Improving Decision Making Skills through Business Simulation.docx

Improving Decision Making Skills
through Business Simulation Gaming and Expert Systems
Alexander Fuchsberger
University of Nebraska, Omaha
[email protected]
Abstract
Business simulations as experimental learning tools
are common, but they usually train specific
predetermined aspects. Research on artificial
intelligence among business simulations is rare, and
therefore, featured in this paper. The purpose of this
research is to explore the use of business simulations
games as an experimental learning tool through a
contemporary, web-based application featuring
artificial intelligence and mobile support. An expert
system guides and advises the players, while they
manage their virtual business in a competitive market
against other participants. The core element is the
design process of an artifact, based on the Design
Science methodology. The training and learning
effects on the participants are observed via the
artifact itself in a series of experiments and an
additional survey. Twenty-six students in Austria
were chosen as the sample group to reveal and
measure the improvements in decision making,
experimental learning capabilities and the biasing
ability of the artificial intelligence.

1. Introduction
Today the decision-making process within
organizations is increasingly complex. All decision
makers in businesses require basic understanding of
organizational structure and how business elements
influence each other. In universities effective work is
done by providing students with the necessary
knowledge about business concepts like production
optimization, marketing, strategies, human resource
management, and so on. But the theoretical
knowledge is rarely put to practice. Avramenko [1]
finds that the educational process in business schools
fails to equip students with employability skills.
Business simulation games encourage teamwork
and decision-making, in a risk-free environment [2].
Players develop a holistic view of the business, they
learn that sometimes alternatives have to be
considered and that losses in an early stage might
lead to higher profit in a later stage. Business games
and simulations became popular over the last 20
years; and they differ in complexity, focus, settings
or intentions. They are web or application-based and
can include random elements.
This research aims to design such a business
simulation, which allows multiple players to train
their management skills in a competitive
environment. No perfect utilization can be reached
only by the player’s actions; other players are
influencing the participant’s outcome as well.
Another core element of this research was to

provide a setting where an expert system can take a
substantial and useful part in such a simulation game.
The idea was to develop a virtual “mentor”, which
acts as an advisor and biases the human player in his
or her decisions. Therefore, the primary research
objectives are:
How can a business simulation game be constructed,
in which…
� ...human players can improve their strategic
management skills through decision-making in
a competitive environment.
� ...an intelligent agent (IA) acts as an advisor to
improve the learning effectiveness of the
players’ skill improvement.
A goal was to prove that participants can improve
decision-making through interacting with a business
simulation with other human participants
(competitors). In order to achieve these objectives, a
scope and balance for the business model had to be
found. The simulation must feature enough
complexity to completely cover all major elements of
a manufacturing company but can’t be too complex,
otherwise the learning effect can no longer be
measured effectively.
A manufacturing company was chosen as
business environment because the basic value
proposition is more straightforward than for example,
the business process of a service provider. In its very
basic form, the value creation process of a
manufacturing company is to purchase resources,

produce goods and then sell them.
It was also essential to study the effects of
integrating an intelligent agent that would act as an
advisor to the participants.
2016 49th Hawaii International Conference on System Sciences
1530-1605/16 $31.00 © 2016 IEEE
DOI 10.1109/HICSS.2016.107
827
To what extend can an artificial advisor bias the
decision making of the participants?
As a part of the Design Science methodology, an
artifact is created through which knowledge is
generated based on the Information Systems
Research Framework described by Henver et.al.
Identifying the environment of this artifact is
necessary; otherwise, it might lead to an
inappropriate design and undesirable side-effects [3].
2. Literature Review
Simulation gaming has been used as a tool by
researchers and industry for more than 50 years.
Many simulations for educational purposes are
without doubt accepted as useful learning tools;

however, qualitative simulation games are hardly
developed with a specific scientific purpose. Design
Science provides an ideal supplement to align such
simulations to the scientific community.
2.1. Design Science Methodology
To study and analyze behavioral decision making
in a business environment Design Science has proven
the most appropriate approach for this research. It can
be described as a problem-solving paradigms which
seeks to create an innovative construct in order to
generate knowledge about a phenomenon [3].
While research is an activity that contributes to
the understanding of a phenomenon, in Design
Science the phenomenon can be created artificially,
and need not occur naturally [4]. In Design Science,
the reality is replaced with an artificial construct that
contributes to answer the research problem. This is
especially useful, when the reality is not suitable or
too costly to be studied directly.
Using this approach allows for a second
significant advantage: part of this research is to learn
about human perception of an intelligent agent. This
agent is already an artificial construct and can
therefore be directly integrated into the artifact. To
ensure Design Science is science and not only design,
it is necessary to prioritize the production of useful
knowledge ahead of the production of the artifact.
Based on the Information Systems Research
Framework by Henver et.al. the business
environment is served by IS research through

