Training Methods Essay

Developments in Business Simulations and Experiential Learning, Volume 32, 2005 DEVELOPING MANAGERIAL EFFECTIVENESS: ASSESSING AND COMPARING THE IMPACT OF DEVELOPMENT PROGRAMMES USING A MANAGEMENT SIMULATION OR A MANAGEMENT GAME John Kenworthy Managing Director, Corporate Edge Asia [email protected]. com Annie Wong Director Corporate Edge Asia [email protected]. com ABSTRACT This research evaluates the effectiveness of using a management simulation, a management game or case studies within a strategic management training programme.

The literature suggests that there is anecdotal evidence that both simulations and games surpass the use of case studies, but there is much criticism of the lack of robust research models used to validate the claims. Using a quasi-experimental design with a reliable managerial competency assessment instrument, the authors assess the impact of different programme groups, the assessed change in workplace behaviour on a 180° basis and participant learning as demonstrated to their own senior managers.

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BACKGROUND AND CONTEXT A large amount of business gaming literature has dealt with how its method fared against the traditional methods for delivering course material (Keys & Wolfe, 1990). For example, the studies by Kaufman (1976), McKenney (1962, 1963), Raia (1966) and Wolfe and Guth (1975) found superior results for game-based groups versus case groups either in course grades, performance on concepts, examinations, or goal-setting exercises.

Although anecdotal evidence suggests that students seem to prefer games over other, more traditional methods of instruction, reviews have reported mixed results. Despite the extensive literature, many of the claims and counterclaims for the teaching power of business games and simulations rest on anecdotal materials or inadequate or poorly implemented research (Gredler, 1996). As reviewed by Keys and Wolfe (1990), these research defects have clouded the business gaming literature and hampered the creation of a cumulative stream of research.

Much of the reason for the inability to make supportable claims about the efficacy of simulations can be traced to poorly designed studies, the lack of generally accepted research taxonomy, and no well defined constructs with which to assess learning outcomes (Feinstein & Cannon, 2001; Gosenpud, 1990). As highlighted by Sales and Cannon-Bowers (2001), there is a somewhat misleading conclusion that simulation (in and of itself) leads to learning; unfortunately, most of the evaluations rely on trainee reaction data and not on performance or learning data.

There are also such a variety of stimuli (e. g. , teacher attitudes, student values, the teacher-student relationship) in the complex environment of a game that it is difficult to determine the exact stimuli to which learners are responding (Keys, 1977). Gosen and Washbush (2004) pointed out that although it seems appropriate to undertake research assessing the value of simulations, the majority of early studies have focused on performance in the simulation (including aptitude scores in the form of SATs, grades, and other INTRODUCTION

The use of computer-based simulations has received attention more recently for both their increasingly sophisticated design and their promotion of participant interest (Mitchell, 2004). However, one of the major problems of simulations is how to “evaluate the training effectiveness [of a simulation]” (Feinstein & Cannon, 2002) citing (Hays & Singer, 1989). Although for more than 40 years, researchers have lauded the benefits of simulation (Wolfe & Crookall, 1998), very few of these claims are supported with substantial research (Butler, Markulis, & Strang, 1988; Miles, Biggs, & Schubert, 1986).

Many of the above cited authors attribute the lack of progress in simulation evaluation to poorly designed studies and the difficulties inherent in creating an acceptable methodology of evaluation. This paper is from an on-going research study comparing the use of different types of simulation and case studies in a quasi-experimental design assessing learning and behaviour change in the workplace following a development programme intervention. 164 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 measures of academic abilities).

However, research on the relationship between learning and performance has strongly suggested that the two variables do not co-vary, performance is not a proxy for learning, and it is inappropriate to assess simulations using performance as a measure of learning (Washbush & Gosen, 2001; Wellington & Faria, 1992). There is thus evidence to suggest that computer-based simulations are effective, but the studies showing these results do not meet the highest of research design and measurement standards, and any conclusion about them must be tentative (Gosen & Washbush, 2004).

