Marketing simulation games:
student and lecturer perspectives
Lynn Vos and Ross Brennan
Middlesex University, London, UK
Purpose – The paper aims to contribute to the wider adoption of simulation games in marketing
teaching. The purposes of the research reported here are to understand marketing students’
perceptions of the learning achieved from the use of simulation games, and marketing lecturers’
perceptions of the barriers to increased use of simulation games.
Design/methodology/approach – A structured questionnaire was administered to 137 ﬁnal-year
marketing undergraduates studying at two British universities and eight semi-structured interviews
were conducted with marketing lecturers currently using simulation games in their marketing teaching.
Findings – Students perceive the simulation game to be a highly effective learning method,
delivering valuable knowledge and skills. In addition, students ﬁnd the game to be an enjoyable
learning approach. Lecturers are enthusiastic about this learning method, but note some barriers to
adoption; particularly cost, the steep learning curve, and the difﬁculty of ﬁnding unbiased advice
about suitable games to deliver desired learning outcomes.
Research limitations/implications – Limitations are that the empirical base for the quantitative
study was only two universities in the UK, and the questionnaire concerned only student perceptions
of their learning, not an objective assessment of actual learning. It is recommended that the study be
extended to a wider sample of universities, and that the approach be widened to include an assessment
of the measurable learning outcomes achieved rather than just student perceptions.
Originality/value – The degree of student enthusiasm for simulation games is striking. Lecturers
also ﬁnd the method very engaging, but acknowledge that there are important barriers to more
widespread simulation game adoption.
Keywords Marketing, Simulation, Video games, Action learning, Undergraduates, United Kingdom
Paper type Research paper
Marketing educators have long accepted that they cannot rely solely on didactic
methods; the nature of the subject necessitates that, in addition to addressing a body of
knowledge through lectures and reading, students must engage in active learning
(Wright et al., 1994; Smith and Van Doren, 2004). Several different pedagogic techniques
are harnessed for this purpose, including historical case studies, live case studies (where
students develop the case studies themselves), real-world research and consultancy
projects, in-basket exercises, role playing, and educational drama (Daly, 2001; Kennedy
et al., 2001; Baruch, 2006; Pearson et al., 2006). The simulation game is a widely used
active learning technique. The characteristics of simulation games include a simulated
competitive environment in which rival companies make periodic decisions; the
decisions provide the inputs to a software package that produces management
information (such as proﬁt and loss statements and analyses of sales patterns) which
provides the basis for the next round of decision making. What differentiates the
simulation game from most other active learning techniques is that by its very nature it
mimics certain aspects of the business world that are otherwise very difﬁcult to bring to
The current issue and full text archive of this journal is available at
Received February 2010
Accepted March 2010
Marketing Intelligence & Planning
Vol. 28 No. 7, 2010
qEmerald Group Publishing Limited
the classroom, notably working to deadlines, often in teams, to make concrete decisions
under competitive conditions, and then having to live with the consequences of those
In addition, team-based simulations allow students to practice speciﬁc skills valued
by employers – communication, problem solving, critical thinking, and analysis of
both verbal and ﬁnancial data – within an environment that allows for failure to be
redressed, and for alternative strategies to be employed without the possibility of
long-term punitive consequences. Given the degree of complexity, games encourage
students to integrate concepts successfully within their own discipline and to think
cross-functionally, the latter being an outcome that is more difﬁcult to achieve through
other learning methods (Chakravorty and Franza, 2005).
In the project described here, we investigated both the undergraduate student and
the marketing educator perspectives on the use of a marketing simulation game
(“The Marketing Game!” (“TMG!”)). The purposes of this paper are to explain the
background, rationale, research objectives, and research methods for the project, to
present and discuss ﬁndings from the survey concerning student perceptions of learning
methods generally and of “TMG!” in particular, and to discuss the ﬁndings from a
qualitative study of marketing educator views on the use of simulation games. In the
following section, we examine prior studies of simulation games, with a focus on their
use in marketing education speciﬁcally. The subsequent section explains the research
objectives and the research methods employed in the present study. Following this, we
move on to discuss the results from the empirical phases of our study, and to draw
conclusions for educational practice.
Prior research into the use of business and marketing simulation games
Business simulation games have been in use in higher education for at least 50 years,
with the ﬁrst documented use at the University of Washington in 1957 (Faria, 2006).
By 1998, up to 97.5 per cent of all accredited business universities in the USA were
using business games as a learning tool. Marketing simulation games are particularly
popular and Faria and Wellington (2004) found that 64.1 per cent of 1,085 faculty
members in American Universities were using games with a focus on marketing.
In a more limited and earlier study carried out in the UK, Burgess (1991) found that
computerised simulation game were used in 92 per cent of the 272 business and
management departments that responded to his survey.
