Measuring the Impact of a Marketing Simulation Game

Abstract

Background. The evidence from past research suggests that business simulation games (BSGs) do offer a meaningful educational experience. One characteristic lacking across past research studies is the trait of indecisiveness.

Aim. This study sought to explore whether business students would self-report a change in their perceptions of their indecisiveness after participating in a business simulation games (BSG). In addition, whether higher performance simulation decision makers would self-report being less indecisive (i.e. able to make decisions in a timely manner) than lower performance simulation decision makers.

Method. Using a pre-test and post-test design with a comparison to an untreated control group, the change in 386 business students’ perceptions of their indecisiveness was assessed using a self-reporting questionnaire.

Results. The findings showed a statistically significant reduction in the level of perceived indecisiveness as a result of the simulation experience. The higher performance students reported being less indecisive than lower performance students while both higher performance and lower performance students reported a reduction in perceived indecisiveness. The level of self-reported perceived indecisiveness amongst a control group of 137 business students indicated no significant change.

Conclusion. If the combination of practice and positive reinforcement increases the comfort level (reduce feelings of risk and threat) of decision makers then perceived indecisiveness should decrease as a result of simulation participation, which may generalize across situations demanding decisions.

Keywords

business simulation game decision makers indecisiveness performance simulation

Introduction

Business simulation games have been in use in university classes in North America since 1957 (Watson, 1981). In 1961, it was estimated that more than 100 business simulation games were in use in the U.S. alone and had been played by over 30,000 business executives and countless students (Kibbee, Craft, & Nanus, 1961). The Guide to Simulation/Games for Education and Training (Horn & Cleaves, 1980) described 228 business simulation games then in use at universities, community colleges and by business firms for management training purposes. Published reports indicate that business simulation game usage continued to increase up to 1998 (Faria, 1998). A 2004 e-mail survey sent to 14,497 university business professors, yielding 1085 returns, reported that 47.4% of the survey respondents had used one or more business simulation games during their teaching careers (Faria & Wellington, 2004). A more recent survey reported in a working paper by Wellington, Hutchinson, and Faria (2016) involving 30,137 business faculty members from 426 AACSB schools across all business disciplines and producing 1024 fully complete responses reports that 56.8% of the respondents have used one or more business simulation games during their teaching careers.

The high use of business simulation games can be attributed to a number of learning benefits offered by this teaching approach. One important learning benefit of business simulations is that they provide decision making experience. This is a benefit not generally provided by traditional lecture and textbook instructional approaches nor by videos, report writing, plant tours and most other teaching approaches. Case studies do provide varying degrees of decision making experience. However, it is not the same as the experience created in a simulation gaming environment in which the decision makers see the results of their decisions, must live with those results, and then proceed on to make additional decisions. This iterative nature of decision-making in uncertain situations, which is part of the business simulation gaming experience, is one of the unique contributions of this teaching approach.

The educational benefit of the simulation gaming approach arises from the fact that the difference between a good executive and a poor executive is not the number of business definitions, principles or facts that can be recited but the quality of the decisions made by the executive in a decisive and timely manner. Through participation in business simulation games, it is expected that business students will have the opportunity to apply the fundamentals of sound decision-making and become more experienced and astute decision makers (Anderson & Lawton, 2009).

When used, business simulation exercises generally take up significant class time and contribute in a significant fashion to each student’s final grade. Across a sample of 220 professors using business simulation games in their classes, on average, 23.8% of class time and 25.1% of the final course grade were accounted for by the simulation exercise (Faria & Wellington, 2004). If business simulation games are to merit this amount of course time and contribute this significantly to final course grades, one would hope that business decision making skills are improved as a result of the simulation experience.

A major problem in demonstrating this relationship, of course, is that decision making skills are very hard to measure in an objective fashion (Michel, 2007). According to Del Missier, Mantyla, and de Bruin (2010), decision making is a complex high-level process involving the analysis of alternatives and only sporadic attempts have been made to develop and validate measures of decision-making competence. To date, published research has not conclusively demonstrated that simulation games, or any teaching approach for that matter, leads to better decision-making in a working environment.

Most business simulation game administrators would likely agree that better simulation performance outcomes are the result of better strategies and better decisions. However, better performance outcomes do not tell us whether the simulation participants improved their decision-making skills via the simulation experience. One could certainly argue that the simulation exercise simply identified those participants who were already better decision-makers entering the simulation competition.

Indecisiveness

To be a good decision maker, one must first be able to make a decision. Being decisive has positive connotations while being indecisive has negative ones (Brooks, 2011). Although almost everyone has had difficulty in making a decision at some time, some individuals experience anxiety (Hartley & Phelps, 2012) or even chronic difficulty in making decisions (Patalano, Juhasz, & Dicke, 2010) and indecision is not a desirable trait for a business manager to possess. Businesses do not look for managers who delay or are incapable of making decisions and it is rare that a manager would be commended for not making a decision. Indecisiveness can be defined as “the inability to make decisions in a timely manner across situations and domains” (Patalano & Wengrovitz, 2006, p. 813). If one is indecisive, the ability to make a decision in an uncertain and risk filled business environment is lacking, never mind the ability to make a good decision. Indecisive individuals “take more time to choose among alternatives . . . use less-exhaustive decision strategies . . . require greater cognitive effort to make decisions . . . are more threatened by ambiguous situations . . . and are more likely to postpone decisions . . . compared to those low on indecisiveness” (Patalano & Wengrovitz, 2006, p. 814). Before good decision making can occur, business managers must be able to make decisions.