This research seeks to evaluate the effectiveness of working managers using a management simulation, a management game or case study within a strategic management training programme. The three interventions are compared in a quasi-experimental method with pre-test and post-test and consider in particular, the development of managerial competencies, i. e. the behavioural change of individual managers in the workplace.

This research considers each individual’s preferred learning style (Kolb, 1984) to consider if particular individuals are likely to benefit more or less from a particular method of performance intervention and will take account of each individual’s age, position in the organisation, gender and level of formal qualification to assess if there is a trend as suggested by Aldrich (2002) that younger managers prefer and benefit more from computer-based, immersive technology-based training methods. the two, to meet some market need or opportunity in the face of competition.

Outcomes are typically rewarded for maximizing profitability and/or creating innovative managerial strategies (Feinstein et al. , 2002). Management simulation: an interactive, advanced, symbolic operation model, and outcomes of decisions are based on analysis and research of real companies (Romme, 2003). Cooperation is key; participants have to determine whether they can solve the problems and achieve the goals that the simulation presents from a range of multiple decision/outcome possibilities and levels of online feedback/coaching.

Emphasis is on running experiments, testing different strategies and building a better understanding of key aspects of the real world, and rich futuristic plans and recommendations usually result (Keys, Wells, & Edge, 1994). INSTRUCTOR INFLUENCE A significant research area in the literature includes game administration factors, such as how the instructor creates the companies within the simulation, places the game within the context of a course, and rewards and interacts with the students playing the game (Keys & Wolfe, 1990).

As highlighted by various writers (Certo, 1976; Keys, 1977; McKenney, 1967), instructor guidance must be applied during crucial stages in the development of the teams and at the debriefing stage of the simulation to insure that some degree of closure and summary insights are obtained from the experience. Garris et al. (2002) provided support and found that the role of the instructor in debriefing learners is a critical component in the use of instructional games, as are other learner support strategies such as online help, cues/prompts, and other activities.

SIMULATIONS AND GAMES Lundy (2003) proposed that the critical difference between computer games and simulations is in what the main objective is: entertainment versus skill building. As emphasized by Callanhan (1999), while simulations often have rules ‘for play’, possess room for alternative strategic tactics, and can be fun, they are not, by definition, games. While games generally focus on one intent (i. e. that of winning), simulations stress the complex, real-life situations and array of goals that organizations attempt to implement on a daily basis; in addition, the simulation environment offers opportunities for action and reflection that is not always inherent in a ‘pure play’ environment (Callanhan, 1999). As emphasized by Feinstein et al (2002), simulations cannot be viewed as a collection of methodologies for experiential learning environments if we expect to be able to effectively assess their value.

Specifically, the following two types of computer-based simulations will be considered in this research: Management game: a structured activity in which teams compete within constraints of rules to achieve an objective (Wolfe, 1990). All business games are competitive games in that they are typically turn-based or round-based, where teams compete against each other for a limited amount of resources, against a game facilitator (can be the computer) who is manipulating external variables, or a combination of MEASURING MANAGERIAL COMPETENCIES

Traditionally, the views surrounding the issue of managerial effectiveness have tended to be largely based on the assumptions about what managers do, and what they should do to be successful according to Robotham and Jubb (1996). These assumptions are challenged (Luthans, Rosenkrantz, & Hennessey, 1985) in that rather than relying on an evaluation of managers’ performance that is based on the activities traditionally prescribed for managerial success, a focus on the activities managers actually perform has emerged.

Models abound in the literature for measuring the behaviours and knowledge of managers and provide a suitable basis to measure managerial effectiveness (competence in doing the job of management). In a recent paper, Kenworthy (2003) proposes the use of the Hay/McBer (McBer, 1997) Managerial Competency Questionnaire (MCQ) as a reliable, valid set of scaled competencies that have sets of behaviours ordered into 165

Developments in Business Simulations and Experiential Learning, Volume 32, 2005 levels of sophistication or complexity (Spencer & Spencer, 1993), as a suitable assessment tool to examine the extent to which the different programmes impact on the managerial competency of the individuals participating in the programmes. The Hay/McBer MCQ competencies found to be the most critical for effective managers include (Table 1): Table 1.

