DECISION STYLES AND STUDENT SIMULATION PERFORMANCE: A REPLICATION Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 95 DECISION STYLES AND STUDENT SIMULATION PERFORMANCE: A REPLICATION Robert W. Hornaday Kent E. Curran The University of North Carolina at Charlotte ABSTRACT Hornaday and Wheatley ( 4] compared the usefulness of the Rowe Decision Style Inventory (DSI) with the Rotter locus of control scale in differentiating student performance on a management simulation. They found that students with Conceptual decision styles scored significantly higher on a management simulation than other decision styles and that decision style seemed to be closely related to locus of control. This paper re- ports an effort that failed to replicate those findings with a different sample of students and a different simulation. INTRODUCTION One of the best justifications for the use of simulations or, for that matter, any form of experiential learning is that there is little relationship between traditional academic achievement and later managerial success [7]. If simulations are to be useful, they mist measure something other than the ability to get a good course grade from an instructor. Therefore, student attributes such as cognitive traits and the ability to work within a group need further investigation (13, p. 279]. BACKGROUND Is there a relationship between the cognitive style of the participants and performance in management simulations? In one of the few studies of this problem, Wolfe and Chako [15] found that the cognitive structure, ambiguity tolerance, and category width of 49 business policy students were not related to their performance in a simulation. Wolfe and Chako speculated that the programmed nature of the simulation “overpowered” the cognitive attributes of the participants and rewarded instead scholastic ability and rational decision-making. Other studies have considered the relationship of group characteristics such as cohesion (3;14] and marginality (5] with simulation performance. Hornaday and Wheatley [4] compared the usefulness of the Rowe Decision Style Inventory (DSI) with the Rotter locus of control scale in differentiating student performance on a management simulation. They found that students with Conceptual decision styles scored significantly higher on a management simulation than other decision styles and that decision style seemed to be closely related to locus of control. This paper is an effort to replicate those findings with a different sample of students and a different simulation. THE DECISION STYLE INVENTORY The Decision Style Inventory (DSI) developed by Rowe [12], classifies respondents into four decision styles: Directive, Analytic, Conceptual, and Behavioral. The DSI, used extensively in management development programs, has been completed by more than 10,000 practicing Managers. The DSI is an outgrowth of the work of Driver and Rowe [1] who investigated cognitive decision styles. Rowe developed a cognitive-contingency model to describe how the four decision styles fit within an organizational setting (Table 1). Tests of the DSI support the model (Table 2). Rowe reported that 80 top level executives tended to have Analytic or Conceptual decision styles (12]. Mann [8] found that financial planners in a sample of 30 executives tended to have dominant Analytic decision styles, while strategic planners had dominant Conceptual decision styles. Rowe posits the following characteristics for the four DSI styles: The DSI consists of 20 statements, each with four responses corresponding to Directive, Analytic, Conceptual, and Behavioral decision styles. Respondents must rank the four responses to each statement by scoring the response most “appropriate” for them with an “8,” the next most “appropriate” response with a “4,” then a “2,” and finally, a “1” for the least “appropriate” Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 96 response. The sum of the 20 subscale responses is the score for that decision style. Respondent style scores are compared to the average DSI scores for the population as a whole. A style score that exceeds the average by seven points or more is considered the respondent’s dominant or most frequently used style. Scores within a range of plus or minus seven points from the average are considered back-up styles. Least preferred styles are identified by scores seven points or more below the average. It is possible for a respondent to have two dominant styles or no dominant style. The DSI classified student respondents into decision style categories by identifying each student’s dominant decision style. The Rotter scale contains 29 pairs of forced-choice statements, including six dummy pairs [9]. A higher score on the Rotter scale indicates that the respondent has a more “external” locus of control, that is, he or she feels they are controlled by events. Lower Rotter scale scores identify respondents who have a more “internal” locus of control. They feel more in control of their fates. METHOD Both projects attempted to answer the general research question: Are decision style and Rotter scale score related to student performance in a management simulation? Both studies tested three hypotheses: Hypothesis 1. There is no difference in overall student grade point averages across the four decision styles. Hypothesis 2. There is no difference in student locus of control scores across the four decision styles. Hypothesis 3. There is no difference in student simulation performance across the four