Research into the educational value of games suggests that they give participants a
“valid representation of real world issues facing managers” (Wolfe and Roberts, 1993,
p. 22) including enhanced skills in strategy formulation, analysis of multiple variables,
integration of a range of marketing concepts and tools, manipulating ﬁnancial
concepts, problem solving, communication and team work (Keys and Wolfe, 1990;
Gopinath and Sawyer, 1999; Jennings, 2002; Zantow et al., 2005; Faria, 2006). Other
studies have investigated the value of games in improving student outcomes. Faria
(2001) reported on 79 comparisons between the use of simulations and other teaching
methods including cases, readings, and lectures. End of class exams demonstrated that
students who had engaged in the simulation performed better on average than those
who had been taught using other methods. Drea et al. (2005) found a statistically
signiﬁcant difference in performance on post-game assessment between those who had
participated in a marketing game and those who made up the control group. Cook and
Owens-Swift (2006) drew similar conclusions in a study linked to learning outcomes on
a sales management simulation. The researchers were able to demonstrate high
correlations between statements such as the game “improved analytical skills”,
“improved problem solving”, “helped learn concepts”, “applied what was learned in
class”, and “taught fundamentals”. In comparison with learning from the textbook,
participants perceived the simulation to be considerably more effective in “teaching
course concepts, promoting the development of high-level skill sets, and providing an
overall positive educational experience”.
Most authors agree that active learning approaches including simulation games need
to be underpinned with knowledge gained from more traditional methods such as lectures
and readings (Livingstone and Lynch, 2002; Laverie, 2006), and that for successful
learning to occur, students must also have the opportunity to reﬂect systematically on
their experience and to grasp how it connects to the course content and learning outcomes
(Herz and Merz, 1998; Hatcher and Bringle, 2000; Young, 2002; Peters and Vissers, 2004).
So, successful implementation of a simulation game requires prior lectures and readings
to equip students with the necessary conceptual knowledge, regular reminders of how
the game ﬁts into the learning outcomes, an effective post-simulation debrieﬁng exercise,
and assessment tools used both during and after the game to allow for the reﬂection
needed to solidify and make sense of their learning.
These additional pillars of the simulation experience not only lead to deep learning,
but are also important in the affective domain. Many authors have reported on the
positive emotions that students experience during simulation games (Coleman, 1966;
Brenenstuhl, 1975; Orbach, 1979; Szafran and Mandolini, 1980; Bredemeier and Greenblat,
1981). Research into the advantages of business games compared to other educational
methods indicates greater levels of student enjoyment and commitment than with case
studies, action learningprojects, lectures or readings (Low, 1980; Malik and Howard, 1996;
Jennings, 2002). Fripp (1994) argued that students ﬁnd simulations to be both stimulating
and enjoyable experiences and that this enhances their learning. In their research into
why people use business games, Gilgeous and D’Cruz (1996) found that keeping
participants motivated and interested was a key reason, and games that are best at
encouraging motivation are those that are deemed by students to be both interesting and
“fun”. Furthermore, effective use of simulation games can lead to positive behavioural
changes, such as enhancing students’ ability to get organised, adapt tonew tasks, resolve
conﬂicts and work effectively in groups/teams (King, 1977; Certo and Newgren, 1977;
Teach and Govani, 1988). In terms of behavioural adaptations, Solomon (1993) found that
simulations can also heighten self-awareness and allow students to examine their own
behaviour, particularly when working within a group.
However, Doyle and Brown (2000) reported that simulations can also create anxiety
and frustration in students, particularly when there are periodic administration
difﬁculties with the running of the game. In these cases, student frustration can have a
negative effect on their learning. Wolfe and Chacko (1983) and Jaffe and Nebenzahl (1990)
also reported on the impact of group size and group cohesion on student achievement
and satisfaction. A group size of three appears to be most effective and group cohesion is
often more important than how motivated individual team members were.
Finally, students can feel a lack of control when they undertake a simulation since
they must absorb a relatively large amount of information in a short period of time, and
then act upon it in a way that leads to successful outcomes. Walters and Coalter (1997)
argued that individual characteristics, such as risk propensity, need for achievement,
and locus of control will inﬂuence engagement and satisfaction; however, limited
additional research has been conducted into the effects of negative emotions and
negative emotional experiences during the simulation process, and how these emotions
and experiences affect learning. Where it is discussed in the literature, the general
conclusions are that the instructor must be actively involved with the game, well
prepared and organised, willing to provide support and assistance, and careful to show
the relationships between learning in the game and key course concepts and outcomes
(McKenney and Dill, 1966; Knotts and Keys, 1997). In other words, a positive overall
simulation experience is more likely to occur when instructors ensure that the
additional pillars mentioned above are built in.
Research purpose and objectives
The review of prior literature has demonstrated that marketing educators believe it is
important to provide students with an educational experience that prepares them for the
world of work. A marketing education cannot simply involve the acquisition of a body of
knowledge; it must also make students more employable by endowing them with
work-relevant skills and competences. The literature suggests that marketing simulation
games seem to provide an excellent opportunity to deliver valuable skills through a
medium that students ﬁnd highly engaging – in other words, an environment in
which students are primed to learn because of their positive affective response to the
educational experience. The project investigates the validity of this important assertion.
The overall purposes of the study were to understand better how students perceive
and respond to simulation games, and to investigate what barriers marketing lecturers
perceive to the adoption of simulation games, in order to make more effective use
of simulations in the curriculum. The empirical study comprised two phases: ﬁrst,
a questionnaire administered to students who had used TMG! At two British
universities, and, second, qualitative interviews with marketing lecturers who use a
simulation game (although not necessarily TMG!) in their teaching.