Hay/McBer Competencies • Achievement Orientation • Developing Others • Directiveness • Impact and Influence • Interpersonal Understanding • Organisational Awareness • Team Leadership The Hay/McBer MCQ provides a robust, reliable tool to consider as a basis of measuring managerial behaviours suitable for this research study (Kenworthy, 2003). The use of a well-tested competency instrument to assess behaviour change on a 180° basis provides sufficient objectivity (Wimer, 2002) without being overly burdensome to both the participants, the client organisation and the researchers. ffectiveness’ as a personal quality which is potentially objectively measurable, and therefore a quality, the possession of which could be assessed as a matter of fact. While scientific method would suggest that the purest form of test of the Experiential Learning Model would be one that isolates a single learning cycle, Gibbs (1988) suggests that may not be either possible or even desirable, as all experiences (and therefore the interpretation of those experiences) are influenced by the sum of preceding experiences.

Easterby-Smith (1994) suggests that the classic design of experimental research to assess the effectiveness of a particular training intervention would require two groups, one group to be trained (given the treatment) and a comparable group not to be trained (receive no treatment). Individuals within the experiment would be assigned randomly to each group and both groups measured immediately before and after the training. The difference between the groups could then be attributed to the training received.

In any evaluation of experiential learning, the existing portfolio provides the foundation upon which any test must be based (Morse, 2001). This design is based on the “before and after” experimental design methodology commonly used in both education and the social sciences (May, 1993). The test assumes that the background of each participant remains a constant during the cycle and implicitly accepts the existing portfolio of knowledge, experience, motives, traits and values. Therefore, a pre- and post- test seems most appropriate.

Easterby-Smith continues warning against experimental design (1994) stating that there are “innumerable problems in achieving matching of control groups” and cites several studies (Easterby-Smith & Ashton, 1975) and (Bligh, 1971; Partridge & Scully, 1979) (cited in Easterby-Smith & Thorpe, 1997) where difficulties arise in interpreting the results either because the control group was not truly random (Easterby-Smith & Ashton, 1975), the criterion accepted was open to debate (Bligh, 1971), the experiment may have been methodologically flawed (Partridge & Scully, 1979).

However, Easterby-Smith also points to dangers in more qualitative methods citing a study by Argyris (1980) who found that despite best efforts to assess delivery method of faculty according to their own values, that the behaviour of faculty was contrary to their espoused theories. Anderson and Lawton (1997) suggest that there are two models to choose from regarding the assessment of the effectiveness of a simulation, a pre- and post-test design to measure the learning (using an objective measure) or an after-only test design using a random control group.

They advocate the latter approach but recognise that whilst this may highlight the difference between different pedagogies used, it does not measure the learning at an individual level. Since we are likely to be affecting the outcomes anyway by becoming involved (action research) and ethically it is difficult to justify why one would deliberately give (even if randomly) a treatment that one believes is inferior EVALUATING TRAINING INTERVENTIONS

Reviewing the history and development of training evaluation research shows that there are many variables that ultimately affect how trainees learn and transfer their learning in the workplace. Russ-Eft and Preskill (Russ-Eft & Preskill, 2001). suggest that a comprehensive evaluation of learning, performance , and change would include the representation of a considerable number of variables. Such an approach, whilst laudable in terms of purist academic research, is likely to cause another problem, that of collecting data to demonstrate the affects and effects of so many independent variables and factors.

Thus, we need to recognise that there is a trade off between the cost and duration of a research design and increasing the quality of the information which it generates (Warr, Bird, & Rackham, 1970). Hamblin (Hamblin, 1974) points out that a considerable amount of evaluation research has been done. This research has been carried out with a great variety of focal theories, usually looking for consistent relationships between educational methods and learning outcomes, using a variety of observational methods but with a fairly consistent and stable background theory.