decision styles. Statistical testing of the hypotheses controlled for the effects of student academic major and sex. The 1985 Simulation. The simulation used in the 1985 study was developed by Carl Gooding (2], presently at East Carolina University, and further modified by Dan Voich at The Florida State University. Called ENSIM (Environmental Simulation), the game is a highly competitive general management simulation with dynamic environmental constraints. The game offers a realistic simulation of a manufacturing firm producing two products in competition with up to 19 other firms. All firms within a class section were in direct competition with one another. The 1986 Simulation. Students in the 1986 sample participated in The Business Management Laboratory (BML) [6]. BML presents more challenging marketing and production conditions than does ENSIM, but has less demanding environmental and labor constraints. Because BML is limited to a maximum of eight firms per industry, each class section had two industries. BML firms competed directly with a maximum of seven competitors. BML and ENSIM are rough- ly equal in difficulty. Both are more complex than most other general management simulations. The Sample. All business policy students in four class sections (128 students) taught by one instructor at a large Southeastern university during the spring semester of 1985 participated in the ENSIM simulation. Students were grouped randomly into two-member ENS TM teams for the management simulation. Teams completed a six- decision practice cycle for familiarization with the simulation before the actual competition began. The ENSIM team score was based on the growth, profitability, liquidity, and leverage position of each team after 12 decision periods over a calendar time of six weeks. The ENSIM team score represented 30 per cent of each student’s semester grade. Most student work on the ENSIM simulation was done outside of class. The 1986 Sample. Students in five sections of business policy at a mid- sized Southeastern university constituted the 1986 sample. The first author taught three of the sections; the second author the remaining two. The authors grouped the participants into three member teams, attempting to place one accounting major on each team. Due to attrition, nine of the teams finished the competition with only two members. The BML team score counted 20% of each student’s course grade, based upon growth, profitability, liquidity, and leverage measures achieved after 10 decision periods over a 10 week time period. Four practice decisions were completed for familiarization with BML. As with the 1985 sample, most student work on BML was accomplished outside of class. Students in the 1985 sample represented the normal range of business school majors, but the students in the 1986 sample attended a university that offers majors only in Accounting and Business Administration. For direct comparison, all non- accounting students in the 1985 sample were grouped into one category. Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 97 Distribution of Decision Styles. Distribution across dominant decision styles of the two samples were similar (Table 3). Rowe reported average decision style subscale scores for different occupational groups (Table 2). Both samples of business college seniors were more Behavioral and less Conceptual than the general population. Hypothesis Testing. Comparing the results of testing Hypothesis 1 (Table 3) slight differences exist in GPA relationships between the two samples. First, the 1985 students (ENSIM) reported a significantly higher mean GPA than the 1986 students (BML). Second, in the 1986 sample, Analytic styles reported significantly higher GPAS than the other styles. 1985 Analytics also had higher GPAS, but the difference was not significant. In both samples, accounting majors reported higher GPAs than non-accounting majors. Females in both samples reported higher GPAs than male students, but the difference was statistically significant only in the 1986 sample. While students in the 1985 sample reported higher GPAS, the results of testing Hypothesis 1 within each sample compare favorably. Table 4 contains results of testing locus of control scores across the three categories in both samples. As a group, the 1985 sample (ENSIM) had a more internal locus of control than the 1986 sample (BML). Locus of control was significantly related to decision style in the 1985 sample, but not in 1986 sample. The lower Rotter scale scores of the 1985 Directive and Analytic students indicated a sore internal locus of control. The difference in locus of control between male and female students was marked in the 1986 sample: males more internal. The difference in the 1985 sample was not significant. There was considerable difference in the results of testing Hypothesis 2 between the two samples. In 1985, the results of testing Hypothesis 3 revealed that students with Conceptual decision styles scored significantly higher on the management simulation than did students of other decision styles (Table 5). But this difference did not appear in the 1986 sample. In fact, Analytic styles did better in the simulation in 1986, but the difference between the decision styles was not significant. In neither sample did simulation performance appear to be related to sex or academic major. The main findings concerning the relationship between student decision style and simulation performance from the 1985 (ENSIM) study were not supported by