An important proposition to be investigated was that students generally have a
positive affective response to simulation games, and that this primes them to respond
well cognitively. Hence, the enjoyable, competitive atmosphere of the game provides a
strong motivation for students to learn about both speciﬁc marketing topics (notably
consumer behaviour and target marketing, in TMG!) and about general business
matters (notably proﬁt and loss analysis, and forecasting). In addition, speciﬁc
objectives concern the differential responses of different categories of students to
simulation games. We hypothesise that there may be differences in response between
different demographic groups (male/female, ethnic background, and so on), between
those with more or less employment experience, between those with different prior
educational experiences (for example, traditional or vocational school-leaving
qualiﬁcations), and between those from different cultural backgrounds. Prior
research has indicated that student perceptions of the effectiveness of learning
methods can vary with such demographic characteristics (Brennan and Ahmad, 2005).
Sampling and data collection
A questionnaire, administered to cohorts of students at two British universities, asked
students who have played TMG! to rate the learning value of simulations in relation to
other learning methods, and measured student perceptions of their own affective and
cognitive responses to playing the game. The questionnaire employed in this study
was suitably adapted from questionnaires used successfully by the co-researchers
previously to investigate student perceptions of the case study method, and of
educational drama, on marketing courses (Brennan and Ahmad, 2005; Brennan and
Pearce, 2008). The survey was administered in a classroom session at the end of the
module on which TMG! had been used. For the great majority of the respondents
(90 per cent), this had been their ﬁrst experience of playing a business or marketing
The quantitative analysis presented here is based on data collected from a total of
137 students (32 from University A, located in eastern England, and 105 from
University B, located in north London).The demographic characteristics of the
respondents to the questionnaire are shown in Table I. The sample represents the
wider population of undergraduate students at the two universities fairly well. There is
an approximate balance between male and female respondents; the age distribution
represents a characteristic mix of traditional young undergraduates aged in their
early 20s (in the ﬁnal year), and a reasonably large number of mature students.
The diversity of the sample in terms of ethnic origin reﬂects the typical ethnic mix of
“new universities” in southern England. A substantial minority of the respondents
received their secondary education outside the UK.
Table II shows that a substantial minority of the respondents claim to have had
full-time work experience (deﬁned on the questionnaire as working full time for the
same employer for more than six months). All but a handful of respondents claimed to
have worked part time, and 65 per cent were in part-time employment when they
completed the questionnaire; the modal category for part-time employment was
between ten and 20 hours per week. As would be expected, the older respondents were
more likely to have had full-time work experience than the younger respondents
(61 per cent of those aged 25 or over had full-time work experience, compared with
29 per cent of those aged 21 or 22).
Male 76 56
Female 61 44
20-21 51 37
22-23 63 46
24 or older 23 17
Ethnic origin (n¼136)
White 50 37
Asian or Asian British 35 26
Black or Black British 33 24
Other 18 13
Secondary education (n¼137)
In the UK 86 63
Outside the UK 42 31
Partly in UK, partly outside 9 6
The qualitative analysis presented here, concerning the lecturer perspective on
marketing simulation games, is based on semi-structured qualitative interviews with
marketing lecturers who were currently using a simulation game in their marketing
teaching. Eight qualitative interviews were carried out, each of approximately one-hour
duration. The interviews were digitally recorded and professionally transcribed, and the
analysis was conducted on the interview transcripts. All of those interviewed were
full-time faculty members in university business schools, two were female and six male,
seven worked at UK universities, and one at a university in Ireland.
The student perspective on the simulation game
In the discussion of the survey ﬁndings, we ﬁrst compare student perceptions of
different learning methods (including TMG!), and then concentrate on responses
concerning TMG!, looking initially at the mean responses from the whole sample and
subsequently at comparisons between different categories of student (men/women, and
Table III shows the mean score (on a 1 to 5 scale) and the rank (calculated from the
mean scores) for 12 learning methods. For all of these learning methods, the mean score
is signiﬁcantly above the scale mid-point of 3.0 (i.e. using a t-test, we can reject the
hypothesis that the true mean is 3.0). The “business game” learning method is easily
ranked ﬁrst. However, we should observe that this research was conducted with
classes who had just ﬁnished playing TMG!, and for most of the students this had been
their ﬁrst experience of a business game, so that their responses were probably
inﬂuenced by the novelty and immediacy of having recently experienced the game.
Nevertheless, we can state with high conﬁdence that students believe that they learned
a lot from playing the game and found it to be highly effective when compared
with other learning methods. Figure 1 shows this point further, showing that nearly
50 per cent of respondents claimed that they “always learn a lot” when using a
business game. Again, we would advocate caution in interpretation, since, for most
respondents, this was their ﬁrst experience with such a game. Nevertheless, the
principal risk of interpretation here would seem to be that the degree of learning that
the respondents claim to have experienced during the game may be exaggerated
because of the immediacy and novelty of the experience. It is very unlikely that the
respondents’ opinions about the game would change from strongly positive to neutral
or negative with greater distance from the experience of playing the game.