developed instruments. In order to add value to the
knowledge base of the scientific community the
designed artifact(s) has to be justified and evaluated
[3].
In this case, the organizational strategies are the
area of interest in the business environment and they
are highly influenced by external factors. Market,
suppliers, legal regulations and competence
availability all affect a business and are constantly
interacting across the borders of the business. The
question becomes which business environment
components are effectively relevant for improving the
learning process when considering a holistic view of
such a manufacturing business model, and how such
a learning process can become effective through a
scientific artifact like a business simulation.
Creating this artifact also has an impact on
Information Technology. Modern web tools
fortunately provide an ideal framework to create an
artifact like that required for this research. Graff
states that computerized hypertext provides an
explicit structure to the material being learned, which
is advantageous to learners and encourages them to
engage and move around [5]. Using web technologies
also allows use of extensive, open-source
frameworks, bringing functionality like dynamic
charts, sortable tables, animations, form elements and
more. To ensure a contemporary instantiation, the
simulation has to fully support the majority of
modern smartphones.
Researchers in Design Science have to be careful
to support their research goals through an unbiased
design of the artifact. Lainema described this problem

as a challenge of providing acceptable scientific
research to the academic community rather than just
presenting own opinions [2]. It can be difficult for a
simulation designer to distinguish between failure
and success due to the fact that no one designs a
simulation to fail [6].
2.2. Approaches to Simulation Gaming
Thavikulwat defined a simulation as an exercise
of real activities in an artificial environment and a
game as an experience, featuring competition and
rules [6]. The most discussed topics in business
simulation education and learning in recent years are
[7]:
� Experience accumulation
� Strategy aspects
� Decision-making experience accumulation
� Learning outcomes
� Teamwork experience
Simulations follow different patterns, and they
can be classified according to their characteristics.
Thavikulwat classified simulations using computers
828
based on who takes control and how interaction takes
place [6].
Basically, two different types of simulations can
be distinguished: continuous and discrete simulations
[8]. The gaming industry has a more straightforward

terminology: real-time and round-based games.
Specifically, concerning the learning aspect,
much research has been performed. Simulation-based
training is inherently more engaging than other
training methods. Knowledge can be gathered more
quickly and efficiently, and simulations are simpler
to operate but provide a more complex, realistic and
manageable learning environment [9].
Besides all positive advantages, simulation
research is often criticized for measuring the affective
and not the cognitive learning, resulting in challenged
legitimacy and a lack of firm conclusions [10].
Business games are often accidently considered
the same as management games. It is assumed that
this equation is only true when the game actually
features the management of e.g. a firm, organization,
portfolio, teamwork or other factors in the business
area [11]. Therefore, business games are games with
a business environment leading to the training of
players in business skills or the evaluation of players’
performances.
The first known use of games for educational
purposes dates back to about 3000 BC in China [2,
7]. The acknowledged beginning of business games
in the modern sense started in Europe with Mary
Birshstein in 1932, who had the idea of adapting the
concept of war games to the modern business
environment [7].
The artifact in this study builds on a competitive,
interactive, deterministic total enterprise mechanism,
in which manufacturing acts as the theme. It is played

by individuals against each other, supported through
the computer. The simulated time frame is split in
turns or ‘periods’.
Business games have established a reputation as
serious alternative methods for teaching managerial
skills. Global organizations are the industry drivers
and act as meeting points for researchers and
practitioners [10]. Despite proof that simulations are
valid teaching instruments, researchers agree that the
full potential of business simulation games is not yet
revealed [9, 12, 13].
2.3. Expert Systems and Artificial Intelligence
in Simulation Gaming
Artificial Intelligence (AI) can be described as a
broad interdisciplinary field, which may cover
elements from computing disciplines to mathematics,
linguistics, economics, neuroscience and many others
[14]. It is sometimes difficult to decide what really
belongs under the domain. While AI is far more
capable in information processing than a human
being (as long as the information can be transformed
into digital language), it lacks the ability to ‘think’
creatively or critically.
An expert system is computer logic, designed to
gather knowledge and make decisions comparable to
a human expert. Mostly artificial intelligence is used
for processing data into useful knowledge.
AI adaptations in computer games are as old as
computer games themselves. “Nimatron,” a machine

