GROUP COGNITIVE STYLE AND COMPUTERPHOBIA IN FUNCTIONAL BUSINESS SIMULATIONS Development In Business Simulation & Experiential Exercises, Volume 21, 1994 48 GROUP COGNITIVE STYLE AND COMPUTERPHOBIA IN FUNCTIONAL BUSINESS SIMULATIONS Nancy H. Leonard, Providence College Thomas L. Leonard, Bryant College ABSTRACT Performance in a business simulation was examined in relation to trait anxiety, computer anxiety and cognitive style, determined at the group level of analysis. Groups were classified as high/low trait anxiety, high/ low on three dimensions of computer anxiety, arid sensing/intuitive and thinking/feeling cognitive style. Results indicated that there was a significant relationship between trait anxiety and group performance and a significant relationship between thinking/feeling groups and group performance. INTRODUCTION Since the first business simulation game was used in a college class at the University of Washington in 1957(Watson, 1981), the number and variety of business games available for classroom use has grown enormously (Whiteley & Faria, 1989). Based on a recent survey entailing approximately 1,500 mail and telephone contacts, Faria (1987) estimated that over 200 business games are currently being used by approximately 8,755 instructors in over 1.900 four-year business schools. Research related to the use of computer simulations in the classroom has also increased greatly. This research has generally focused on identifying environmental, individual and group variables, which affect performance, as well as the relative educational benefits of simulation games versus other teaching approaches. Individual variables which have been examined include personality characteristics of the simulation team members (Patz, 1989), ethnic origin of team members (Faria, 1986), previous business experience (Trinkaus, 1981), selected analytical skills (Hall, 1987), and Grade point averages and aptitude scores (McKenney & Dill, 1966; Gray, 1972). One of the main weaknesses of much of this work is that the most common outcome variable is team performance, a group variable which is examined in light of individual variables. In a recent review of management games used in education and research, Keys & Wolfe (1990) suggest that rather than examining individual variables as predictors of group performance, a more useful analysis would be to examine the dominant thinking and decision making style that prevails on a given team. Because management games and actual management decision making are group experiences and groups must work together to achieve optimal learning and success, it is essential that we understand the group variables which affect performance. Three such group composition factors were examined in this study, general or trait anxiety, computer anxiety and cognitive style. Each of these factors was examined at the group level by creating composite scores for each group. Computer Anxiety As students are required to use computers, it has become evident that not all students are comfortable in doing so. Many students’ experience varying degrees of anxiety when required to use or even learn about computers. Jay (1981) has referred to these negative reactions as Computerphobia”. Loyd and Gressard (1984, p. 67) define computer anxiety as “resistance to thinking about computer technology, fear of computers, and hostile or aggressive thoughts about computers”. It has also been suggested in the literature that anxiety toward a subject area such as mathematics (Fennema & Sherman, 1976) may influence the learning process. Therefore, it seems likely that student’s attitude toward computers and toward simulations, which involve computers, may be an important factor in their success or failure on computer-based management simulation games. Trait Anxiety While computer anxiety can be traced to situations in which computers are encountered, trait anxiety is viewed as anxiety which occurs in a wide variety of situations not necessarily related to situations involving computers. Taylor (1953, p. 285) defines trait, or general anxiety as a relatively constant level of internal anxiety or emotionality”. Individuals who score high on trait or general anxiety tend to perceive the environment as threatening and uncontrollable and are less able than those who score low on general anxiety to control autonomic reactions to stress situations. In stressful situations, high scorers tend to feel anxious, tense, and jumpy and are likely to experience some physiological changes, such as excessive perspiration, increased pulse rate, and greater emotional discomfort (Dahlstrom, Welsh, & Dahlstrom, 