE SIMULATION
The Capstone Simulation was originally developed for management training seminars in large corporations, and has been
adopted for use by over 500 colleges and universities to help
teach business strategy.1 For a simulated eight-year span,
competing management teams run $100 million electronic
sensor manufacturing companies. A series of decisions are
made each year of the simulation in the functional areas
of research and development, marketing, production, human
resources, finance, process management, and total quality.
Furthermore, there are five market segments, each having
different customer requirements and the teams are provided
with periodic financial reports. Once the decisions are uploaded for each team, they are processed. The results are
automatically entered into the matrices for the beginning of
the next period’s decisions. The results are also made available to each team through the Capstone Courier detailing
each team’s decisions and performance. The teams take the
data from the updated spreadsheets and the Capstone Courier
reports and use the data to develop the next period’s decisions.
Sample
One objective of the present research was to identify the differences in student performance resulting from a simulation
exercise in the classroom from a traditional group paper as
has been the practice in our school for more than a decade.
To accomplish this objective an experiment was designed in
which two sections of the same course, taught by one instructor, comprised the control and treatment groups of the
experiment. Each class was divided into teams of four or five
members: six teams in the larger class (the control group)
and five teams in the smaller class (the treatment group). The
five teams from the treatment group competed against each
other in the Capstone Simulation (Management Simulations,
2008).
Each class was taught in the traditional lecture–
paper–exam manner and during the last two weeks of the
course the control group was assigned a project where each
team was assigned to analyze the strategy, performance, and
potential of a competitor in the golf equipment industry.
The analysis and recommendations were documented in a
short paper and presented in class. The competitor teams
questioned the work of the presenters and were asked how
their company or client would or should respond to their
competitor’s plans. The treatment class had the same lectures and assignments up until the golf equipment company
assignment, which was replaced with the simulation. Each
section was given identical exams throughout the course,
including the final exam, which was administered in the
week following the last presentation by the teams in each
class.
The simulation grades were based on market share, stock
price, and return on assets over the life of the simulation,
and the explanations given by the management team of what
processes they would use to improve their company’s performance in these measures. The grading for the strategic review
case was based on the team’s ability to identify problems and
recommend solutions that were available to their assigned
client company. In the design of the experiment the grade for
the simulation component and the industry strategic review
were not considered as part of the experiment. The hypothesis was that the treatment section would perform better on
the final exam than the control group.
Null Hypothesis: There would be a difference in student
performance on the final exam in the capstone business strategy course between the treatment and control
groups.
The final exam consisted of a case analysis; the case was
given to the students one week prior to the final exam period
in which a series of questions about the strategy used in the
case were administered.
The quantitative data collected consists of the exam
scores, which included the grade received from responding to questions about the case analyzed for the final, which
was identical for both class sections. In addition to the quantitative data, qualitative data were collected in the form of
course evaluations and comments solicited from the students
regarding how they felt about their experience.
CAPSTONE TEACHING MODELS
TABLE 1
Test Results
The data generated in this experiment consisted of the means
of the exam scores given at three times during the course.
The scores are summarized in Table 1. The KolmogorovSmirnov (K-S) test was used to validate the assumption of
a normally distributed data set, which allowed the use of
parametric statistical tests of the means.
The K-S test indicates that Exam 3 should not be assumed
to have a normal distribution based on the data obtained, and
in fact is a skewed distribution with six scores of 95% or
better. In Class 2 both the second and third exams had clusters of scores that appeared to make the distribution bimodal.
Although these deviations are worth noting, it was not perceived as significant enough to alter the conclusions of the
test. As explained by Hays (1994), “so long as the sample
size is even moderate for each group, quite severe departures
from normality seem to make little practical difference in the
conclusions reached” (p. 327).
Following this check of the data, t tests were conducted
to determine if there was a significant difference in the mean
exam scores obtained and this calculation is summarized in
Table 2.
These results do not detect a difference in the mean exam
scores at any point during the semester between the control
group and the treatment group. Although not shown here,
similar results are obtained if the assumption of equal variances between the two classes is relaxed.
In the test results table, the Shapiro-Wilk test result was
given in addition to the K-S test result, which indicates that
the exam results for Class 2 Exams 1 and 2 also fail the
assumption of normality, even though they pass the K-S test.
According to Field (2000, 2009), the Shapiro-Wilk test is
more accurate.2 Because of this result, two nonparametric
tests of the data were conducted to identify differences in the
mean scores of the final exam, the Mann-Whitney test and
the two-sample K-S test, which both resulted in rejecting
the hypothesis that there would be a significant difference
between the means of the final exam scores.
This segment of the results section is on statistical power,
as our sample size was limited to the number of students
registered in the class, and confined to two sections; it is important to determine the ability of our experiment to reject the
null hypothesis when it is false. The observed power of this
analysis is .150—a dismally low number. To raise the power
to .8, the value typically accepted as a minimum; a sample
size of approximately 122 (61 per treatment group) would be
required to detect a 10% grade shift with a 95% confidence
level (Hays, 1994). This target is not possible at this institution as a one-time experiment, as at peak enrollment there
were only 100 students registered for the course. An administrative constraint is that present faculty teaching load does
not typically exceed three classes equivalent to a maximum
of 75 students. The result is that the design of the experiment
would have to be significantly modified, affecting variables
in the environment that are of no interest in the analysis.