capable of playing the game “Nim,” was one of the
first computerized games, developed in 1940 [15].
Until the 70s, AI could only be found in
computerized two-player games like checkers or
chess [16]. Later in the 70s, arcade games popped up,
featuring single-player gaming against computer-
controlled enemies [17]. In the 90s AI was assigned
with complex tasks like dealing with incomplete
information, path-finding or real-time decision
making and economic planning [18].
Intelligent agents (IA) mark the class of AIs that
are often used in simulation gaming and are therefore
of special interest for this research. Tecuci developed
a comprehensive definition of an intelligent agent
[14]: “an agent is a knowledge-based system that
perceives its environment […] and acts upon that
environment to realize a set of goals or tasks for
which it has been designed.”
Summers adds, that an IA is defined by its ability
to determine its own behavior [21]. Artificial agents
can be complex, combining multiple different
decision-making models. The model of a knowledge-
based agent shows how the agent acts with the
environment, as can be seen in Figure 1:
Figure 1. Main modules of a knowledge-based
agent / expert system [14]
Modern intelligent agents are capable of learning
from the players’ behavior and switch their tactics
accordingly. Also phenomena from reality are
transferred into computer games. StarCraft 2 oriented
their unit movement after the flow of water in a
stream; if there is an obstacle in the path, the water

829
flows with reduced speed around the obstacle,
considering the available space through the
environment and other water particles.
Simulations have been the testing environment for
many AI applications like neural networks or crowd
simulation [19, 20]. AI in educational business
simulations has mostly taken coaching roles so far,
and there is a trend towards the integration of AI as
decision-support and expert systems [21].
Based on a set of input variables, a knowledge
base and a processing algorithm(s) output is
generated. Knowledge-based agents may additionally
support complex modules like a learning or problem
solving engine.
Artificial intelligence continues to be an emerging
domain in computer science. Given the capabilities of
AI and intelligent agents, much progress in
simulation-based gaming and learning is expected.
Contemporarily, AI research benefits from
advancements coming from the highly profitable
gaming industry [7].
3. The Artifact – Architecture and Design
According to the guidelines for design science in
IS research [3], the artifact has to be innovative and
purposeful. The web-based business simulation

suggested in this research has allow students to
improve their decision-making skills in an intuitive
way. To demonstrate the utility, quality and efficacy
of the simulation, methods have to be implemented to
allow for a serious evaluation. This is done by
measuring the effective improvements of participants
(feedback through simulation) and their perceived
improvements (feedback through participants).
The Business Simulation for this research is
designed for desktop and mobile browsers and
provides a simple interface to give participants
control over various aspects of their business (Figure
2). A simulation game consists of four participants
competing in a closed market. The ultimate goal is to
finish with the highest accumulated revenue over the
period of ten timed rounds in which participants can
decide on eight different corporate strategies.
Figure 2. The Business Simulation in Action
The simulation provides the participants with a lot
of information on their and the competitors’
businesses, mostly derived from charts and tables.
Besides that, the artificial intelligence tries to provide
feedback on advantages and disadvantages of
recognized strategies. In each round (period), the
same decisions are available, but an ideal strategy is
impossible to predict or achieve since the users have
to deal with incomplete information, and the
competitors’ actions for the current turn can’t be
predicted. After the simulation, an extensive analysis
is provided, showing all the decisions made along
with additional charts and tables.

3.1. Decision-Making
Decision-making in the Business Simulation is
designed with the goal to increase usability to a
maximum through a simple, intuitive interface.
Learning should happen by understanding the
impacts of decisions on the business, not through
dealing with numbers and complex mechanisms.
Typical organizational strategies build the core of the
simulation and they usually feature two extremes
which are mutually exclusive. For example managers
can focus entirely on product A or B or they have to
split available resources among them. Through a
slider, users can declare if they fully support strategy
A, strategy B or if they are indifferent. Figure 3
shows the mechanism of such a slider:
Figure 3. Decision-making Through Sliders
Every slider has five stages (increments), and
there are a total of seven such strategic decisions that
have to be made. The simulation is designed to allow
advantages and disadvantages for each position on
the spectrum. It is possible that a specific decision is
favorable at an early stage in the game, but the
opposite strategy is beneficial at a later stage.
Additionally, players can toggle on/off a competitor
analysis as the eighth decision. The artifact was
designed to provide a holistic set of realistic
decisions, and every decision features advantages and
disadvantages. A major challenge in designing the
artifact was balancing out the strategies, to give each