1975). Because the literature does not make a clear distinction between general and situational anxiety, both trait and computer anxiety were addressed in this study. In both cases, it is hypothesized that anxiety will be negatively related to performance. Support has been found in the past for this relationship (Sarason, Mandler & Craighill, 1952; Alpert & Haber, 1960). Therefore, the following hypotheses are suggested: H1: Performance in the simulation will be negatively related to computer anxiety. H2: Performance in the simulation will be positively related to computer liking. H3: Performance in the simulation will be positively related to computer confidence. H4: Performance in the simulation will be negatively related to trait anxiety. Cognitive Style One of the aspects of personality that would logically be relevant to group task behavior is that of cognitive style. Cognitive style refers to the way in which one goes about organizing and processing information to complete a decision task. Conceptually, cognitive style has been defined as “the way in which people process and organize information and arrive at judgments or conclusions based on their observations of situations” (Steers, 1988). These styles are viewed as relatively stable dispositions which lead to differences in behavior in the decision making process. These dispositions and the characteristic processing presumed to underlie them are referred to as cognitive “styles” to indicate that this stability extends over a variety of tasks with similar task demands and/or stimulus properties (Shipman & Shipman, 1985). Historically, there have been numerous operationalizations of cognitive style. These include such unidimensional measurements as “ways of reasoning” termed analytic versus heuristic (Huysman, 1970), information processing capacity” measured as abstract versus concrete (Schroeder & Suedfeld, 1971), and ability to separate an object from its environment (Witkin, 1967). A number of researchers have developed multi-dimensional measures of cognitive style, including Driver and Mock (1975), who examined cognitive style in terms of the number of solutions generated and the amount of information used, McKeeney and Keen (1974) who operationalized cognitive style in terms of information gathering and information evaluation, and Mason and Mitroff (1973) who examined cognitive style based on the Jungian dimensions of sensing/intuition and thinking/feeling (Jung, 1970) These two dimensions relate to types of information processing and decision making. In later work, Mitroff Development In Business Simulation & Experiential Exercises, Volume 21, 1994 49 (1983) identified four distinct cognitive styles as ST (sensing/thinking), SF (sensing/feeling), NT (intuitive/thinking), and NF (intuitive/feeling). Sensing (S) refers to perception of the immediate experience, an awareness of what is occurring in the present, and a concern for details. Sensing individuals prefer structured problems, which involve routines and details. Intuition (N) refers to perception of possibilities, going beyond what is immediately present ins situation, and an appreciation of the overall meaning of a situation. Intuitives prefer unstructured problems. Thinking (T) refers to the use of logic and objective analysis, and tends to be impersonal, and feeling (F) refers to an emphasis on individual and group values and subjective impressions, arid tends to be personal. Recently, researchers have begun to examine the concept of cognitive style as it relates to simulations used in the classroom setting (Paw 1989; Henderson & Null, 1980). Grove, Stephen & Knowles (1990) used the MBTI to examine cost performance in a computer simulated business environment. These authors found a significant difference in cost performance for different cognitive styles. They hypothesized that ST’s would perform best, followed by NT, SF, and NF styles. The results of their study indicated that overall, S’s performed more efficiently than N’s. Patz (1989) created team personality types based on the MBTI dimensions of sensing/intuition and thinking/feeling and found that intuitive/ thinking (NT) teams established and maintained an early lead in a total enterprise simulation. One possible explanation for these differing results may lie in the type of tasks required in the simulations. In the simulation used by Groves et. al (1990) the tasks may have been relatively well defined, requiring attention to detail as preferred by sensing individuals, while the tasks in the total enterprise simulation used by Patz (1989) may have been unstructured tasks, requiring intuitive