According to Patalano et al. (2010), “Indecisiveness is a fascinating phenomenon for the field of decision making but it has been given very little attention by decision making researchers” (p. 367). Business games require participants to make decisions in a simulated business environment which is both risky and uncertain allowing varying degrees of indecisiveness to manifest itself in participants. If through the decision making experience gained from participation in a business simulation exercise, students become less indecisive, then simulation games would be providing an additional meaningful classroom experience and their usage would be further justified. The exploratory research reported on in this article will examine participants’ perception of their indecisiveness and will attempt to determine whether the experience of playing a business simulation game in a classroom setting reduces their perceived indecisiveness. The research will also evaluate whether there is a linkage between an individual’s performance in a business simulation game and the participant perceptions of their level of indecisiveness.

Literature Review

Despite the widespread use of simulation games in business courses, an important issue confronting both simulation game users and non-users is the degree to which participation in such exercises provides a meaningful experience. The evidence from past research would suggest that business simulation games do offer a meaningful educational experience (Faria, Hutchinson, Wellington, & Gold, 2009). However, one characteristic conspicuously lacking across past research studies is the trait of indecisiveness. The present exploratory study examines participant perceived indecisiveness and whether this perception changes through participation in a business simulation game and whether it is related to simulation game performance. As cited earlier, Patalano and Wengrovitz (2006) define indecisiveness as “the inability to make decisions in a timely manner across situations and domains” (p.813). Under this definition, indecisiveness does not include or involve the quality of a decision. Rather, it is the willingness, or ability, to make a decision. As such, this study will not focus on whether participant decision-making quality improves after participating in a simulation competition but rather on whether there is a change in participant perception of their indecisiveness after participating in the competition and, in addition, if this change is linked at all to simulation game performance.

According to Elaydi (2006), “…research studying indecisiveness is sparse at best” (p. 1373). Germeijs, Verschueren, and Soenens (2006) state that indecisiveness can be described “as a chronic problem with making decisions across different situations” (p. 397) and individuals can have different levels (high to low) of indecisiveness that can change over a period of time. Leong and Chervinko (1996) suggest that indecisiveness is a multidimensional construct and that personality traits and “personal problems are often intertwined” (p.324) with decision making. Bacanli (2006) suggested that there are two types of indecisiveness: exploratory and impetuous. “Exploratory indecisiveness refers to a long decision-making process even though all options have been explored thoroughly, as well as having difficulties in making decisions, while impetuous indecisiveness refers to quick decision making and giving up on such decisions easily” (Bacanli, 2006, p. 321). Jones (1989) also supports the notion that indecisiveness is a multi-dimensional construct and suggests that long term trait anxiety is related to how comfortable individuals are with their decisions. According to O’Hare and Tamburri (1986), anxiety is the consequence or outcome of the inability to cope with stressful situations such as decision making. This study aims to explore whether individuals are able to adapt to the stress of making a decision and, therefore, feel less indecisive after participating in a business simulation game.

While the literature reports on the characteristics of successful simulation game performers, there is no reporting on simulation usage and indecisiveness. It is not known if participation in a simulation game has a measurable effect on indecisiveness. However, given that real-world business decisions are often made in uncertain business and competitive situations and that these environments are typically modeled in business simulation games, indecisiveness is likely to manifest itself amongst participants in business games and can be studied.

Self-reporting subjective mental constructs such as attitudes and intentions via surveys is commonly used in research (Burns & Bush, 2003). Using surveys to measure or sample the perceptual attitudes of a large number of individuals is generally considered to be cost effective, practical, quick and easy. The self-reporting survey method was incorporated into the current study because it was an exploratory study of a mental construct of a large number of potential respondents over a relatively short period of time (a university semester) on a limited research budget. Other possible research methods such as Neuroscience methods (Riedl, Davis, & Hevner, 2014) as well as the Facial Expression Coding System (Kaliouby & Robinson, 2004) were considered to be beyond the scope of this exploratory study.

While no past research on indecisiveness and simulation game participation could be found, Rassin (2007) reports on the body of research on the “concept of indecisiveness” (p. 1) and then offers a theory of indecisiveness. Rassin (2007) presents indecisiveness as a psychological concept composed of predispositions, perceptions and behaviours that are affected by moderators. The actual trait involves perceptions that are measured through the use and application of psychometric measurement scales. In his model, Rassin (2007) presents the situation of maximizing behavior, compensatory choices and intolerance of uncertainty as predispositions which affect three perceptions which comprise the trait of indecisiveness: 1) a perceived lack of information, 2) valuation problems, and 3) outcome uncertainty. The perceptions then result in three types of behavior: 1) delaying decision making by procrastinating, avoiding the decision, and gathering more information; 2) narrowing the decision by ignoring information, focusing in on one choice, and using one dimension to compare multiple choices; and 3) post-decision behaviours such as worrying about its correctness, checking over the decision, and changing the decision. The three main relationships of predispositions, perceptions and behaviours are influenced by moderator variables such as time pressure and the perceived importance of the decision at hand (Rassin, 2007).