strategy validity at some point, and to give them
830
serious consideration in specific situations. Once a
decision is altered, it is immediately updated in the
database. Since the simulation is purely discrete and
there are no random elements, only the decisions
have to be stored in the database. Calculations on
outcomes like revenue can be done on demand,
which simplifies the data structure and increases the
performance of the app.
3.2. Timing and Tutorials
The business simulation is time controlled. Users
who are done with their decisions before the time has
run out have the opportunity to end their turn
prematurely. If all four participants finish early, the
simulation immediately processes to the next round.
Each of the ten rounds is limited to two minutes,
after which the game processes to the next round
automatically. The first round makes an exception,
for which the time limit is five minutes to allow new
players more time to get familiar with the simulation
elements. This feature has been aligned with the
tutorial, which is only displayed in the first round.
Instead of the charts, which wouldn’t be of much use
in the first round anyway, the players experience
tutorial-like text information. This information aims
to explain what can be done in each section and what
is important.

3.3. Artificial Intelligence (“Eddie”)
The AI is implemented as an expert system using
deductive rules to observe the market and decisions
of the player as well as critical variables resulting
from decisions in previous rounds. It is integrated as
an ‘advisor’ and it has a face and a name (“Eddie”).
Figure 4. Charts, Tables and Decision Feedback
As knowledge base all variables associated to the
player decisions from the current and last round are
taken in consideration. This includes:
� Player Decisions
� directly calculated outcomes (e.g. costs of
employees based on amount, salary and amount
of extra hours)
� Indirectly calculated / implied outcomes (e.g. a
resulting market share cumulated over all
previous rounds)
The most appropriate feedback is determined
when the engine is loading all the game variables at
the beginning of a new round. An inference engine
then generates the outcome in form of subjective
feedback to the player. The feedback is a brief verbal
statement, either a warning, suggestion or
information on current issues the player might look
into. The engine compares and evaluates the

relevance of a total of 30 different options. During
several pre-experiments these 30 feedbacks were
identified as most fitting and helpful based on
strategic mistakes the players made. The inference
engine works by determining and assigning a priority
value for the relevant feedback statements
independently from other statements. High priority
results are then compared by including the relevant
conditions and variables from the other high-priority
statements with a reduced impact and a new priority
value is generated. The statement which has the
highest priority by the end of this second step wins,
and is displayed to the player. This feedback is
selected by best-fit to the current situation, and it is
ensured that Eddie never gives the same advice twice
in the simulation, should this mathematically happen,
Eddie suggests the next best (not already displayed)
feedback. He appears to have a personality and may
be described as provocative, bold, funny or pushing.
His behavior and capability to bias participants is one
of the primary research areas in this study.
Feedback Examples (11 out of 30):
� Pushing up your marketing expenses at the end
of the product life cycle is lost money!
� Rapid expansion in the beginning might result
in huge personal costs and overproduction!
� Focusing on one product is dangerous. Only do
it, when you see potential in this market!
� Your moral is dangerously low. The
productivity in your firm is suffering!

� You are producing more than you are selling.
Try do reduce production to save costs!
� If you don't start expending soon, you will get
behind!
831
� We are losing grip on the market. We need
more sales!
� Only risk extra hours, if you need them and you
can pay for them!
� We can't compete with the prices of your
competitors. Why not trying out some product
development?
� Your quality is already amazing. Maybe it is
time to increase the production and lower costs!
� You have a good moral! It might be save to ask
for a little more of your employees.
3.4. Screens / Pages
The simulation is split into five tabs, each
focusing on one department of the business. A lobby
(before the game starts), a final score page (when the
game ends), and a brief description of the
implementation of the survey are also included to

give a holistic view.
3.4.1. Lobby. The lobby serves as a starting point to
direct the participants into the simulation. On this
first page instructions and a chat are accessible.
Header and Footer provide compact information:
� Language Switch (German and English)
� Timer
� Actual Round / Total Rounds
� Navigation Tabs
3.4.2. Progress / AI. This initial screen displays a
selection of charts that shows, the actual progress in
terms of revenue, turnover and cost. A simplified
balance sheet is available, and it is also the place
where Eddie, the AI, can be found.
3.4.3. Production. On this screen, the player can
choose a production focus and the amount of product
development. The company produces two different
types of goods, products A and B. They have
different resource demands, production times and
selling prices. High product development increases
the quality of the product and, therefore, the price for
which it can be sold, but comes at the cost of a
reduced production capacity.
3.4.4. Marketing. In this section, the user is able to
influence the whole market. All players start with 10
shares (25% market share each). They can increase
their amount of shares between 0 and 4 per round,
depending on how much they invested into