capabilities. Given these mixed results, it is dear that further research concerning the relationship between cognitive style and simulation performance is necessary. The Brands simulation used in this study involved largely unstructured problem solving, and required logic and objective analysis. The Brands simulation has a reputation for modeling very extensive marketing research capabilities. Therefore, it was reasoned that similar to the results of Patz’s study, intuitives would perform the task better than sensing groups, and thinking groups would perform better than feeling groups. The following hypotheses are proposed: H5: Performance in the simulation will be better for intuitive groups than for sensing groups. H6: Performance in the simulation will be better for thinking groups than for feeling groups. METHOD Sample Subjects were 70 undergraduate students enrolled in two sections of an undergraduate marketing strategy course and one section of a marketing policy course at two eastern universities. In all classes, participation in a simulation game named Brands: A Marketing Simulation (Chapman, 1992) constituted 40 %of the students’ grades. Dependent Measure Overall team performance was measured by a weighted composite score based on return on investment, operating efficiency, and change in market share. Independent Measures Cognitive Style. The MBTI is a tool, which was developed by Myers (1962) as an operationalization of Jung’s psychological theory. The MBTI measures personality on four dimensions (introvert/extrovert, sensing/intuition, thinking/feeling, and perception/judgment). Although the complete form was administered, only two dimensions, sensing/ intuition and thinking/feeling were used in the analysis. Support for the use of only two dimensions (sensing/intuition and thinking/feeling) as a measure of cognitive style can be found in the work of Mason & Mitroff (1973) and Ruble & Cosier (1990). Reliability and validity issues are discussed in detail by Carlyn (1977) and by Carlson (1985). Computer Anxiety. Although it was believed that computer anxiety would have a direct affect on simulation performance, it was not clear whether measures of computer anxiety reflect only computer anxiety, or might also reflect general anxiety. Therefore, a scale was administered which included twenty questions which measure general anxiety (Manifest Anxiety Scale: Taylor, 1953) and twenty-five questions which measure computer anxiety (Computer Anxiety Scale: Loyd & Gressard, 1984). The Computer Anxiety Scale (CAS) developed by Loyd and Gressard (1984) contains three subscales: fear and anxiety about computers (computer anxiety), enjoyment in working with computers (computer liking), and confidence in the ability to use and to learn about computers (computer confidence). The instrument contains thirty Likert-type items and six-point ordered response scales that are anchored by the terms, “Strongly Agree” and “Strongly Disagree”. Fifteen of the items are content reversed. In their original study, Loyd and Gressard (1984) reported that the reliabilities were .86, .91. .91, and .95 for the computer anxiety, computer liking, and computer confidence subscales, and the total score, respectively. General Anxiety. Taylor (1953) developed a measure of trait anxiety based on the Minnesota Multiphase Personality Inventory. The MAS was originally comprised of fifty items but was later reduced to a twenty-item scale by Bendig (1956). This twenty-item scale was found to be as reliable as the original scale. The reliability scales were .78 (50-item scale) and .76 (20-item scale) with intercorrelations among the scores of .93 and .91. Because the twenty-item scale was more parsimonious of testing time, it was utilized in this study. Procedure The authors of this article administered the MBTI, CAS and MAS in classroom settings, along with a questionnaire, which provided demographic data. Brands is a marketing simulation designed for use in a senior-level capstone course. For the simulation, teams were formed during the third week of the semester. The teams were formed randomly. Twenty-one student teams, composed of tour to five students, undertook marketing research to ascertain consumer preferences, and manufactured two products, which were sold in three geographic markets. Decisions were made concerning product characteristics, price, sales force size and compensation, advertising and sales promotion and product distribution. The game horizon included twelve decision periods with each decision period representing one quarter (three months) of business activity. The major objectives of each team were