Rather than attempt to develop a new scale to measure the trait of indecisiveness with all the attendant reliability and validity issues, the authors chose to search for psychometric measures in the literature which had established properties and could be adapted for this study. The review of the literature revealed a few studies that provided survey scales to measure this tendency (Bacanli, 2006; Cooper, Fuqua, & Hartman, 1984; Frost & Shows, 1993; Germeijs & DeBoeck, 2002). After reviewing these measurement scales with respect to their reported levels of reliability and the nature of the populations they were applied to, it was decided to use the scale developed by Germeijs and DeBoeck (2002) for measuring indecisiveness in this study as this scale had undergone a validation process; had a very high reported internal consistency alpha reliability (.91); and was developed using a student population which is the group of interest in this study.

Purpose and Hypotheses

Past research on indecisiveness has looked at the causes of indecisiveness or on ways to cure indecisiveness (Picard, 2012). Mill (2007) reports that company recruiters find undergraduate business students lacking in “non-technical skills such as creativity, oral and written communication, decision-making and leadership” (p.1). Conversely, a quick perusal of the published learning goals of many AACSB accredited business schools reveals that many of them identify critical thinking and decision making skills as learning outcomes for their undergraduate business students (Association to Advance Collegiate Schools of Business, 2013). If an objective of business schools is to produce good decision makers, then indecisiveness is a problem that must be addressed. The purpose of the present exploratory study is to determine, in a classroom setting, whether the experience of participating in a simulation game will impact the level of perceived indecisiveness of the game participants. Based on past research findings, and some amount of logic, the following hypotheses were selected for testing within a business simulation game environment:

Hypothesis 1 (H1): As a result of participating in a simulation game over a one semester period, students will self-report being less indecisive within the game setting by the conclusion of the simulation experience than they were at the beginning.

Hypothesis 2 (H2): A control group of students who do not participate in a simulation game will show no significant change in self-reported indecisiveness over a one semester period.

Hypothesis 3 (H3): The students who participate in a simulation game experience will self-report being less indecisive at the end of a semester than a control group of students who do not participate in a simulation game experience.

Hypothesis 4 (H4): Higher performance students in a simulation game will self-report being less indecisive at the conclusion of the simulation experience than lower performance students.

Methodology

The subjects for the research to be reported on here were 946 undergraduate students who completed a principles of marketing course taught by the same instructor (part of a team of three primary researchers and eight teaching assistants) over three different semesters. The same textbook and course structure was utilized over the three semesters. In two of the three semesters students played a simulation game during the course. In the third semester the students (control group) did not participate in a simulation game. As such, the experimental design was an untreated control group design with pre-test and post-test samples (Cook & Campbell, 1979, pp. 103-112). The requirement that a control group be measured in a different semester was necessitated by a university policy that requires all course sections offered in the same semester to have fully equivalent syllabi.

The simulation participant treatment group was comprised of 632 undergraduate students from three different class sections, over two different semesters, who took part in a decision making experience by playing a marketing simulation game. A control group of 314 students from one class section in a different semester took part in a course that did not involve playing a simulation game but undertook some online assignments instead. The simulation used in the course for the treatment group was MERLIN: A Marketing Simulation (Anderson, Beveridge, Lawton, & Scott, 2004), a simulation designed for an introductory marketing course. The game presents six (two products by three territories) decision making environments within which students make approximately 120 business operating decisions over the course of the game - eight simulation periods representing two years of simulated competition. Participants are provided detailed operating results from their decision-making including income statements, balance sheets, cash flow reports, inventory reports, and product contribution statements. The MERLIN simulation also routinely provides competitive performance results in terms of sales, earnings, return on sales and forecasting errors. Decision makers may also purchase a number of market research reports. The participants played as individuals operating their own firms and were divided into industries of six or seven single person companies.

The simulation participant treatment group of students was asked to voluntarily complete a self-report questionnaire (see Appendix 2) at the beginning and end of the simulation exercise while the control group of students was also asked to complete the same self-report questionnaire (see Appendix 3) at the beginning and end of the marketing class. The questionnaire for the simulation participant group contained measures of their indecisiveness and during the introduction of the survey they were instructed to consider their Merlin simulation gaming experience. The control group questionnaire included the same items but no specific reference to the Merlin simulation. Instead, the control group was presented with the items within a context of potentially playing a marketing simulation game in the class.

Permission was obtained to employ a 22 item indecisiveness scale (see Appendix 1) developed by Germeijs and DeBoeck (2002) with a reported alpha reliability of .91. The survey items were measured using a Likert style seven point Strongly Agree (1) to Strongly Disagree (7) scale.

For the simulation participant group, only students who actually returned both the pre-competition and post-competition questionnaires were included in the data analysis. Likewise, for the control group, only students who completed both the beginning course and ending course questionnaires were included in the data analysis. Incomplete questionnaires and questionnaires containing highly inconsistent responses were removed from the data analysis. An example of an inconsistent response would be where a respondent answered every question with the same response, even a reverse coded question. These procedures produced a usable sample size of 386 students, composed of 249 students in the simulation participant treatment group (39.4% of the 632 enrolled students) and 137 students in the control group (43.6% of the 314 students). As such, the participation rate between the simulation participant and control groups was very similar.