Have you ever worked full time for more than six months? (n¼136)
Yes 57 42
No 79 58
Have you ever worked part time? (n¼137)
Yes 129 94
No 8 6
Are you working part time now? (n¼137)
No 49 36
Yes, ,ten hours per week 20 15
Yes, ten to 20 hours per week 56 41
Yes, .20 hours per week 12 9
The next step in the analysis was to investigate in what ways the students felt that
their learning had beneﬁted from playing TMG! This was measured using a battery of
23 Likert-scale items adapted from a questionnaire used by Brennan and Ahmad (2005)
to study student perceptions of the case study method. The ten items with the highest
mean scores are shown in Table IV. The scores for all of the items shown in Table IV
are signiﬁcantly above the scale mid-point of 2.5 (i.e. using a t-test, we can reject the
hypothesis that the true mean is 2.5). From these results, we conclude that the
respondents were strongly of the opinion that TMG! had been a useful learning
experience, and that the nature of the learning included many experiential components,
such as understanding how business decisions are made, improving team-working
skills, and illustrating how strategic decisions are made in the real world.
Learning method Rank Mean score
Business game (e.g. TMG!) 1 4.21
Question and answer sessions in seminars 2 4.06
Assignment-based research 3 4.02
Discussions with other students 4 3.88
Private reading (e.g. textbooks and articles) 5 3.78
Case-study analysis 6 3.75
Group work 7 3.64
Presentations 8 3.60
Lectures 9 3.56
Self-guided research 10 3.55
Watching a video 11 3.22
Computer-based learning (e.g. blackboard) 12 3.16
Notes: Question asked: “what is your opinion of these learning methods?” Rate each learning method
on a scale from 1 to 5 where 1 means “I never learn anything when this method is used” and 5 means
“I always learn a lot when this learning method is used”; mean score is on a scale from 1 “never learn
anything” to 5 “always learn a lot”
Rank order of student
perceptions of learning
Student perceptions of
4 Always learn
Note: Business game, e.g. TMG!
We were also interested to establish how students responded affectively to “TMG!”.
Figure 2 shows the responses to a question about the enjoyment of “TMG!”. The great
majority of the responses, on a scale from 1 (low enjoyment) to 10 (high enjoyment) were
between 7 and 10, with 8 as the modal response and a mean response of 8.01. Clearly, the
results from our survey indicate that our sample of students believed that the game was
an effective learning method, which delivered a wide range of learning beneﬁts, and
which they enjoyed playing.
Differences in response between categories of students
An exploratory factor analysis (using principal factor analysis and varimax rotation)
conducted on the 23 items used to measure student perceptions of learning beneﬁts
identiﬁed six factors with eigenvalues greater than 1.0, explaining 63 per cent of the
variance in the data. The two factors explaining the largest percentage of the variance
(40 per cent in all) were interpreted, based on the items of which they were comprised, as
“analysis” (28 per cent of variance) and “skills” (12 per cent of variance). The mean score
To what extent do you agree that Rank
“Business games are a good way to practise using analytical tools” 1 3.34
“Business games help me understand how business decisions are made” 2 3.32
“Business games illustrate how business/marketing strategy works in the
real world” 2 3.32
“Business games help me understand theoretical concepts” 4 3.30
“Doing analysis for business games helps me to develop useful business skills” 5 3.25
“Working on business games has helped me to develop my team working skills” 6 3.24
“Working on business games has helped me to develop my skills in business
analysis” 7 3.23
“I usually contribute to business game discussions in class” 8 3.21
“Working on business games gives me the conﬁdence to express opinions” 9 3.11
“Business games are a useful way to discuss business problems in class” 10 3.08
Note: Mean score is on a scale from 1 “disagree strongly” to 4 “agree strongly”
Rank order: student
perceptions of learning
beneﬁts from business
Using a scale from 1 (did
not enjoy at all) to 10
(absolutely great!) indicate
how much you enjoyed
01 to 5 6 7
Note: Mean score on 1-10 scale = 8.01, SD =1.48
(on a scale from 1 to 4 as deﬁned in Table IV) for “analysis” was 3.22, and the mean score
for “skills” was 3.06. These factors were used in the subsequent analysis totest differences
between sub-groups of the sample. The results of the between-group comparisons are
shown in Table V.
Perhaps, the most striking characteristic of Table V is that few of the between-group
differences are statistically signiﬁcant. That is to say that, in most cases, one cannot
reject the null hypothesis that there is no difference between the mean scores of the
categories. This suggests that, while there may be subtle differences between the
responses of different student categories to the game, overwhelmingly all categories of
student perceive it to be a beneﬁcial and enjoyable learning method thatdelivered learning
beneﬁts in terms of both analysis and skills. There is some evidence that students who
described their ethnicity as “Asian or Asian British” were signiﬁcantly more positive
about the game than students who described themselves as “White” (note that the Asian
category does not include “Chinese”, since this was included as a separate category, but
there were too few respondents in this category to use it for analysis). From Table V,
we can see that there may be some difference between students who were educated at
secondarylevel in the UK, and those who were educated outside the UK (thelatter recorded
higher scores), and between those with and without full-time employment experience
(those with full-time experience recorded higher scores).