to maximize profits and to manage an efficient organization. Each game lasted approximately three months in real time, with each decision or iteration made at one-week intervals for a total of twelve decisions. After each decision, participants received detailed balance sheets, profit and loss statements, operating analysis and market data. Development In Business Simulation & Experiential Exercises, Volume 21, 1994 50 RESULTS The data analysis proceeded in three phases: 1) the factor analyses of the anxiety items, 2) classification of student groups according to group cognitive style and anxiety level, and 3) hypotheses testing. Factor Analyses Three factor analyses were performed, one with all 45 anxiety items, one with just the computer anxiety items and one with only the trait anxiety items. The objective of the first factor analysis was to examine the structure of the 45 anxiety items in terms of computer anxiety versus general anxiety. The question addressed by this analysis was whether or not computer anxiety and general anxiety are two distinct constructs. A principal components analysis, with a varimax rotation, was employed. Eleven factors had eigenvalues over 1.0. The scree plot indicated a three-factor solution. An examination of the item loadings, however, showed that all 25 computer anxiety items loaded solely on the first factor while no trait anxiety items loaded on factor one except for one which loaded on both factor one and factor two. This was regarded as strong evidence that the computer anxiety items and trait anxiety items were from separate domains. The second factor analysis was a principal components analysis of the 25 computer anxiety items. Four factors had eigenvalues greater than 1.0 and the scree plot suggested two factors. Two-, three-And four-factor solutions with varimax rotations were examined. The three-factor solution closely approximated results of others (Lloyd and Gressard, 1984) and accounted for 17% of the variance (See Table 1). The three factors were interpreted to represent computer anxiety, computer liking, and computer confidence. This interpretation is more consistent with Lloyd and Gressard than that of Bandalos and Benson (1990). The third principal components analysis was performed on the 20 trait anxiety items. Six factors had eigenvalues over 1.0. The scree plot suggested a two-factor solution. Examination of two-, three-, and four- factor varimax-rotated solutions, however, showed unstable item loadings. A number of items loaded on two factors and others switched from factor to factor as the solution dimensionality was increased. Most convincingly, an examination of item contents defied interpretation. TABLE 1 FACTOR ANALYSIS OF COMPUTER ANXIETY ITEMS Factor 1 Anxiety Factor 2 Liking Factor 3 Confidence 2 I get a sinking feeling when trying to use a computer. 0.85 24 Computers usually make me feel nervous and uncomfortable. 0.70 -0.40 34 I am no good with computers. 0.58 -0.51 14 I’m not the type to do well with computers. 0.56 -0.53 3 I do not think I could handle a computer course. 0.55 -0.39 -0.39 9 I would feel comfortable working with a computer. -0.53 0.52 29 It would not bother me at all to take a computer course. -0.58 0.44 0.45 18 I have lots of self confidence when it comes to working with computers. -0.60 23 Generally, I would feel ok about trying a new problem on a computer. -0.64 0.47 0.47 25 I would feel at ease in a computer class. -0.66 0.42 0.42 37 Once I start working on the computer, I find it hard to stop. 0.75 0.75 Factor 1 Anxiety Factor 2 Liking Factor 3 Confidence 41 I would like working with computers. -0.44 0.70 0.70 35 I think working with computers would be enjoyable and stimulating. -0.49 0.67 0.67 32 I’m sure I could do advanced work in computers. 0.57 6 I do not enjoy talking with others about computers. -0.53 -0.44 43 I will do as little work with computers as possible. 0.42 -0.53 44 The challenge of solving problems with computers does not appeal to me. 0.45 -0.60 8 Figuring out computer problems does not appeal to me. 0.45 -0.64 13 I do not understand how some people can spend so much time working with computers and seem to enjoy it. -0.70 27 I am sure I could learn a computer language. 0.74 39 I am sure I could do work with computers. 0.40 0.71 40 I could get good grades in a computer course. 0.48 0.69 11 Computers do not scare me at all. -0.44 0.67 30 I do not feel threatened when others talk about computers. 