An internal consistency co-efficient alpha reliability analysis (SPSS v.19) of the 22 item measurement scale for the indecisiveness trait was undertaken. For the control group of 137 students, the alpha reliabilities for the pre-test and post-test results were .917 and .907. For the simulation participant group of 249 students the alpha reliabilities of the 22 item scale for the pre-test and post-test results were equal with a value of .761. There was a concern as to why the scale reliabilities were so different between the simulation participant and control groups. It was clear that the measurement of indecisiveness for the simulation participant group was occurring within the context of the actual decision making framework associated with a marketing simulation game and thus the interpretation of the indecisiveness measures for this group was likely affected by this. In contrast, the measurement of the control group was occurring within the context of a hypothetical situation of potentially playing a simulation game.

The notion that participation in the simulation could have an impact on the reliability of the indecisiveness scale was illustrated by the finding that one of the 22 items on the indecisiveness scale (Germeijs & DeBoeck, 2002) was slightly reducing the alpha reliability of the entire scale in the simulation participant group. The wording of the scale item was “I don’t know how to make decisions.” In comparing this item to the other 21 scale items, it was noted that within the context of the simulation experience, the outlying item was likely confounding because it might suggest that the participants did not physically know how to enter a simulation decision. All participants would have had to gain this skill to compete. In light of this possible interpretation and its poor contribution to the scale’s reliability, the item was removed from further analysis. The alpha reliability values of the simulation participant group for both the pre-test and post-test questionnaire of the now 21 item scale drawn from the literature remained essentially equal and improved slightly to .78 which was considered an acceptable level of reliability (Table 1). As such, it was decided to employ the modified 21 item scale as presented by Germeijs and DeBoeck (2002) as a unidimensional construct using the average value of the 21 scale items for hypotheses testing of both the treatment and control groups. Further discourse with respect to the finding of a difference in internal consistency reliability measures between the simulation participant and control groups will be presented in the discussion section.

Table 1.

Pre-Test and Post-Test Measurement Scale Reliabilities.

Respondent Group and Scale	N	Number of Items	Alpha Reliability
All Respondents
Pre-test Indecisiveness	386	21	.868
Post-test Indecisiveness	386	21	.862
Simulation Group
Pre-test Indecisiveness	249	21	.778
Post-test Indecisiveness	249	21	.780
Control Group
Pre-test Indecisiveness	137	21	.920
Post-test Indecisiveness	137	21	.924

In the MERLIN simulation competition, performance is measured using a ranking based on an index of company sales, earnings, the return on sales ratio, and forecast accuracy. These indices were weighted 5%, 85%, 5% and 5%, respectively. Based on each student’s score, each participant/company was ranked from first place to last place within his/her industry. For each industry, the top three companies were considered higher performance companies while the bottom three companies were considered lower performance companies.

H1 and H2 were tested using a repeated-measures MANOVA analysis to compare how perceived indecisiveness changed over time for both the simulation participant and control groups as measured on the modified 21 item scale as presented by Germeijs and DeBoeck (2002). H3 was tested using a repeated-measures MANOVA analysis to compare and contrast the beginning and ending perceived indecisiveness levels of the simulation participant group and the control group composed of non-simulation game players. H4 was tested using a repeated-measures MANOVA analysis of the simulation participant group to compare and contrast how perceived indecisiveness changed over time in relation to higher versus lower simulation performance results. Finally, a comparison to assess differences in perceived indecisiveness levels between the pre-test and post-test measures of the control group versus the higher performance simulation participant group and the lower performance simulation participant group was made using a Bonferoni contrast comparison.

Findings

The mean levels of perceived indecisiveness amongst all of the groups of student respondents across the pre-test and post-test questionnaires are reported on 1-7 point scales where the lower numbers indicate less indecisiveness. The mean perceived indecisiveness scores across all student simulation participants declined from 3.97 (SD = .640) on the pre-test to 3.71 (SD = .652) on the post-test. The control group exhibits a very modest decline in mean pre-test to post-test indecisiveness of 3.43 (SD =.939) to 3.37 (SD = .948). Lastly, when the simulation participants were divided into higher performing and lower performing groups it can be seen that there was a decline in indecisiveness across both groups with the mean indecisiveness of the higher performing group changing from 3.93 (SD = .605) to 3.55 (SD = .648) while the lower performers demonstrated a more modest decline in mean indecisiveness from 4.01 (SD = .670) to 3.90 (SD = .605).

In order to examine the statistical significance of these changes and differences as well as test the four hypotheses presented, a series of repeated-measures MANOVA’s were used. The first hypothesis (H1) was that the perceived indecisiveness of business students would be reduced through participation in a simulation game and the second hypothesis (H2) was that the perceived indecisiveness of a control group of business students would remain unchanged. The reduction in perceived indecisiveness for simulation participants is statistically significant, F(1, 249) = 4.87, (p = .028), and provides support for H1 (see Table 2). As a group, the simulation game participants reported becoming less indecisive after participation in the simulation competition. While the mean perceived indecisiveness of the control group declined slightly from 3.43 (SD =.939) at the beginning of the course to 3.37 (SD = .948) at the end of the course, the MANOVA results F(1,137) = .963, (p =.337), indicate that the difference was not statistically significant. While there is no conclusive evidence to support the null hypothesis (H2), the results do suggest that students who did not play a simulation game did not appear to experience a change in perceived indecisiveness.

Table 2.

H1, H2, H3 & H4: Repeated-measures MANOVA Comparison of Changes in Perceived Indecisiveness of Simulation, Control and Simulation Performance Groups.