However, the underlying theory has been taken from behaviouralist psychology summed up as the ‘patient’ – here the essential view is that the subject (patient) does (behaviour or response) is a consequence of what has been done to him (treatment or stimulus). Another approach according to Burgoyne (Burgoyne & Cooper, 1975) which researchers appear to take to avoid confronting value issues is to hold that all value questions can ultimately be reduced to questions of fact.

This usually takes the form of regarding the quality of ‘managerial 166 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 (researcher bias) – such methodological approaches are METHOD unethical (Remenyi, Williams, Money, & Swartz, 1998). Based on these insights, this research aims to add to our understanding of the effectiveness of computer-based simulations across different learning styles and assess changes in work-place behaviour.

Given the realities of the training world and the difficulties in assigning individuals to random groups mean that a true experimental design is not feasible (Easterby-Smith, Thorpe, & Lowe, 1991) and precluded (Ross & Morrison, 2003). As such, this research will be a quasi-experimental design. Pre-testing of each individual presents the opportunity to qualify the similarities of the groups and the benchmark of the basis for the posttest to establish change in individuals’ behaviour at the workplace according to self-assessment and a 180? hird party assessment measuring if two types of computer-based simulations will be more or less effective for individuals with a preferred learning style (Kolb, 1984). Learning measure is post-test only (Anderson & Lawton, 1997) and is the assessment of Strategy Presentations made by participants at the end of each programme. Assessors represent the senior management of the client organisation and rate presentations on a 7 point scale with 1 being the lowest to 7, the highest. Making Strategy Presentations to Senior Management is one specific Learning Outcome of the programme (Table 2).

It has been suggested that management simulations have advantages over games (Mitchell, 2004). Such complex computer-based simulations encourage cooperation in experimenting with making decisions and immersing learners in an environment in which they actively participate in acquiring knowledge. In addition, management simulations allow learners to visualize situations and see the results of manipulating variables in a dynamic environment that cannot be duplicated in the typical turn-based competition strategies of management games (Feinstein et al. 2002). The pervasiveness of Kolb’s learning styles theory is well represented in the literature and for this reason, it has been chosen here as the basis to determine the effectiveness of computer-based simulations across different learning styles. However, the Kolb LSI, the subject of much criticism (Freedman & Stumpf, 1980; Lamb & Certo, 1978) yet widely used (Hunsaker, 1981) as a self-perception instrument may not be robust enough to hypothesize that a particular learning style would enjoy and benefit more from using a simulation than ther learning style preferences. Byrne and Wolfe (1974) established that with regard to the design of optimal learning experiences, individuals have different needs for learning, both with regard to the content and to the preferred method of learning. Learning styles can potentially influence the learners’ preference for training delivery mode, and it follows that learning environments that are not consistent with an individual’s style are more likely to be rejected or resisted by the individual (Brenenstuhl & Catalanello, 1977).

The research study investigates two specific programmes: A strategic management training programme using Imparta’s Strategy CoPilot™ simulation, blended with theory and sessions specific to the application of the theory to the clients own organisations. This is compared with a group undertaking similar programme using a strategic management game developed by CELSIM – Strategy Management Edge. The third group undertook the same programme using paper-based case studies. Learning objectives for each of the programmes were the same (Table 2): Table 2. Programme Outcomes Strategy Programme Outcomes • • • • • •

Identify and prioritise critical strategic issues Generate and evaluate creative ideas for new strategic directions Build the assets, relationships and capabilities required to sustain superior returns Plan an achievable implementation strategy How to align organisation strategy and stakeholder needs Present new strategic plans to senior management The choice of groups was made by client companies on the basis of their training and development needs and budget. The background of the individuals represents a cross-section of Singapore and Malaysian society and is broadly similar to participants on short course simulation based programmes.