0.63 16 I think using a computer would be very hard for me. 0.50 -0.40 -0.56 Therefore, it was concluded that, in conformity with the literature (no published factor analyses of this scale), these items were best treated as a multi-item index of a single dimension. Scores for the 20 items were summed (reverse-scored items were subtracted) to yield a trait anxiety index for each of the 70 subjects. Scores ranged from 1 to 54, with a mean of 25.7. Cronbach’s Coefficient Alpha for this scale was 0.52. Classification of Groups Each of the 21 student groups was classified as characterized by one end or the other of six dichotomous dimensions: two cognitive style dimensions, sensing/intuition (S/N) and thinking/feeling (T/F); and four anxiety dimensions: ‘hi’ versus ‘lo’ computer anxiety, computer liking, computer confidence, and trait anxiety. Cognitive Style Grouping. Three different methods were derived or modified from the literature to suggest groupings. Method One was to classify each group as sensing or intuitive and as thinking or feeling according to the simple majority of team members’ raw MBTI scores. Method Two was similar to the first, except that only students whose raw MBTI scores were distinctly greater than zero were used. The 23 highest (one-third of our sample) MBTI scores in each dimension were allowed to retain their cognitive style designations, and the remaining ones were grouped into middle, ‘on-the-fence’ groups. A simple majority was then used to categorize the group’s cognitive style. In Method Three each of the two cognitive style pairs was treated as a bipolar dimension. Team members’ raw MBTI measures for opposing styles were simply netted together. Teams were then categorized according to the direction of their total scores. The results of these grouping methods for the sensing/intuition style is illustrated in Table 2. Development In Business Simulation & Experiential Exercises, Volume 21, 1994 51 TABLE 2 CLASSIFICATION OF GROUPS AS SENSING/INTUITION Method One Simple Majority Method Two Majority, Excluding Middle- of-the-Roaders Method Three Summed Raw Score Group Style S N S M N S N 1 N 1 2* 1 1 1 2.3 2 N 1 1 0 2 0 4.0 3 N 2 3* 2 1 2 0.2 4 N 2 2 2 0 2 0.5 S N 1 2* 1 0 2* 14.3 6 S 2 2 *1 3 0 7.5 7 S *3 0 *2 1 0 17.7 8 S *2 1 *2 1 0 11.7 9 N 2 2 1 1 2* 6.5 10 N 1 1 0 1 1* 60 11 S 1 1 1 0 1 5.0 12 S *2 1 *1 2 0 10.3 13 S *4 0 *1 3 0 13.5 14 S *3 2 2 1 2 34 15 N 0 3* 0 0 3* 17.0 16 S *2 1 *1 2 0 7.0 17 S *3 0 *2 1 0 14.3 18 N 0 4* 0 1 3* 16.0 19 N 2 2 2 0 2 3.0 20 N *2 1 0 2 1* 2.3 21 S *2 0 *1 2 0 117 As Table 2 shows, there was almost total consistency among the different methods of categorizing groups* cognitive styles. In all, there were only two cases of contradictory groupings, one each for sensing/ intuition (S/N) and thinking/feeling (T/F). In both cases Method One deviated from the other two. In one case two team members with marginal raw MBTI scores for sensing comprised a majority against the third team member’s substantial intuition rating. Method One thus classified the team sensing whereas Methods Two and Three classified the team intuitive. In the other case a similar discrepancy occurred for a thinking versus feeling classification. In both instances, the groups were classified according to Methods Two and Three. Anxiety Grouping. Essentially the same three methods were used for categorizing groups according to computer anxiety, computer liking, computer confidence, and trait anxiety. Groups were characterized as either *hi* or *lo* on each of these dimensions. As was the case for cognitive style, conflicting grouping was not a problem, only one conflict occurred. Once again, Method One results conflicted with Methods Two and Three and the group was classified according to Methods Two and Three. Hypothesis Testing The dependent variable in all the hypotheses was a group performance measure automatically assessed by the Brands simulation. Scores for the 21 teams ranged from 80 to 93 and averaged 86. Computer Anxiety Hypotheses. Using simple t-tests none of the three hypotheses for computer anxiety, computer liking, and computer confidence were supported by the data Trait Anxiety Hypothesis. Hypothesis Four stated that performance should be negatively related to trait anxiety. *Hi* trait anxiety groups scored an average performance rating of 83.7 while *lo* trait anxiety groups averaged 88.5. This is a significant difference. The t-statistics was 2.99 (p < 0.005) Cognitive Style Hypotheses. For Hypotheses Five and Six, it was hypothesized that intuitive groups would score higher than sensing Groups and thinking groups would score higher than feeling groups. This was tested using an unbalanced 2x2 factorial ANOVA. Table 3. TABLE 3 COGNITIVE STYLE ANALYSIS OF VARIANCE With Interaction Term: Source DF Sum of Squares