Hypothesis and Comparison		df	F	Sig.
H1-Simulation Group	Within Subjects - Over time	1	4.87	.028^a
H2-Control Group	Within Subjects - Over time	1	0.93	.337
H3-Simulation Vs Control	Within Subjects - Over time	1	20.75	.000^b
	Within Subjects -Time x Group	1	7.93	.005^b
	Between Subjects - Group	1	35.10	.000^b
H4-Performance Groups	Within Subjects - Over time	1	40.13	.000^b
	Within Subjects -Time x Performance	1	13.07	.000^b
	Between Subjects – Performance	1	8.97	.003^b

P < .05.

P < .01.

The overall findings from the repeated-measures MANOVA analysis of changes in perceived indecisiveness by simulation participant versus control group for H3 (which suggests simulation players would have a lower level of perceived indecisiveness at the conclusion of a simulation game than a non-simulation playing control group) are reported on in Table 2. These findings indicate that there were significant changes in the simulation participant group over time and also that the level of perceived indecisiveness between the simulation participant and control group was significantly different. Although the hypothesis indicated there would be similarities between the simulation participant and control groups at the beginning of the course and differences between the simulation participant and control groups at the end of the course, the unexpected finding was that right from the beginning of the course the level of perceived indecisiveness was actually lower in the control group than in the simulation participant group. The study was designed in anticipation that for both the simulation participant and control groups the levels of perceived indecisiveness would be equal before beginning the simulation. This goes against the hypothesized expectation and, as such, H3 is rejected.

The overall findings from the repeated-measures MANOVA analysis of changes in perceived indecisiveness by simulation participant performance group for H4 are reported on in Table 2. At the start of the simulation game, both higher performance simulation participants (top three students in each industry) and lower performance students (bottom three students in each industry) were not significantly different in their reported levels of mean perceived indecisiveness (M = 3.93, SD = .605 vs M = 4.01, SD = .670; Bonferroni Contrast Significance of 1.0). Both groups also reported becoming less indecisive through participation in the marketing simulation competition; the higher performance students reduced their mean level of perceived indecisiveness by a greater amount (M = 3.93, SD = .605 to M = 3.55, SD = .648) on the indecisiveness scale than the lower performance students (M = 4.01, SD = .670, to M = 3.90, SD = .605). As well, the higher performance students were less indecisive at the conclusion of the simulation competition than the lower performance students ((M = 3.55, SD = .648 vs M = 3.90, SD = .605; Bonferroni Contrast Significance of .001). All of these comparative differences are statistically significant and provide support for H4.

Unexpectedly on the pretest survey, the beginning levels of perceived indecisiveness between the control group and both, the high simulation participant performance group (M=3.43, SD =.939 vs M = 3.93, SD = .605; Bonferroni Contrast Significance of .000) and the low simulation participant performance group (M=3.43, SD =.939 vs M = 4.01, SD = .670; Bonferroni Contrast Significance of .000) were different and statistically significant. However, the post-test survey results suggest that the ending level of perceived indecisiveness for the higher performance group was not significantly different from the control group’s ending perceived indecisiveness (M=3.55, SD = .648 vs M=3.37, SD = .948; Bonferroni Contrast Significance of .154) but the lower performance group’s ending perceived indecisiveness was still higher than the control group and the difference was statistically significant (M = 3.90, SD = .605 vs M=3.37, SD = .948; Bonferroni Contrast Significance of .000). Despite this, the lower performance group’s post-test level of perceived indecisiveness had been reduced from the beginning of the simulation game but not by as much as the higher performance group.

Discussion and Conclusions

The use of business simulation games in business courses requires considerable instructor time to administer and considerable student time for data analysis and decision-making. If this amount of instructor and student time is to be devoted to a simulation exercise, instructors must have confidence in the relevance and meaningfulness of this teaching tool. As the literature review suggested, there are a number of meaningful teaching benefits from simulation games. The research reported here sought to examine whether students, when participating in a simulation game perceived that they became less indecisive. Indecisiveness is an issue that has not been explored in past simulation gaming research nor is it a prominent topic in the general literature on decision-making (Elaydi, 2006). If the combination of practice and positive reinforcement can increase the comfort level (reduce feelings of risk and threat) of decision makers then perceived indecisiveness should decrease as a result of simulation participation.

Past simulation research has not conclusively demonstrated that participation in a simulation game will improve decision making ability. However, if we believe that you learn from doing and become better and more comfortable with practice, then simulation games do provide decision making practice or experience which should be useful in the business careers of our students. Rassin and Muris (2005) suggest that in ambiguous situations individuals that perceive the situation to be threatening are more uncomfortable and indecisive. As such, if decision makers gain experience and comfort with making decisions in an uncertain environment, it might be expected that their level of indecisiveness should decrease.

To explore whether business students believed that they were less indecisive after participating in a business simulation, an exploratory Pre-test and Post-test experiment was designed. Four hypotheses were formulated and tested within a business gaming environment. To provide a large participant sample for analysis, business simulation performance outcomes from participants in a marketing course taught across three different semesters by the same instructor using the same simulation game were gathered and analyzed. In total, usable data were gathered from a sample of 249 undergraduate student participants in a marketing simulation game to test the hypotheses. A control group sample of 137 undergraduate student participants who took the same course from the same instructor but did not participate in a simulation gaming exercise were also measured with regard to perceived indecisiveness.