There is some evidence in our data of covariance between age, full-time work
experience, and a positive response to “TMG!” We observed above that age and full-time
work experience are correlated, and Table V shows a weak association between full-time
work experience and a positive response tothe game. In Table VI, we show the correlation
coefﬁcients between the factors “analysis” and “skills”, and the variables “age” and
Enjoyment Learning: analysis Learning: skills
Comparator variable t-value Signiﬁcance t-value Signiﬁcance t-value Signiﬁcance
Men/women 0.93 n.s. 0.98 n.s. 1.31 n.s.
White/Asian 1.87 0.07 1.92 0.06 2.56 0.01
White/Black 0.21 n.s. 1.13 n.s. 1.33 n.s.
Asian/Black 1.36 n.s. 0.53 n.s. 1.39 n.s.
In UK/outside UK 0.91 n.s. 1.76 0.08 0.82 n.s.
A levels/GNVQ 0.47 n.s. 0.47 n.s. 0.32 n.s.
Worked full time?
Yes/No 0.57 n.s. 0.57 n.s. 1.67 0.09
Comparative analysis of
responses to “TMG!”
Variable 1 Variable 2 Correlation coefﬁcient Signiﬁcance
Learning: skills Age 0.07 n.s.
Learning: skills Enjoyment 0.476 0.00
Learning: analysis Age 0.194 0.03
Learning: analysis Enjoyment 0.505 0.00
Correlations of perceived
learning, age and
enjoyment of “TMG!”
“enjoyment”. Enjoyment is highly correlated with both “analysis” and “skills”, consistent
with the hypothesis that a positive affective response to the game is associated with a
strong cognitive response. Age shows no signiﬁcant correlation with “skills”, but a
moderately strong correlation with “analysis”. This provides some tentative support
for the hypothesis that older students found the game to be a more effective learning
experience, particularly for learning about analytical methods, than younger students.
The lecturer perspective on simulation games
The interview phase of the project revealed a great deal of enthusiasm among those
who have adopted marketing simulation games. In several of the universities visited,
the interviewee was more or less a lone enthusiast. There was a striking level of
commitment from the majority of the interviewees to the use of simulation games,
with most believing that marketing educators should make more use of simulations.
The types of learning outcomes sought from the use of the game were universally
of a practical nature, usually to do with analytical, team working and personal skills
development. Simulation games are seen, by their proponents, to be an unrivalled
method for teaching a number of skills which are considered to be valuable, and which
cannot readily be taught using other learning methods. Skills of the following types
were mentioned: the ability to meet deadlines, the ability to work in teams of
individuals with mixed skills towards a concrete and time-constrained goal, the ability
to assimilate and to analyse numerical data of the type frequently encountered by
marketing managers (for example, sales revenue and gross proﬁt margin, gross
contribution, and market share). It is the concrete, decision-orientated and data-driven
nature of the marketing simulation experience that game adopters believe to be
particularly valuable for their students’ learning. A common thread in the interviews
was the belief that the skills learned in the course of playing the simulation game were
of value in the work-place, so that students who played the game would be better
prepared for junior marketing or sales positions than those who had not.
The game adopters, although very enthusiastic, were not uncritical about game
design. For example, a sales management simulation was considered to be in some
respects “unrealistic”, because it over-simpliﬁed the nature of the sales management
task and offered the game player too much ﬂexibility in adjusting elements of
sales remuneration. Nevertheless, the lecturer still considered the game to be a valuable
exercise because of the skills (data analysis, working to deadlines, and so on) that were
involved. She observed, however, that the “unrealistic” aspects of the game would
make it unsuitable with certain students, notably post-experience students, since they
would be likely to dwell on the ﬂaws in the game design rather than participate
enthusiastically. At the other extreme, an international marketing simulation which was
considered to be highly realistic, came in for criticism because it provided the students
with so much data that they were tempted to devote too much of their time to playing the
game – so diverting time from other important learning tasks on the course. This
highlights one potential, and unexpected, drawback to playing a simulation game,
namely the “opportunity cost” involved. Clearly, playing a simulation game requires
learning time to be devoted to it. Mostly, this is a mixture of in-class time and student
private-study time. However, on the majority of courses the simulation game is one out
of several learning methods employed, with different learning methods designed to
achieve different learning outcomes. Since students often ﬁnd the simulation game to be
highly involving, there is a risk that they will divert more of their study time to the game
than the lecturer intended, and may spend less time on other learning methods. Only one
of our interviewees had overcome this possibility entirely, by devoting the entirety of a
12-week module to playing the simulation game. In all other cases, the game was a
component of a module (or course).
A particular concern for the interview phase was to identify the barriers to adopting
simulation games. The key barriers that emerged were as follows:
(1) ﬁnancial cost;
(2) searching for and evaluating games;
(3) concerns about the learning curve:
.concerns about ability to facilitate the learning process;
.concerns about administrative work-load; and
.prevalence of necessary skills among marketing lecturers.
(4) uncertainty about learning outcomes.
Financial cost was not a universal concern, but in the ﬁnancially uncertain times
when the interviews were conducted (Summer 2009) the matter was raised by several
interviewees. The nature of the course on which the simulation is used makes a
difference: for example, where MarkStrat is used on an executive education course, cost
is unlikely to be an issue because of the revenue directly generated by the course.