advertisement and marketing. Additionally, to the
variable individual increase, the market also has a
natural lifecycle. The market demand increases with
the total amount of shares and the market share
defines how many products of this global demand
customers are ready to purchase from the players.
Should the player produce less than he or she can sell
on the market, the remaining demand can be
exploited by his or her competitors. The second
decision the player can make here is to undertake a
competitor analysis. This enabled additional
information on competitors on various charts for the
price of some variable costs.
3.4.5. Personnel. On this screen, players can handle
two strategies concerning human resources. They can
set a salary level and order extra hours to increase
productivity. Both decisions influence the morale
(effectiveness) of the workers. A high salary has the
disadvantage of causing additional personnel costs,
but is necessary if the morale drops low in the
company. The morale is a general indicator for the
productivity of each worker. Extra hours increased
the production capacity but decreased the morale and
caused additional costs.
3.4.6. Strategy. On this screen, players have the
opportunity to make strategic corporate decisions.
The first choice is between a rapid expansion over
improving the business. Expansion leads to more
employees, and therefore, to a higher production
capacity. Too rapid expansion results in unaffordable
personnel costs. Improving the business, on the other
hand, simulates improvements in internal processes
and reduces the time needed to produce products.

Users can also decide on a price or a quality
focus. A price strategy (cost leadership) leads to
discounts, making the purchase of resources more
affordable, while a quality focus improves the quality
of the products resulting in an alternative method to
increase selling prices.
4. Evaluation and Verification
Referring back to the research problem, the first
four issues were dealt with by developing an artifact
that is capable of training human players in decision-
making in a competitive environment. The artifact
was accessible from any device with internet access,
and German and English were implemented as
languages. An intelligent agent increased the learning
potential further by advising the player on important
decisions based on developments during the
simulation. These four issues were addressed by
832
collecting feedback from participants in controlled
experiments performed in two stages.
In the first stage, two business intelligence
university courses with a total of 26 students, held at
the Management Center Innsbruck in Austria, served
as an environment for the experiments. The
simulation was first explained to the students who
then performed one game. The students were then led

to a survey that included qualitative and quantitative
feedback related to the simulation and AI. After the
first simulation and the survey, the students were then
randomly mixed and prepared for a second game.
The purpose was to see if the overall performance in
the second game increased through learning and
adaptation of the game mechanics. Also, potentially
interesting behavioral patterns were identified.
4.1 Survey
The survey served two main purposes: first,
players were asked about good strategies for the
early, middle and late game. The survey tested the
artifact against the issues identified in the research
problem. The responses were compared to the
observed behavioral changes between the first and
second round of experiments. This was done to verify
if the players had understood and adapted the
concepts of the business simulation. Table 1 shows
the number of participants who evaluated the chosen
strategy as strong during the early, mid or late game.
For example only one person felt that using a
strong quality-focus (5) over price-leadership was a
beneficial strategy in the early game. The market
research strategy is excluded from the Table since it
only had two states (on and off).
Table 1. Efficient* Game Strategies
Decision Strategy / # participants
Early Game (Round 1-3) 1 2 3 4 5
Product Development 1 3 3 11 8
Marketing 1 5 2 7 11

Salaries 7 11 5 2 1
Extra hours 10 6 5 5 0
S1: Improve / Expand 9 5 4 5 2
S2: Price / Quality 5 7 2 10 1
Midgame (Round 4-7) 1 2 3 4 5
Salaries 0 5 14 7 0
Endgame (Round 8-10) 1 2 3 4 5
Salaries 5 2 10 8 1
n=26; (*) participants evaluated with 4 or 5
Second, data about previous experiences and
knowledge of business management, along with
qualitative feedback related to aspects of the
simulation was collected. The last screen included
two questions about artificial intelligence to find out
about its place in business intelligence and
management. The survey asked the students for one
positive and three negative observations they made
during the experiments. The results were grouped and
counted based on similarity. Table 2 shows the
amount of individual feedback for the derived group:

Table 2. Qualitative Feedback on Simulation
Positive Feedback Count
Artificial Intelligence 3
Charts 3
Competition 6
Design & Structure 7
General / Idea 4
Learning effects 4
Total (26 expected) 27
Negative Feedback Count
Artificial Intelligence 1
Bugs in the Simulation 5
Calculations / Mechanics 4
Design & Structure 15
Explanations 13
Limited Time 7
Total (78 expected) 45
n=26; categories based on similarities in feedback
4.2 Simulation Decision Results
The survey and the simulation were both designed
to supplement each other. Since the survey asked the
students for the perceived best strategies in early, mid
and late game, the same aspect was analyzed from the
actual performance of the participants.
Decisions from the first and second round of
experiments were separated to find the behavioral

changes influenced by adaptive learning and the
impulse through the survey. Evaluating all the
decisions turned out to be challenging. The limited
833
number of options for each decision helped to reduce
complexity in the process. Clear changes in decision
behavior were identified, both between the different
stages in the simulation, as well as between the two
rounds of experiments. Table 3 shows these
behavioral changes in terms of absolute participants,
who have chosen the specific strategy:
Table 3. Simulation Data: Decision Strategies
Decision Experiments (First Round) Experiments (Second
Round)
Early Game (Round 1-3) 1 2 3 4 5 1 2 3 4 5
Competitor Analysis* 5 64 6 60
Production Focus 10 22 15 21 1 13 12 28 5 8
Product Development 10 21 19 17 2 13 14 11 13 15
Marketing 4 17 22 17 9 10 6 8 19 23
Salaries 0 35 32 2 0 9 29 16 7 5
Extra hours 21 38 10 0 0 27 25 12 2 0
S1: Improve / Expand 10 24 19 13 3 15 17 15 13 6
S2: Price / Quality 4 13 16 25 11 6 22 11 13 14
Midgame (Round 4-7) 1 2 3 4 5 1 2 3 4 5
Marketing 16 19 17 17 23 17 17 13 25 16

Salaries 1 34 38 15 4 8 33 34 11 2
Extra hours 32 30 27 3 0 29 32 18 6 3
S1: Improve / Expand 11 26 26 21 8 17 18 29 18 6
S2: Price / Quality 5 27 23 28 9 10 29 30 8 11
Endgame (Round 8-10) 1 2 3 4 5 1 2 3 4 5
Marketing 25 23 7 2 12 19 16 12 15 4
Salaries 4 24 31 7 3 18 12 24 7 5
Extra hours 30 19 11 9 0 18 20 17 7 4
S1: Improve / Expand 14 18 16 13 8 19 11 10 9 17
S2: Price / Quality 12 19 16 18 4 22 19 11 6 8
This table represents how players decided (1...5) in each stage
of the game. The values are representing the
absolute amount of occurrences for each strategy chosen.
(*) Competitor analysis has only has two values, true (2) and
false (1)
The competitor analysis was a feature used
consistently by most players throughout all
simulations. The decisions for production focus were
more diversified. Producing Product A rather than B
was noticeable at all stages.
A clear learning effect can be observed in the
product development strategy. Players realized
correctly that this strategy improved the total
revenue, especially in the early and midgame, and
changed their behavior in the second simulation. The
marketing strategy showed an even superior learning
effect. While players focused on an average
marketing investment strategy during the first round
of experiments, they reduced their efforts in the late
game. An analysis of the salary showed that most

decisions concerning the salary were set statically.
Using Extra Hours is a feature designed to generally
have most effect in later stages of the game. This was
recognized by the players. The final two strategies
were more challenging to analyze. There was no
‘ideal’ way to handle these strategies. Players had
strong and very different opinions on the usefulness
of all strategies; the only thing they agreed on was
that taking no preference is a disadvantage.
Improving the business was considered a better
alternative in the early game rather than expanding it.
This changed in the midgame; in the endgame, the
results were equal to those in the early game.
A similar result was identified in the second
business strategy (Price / Quality). According to the
survey, players believed either in the success of a
price focus or a quality focus in the early game. In
834
the midgame, people were more indifferent, resulting
in a ‘slight’ or no-preference strategy. Players were
more experimental again in the endgame, featuring
either a price strategy or a quality strategy.
Table 4 shows the relative change in % for a
specific strategy from the first to the second round of
experiments. For example, only 79 % of the original
players who had chosen a light quality strategy (4) in
the midgame had chosen it again in the second round.
However, these players are not necessarily the same.