the 1986 (BML) study. Simultaneous testing of all the variables using two way Analysis of Covariance is reported in Table 6. The results show that after controlling for GPA, locus of control (Rotter Score), and academic major, Conceptual decision styles outscored the other decision styles in the 1985 ENSIM competition. The same results did not occur in the 1986 BML competition. The covariance model had considerably less explanatory power with the 1986 sample data than with the 1985 sample data. The r2 dropped from .127 (1985) to .030 (1986), indicating Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 98 the ANCOVA model was much less useful in explaining variation in simulation performance. DISCUSSION Before rushing to abandon the DSI as a method of measuring decision styles of business policy students, two other major conditions must be considered which may have made it difficult to compare the two samples. The testing conditions were dissimilar. Different simulations were used (ENSIM and BML). The ENSIM study used two- member teams, six practice decisions, and 12 actual decisions; while the BML study used three-member teams, four practice decisions, 10 actual decisions, and counted 10* less for each student’s course grade. The change from two- member teams (ENSIM) to three- member teams (BML) undoubtedly had an effect on the importance of group dynamics as opposed to individual decision styles. Still, if the DSI is to have any value in measuring cognitive traits of business policy simulation participants, it must be robust enough to overcome these relatively minor variations. The other major external condition is probably more important. Perhaps the failure to replicate the ENSIM results with the BML sample has more to do with respective student populations than with DSI scores. While both samples consisted of senior business policy students at AACSB business schools, the 1985 sample had higher grade point averages and lower Rotter scores. The ENSIM sample came from a major university located in a small city with a traditional student body of over 20,000 students who live away from home, do not work during the semester, and avidly cheer their usually successful big-time football team. The BML sample, on the other hand came from a mid-sized (10,000 students) “computer campus” in a large city where most of the students work during the school year. A large percentage live at home and have no football team at all. It is possible that the characteristics of these two very different student groups may have important effects on student performance, especially in activities as dynamic as a simulation. The different test results may be symptomatic of the larger problem of generalizing research results based upon student samples to the problems faced by practicing managers. If research results cannot be replicated with other student samples, how can they apply to the real world? CONCLUSIONS Three major conclusions are drawn from this study. 1. The use of the DSI in measuring cognitive traits among business policy students is suspect. It is apparently not robust enough to be used across different sample schools or even relatively minor differences in simulation conditions. In addition, 1986 sample produced unacceptably low reliability scores. 2. Conceptual decision styles did not perform better on the simulation than the other decision styles in the 1986 sample. 3. The strong relationship between locus of control and decision style indicated in the 1985 sample did not occur in the 1986 sample. RECOMMENDATIONS FOR FUTURE RESEARCH The DSI should not be abandoned for use with business policy students without further testing. It is short, easy to administer and provides a base line for comparison with various occupational groups. The search for the effects of other cognitive traits on student simulation participation should continue. Developments in Business Simulation & Experiential Exercises, Volume 14, 1987 99 REFERENCES [1] Driver, M. and A. J. Rowe, “Decision-Making Styles: A New Approach to Management Decision Making,” in Cary Cooper (editor), Behavior Problems in Organizations (Englewood Cliffs, N.J.: Prentice-Hall, 1979), pp. [2] Gooding, C. “Decision Making Under Environmental Constraints: A Management Simulation Game,” Unpublished Doctoral dissertation, University of Georgia, 1976. [3] Gosenpud, J. J. “Personality Variables on Group Cohesion, Team Participation and Total Learning,” Proceedings, Thirteenth ABSEL Conference, 1986, pp. 115-118. [4] Hornaday, R. W. and W. J. Wheatley, “Exercise Right Brain Skills in the Classroom: Use a Simulation,” Proceeding, 46th Annual Meeting of the Academy of Management, 1986, p. 371. [5] Hornaday, R. W. and W. J. Wheatley, “Four Factors Affecting Group Performance in Business Policy Simulations,” Proceedings, Thirteenth ABSEL Conference, 1986, pp. 17-21. [6] Jensen, R. L. and D. J. Cherrington, The Business Management Laboratory (Plano, Texas: Business Publications, 1984). [7] Livingston, J. S. “Myth of the Well-Educated Manager,” Harvard Business Review, Jan-Feb, 1971, pp. 79-87. [8] Mann, H. B. “Relationship Between the Decision Making Styles of Corporate Planners and Other Planners,” Unpublished Doctoral dissertation, University of Southern California, 1982. [9] Rotter, J. B. “Generalized Expectancies for Internal Versus External Control of Reinforcement,” Psychological Monographs: General and Applied, 1966, Vol. 80, No. 1, Whole Number 609. [10] Rowe, A. J. and J. D. Boulgarides, “Decision Styles: A Perspective,” Leadership and Organizational Development Journal, 1983, Vol. 4, pp. 3-9. [11) Rowe, A. J., W. Bennis, and J. D. Boulgarides, “Desexing Decision Styles,” Personnel, 1984, Vol. 61, pp. 43-52. [12] Rowe, A. J., R. 0. Mason, and K. E. Dickel, Strategic Management & Business Policy: A Methodological Approach, (2d ed.) (Reading, Mass: Addison- Wesley, 1985). [13] Wolfe, J. “The Teaching Effectiveness of Games in Collegiate Business Courses: A 1973-1983 Update,” Simulation & Games, September, 1985, pp. 251-288. [14] Wolfe, J. and T. M. Box, “Relationships Between Team Cohesion Dimensions and Business Game Performance,” Proceedings, Thirteenth ABSEL Conference, 1986, pp. 11-16. [15] Wolfe J. and T. I. Chacko, “Team-Size Effects on Business Game Performance and Decision-Making Behaviors,” Decision Sciences, Vol. 14, pp. 121-133. Table of Contents Volume 14, 1987 Simulation Gaming as An Experimental Context for the Study of Multicriteria Decision Making How to Multiply Your Management Game Grading the Personnel In-Basket Exercise Simulation: The Players™ Perspective The Acute Susceptibility of Nominal Grouping to Negativity The Relative Value of the National ABSEL Meetings: An Analysis of Perceptions by Faculty and Deans A Survey of Behavioral Labs Used by American Business Schools A Structured Framework for Applying Conceptual Models to Problem Analysis: Hardening Up An Expert System Approach for Teaching Marketing Case Analysis From Theory to Practice: A Model for Teaching Beginning Advertising The Use of a Simple Forecasting Technique During an Interactive Computerized Business Game An Investigation of the Relationship Between Formal Planning and Simulation Team Performance and Satisfaction The Exponential Logarithm as an Algorithm for Business Simulations Learning macroeconomic Theory and Policy Analysis Via Microcomputer Simulation Laptop: A Principles of Marketing Simulation Teaching About Sampling in a Marketing Research Class Integrating Decision Support Systems and Business Games Demand Generation in a Service Industry Simulation: An Algorithmic Paradox ABSEL - At a Crossroads? Research on Predicting Performance in the Simulation An Expert System for Financial Planners "Developing Various Student Learning Abilities Via Writing, the Stock market Game, and Modified Marketplace Game in Beginning Macroeconomics" Professional Activity Reports: Getting back to Basics Œ A Preliminary Investigation "A Comparison of Performance, Attitudes, and behaviors of MBA and BBA Students in a Simulation Environment: A Preliminary Investigation" Decision Styles and Student Simulation Performance: A Replication Personal Power Strategies: An Experiential Exercise LTL I: A Trucking Simulation Game Total Enterprise Business Games: An Evaluation Can the Skill of Management be Taught? Developing Critical Thinking and Effective Communication Meaningful Notebooks and Enhanced Learning Using a Totally Menu-Driven Data base and Decision Support (DSS) Program Using a Computerized Menu-Driven Data Entry and fiWhat-Iffl Program as a Pedagogical Enhancement for Management Simulation Using a Joint Project Involving MBA Marketing Management and Undergraduate Marketing Research Students to Teach Marketing Research Testing the PAGE Technique: Results and Further Developments A Comparison of Student Perceptions with Accepted Expectations for Business Simulations "Conflict Management in Action: Verbal Strategies, Nonverbal behaviors and Conflict Styles" Dramatic Monologues as Surrogates for Experiential Learning Influencing One™s Superior: An Assessment and Exercise Personality Traits and Organizational Cultures: Two One-Hour Experiential Learning Exercises Computype: A Strategic Marketing Game Open System Simulations and Simulation Based Research "Teaching Forecasting to the Masses: Little Decision Support Training, But a lot of Answers" Goal Setting and Performance Evaluation with Different Starting Positions Œ The Modeling Dilemma The Gamesmanship of Pricing: Building Pricing Strategy Skills Through Spreadsheet Modeling Teaching MPR Experientially Through the Use of Lotus 1-2-3 Business Simulation Through Activities of a Manufacturing Company Simulated Stock Broker Teaching Employee Counseling Skills to Management Students: A Simulation Airline: A Strategic Management Simulation Developing Simulations and Experiential Exercises on the Personal Computer: Some Critical Issues A Powerful Tool Œ For What? An Integrative Systems Approach to Simulating Free Enterprise in a Small Business Course in a Business School Profits: The False Prophet The Use of Expert Systems to Develop Strategic Scenarios: An Experiment Using a Simulated Market Environment An Interdisciplinary Workshop: Spreadsheet Modeling and Case Analysis Techniques for Beginning Executive MBA Students An Innovative Approach to an O. D. Class Œ The Creative Interaction The Application of Spreadsheet Software Technology to Complete Taxpayer Elections Experiential Learning as the Cornerstone of an Industry Management Course "Identifying, Exercising and Enhancing Right Brain Skills in the Business Classroom" Utilizing Envisionary Technology in the Business Policy Course: An Experiential Pedagogy Business Simulation in the Policy Course: A Survey of American Assembly of Collegiate Schools of Business The Moving Van: Vehicle to Success of Disaster for the Two-Gender Work Force Oil and Gas Well Investment Analysis Using the Lotus 1-2-3 Decision Support System Team Cohesion Effects on Business Game Performance