However, for core undergraduate teaching, the cost of the simulation may be more of
an issue. At one institution, the interviewee wanted to switch to a new simulation game
which he had evaluated as preferable in important respects to the simulation that he
had been using for ten years. However, he had encountered considerable difﬁculty in
obtaining the budgetary authority to make the necessary investment.
Searching for and evaluating games was considered to be a lengthy,
time-consuming and uncertain process. If the lecturer has certain learning outcomes
in mind, and believes that a game would be the best way of delivering them, then a
search process will be initiated. However, there is no guarantee that a game exists that
will deliver the desired learning outcomes, and reliable sources of information about
the objective merits of different games are difﬁcult to ﬁnd. Information provided by
game suppliers is regarded as unreliable, since they are perceived to be in the business
of selling games, rather than helping lecturers to achieve their educational goals.
There are several concerns about the learning curve. First, with the majority of games
lecturers perceive there to be a substantial amount to be learned by the tutor before they
can effectively facilitate student learning (and there is anxiety that they may not learn
enough prior to starting the related teaching sessions, and so be unable to answer
student questions effectively). Second, prior to using a simulation game, the lecturer is
uncertain about the weekly administrative workload involved in game administration,
and may be concerned that the workload will be unmanageable. Third, some of the
simulation games (such as TMG! and MarkStrat) provide substantial amounts of
ﬁnancial and numerical data; for some marketing lecturers, providing advice to students
on ﬁnancial and numerical analysis can be daunting. It is possible that some marketing
lecturers, who might otherwise use games, avoid them because they experience anxiety
about the need to advise students on ﬁnancial and numerical analyses.
Finally, prior to using a particular simulation game, the lecturer may well be
uncertain about the learning outcomes that the game will genuinely deliver. This raises
the possibility that a module (course) could be carefully planned, with an integrated
simulation game, but that the lecturer could ﬁnd out during the teaching term (too late to
make adjustments to the teaching and learning strategy) that the game cannot deliver
the intended outcomes. This barrier is closely related to the issues associated
with searching for and evaluating games, and the availability of objective, reliable
information about what learning outcomes a game can deliver.
The interviewees were asked what advice they would provide to newcomers to
marketing simulation games to overcome these barriers. For the most part the
responses were, although perfectly reasonable and no doubt correct, essentially
platitudes – for example, to be fully prepared before starting to use the game, and to be
clear about what learning outcomes one wishes to achieve through the game. However,
one thing that stands out from the interviews is the value of obtaining advice from
a lecturer who has already used the game that one is thinking of adopting, and who
can therefore provide trusted and practical advice about the educational value of the
game, the administrative burden associated with the game, and the steepness of the
learning curve the lecturer can expect.
Simulation games are not an undiscovered educational technique. Nevertheless, we
believe that the potential educational contribution of marketing simulation games has
been far from fully exploited. In particular, from our own practice, we have observed
that this is one of the most effective tools for engaging students actively in the learning
experience. Many of the students who play marketing simulation games become absorbed
in the game, determined to improve their team’s performance, and realise quickly that
in order to achieve good performance they need to understand and apply important
marketing principles. The results from the survey of students support these contentions.
Of course, we must emphasise that what we have measured are student perceptions of
their enjoyment, overall learning from, and components of learning from a single
marketing simulation game. These clearly represent limitations on this study. In addition,
the sample of students was taken from only two universities in the south-east of England,
and the generalisability of the ﬁndings is therefore strictly limited.
Within those limitations, we have conﬁrmed the general ﬁndings from earlier
literature that students enjoy simulation games and believe that they learn a lot from
them. Going beyond these earlier studies, we have provided some evidence that student
perceptions of their learning from simulation games seem to be particularly strong in
terms of aspects of analysis and aspects of skill development.
Of particular concern is the use of simulation games with an increasingly diverse
population of students in the UK. The once homogeneous UK higher education body of
students has become increasingly heterogeneous, partly reﬂecting the increasing
diversity of UK society in general, and partly reﬂecting government policy to widen
access to higher education. It is important for marketing educators to understand how
different categories of student are likely to respond to marketing simulation games.
Our ﬁndings in this respect are encouraging, since there did not seem to be marked
differences between different demographic categories of students in their response to
the game. There was some limited evidence that “Asian or Asian British” students,
students who received their secondary education outside the UK, and mature students
had more favourable attitudes towards the game. However, these ﬁndings are tentative
and further research would be needed to establish how valid they are.
Marketing simulation games are not for everyone and they are not for every module.
Clearly, they are most useful where the lecturer has learning outcomes related to the
world of marketing management practice, understanding the decision-making process,
analysing and interpreting ﬁnancial and marketing data, working to deadlines, and
working in teams. Often, this will be on ﬁnal-year undergraduate modules and on
postgraduate modules where the lecturer wants to give the students a taste of practical
marketing decision making before they enter the work-place. The more complex
games, such as MarkStrat, are also suitable for use with post-experience MBA and
executive education groups, to enable those who already have management experience
to test out alternative marketing strategies in the safe environment of the simulation.