Table 4. Relative Behavioral Changes in Decision-
making between the two Rounds of Simulations
Early Game 1 2 3 4 5
Competitor Analysis 2 -2
Production Focus 5 -14 21 -23 11
Product Development 5 -9 -11 -5 20
Marketing 9 -16 -20 4 22
Salaries 14 -7 -22 8 8
Extra hours 10 -17 4 3 0
Improve / Expand 8 -9 -5 1 5
Price / Quality 3 14 -7 -17 5
Midgame 1 2 3 4 5
Competitor Analysis -17 17
Production Focus 4 -6 12 -7 -3
Product Development -5 -7 -5 10 6
Marketing 2 -1 -4 10 -7
Salaries 8 1 -3 -4 -2
Extra hours -2 4 -9 4 3
Improve / Expand 7 -8 5 -2 -2
Price / Quality 6 4 9 -21 3
Endgame 1 2 3 4 5
Competitor Analysis -14 14
Production Focus 19 -11 -7 -4 3
Product Development 0 5 -18 -5 19
Marketing -7 -9 8 20 -11
Salaries 21 -17 -9 0 3
Extra hours -16 3 10 -2 6
Improve / Expand 8 -9 -8 -5 14
Price / Quality
16 1 -7 -17 6
The values represent the relative change in number

of occurrences per strategy in %
Evaluating the artifact as an instrument was done
by collecting and analyzing qualitative feedback of
the participants in the experiments. Eddie proved to
be valuable as an advisor and gained mostly positive
feedback as well. Four players mentioned Eddie or
the artificial intelligence as a positive aspect of the
simulation. Two players approved the intelligent and
useful help of Eddie after each round; one player
wished the advices to be more specific.
5. Conclusion
A major challenge in this research was to avoid
creating an outcome that automatically favors and
validates the research method. With that in mind, this
research tried to judge success and validity through
two different data sources. The success should be
measured on how the players experienced the artifact
based on their own judgment (survey) as well as how
they experienced it based on the decisions they
actually made.
The research clearly shows that students were
adapting strategies as intended by the simulation
design. In cases where those general statements were
valid (Marketing, Product Development, CA, Salary
and Extra Hours) the participants showed a clear
learning effect from the first to the second round of
experiments (Marketing, Product Development and
CA) or a partial learning effect (Salary and Extra
Hours). In strategies that were intended to be
situation-dependent, people acted very differently

and also claimed different strategies as “optional” in
the survey. Considering these clear results and the
aforementioned academic problems, the research
suggests that all points defined in the research
problem could be successfully dealt with, but the
final validation has to come from an outside observer.
It was also important to find out if players trusted
the expert system during the simulation and allowed
it to bias their decisions. The results clearly exhibited
a user bias, most decisions changed in the second
round of experiments according to the advice of
Eddie. Also the positive feedback on Eddie indicates
the acceptance of the intelligent agent among the
players. The participants stated their trust in a
potential artificial intelligence in various areas of
business management.
The students prioritized Market Analysis, Logistic
Optimization and Process Optimization as areas with
the highest potential to effectively use Artificial
Intelligence to support the outcome.
6. Limitations of the Study
A major challenge was balancing the game
variables such that each slider became valid, useful
and realistic. Intelligent Agent Eddie, although
accepted positively and successful in biasing the
players, had very limited capabilities behind the
scenes. Eddie was not able to learn from the
decisions humans made during the simulations, and
couldn’t advise based on considering more than just
the latest round. Since feedback was only shown once

835
per simulation, the most relevant feedback might
have been missed out on future rounds.
This research, and Design Science in general,
may lag behind theory-based research in gathering
specific useful findings; however, the findings
showed a clear learning effect among the participants
of the experiment and a positive acceptance of the
artifact and the artificial intelligence. It may also be
criticized for not studying the aspect of artificial
intelligence in enough deep. The expert system had
its valid place in the artifact, and the research tried to
identify its acceptance and biasing capabilities as an
advisor during the simulation. The focus in this
research, however, was not artificial intelligence, but
the design of the artifact itself.
7. Future Research
The researcher believes that this research is the
start of a far more comprehensive study involving
real AI and a remodeling of the artifact. The purpose
of the experiments was, besides providing data for
this research, to reveal the weaknesses and problems
of the artifact. A lot of feedback has been provided
by the participants, making a remodeling of the
artifact tangible and viable. In such an upgrade, the
following aspects could be addressed:
� Improved artificial intelligence
� Fixing of gaming mechanics and variables