To supplement the statistical analysis, each group was
given the opportunity to evaluate the course, and the treatment groups were asked specifically to comment on their
experience with the simulation. A summary of the comments
appears in Appendix A, and the comments and observations
of the instructor suggest that the students treated to the simulation were significantly more engaged with their assignment.
They were much more emotional about the periodic feedback
and protective of their strategic plans. They had complete discretion of when their team met and how they made decisions,
with the only requirements being to have their decisions input
by the announced deadline and to respond to any correspondence received from the instructor (in the form of a major
stockholder, government regulator or other stakeholder). The
most common complaint was there was insufficient time to
meet (as a group) to review and analyze the available information before the next set of decisions was due. This was not
an issue in the class with the case assignment.
TABLE 2
Results of t Test
DISCUSSION
Class
Exam 1 Exam 2
Class 1
M
SD
Sample size
Kolmogorov-Smirnov test
Shapiro-Wilk
Class 2
M
SD
Sample size
Kolmogorov-Smirnov test
Shapiro-Wilk
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181
aSignificant
Exam 3
69
10
26
.200
.140
68
12
26
.200
.755
82
12
26
.043a
.030a
75
12
21
.200
.334
70
13
21
.083
.028a
79
07
21
.101
.020a
departure from normal distribution.
RESULTS
Exam
1
2
3
t
df
−1.646 45
−0.614 45
0.933 45
Significance M difference Confidence interval
.107
.543
.356
−.0511
−.02315
.02767
[−.1136, .0014]
[−.0991, .0528]
[−.0320, .0874]
The net result of this research is that we are unable to objectively identify a difference between using a simulation
method versus the traditional term paper or written assignment in a controlled experiment. Using the subjective evidence, there is heightened sense of engagement among the
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M. REID ET AL.
students that seems to be fueled by the competitive atmosphere created in the simulated environment. This is consistent with other findings in the literature (Anderson & Lawton, 1992; Chapman & Sorge, 1999; Faria, 2001; Gopinath
& Sawyer, 1999; Hornaday & Curran, 1996; Knotts & Keys,
1997; Washbush & Gosen, 2001) in which objective statistical evidence of the effect of including simulation in a
management strategy course was inconsistent.
Our approach in which the variation in all the environmental variables, textbook, instructor, and lectures were minimized is a rigorous attempt to isolate the effect of the simulation, but still requires more students, more sections of the
course, and more instructors than our student and faculty population on campus have to generate results sufficient to generalize the findings to a larger population. To achieve this level
of statistical accuracy it is necessary to introduce more variation in the number of instructors, lecture style, and course
content emphasis. An alternative way to achieve a larger
sample would be to replicate the experiment over multiple
terms with one instructor, which could introduce variation in
lecture content and emphasis plus introduce bias in student
selection of the course, where students select the style of
course in which they feel more comfortable or successful.
Another aspect of this research that bears inspection is the
type of assessment used to gauge student comprehension of
the model taught in class. The posttreatment exam consisted
of a short case analysis designed to demonstrate the students’
understanding of the application of the strategy model taught
in the class. The questions included critiquing the external
environment, the internal environment and making recommendations that would support their understanding of the
mission of the organization in the case.
The application of the knowledge gained during the simulation is different than the way knowledge is gained in a case
study, which could bias the control group to perform better
at recognizing the issues in the case environment, whereas
the treatment group (those in the simulation) had more emphasis on implementing strategy. Students are taught that
implementation is at least as important as devising a good
strategy and the thought that a mediocre strategy well executed can beat an excellent strategy poorly executed was
mentioned in lecture. Given this bias, is the posttreatment
exam of a case analysis the best way to assess a student’s
understanding of the material? More emphasis on the implementation of strategy could have been included in the final
exam, although having feedback loops that are important in
implementation, are impractical for a simple written exam.
Although the identification and implementation of strategic
issues and initiatives are taught, testing and lecture topics are
biased toward identification.
A second concern in this experiment is that this research
used teams of students to prepare all the papers written in
the course yet tested students on their individual knowledge
of the theoretical concepts. In the posttreatment exam the
students were individually tested on their ability to identify
problems and suggest recommendations. The group versus
individual dynamic has been identified and recognized as
a factor (Thorelli, 2001) in the performance difference between groups and the individuals that comprise the group.
The method chosen for this research was to base the performance metric on individual performance and the group
performance was not examined.
The subjective comments about the simulation were generally very positive and during the sessions that were observed by the instructor the involvement of the entire team
was a positive experience (in terms of course objectives,
listed in the Appendix B). Some teams organized functionally (with each member assigned a discipline) for the decisions to be made and others used a more democratic process
(each member contributed to all the decisions to be made).