What are the factors that inhibit marketing simulation games from being used more
widely? First, there is the matter of cost: generally there is an annual fee per student for
the simulation, so that the overall cost can be high if the simulation is used with a large
cohort of students. However, perhaps, more serious than the direct ﬁnancial costs are
the indirect costs associated with setting up a marketing simulation game from
scratch. This is a serious undertaking which needs to be approached professionally. It
must be understood that the a lecturer who evaluates candidate games, selects one for
implementation, arranges for any necessary software installation, and then learns the
game sufﬁciently well to be able to administer it and to advise student participants on
playing the game, has undertaken a major task. This is not a job to give to someone
who is already over-loaded, and it is not a job to skimp on, since our research has
shown that marketing simulation games require thorough lecturer preparation if they
are to be implemented successfully.
Baruch, Y. (2006), “Role-play teaching: acting in the classroom”, Management Learning, Vol. 37
No. 1, pp. 43-61.
Bredemeier, M.E. and Greenblat, C.S. (1981), “The educational effectiveness of simulation
games”, Simulation and Games, Vol. 12, pp. 307-32.
Brenenstuhl, D.V. (1975), “Cognitive vs affective gains in computer simulations”, Simulation and
Games, Vol. 6, pp. 303-11.
Brennan, R. and Ahmad, S.J. (2005), “Using case studies in management education: the student
perspective”, International Journal of Management Education, Vol. 4 No. 3, pp. 21-30.
Brennan, R. and Pearce, G. (2008), “Educational drama: a tool for promoting marketing
learning?”, International Journal of Management Education, Vol. 8 No. 1, pp. 1-10.
Burgess, T.F. (1991), “The use of computerized management and business simulation in the
United Kingdom”, Simulation Gaming, Vol. 22 No. 2, pp. 174-95.
Certo, S.C. and Newgren, K.N. (1977), “Interpersonal skill development: the experiential training
unit (ETU) and transfer of training”, New Horizons in Simulation Games and Experiential
Learning, Vol. 4, pp. 72-81.
Chakravorty, S.S. and Franza, R.M. (2005), “Enhancing cross-functional decision making:
a simulation approach”, Decision Sciences Journal of Innovative Education, Vol. 3 No. 2,
Coleman, J.S. (1966), “In defence of games”, American Behavioural Scientist, Vol. 10, pp. 3-4.
Cook, R. and Owens-Swift, C. (2006), “The pedagogical efﬁcacy of a sales management
simulation”, Marketing Education Review, Vol. 16 No. 3, pp. 37-46.
Daly, S.P. (2001), “Student-operated internet businesses: true experiential learning in
entrepreneurship and retail management”, Journal of Marketing Education, Vol. 23 No. 3,
Doyle, D. and Brown, F.W. (2000), “Using a business simulation to teach applied skills – the
beneﬁts and the challenges of using student teams in multiple countries”, Journal of
European Industrial Training, Vol. 24 No. 6, pp. 330-6.
Drea, J.T., Tripp, C. and Stuenkel, K. (2005), “An assessment of the effectiveness of an in-class
game on marketing student’s perceptions and learning outcomes”, Marketing Education
Review, Vol. 15 No. 1, pp. 25-33.
Faria, A.J. (2001), “The changing nature of business simulation gaming research: a brief history”,
Simulation & Gaming, Vol. 32 No. 1, pp. 97-110.
Faria, A.J. (2006), “History, current usage, and learning from marketing simulation games:
a detailed literature review”, Proceedings of the Marketing Management Association,
Nashville, TN, pp. 138-9.
Faria, A.J. and Wellington, W.J. (2004), “A survey of simulation game users, former-users, and
never-users”, Simulation and Gaming, Vol. 35 No. 2, pp. 178-207.
Fripp, J. (1994), “Why use business simulations”, Executive Development, Vol. 7 No. 1, pp. 29-32.
Gilgeous, V. and D’Cruz, M. (1996), “A study of business and management games”, Management
Development Review, Vol. 9 No. 1, pp. 32-9.
Gopinath, C. and Sawyer, J.E. (1999), “Exploring the learning from an enterprise simulation”,
Journal of Management Development, Vol. 18 No. 5, pp. 477-89.
Hatcher, J.A. and Bringle, R.G. (2000), “Reﬂection: bridging the gap between service and
learning”, College Teaching, Vol. 45 No. 4, pp. 153-8.
Herz, B. and Merz, W. (1998), “Experiential learning and the effectiveness of economic simulation
games”, Simulation & Gaming, Vol. 29 No. 2, pp. 238-50.
Jaffe, E.D. and Nebenzahl, I.D. (1990), “Group interaction and business game performance”,
Simulation and Gaming, Vol. 21 No. 2, pp. 133-46.
Jennings, D. (2002), “Strategic management: an evaluation of the use of three learning methods”,
Journal of Management Development, Vol. 21 No. 9, pp. 655-65.
Kennedy, E.J., Lawton, L. and Walker, E. (2001), “The case for using live cases: shifting the
paradigm in marketing education”, Journal of Marketing Education, Vol. 23 No. 2, pp. 145-51.
Keys, B. and Wolfe, J. (1990), “The role of management games and simulations in education and
research”, Journal of Management, Vol. 16 No. 2, pp. 307-36.
King, A. (1977), “Experiential exercises on values, attitudes and conﬂict resolution”,
New Horizons in Simulation Games and Experiential Learning, Vol. 4, pp. 353-60.
Knotts, U.S. Jr and Keys, J.B. (1997), “Teaching strategic management with a business game”,
Simulation & Gaming, Vol. 337, p. 394.