� Broader and more diversified group of
participants
� Modifications and additions to the artifact
� Linking the feedback to related decisions for
more academic insights
Stepping away from this specific research and
back into the area of business simulation research,
academics anticipate a lot of new development over
the next years [21].
Academic research involving both artificial
intelligence and simulation gaming is still rare. This
study was created to delve into this niche and provide
a basis on which further research can be continued.
References
[1] Avramenko, A., "Enhancing students' employability
through business simulation", Education + Training, 54(5),
pp. 355-367, Emerald Group Publishing Limited 2012.
[2] Lainema, T., “Enhancing organizational business
process perception: Experiences from constructing and
applying a dynamic business simulation game”, Turku
School of Economics and Business Administration, 2003.
[3] Hevner, A. R., S. T. March, J. Park, and S. Ram,
“Design science in information systems research”, MIS
quarterly, 28(1), pp. 75-105, Springer, 2004.
[4] Vaishnavi, V. K., and W. Kuechler. “Design science
research methods and patterns: innovating information and

communication technology”. CRC Press, 2015.
[5] Graff, M., E. Sadler-Smith, and C. Evans, "Constructing
and maintaining an effective hypertext-based learning
environment: Web-based learning and cognitive style",
Education+ Training, 48(2/3), pp. 143-155, 2006.
[6] Thavikulwat, P., "The architecture of computerized
business gaming simulations", Simulation & Gaming,
35(2), pp. 242-269, 2004.
[7] Faria, A. J., D. Hutchinson, W. J. Wellington, and S.
Gold, "Developments in Business Gaming A Review of the
Past 40 Years", Simulation & gaming, 40(4), pp. 464-487,
2009.
[8] B. Jovanoski, R. Nove Minovski, G. Liechtenegger, and
S. Voessner, "Managing strategy and production through
hybrid simulation", Industrial Management & Data
Systems 113(8) pp. 1110-1132, 2013.
[9] Salas E., J. L. Wildman, and R. F. Piccolo. "Using
simulation-based training to enhance management
education", Academy of Management Learning &
Education, 8(4), pp. 559-573, 2009.
[10] Clarke T., and E. Clarke, "Learning outcomes from
business simulation exercises: Challenges for the
implementation of learning technologies" Education+
Training, 51(5/6), pp. 448-459, 2009.
[11] Greco, M., N. Baldissin, and F. Nonino, "An
exploratory taxonomy of business games", Simulation &
Gaming, 44(5), pp. 645-682, 2013.
[12] Bell, B. S., A. M. Kanar, and S. W. J. Kozlowski,

"Current issues and future directions in simulation-based
training in North America", The International Journal of
Human Resource Management, 19(8), pp. 1416-1434,
2008.
[13] Vos, L, and R. Brennan, "Marketing simulation
games: student and lecturer perspectives", Marketing
Intelligence & Planning, 28(7), pp. 882-897, 2010.
[14] Tecuci, G. “Artificial intelligence”, WIREs Comp
Stat, 4, pp. 168–180, 2012.
[15] Grant, E. F., and R. Lardner, “The Talk of Town, ‘It’",
The New Yorker, pp. 18–19, 1952.
[16] Wallace, S. A., R. McCartney, and I. Russell, “Games
and machine learning: a powerful combination in an
836
artificial intelligence course”. Computer Science
Education, 20(1), pp. 17–36, 2010.
[17] Lowood, H., "Video Games in Computer Space: The
complex history of Pong–2005", Videoludica Vintage,
15(73), 1984.
[18] Schwab, B., “AI game engine programming”. Cengage
Learning, 2009.
[19] Lam, H.K., and H. T. Nguyen, “Computational
intelligence and its applications: evolutionary computation,
fuzzy logic, neural network and support vector machine
techniques”, World Scientific, 2012.

[20] Rao, Y., C. Leiting, L. Quihe, L. Weiyao, Z. Jun,
"Real-time control of individual agents for crowd
simulation", Multimedia Tools and Applications, 54(2), pp.
397-414, 2011.
[21] Summers, G. J. "Today’s business simulation
industry.", Simulation & Gaming, 35(2), pp. 208-241,
2004.
837

Improving Decision Making Skills through Business Simulation.docx

Recommended

Recommended

More Related Content

Similar to Improving Decision Making Skills through Business Simulation.docx

Similar to Improving Decision Making Skills through Business Simulation.docx (16)

More from sheronlewthwaite

More from sheronlewthwaite (20)

Recently uploaded

Recently uploaded (20)

Improving Decision Making Skills through Business Simulation.docx