Hypothesis 1 is at the core of the research reported in this study. As business executives are expected to be able to make decisions, it is important that we graduate business students who have some decision-making experience. While traditional teaching approaches do a good job of providing definitions, principles, concepts and the ability to understand strategic issues in business, it is important that some approach used in our classes addresses the issue of business decision making. The results from the research reported in this study demonstrate that business simulation games provide experience in making decisions and the experience is associated with a reduction in the overall level of participant indecisiveness (see results reported in Table 2). As a group, the business student simulation participants in this study reported becoming less indecisive through their participation in a business simulation competition and this reduction in perceived indecisiveness was statistically significant.

As expected, a control group of students who did not participate in a business simulation game exhibited no change in their level of perceived indecisiveness thus suggesting support for the second hypothesis.

The rejection of the third hypothesis (that the level of perceived indecisiveness amongst a control group would initially be the same as the treatment groups but when measured at the end of the course would be higher) was unexpected. The level of perceived indecisiveness of the control group at the beginning and end of the simulation exercise was lower and was significantly different from the treatment group. It is possible that measurement error could be a partial explanation given that the scale reliability of the indecisiveness measure was much higher for the control group than it was for the treatment group. However, the reliability measure of the scale for the treatment group was in the acceptable range so the degree of difference is not thought to be attributable to this.

In retrospect, the authors probably should have anticipated a difference between the treatment and control groups attributable to an interaction between testing and treatment and/or an interaction of selection and treatment as described by Cook and Campbell (1979). For example, Rassin and Muris (2005) reported that ambiguous situations can raise the level of indecisiveness. By their very design, business simulation game exercises present students with ambiguous situations that they must cope with. On the other hand, the control group experienced a business course which was very much akin to the typical instructional approaches of most of the courses they had previously encountered (i.e, read and attend lectures and become familiar with new concepts and theories and verify knowledge on examination measures of the concepts and theories). As such, they would not be faced with any of the three predispositions for indecisiveness (maximizing behavior, compensatory choices and intolerance of uncertainty) that Rassin (2007) identifies. In contrast, all three of these predispositions would be present for the students taking the course that included playing the simulation game.

The interaction of selection and treatment might also have been critical. For example, Mojgan, Kadir, and Soheil (2011) looked at anxiety and career indecision amongst a group of students. They report a strong relationship between the level of anxiety and the level of career indecision. In this study, a student taking a required class where they had to play a business simulation game and be evaluated according to performance would be expected to produce greater anxiety for students than in the control group wherein students did not play the simulation and were evaluated primarily on memorization of academic material. This higher level of anxiety amongst simulation participants could well be expected to produce a higher level of perceived indecisiveness amongst this group from the outset.

Hypothesis 4 was supported as the study results showed that higher performing student simulation participants reported that they were less indecisive at the completion of the competition than lower performing participants. The non-simulation playing control group in this study could be considered the baseline ‘comfort zone’ relative to the simulation participant groups. The Bonferroni Contrast results comparing the pre-test and post-test perceived indecisiveness measures of the control group versus both the higher and lower performance simulation participants groups are very interesting. In the pre-test, the results indicate that there is a significant difference in perceived indecisiveness levels between the control group and both the higher and lower ending performance groups while there was no significant difference in perceived indecisiveness between the higher and lower performance simulation participant groups at the outset of the simulation experience. However, at the conclusion of the simulation experience, the Bonferroni Contrast results indicate that the higher performance group’s post-test perceived indecisiveness level is not significantly different from the control group’s post-test level of indecisiveness. In contrast, both the control group and higher performance group have a much lower level of post-test perceived indecisiveness than the lower performance group.

These findings suggest that business simulation participants were initially anxious and thus self-reported being more indecisive than the non-playing control group. However, as the simulation progressed, higher performing participants learned to cope with the situation (decision making, competition, and uncertainty) and become more comfortable. The fact that the most successful of these students ended up reporting a level of indecisiveness which was not significantly different from the control group suggests that they benefited from the experience.

In summary, the results of this study indicate that students participating in a simulation game undergo an experience which may actually increase their level of perceived indecisiveness which then becomes reduced through experience. If indecisiveness is considered a personality trait that generalizes across situations demanding decisions as asserted by Osipow (1999), then participation in business simulation games would appear to offer a teaching benefit that future researchers might further investigate.

Previous research has justified the use of business games through an examination of what is learned by participants as a result of the gaming experience, by comparing common final exam performance scores across course sections using business games and sections using other teaching approaches, as well as by examining the internal and external validity of business simulation games (Faria et al., 2009). To this body of research, this exploratory work adds the finding that the decision-making experience brought about through a business simulation competition can lead to changes in the level of perceived indecisiveness amongst the game participants. The findings reported with regard to the lessening of perceived indecisiveness amongst the game participants were positive and statistically significant. As such, the results from the current research study indicate one additional instructional benefit to add to the many other previously identified instructional benefits of simulation exercises.