Laverie, D.A. (2006), “In-class active cooperative learning: a way to build knowledge and skills in
marketing courses”, Marketing Education Review, Vol. 16 No. 2, pp. 59-70.
Livingstone, D. and Lynch, K. (2002), “Group project work an student centred action learning:
two different experiences”, Journal of Geography in Higher Education, Vol. 26 No. 2,
Low, J.T. (1980), “Guidelines for the use of business simulation games”, Journal of Marketing
Education, Vol. 30 -37.
McKenney, J.L. and Dill, W.R. (1966), “Inﬂuences on learning in simulation games”, American
Behavioural Scientist, Vol. 10 No. 2, pp. 28-32.
Malik, D. and Howard, B. (1996), “How do we know where we’re going if we don’t know where
we’ve been: a review of business simulation research”, Developments in Business
Simulation and Experiential Exercises, Vol. 23, pp. 49-53.
Orbach, E. (1979), “Simulation games and motivation for learning”, Simulation and Games,
Vol. 10, pp. 3-40.
Pearson, M.M., Barnes, J.W. and Onken, M.H. (2006), “Development of a computerized in-basket
exercise for the classroom: a sales management example”, Journal of Marketing Education,
Vol. 28 No. 3, pp. 227-36.
Peters, V. and Vissers, G. (2004), “A simple classiﬁcation model for debrieﬁng simulation
games”, Simulation and Gaming, Vol. 34 No. 1, pp. 70-84.
Smith, L.W. and Van Doren, D.C. (2004), “The reality-based learning method: a simple method for
keeping teaching activities relevant and effective”, Journal of Marketing Education, Vol. 26
No. 1, pp. 66-74.
Solomon, C.M. (1993), “Simulation training builds teams through experience”, Personnel Journal,
Vol. 72 No. 6, pp. 100-7.
Szafran, R.F. and Mandolini, A.F. (1980), “Student evaluation of a simulation game”, Teaching
Sociology, Vol. 8, pp. 21-37.
Teach, R. and Govani, G. (1988), “Simulation game performance: an examination of the effect of
time pressure, method of team formation, and formal planning”, Developments in Business
Simulation & Experiential Learning, Vol. 21, pp. 83-5.
Walters, B.A. and Coalter, T.M. (1997), “Simulation games in business policy courses: is there
value for students?”, Journal of Education for Business, Vol. 72 No. 3, pp. 170-4.
Wolfe, J. and Chacko, T.I. (1983), “Team-size effects on business game performance and decision
making behaviours”, Decision Science, Vol. 14 No. 1, pp. 121-33.
Wolfe, J. and Roberts, C.R. (1993), “A further study of the external validity of business games:
ﬁve-year peer group indicators”, Simulation & Gaming, Vol. 24 No. 1, pp. 21-33.
Wright, L.K., Bitner, M.J. and Zeithaml, V.A. (1994), “Paradigm shifts in business education:
using active learning to deliver services marketing content”, Journal of Marketing
Education, Vol. 16 No. 3, pp. 5-19.
Young, M.R. (2002), “Experiential learning¼hands-onþminds-on”, Marketing Education Review,
Vol. 12 No. 1, pp. 43-51.
Zantow, K., Knowlton, D.S. and Sharp, D. (2005), “More fun and games: reconsidering the virtues
of strategic management simulations”, Academy of Management Learning and Education,
Vol. 4 No. 4, pp. 451-8.
Anderson, P.H. and Lawton, L. (1992), “The relationship between ﬁnancial performance and
other measures of learning on a simulation exercise”, Simulation and Gaming, Vol. 23 No. 3,
Burns, A.C. and Gentry, J.W. (1980), “Moving towards a ‘theory’ of the use of simulation games
and experiential exercises”, Experiential Learning Enters the Eighties, Vol. 7, available at:
Faria, A.J. (1998), “Business simulation games: an update”, Simulation and Gaming, Vol. 29
No. 3, p. 295.
Faria, A.J. and Wellington, W.J. (2005), “Validating business gaming: business conformity with
PIMS ﬁndings”, Simulation and Gaming, Vol. 36 No. 2, pp. 259-73.
Faria, A.J., Wellington, W.J., Hutchison, D. and Whiteley, T.R. (2003), “Achieving course learning
objectives through the use of business simulation games”, Proceedings of the Marketing
Management Association, Nashville, TN, p. 67.
Gosenpud, J. (1990), Guide to Business Gaming and Experiential Learning, Nichols/GP,
New York, NY.
Krathwohl, D., Bloom, B. and Masia, B. (1956), Taxonomy of Educational Objectives. Handbook II:
Affective Domain, David McKay, New York, NY.
Mason, C.H. and Perreault, W.D. (2001), The Marketing Game, McGraw-Hill, New York, NY.
Wentworth, D.R. and Lewis, D.R. (1975), “An evaluation of the use of the marketplace game
in junior college economics”, The Journal of Economic Education, Vol. 6 No. 2, pp. 113-9.
Wolfe, J. (1976), “Correlates and measures of the external validity of computer-based business
policy decision-making environments”, Simulation and Gaming, Vol. 7 No. 3, pp. 411-38.
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