Mean Square F Value Pr > F Sensing/ Intuition 1 17.5 17.5 1.07 0.31 Thinking/Feeling 1 61.5 61.5 3.76 .007 SN X TF 1 16.3 16.3 1.00 0.33 Error 17 277.7 Corrected Total 20 372.0 R-Square: 0.25 Without Interaction Term: Source DF Sum of Squares Mean Square F Value Pr > F Sensing/ Intuition 1 16.1 16.1 0.99 0.33 Thinking/Feeling 1 58.9 58.9 3.61 0.07 Error 18 294.0 Corrected Total 20 372.0 0.21 Cell Means: Sensing Intuition Total Thinking 82.4 86.0 84.2 (n=5) (n=5) Felling 87.2 87.7 87.6 (n=5) (n=6) Total 85.0 86.9 86.0 Development In Business Simulation & Experiential Exercises, Volume 21, 1994 52 As indicated in the table, neither the sensing/intuition-by-thinking/ feeling interaction nor the sensing/intuition main effects were significant. Thinking/feeling did show a moderately significant main effect. Examining group means reveals, however, that the thinking/feeling effect occurred in the direction opposite to that hypothesized. Feeling groups performed better than thinking groups, with feeling groups averaging 87.6 to thinking groups 84.2. TABLE 4 THINKING/FEELING AND TRAIT ANXIETY ANALYSIS OF VARIANCE With Interaction Term: Source DF Squares Sum of Square Mean F Value Pr > F Thinking/Feeling 1 41.0 41.0 3.30 0.09 Trait Anxiety 1 95.9 95.9 7.72 0.01 TF x TA 1 1.3 1.3 0.10 0.75 Error 17 211.2 Corrected Total 20 372.0 R-Square: 0.43 Without Interaction Term: Source DF Square s Sum of Square Mean F Value Pr > F Thinking/Feeling 1 40.2 40.2 3.41 .009 Trait Anxiety 1 97.7 97.7 8.28 .001 Error 18 212.4 Corrected Total 20 372.0 R-Square: 0.43 Cell Means: Thinking Feeling Total Low Trait Anxiety 86.5 89.8 88.5 (n=4) (n=6) High Trait Anxiety 82.7 85.0 83.7 (n=6) (n=5) Total 85.0 86.9 86.0 A final ANOVA was used to investigate the possibility of an interaction between thinking/feeling and trait anxiety. As Table 4 shows, there was no interaction. DISCUSSION AND CONCLUSIONS The research question addressed in this study was whether business simulation performance was related to general anxiety, computer anxiety and/or cognitive style. The findings indicated that computer anxiety is a multi-dimensional construct composed of computer anxiety, computer liking and computer confidence, and that trait anxiety is best represented as a single dimension. This finding is consistent with previous work (Loyd & Gressard, 1984; Taylor, 1953). The results also indicate that the three methods utilized in previous research for categorizing groups according to individual MBTI scores produce similar groupings. Although the hypotheses concerning computer anxiety were not supported, this may have been due to the role that computers played in this particular simulation. Computers were only used for inputting decisions and were not an integral part of the simulation experience. Future research is needed to determine the role of computer anxiety in simulations where computer interaction is central. As hypothesized, performance was negatively related to trait anxiety and therefore, instructors who use simulations in the classroom should be sensitive to the effects of students* trait anxiety. The results of the group cognitive style on simulation performance were counterintuitive. The relationship between feeling groups and group performance was surprising. It may be explained by the nature of the game and the spirit of the class. The lack of results for sensing versus intuitive groups is less easily explained. It was hypothesized that intuitive groups would perform better than sensing groups. The results did not bear this out. The intuitive groups may have been experimenters while sensing groups may have been more cautious in their strategies. Limitations of the Study A main limitation of this study was the sample size. A sample of seventy is only marginally sufficient for three factor analyses of 45 items. A larger sample may have led to a stable solution for the trait anxiety items. In the case of the ANOVA’s, twenty-one teams were used in the analysis. A larger sample would have allowed us to increase the number of levels per factor, or perhaps include an anxiety component in the ANOVA as either a distinct factor or as a covariate. REFERENCES Alpert, P. & Haber, R. (1960). Anxiety in academic achievement situations. Journal of Abnormal and Social Psychology, 61(2): 207-215. Bandalos, D. & Benson, J. (1990). Testing the factor structure invariance of a computer attitude scale over two grouping conditions. Educational and Psychological Measurement, 50, 49-60. Bendig, A.W. (1956). The development of a short form of the manifest anxiety scale. Journal of Consulting Psychology, 20(5): 384-361. Carlson, J.G. (1985). Recent