There are a number of limitations in this study which future studies may want to address. To begin with, a clearer explication of the indecisiveness concept and how it is measured would seem to be called for. This study employed a single measure of perceived indecisiveness in the form of a self-reported psychometric scale. A broader set of measures for indecisiveness might add to the validity of the research especially if these measures allow for a more objective assessment of this trait. For example, in a more recent study Sarig, Dar, and Liberman (2012) used time to complete a task as a measure of indecisiveness. Similarly, Danan and Ziegelmeyer (2006) developed a behavioral measure of indecisiveness based on a “behavioral test of the axiom of completeness of individual preferences.” Finally, Liu et al. (2015) present the notion of “observed indecisiveness” which they studied using a database supplied by online retailer Alibaba which involved online business to consumer purchase activity. Liu et al. (2015, p. 283) present the following three characteristics of observed indecisiveness: “1) The more actions in this session, the higher indecisiveness of the corresponding customer (‘takes a long time’ or ‘delaying’); 2) The more balanced distribution of the actions on different items, the higher indecisiveness (‘feeling uncertain’); 3) The more transitions between the actions of different items, the higher indecisiveness (‘reconsideration’).” As such, future studies involving a business simulation game setting could employ self-reported psychometric scale measures of indecisiveness combined with measures of indecisiveness behavior by looking at the amount of time taken to make decisions and the number of changes in decision variables.

Aside from actual indecisiveness behavior there are a number of personality traits that are correlated with indecisiveness such as state or trait anxiety, or exploratory and impetuous indecisiveness as described by Bacanli (2006) that could be measured in conjunction with the indecisiveness measures employed. Had some of these additional measures been employed it could have helped the researchers in this study to better understand why the control group had a lower baseline level of indecisiveness than that of the simulation participant group.

Finally, the one critical difference between learning through the decision making in a business simulation game versus learning through traditional memorization is that there is far more uncertainty in the learning outcome of a business simulation game than in learning outcomes related to memorization. The researchers had expected the study to provide evidence that decision making experience would reduce indecisiveness and that less indecisive decision makers might also be better performers than more indecisive decision makers. This expectation was based on the idea that indecisiveness was a personality trait that generalizes across situations demanding decisions as asserted by Osipow (1999). Absent the control group, this conclusion might be drawn. However, the study results seem to demonstrate that indecisiveness might be more of a situational variable because students participating in a business simulation game were faced with an experience which actually increased their level of perceived indecisiveness in comparison to a control group. The implication for future research of this finding suggests a study of whether the experience of playing a business simulation game causes indecisiveness to occur when it wasn’t present before. A potential ethical dilemma for business simulation game users is whether it is an acceptable risk in an educational setting to produce an outcome where indecisiveness is actually induced by the activity as opposed to simply unmasking its presence?

Footnotes

Appendix 1

Appendix 2

Appendix 3 Author’s Note

The research methodology was reviewed by the University of Windsor’s Research Ethics Board (REB).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

William J. Wellington, PhD, associate professor, Marketing Area, and director of Masters of Management program at the Odette School of Business, University of Windsor. Past president of the Association for Business Simulation and Experiential Learning (ABSEL).

Contact: r87@uwindsor.ca

David B. Hutchinson, PhD, associate professor, Marketing Area at the Odette School of Business, University of Windsor. Past board member of the Association for Business Simulation and Experiential Learning (ABSEL).

Contact: dhutch@uwindsor.ca

Anthony J. Faria, PhD, professor emeritus, and director of the Office of Automotive and Vehicle Research at the Odette School of Business, University of Windsor, Windsor, Ontario, Canada. A fellows member of the Association for Business Simulation and Experiential Learning (ABSEL).

Contact: ad9@uwindsor.ca

References

Anderson

P. H.

Beveridge

D. A.

Lawton

Scott

T. W.

(2004). MERLIN: A marketing simulation. New York, NY: McGraw-Hill.

Anderson

P. H.

Lawton

(2009). Business simulations and cognitive learning: Developments, desires, and future directions. Simulation & Gaming, 40(2), 193-216. doi:10.1177/1046878108321624

Association to Advance Collegiate Schools of Business. (2013). AACSB assurance of learning standards: An interpretation (AACSB White Paper No.3). AACSB International Accreditation Coordinating and Quality Committees. Retrieved from http://www.aacsb.edu/~/media/AACSB/Publications/white-papers/wp-assurance-of-learning-standards.ashx

Bacanli

(2006). Personality characteristics as predictors of personal indecisiveness. Journal of Career Development, 32(4), 320-332. doi:10.1177/0894845305282941

Brooks

(2011). Management indecision. Management Decision, 49(5), 683-693. doi:10.1108/00251741111130788

Burns

A. C.

Bush

R. F.

(2003). Marketing research: Online research applications. Saddle River, NJ: Prentice Hall.

Cook

T. D.

Campbell

D. T.

(1979). Quasi-experimentation: Design & analysis issues for field settings. Boston, MA: Houghton Mifflin.

Cooper

S. E.

Fuqua

D. R.

Hartman

B. W.

(1984). The relationship of trait indecisiveness to vocational uncertainty, career indecision, and interpersonal characteristics. Journal of College Student Personnel, 25, 353-356.

Danan

Ziegelmeyer

(2006). Are preferences complete? An experimental measurement of indecisiveness under risk (No.93/01, Papers on Strategic Interaction). Max Planck Institute of Economics, Strategic Interaction Group.

10.

Del Missier

Mantyla

de Bruin

W. B

. (2010). Executive functions in decision making: An individual differences approach. Thinking & Reasoning, 16(2), 69-97. doi:10.1080/13546781003630117

11.

Elaydi

(2006). Construct development and measurement of indecisiveness. Management Decision, 44(10), 1363-1376. doi:10.1108/00251740610715696

12.

Faria

A. J.

(1998). Business simulation games: Current usage levels—An update. Simulation & Gaming, 29(3), 295-308. doi:10.1177/1046878198293002

13.