The organization of the team required the use of management techniques learned in other classes, as did understanding the periodic reports generated as a result of the decisions
they input; using these skills were additional benefits of the
simulation. In reviewing the strategies the student teams employed it was interesting to see that some teams consciously
chose strategies that were discussed in class as being nonoptimal choices: for example, choosing to compete in the most
sophisticated product market with minimal research and development spending. This presented a learning opportunity
when they lost market share, revenue, and shareholder value
until they realized their error. This experience was significantly more meaningful to the team than missing a question
on a test. This type of experience also emphasized to the
instructor the value in more extensive debriefing of the simulation to insure that the teams recognize the errors they made
and how to correct their errors.
CONCLUSION
This experiment had a positive impact on the student participants and on the instructor. The theory that was assumed
to be understood by the instructor clearly was not understood by the class, and the simulation highlighted many of
these areas. The design of the assessment measures used in
the course has limitations that do not sufficiently incorporate
strategic implementation and the use of feedback systems
to insure a strategy is moving toward the desired objectives.
Communication within the teams works differently when
there is additional stress put on the organization, and the
stress created on the term paper group was a different type
of stress than the stress created in the simulation. The stress
was different because of the multiple deadline periods, the
unstructured and variable problems that the teams had to
identify and resolve that appeared erratically within the simulation, and the required response to competitive teams that
were out to satisfy the same universe of customers. These
characteristics make the two approaches very different, and
the simulation has value just because it is a different way of
creating a learning environment that incorporates the same
learning goals through a different delivery method.
CAPSTONE TEACHING MODELS
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The initial objective of this study was to review the use of
simulation in a business classroom setting and compare the
method to the case and lecture method. Although our findings
are consistent with the majority of the existing literature, we
note that there is a lack of research that explores the student
performance effect of simulation use in a statistically reliable
and valid way. In the preface to the 17th edition of Crafting
and Executing Strategy, Thompson, Strickland, and Gamble (2010) stated that “a three-pronged text-case-simulation
course model has significantly more teaching/learning power
than the traditional text-case model” (p. xvii) and the way this
power is reflected in student performance needs to be more
effectively documented.
NOTES
1. Capsim Management Simulations Inc., http://www.
capsim.com
2. “The Shapiro-Wilk Statistic yields exact significance
values where as the K-S Test sometimes gives an approximation of .2 for the significance because SPSS
cannot calculate exact significances. This finding highlights an important difference between the K-S Test and
the Shapiro-Wilk Test: in general the Shapiro-Wilk Test
is more accurate” (Field, 2009, p. 546).
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APPENDIX A—SUMMARY COMMENTS MADE
BY THE STUDENT TEAMS AT THE END OF
THE SIMULATION
Team A
• We tried to focus on two markets with insufficient resources to satisfy market demand and other customer requirements
• We consistently priced our product too high relative to the
competition
• We had poor communication processes among the management team
• There wasn’t enough time to fully analyze the environment
prior to making decisions
Team B
• Focused on one market
• Extremely satisfied with company performance; ROS of
14.9% over the life of the simulation, ROA of 24.5% and
shareholder value quadrupled.
• Developed highly automated production and a low tech
product for cost leadership in the market sector
• Emphasized use of research and development (high spending level each period)
• Did not use price as a competitive weapon
• Committed to human resource development
• Issued stock to fund R&D and manufacturing plant capacity growth
• Invested in Total Quality Management to keep production
costs low
Team C
• Identified strategic choice as maintaining competitiveness
without dominating any one market segment
• Communication problems at the start of the simulation
resulted in a more autocratic/hierarchical management
structure
• Choose to participate in two markets
• Made poor forecasts one half way through the simulation
that put the team behind the competition (firm performance)
• Enjoyed the financial modeling aspect of the simulation.
Team D
• Strategy: Low research and development investment,
while competing in the high tech segment
• Use competitive intelligence to maintain competitiveness
◦ Note: This team had strategies for R&D, Marketing,
Production and Finance that were not complementary
• Group decisions had better outcomes than individual decisions; but the time constraints detracted from the experience
Team E
• Identified the mission as market leader in price and sales
• Focus investment in production, and marketing
• Inflexibility of automated production became a problem
as market technology improved making the manufacturing
facilities obsolete
• Used a democratic management style, team vote on all
decisions
• Kept team member participation high
• First five periods reinvested all earnings then declared a
dividend in the remaining periods.
APPENDIX B—CAPSTONE COURSE
OBJECTIVES
• To develop your capacity to think strategically about a
company, its environment, objectives and direction.
• To integrate the skills you have acquired in various business disciplines to solve real world complex
problems. To demonstrate the advantages of managing
the different parts of a business with complimentary
strategies.
• To sensitize you to the importance of ethics and values in
management practices.
• Provide students with an ability to:
◦ Approach and analyze problems in a systematic manner
◦ Communicate with analysts in other disciplines
◦ Understand the solutions obtained