assessments of the Myers-Briggs type indicator. Journal of Personality Assessment, 49, 356-365. Carlyn, M. (1977). *An Assessment of the Myers-Briggs Type Indicator,” Journal of Personality Assessment, 41, 461-473. Chapman, Randall (1992). Brands: A marketing game. Prentice Hall, Englewood Cliffs, NJ. Dahlstrom, W., Welsh, G. & Dahlstrom, L. (1975). An MMPI handbook volume II: Research applications. Minneapolis: University of Minnesota Press. Driver, M.J., and Mock, T.J. (1975). *Human Information Processing, Decision Style Theory, and Accounting Information Systems,” Accounting Review, July, 490-508 Faria, A.J. (1986). A test of student performance and attitudes under varying game conditions, in A.C. Bums and L. Kelley (eds.) Proceedings of the Thirteenth Annual Conference of the Association for Business Simulation and Experiential Learning. Faria, A.J. (1987). A survey of the use of business games in academia and business. Simulation £0 Games, 18 (2): 207-224. Fennema, M. & Sherman, J. (1976). Fennema-Sherman mathematics attitudes scales: JSAS (Ms. No. 1225). Catalog of Selected Documents in Psychology, 6 (1): 31. Gray, CF. (1972). Performance as a criterion variable in measuring business gaming success: An experiment with a multiple objective performance model. Proceedings of the Southeastern AIDS Conference: 193-198. Grove, Stephen & Knowles, Patricia (1990). An Experimental Application of personality Type as an Analogue for Decision-Making Styles, Psychological Reports, 66, 167-175. Development In Business Simulation & Experiential Exercises, Volume 21, 1994 53 Hall, DR. (1987). Developing various student learning abilities via writing, the stock market game, and modified marketplace game, in L. Kelley and P. Sanders (eds.) Proceedings of the Fourteenth Annual Conference for the Association for Business Simulation and Experiential Learning. Henderson, JC. & Nutt, P.C. (1980). *The Influence of Decision Style on Decision Making Behavior,” Management Science, 26, 4, 371-386. Huysman, J. (1970). The Implementation of Operations Research, Wiley- lnterscience, New York. Jay, T. (1981). Computerphobia: What to do about it. Educational Technology, 21, 47-48. Jung, C. (1970). Collected Works: Six Psychological Types, W. McGuire, ed, Princeton University Press, Princeton, NJ. Keys, B- & Wolfe, J. (1990). The role of management games and simulations in education and research, Journal of Management, 16 (2): 307i-336. Loyd, B. H. & Gressard, B.H. (1984). Reliability and factorial validity of computer attitude scales Educational and Psychological Measurement, 44, 501-505. Mason, R.W. and Mitroff, 1.1. (1973). A program of research on management information systems, Management Science, 19(5): 475- 487. McKenney, JL. and Keen, P. (1974) *How Managers* Minds Work,” Harvard Business Review, (May-June), 79-90. McKenney, J.L. & Dill, W.R. (1966). Influences on learning in simulation games. American Behavioral Scientist. 10(2): 28-32. Mitroff, II. (1983). Archetypal social systems analysis: On the deeper structure of human systems. Academy of Management Review, 8, 387- 397. Myers, I. (1962). The Myers-Briggs Type Indicator Palo Alto, CA: Consulting Psychologists Press. Myers, I.B., and McCaulley, M.H. (1985). Manual: A guide to the development and use of the Myers -Briggs type indicator Palo Alto, CA: Consulting Psychologists Press. Paz A.L. (1989). Group personality composition and total enterprise simulation performance. In J. Wingender & W. Wheatley (Eds.), Developments in business simulation and experiential exercises: 136- 137. Sillwater, OK: Oklahoma State University. Ruble, Thomas L. & Cosier, Richard A. (1990). Effects of cognitive styles and decision setting on performance. Organizational Behavior and Human Decision Processes, 46,283-295. Sarason, S., Mandler, G. & Craighill, P. (1952). The effect of differential instructions on anxiety and learning. Journal of Abnormal Social Psychology, 47, 561-565. Schroeder, H.M. & Suedfeld, P. (1971). Personality theory and in formation processing. New York: Ronald Press. Shipman, KS. &Shipman, V.C. (1985). Cognitive styles: some conceptual, methodological and applied issues. In E.E. Gordon (Ed.), Review of research in education. Vol. 12. Washington, DC: American Educational Research Association, 229-291. Steers, R.M. (1988). Introduction to organizational behavior(3rd ed.). Glenview, IL: Scott Foresman. Taylor, J.A. (1953). A personality scale for manifest anxiety. Journal of Abnormal Psychology, 48, 285-290. Trinkaus, J.W. (1981). Participant type differences in response to experiential methods, in D.C. Brenenstuhl and W.D. Biggs (eds.) Proceedings of the Seventh Annual Conference of the Association for Business Simulation and Experiential Learning. Whiteley, T.R. & Faria, A.J. (1989). A study of the relationship between student final exam performance and simulation game participation. Simulation and Games, 20(1): 44-64. Witkin, H.A. (1967). A cognitive style approach to cross cultural research, International Journal of Psychology, 2, 233-250. Watson, H.J. (1981). Computer simulation in business. New York: John Wiley. Table of Contents Volume 21, 1994 ABSEL: The Way We Were and Need to Be The Intellectual Structure of ABSEL: A Bibliometeic Study of Author Cocitatons Over Time Activity-Driven Time in Computerized Gaming Simulations An Assessment Framework for Determing the Effectiveness of Total Enterprise Simulations Attributes of Learning Organizations: Simulating the Relationships Business Policy/Strategy Case Extension using Pro-Forma Planning: A Computer Based Model Complexity: Is it really that Simple? A Random-Strategy Criterion for Validity of Simulation Game Participation Enhancing a Computer Simulation with a Structured Reporting Environment Experiencing a Foreign Culture: A Cross-Cultural Simulation Group Cognitive Style and Computerphobia in Functional Business Simulations Human Issues in Technology Implementation Management Simulator Incorporating Advertising Strategy into Computer-Based Business Simulations: A Validation Study Increasing the Effectiveness of Performance Evaluation Through the Design and Development of Realistic Finance Algorithms The Packer-Feeder Game: A Commodity Market Simulator Relationships Between R&D and Profitability: An Exploratory Comparison of Two Business Simulations with Two Real-World, Technology Intensive Industries Simulation of the Predictive Value of Mammography Simulation Performance and Learning Revisited Strange Bedfellows: Competency Models and ACBSP Accreditation Standards Using a Business Simulation to Study the Determinants of Ethical Behavior What Simulation Users Think Players Should be Learning from the Simulations ADA and its Implications for Experiential Training Boss/ Subordinate Perceptions of Instrumental and Supportive Leadership Behaviors in Relation to Myers-Briggs Thinking Type Cluster Analyses of American Universities' Business Core Curricula Structures Utilized to Satisfy Fifteen Curriculum Areas Cooperative Learning or Learning to Cooperate Experiential Learning: Constraining Students with Time Budgets How Different Workplace Experiences Affect Different Worker Values Implications of the Trend Toward Relationship Marketing for Experiential Learning The Increasing Cultural Diversity of the American Workforce: Management's Challenge of the 21st Century Information and Uncertainty as Strange Bedfellows: A Model and Experiential Exercises Leadership as a Medium: It's Emergence and Effect on Performance in Small Leaderless Groups Speed, Depth, and Breadth: Assessing Learning in Learning Organizations Teaching Strategic Planning, Problem Solving, and Decision Making with Envisionary Experiential Exercises Validating an Instrument for Student Evaluation of Teachers: Some Noteworthy By-Products Don't Teach Ethics to Business Students Emotional Reactions Toward a Simulated Layoff: Before and After the Manipulation Enhancing Communication Using a Presentation Package Implementing Marketing Policies through a Business Management Simulation Integrating Action-Based Learning into Executive Development Programs On the validity of using the Microsegmentation Principle in Media Simulationsm Participatory Systems Analysis Some Relationships between Cultural, Organizational, and Educational Experience and Perceptions of Influence The Use of Decision Support Systems with a Marketing Simulation: The Future is Now Astute Business Policy: A Simulation of the Automobile Industry The Business Policy Game CEO II: A Gaming Simulation for Assessment Computer Paced Project Management Simulation Computerized Tutor Support Systems DEAL & GEO: Progressively Integrating Gaming Simulations for Entrepreneurship and International Business An Interactive Simulation Game for Competitive Decision-Making International Operations Simulation/Mark 2000 (INTOPIA) Multimedia Simulation Cuts Training Costs for Anderson Consulting Concepts of Total Quality Management: An Active Learning Exercise Cooperative Learning: The Extended Jigsaw Managing Diversity--Values and Attitudes: An Experiential Exercise in Awareness Navigating the Shoals of International Management Development Evaluating Student Performance in the Use of Computer Simulation Entrepreneurial Simulation Program