Faria

A. J.

Hutchinson

Wellington

W. J.

Gold

(2009). Developments in business gaming: A review of the past 40 years. Simulation & Gaming, 40, 464-487. doi:10.1177/1046878108327585

14.

Faria

A. J.

Wellington

W. J.

(2004). A survey of simulation game users, former-users, and never-users. Simulation & Gaming, 35(2), 178-207. doi:10.1177/1046878104263543

15.

Frost

R. O.

Shows

D. L.

(1993). The nature and measurement of compulsive indecisiveness. Behavioral Research and Therapy, 31(7), 683-692. doi:10.1016/0005-7967(93)90121-A

16.

Germeijs

DeBoeck

(2002). A measurement scale for indecisiveness and its relationship to career indecision and other types of indecision. European Journal of Psychological Assessment, 18(2), 113-122. doi:10.1027//1015-5759.18.2.113

17.

Germeijs

Verschueren

Soenens

(2006). Indecisiveness and high school students’ career decision-making process: Longitudinal associations and the meditational role of anxiety. Journal of Counseling Psychology, 53(4), 397-410. doi:10.1037/0022-0167.53.4.397

18.

Hartley

C. A.

Phelps

E. A.

(2012). Anxiety and decision making. Biological Psychiatry, 82, 113-118. doi:10.1016/j.biopsych.2011.12.027

19.

Horn

R. E.

Cleaves

(1980). The guide to simulations/games for education and training. Beverly Hills, CA: SAGE.

20.

Jones

L. K.

(1989). Measuring a three-dimensional construct of career indecision among college students: A revision of the vocational decision scale—The career decision profile. Journal of Counseling Psychology, 36(4), 477-486. doi:10.1037/0022-0167.36.4.477

21.

Kaliouby

R. E.

Robinson

(2004, January). FAIM: Integrating automated facial affect analysis in instant messaging. Paper presented at the Intelligent User Interfaces, Funchal, Portugal. doi:10.1145/964442.964493

22.

Kibbee

J. M.

Craft

C. J.

Nanus

(1961). Management games. New York, NY: Reinhold.

23.

Leong

F. T. L.

Chervinko

(1996). Construct validity of career indecision: Negative personality traits as predictors of career indecision. Journal of Career Assessment, 4, 315-329. doi:10.1177/106907279600400306

24.

Liu

Zeng

Liu

Zhu

Chen

Xiong

Xie

(2015). Mining indecisiveness in customer behaviors. Proceedings of the 2015 IEEE International Conference on Data Mining, 15, 281-290. doi:10.1109/ICDM.2015.78

25.

Michel

(2007). Understanding decision making in organizations to focus its practices where it matters. Measuring Business Excellence, 11(1), 33-45. doi:10.1108/13683040710740916

26.

Mill

R. C.

(2007). Meeting the needs of business: Are we teaching the right things? Journal of College Teaching & Learning, 4(8), 7-12. doi:10.19030/tlc.v4i8.1552

27.

Mojgan

F. N.

Kadir

R. A.

Soheil

(2011). The relationship between state and trait anxiety with career indecision of undergraduate students. International Education Studies, 4(3), 31-35. doi:10.5539/ies.v4n3p31

28.

O’Hare

Tamburri

(1986). Coping as a moderator of the relation between anxiety and career decision making. Journal of Counseling Psychology, 33(3), 255-264. doi:10.1037/0022-0167.33.3.255

29.

Osipow

S. H.

(1999). Assessing career indecision. Journal of Vocational Behavior, 55, 147-154. doi:10.1006/jvbe.1999.1704

30.

Patalano

A. L.

Juhasz

B. J.

Dicke

(2010). The relationship between indecisiveness and eye movement patterns in a decision making informational search task. Journal of Behavioral Decision Making, 23(4), 353-368. doi:10.1002/bdm.661

31.

Patalano

A. L.

Wengrovitz

S. M.

(2006). Cross-Cultural Exploration of the Indecisiveness Scale: A comparison of Chinese and American men and women. Personality and Individual Differences, 41, 813-824. doi:10.1016/j.paid.2006.03.023

32.

Picard

(2012). Reducing academic and career indecision: The effectiveness of a first-year college program. The Online Journal of Counselling and Education, 1(3), 70-82.

33.

Rassin

(2007). A psychological theory of indecisiveness. Netherlands Journal of Psychology, 63, 1-11. doi:10.1007/BF03061056

34.

Rassin

Muris

(2005). Indecisiveness and the interpretation of ambiguous situations. Personality and Individual Differences, 39, 1285-1291. doi:10.1016/j.paid.2005.06.006

35.

Riedl

Davis

F. D.

Hevner

A. R.

(2014). Towards a NeuroIS research methodology: Intensifying the discussion on methods, tools, and measurement [Special issue]. Journal of the Association for Information Systems, 15, i-xxxv. Retrieved from http://search.proquest.com/docview/1619352427?accountid=14789

36.

Sarig

Dar

Liberman

(2012). Obsessive-compulsive tendencies are related to indecisiveness and reliance on feedback in a neutral color judgment task. Journal of Behavior Therapy and Experimental Psychiatry, 43, 692-697. doi:10.1016/j.jbtep.2011.09.012

37.

Watson

H. J.

(1981). Computer simulation in business. New York, NY: John Wiley.

38.

Wellington

W. J.

Hutchinson

Faria

A. J.

(2016). A survey of simulation game users, former-users, and never-users: An update. In review with Simulation & Gaming.