In addition, the researcher is involved in facilitating both groups eliminating the effects of researcher bias or facilitator interference identified by Argyris (1980). Furthermore, as both groups are facilitated by the author and who gives feedback to each individual regarding their assessments – the concern about control over the feedback nullifies the argument that the process becomes a self-fulfilling hypothesis (Burgoyne & Cooper, 1975). Following the recent literature on evaluation of experiential learning – this research will measure participants at three levels of Kirkpatrick’s odel, Reaction, Learning and Transfer (Kirkpatrick, 1959/60; Kirkpatrick, 1994). The fourth level, business benefits are measured in circumstances where the organisation under study provides confidential access to such data and as such is not considered in this paper. RESEARCH QUESTIONS AND HYPOTHESES The study seeks to test differences in reaction to the programme (enjoyment and usefulness) learning and learning transfer (behaviour change) between different delivery methods, and comparing the results with personal learning styles, gender, current managerial position and prior educational attainment. 67 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 Research Questions: Will participants change their behaviour in demonstrating particular competencies from pre-test to post-test following the treatment? Will there be differences in competency behaviour change, with differences in prior behaviour controlled? Is enjoyment positively related to usefulness, learning and change in behavioural competency? How well do experience, age, gender, and educational qualification predict demonstration of managerial competencies?

Do students with different learning style preferences differ with regard to enjoyment and usefulness of sessions? Hypotheses: H1 The simulation or game treatment group surpass the Case Study group in reaction, learning and transfer H2 The Simulation group will surpass the Game Group in reaction, learning and transfer H3 There will be differences in learning among three groups that learn from simulation, game and the control group H4 Convergent Learners will enjoy the simulation and find it more useful than non-convergent learners. boss, assess the learning demonstrated on a 7 point Likert scale.

Learning transfer is assessed by means of a 180° (self, boss, staff) pre and post test behavioural competencies questionnaire based on the Hay/McBer Managerial Competency Questionnaire instrument (McBer, 1997). the mean 180 degree assessment of the participant competencies before the programme was compared with the mean assessment 8-10 weeks after the programme (Higgs & Rowland, 2001). Learning style are self-assessed using the Kolb LSI version III (Kolb, 1999). RESULTS AND ANALYSIS Data were collected across 6 separate programmes held from late 2003 to mid 2004.

A total of 100 participants completed all assessments, 27 from two Simulation groups, 49 from three Game groups, and 24 from one Case Study group (Control). Table 3 below indicates the statistical test that have been undertaken with the data based on commonly used techniques in research in educational technologies (Ross & Morrison, 2003) and the summary results. T-Test of change in each competency factor, reaction test and learning comparing the simulation, game and control groups (Table 4) show no apparent significant difference between the Simulation and Game in change of competency level.

T-Tests between the pre and post mean competency scores show a significant difference at the 5 % level for every factor across all groups. The results of ANOVA of Simulation Type and for LSI preference for each competency factor change, reaction to enjoyment and usefulness and learning increase (Table 5) suggest that simulation type is a significant differentiator for enjoyment and usefulness, though LSI preference has some significance in enjoyment.

Simulation type is significant at the 5% level for change in Achievement Orientation, Directiveness and Team Leadership – and Impact and Influence at 10% level. MEASUREMENT METHODS To test the questions and hypotheses, programme participants undertook the following assessments: Participants’ reaction to the training event is measured immediately following the event asking for their rating on a five-point Likert scale their enjoyment and usefulness of each separate session within the training event.

Learning is assessed by participants’ bosses at the final presentation. Participants are required, as part of their final presentation to demonstrate what they have learned through the programme by applying their learned understanding of strategic analysis to a real business problem previously identified by the client organisation and allocated to participants. Three organisation bosses, including direct line 168 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 Table 3.

Tests and Summary Results Analysis t test Independent samples t test Dependent samples Analysis of variance (ANOVA) Analysis of covariance (ANCOVA) or (MANCOVA) Pearson r Research Question/Hypothesis H1 The simulation or game treatment group surpass the Case Study group H2 The Simulation group will surpass the Game Group Will participants change their behaviour in demonstrating particular competencies from pre-test to post-test following the treatment?

H3 There will be differences in learning among three groups that learn from simulation, game and the control group Will there be differences in competency behaviour change, with differences in prior behaviour controlled? Is enjoyment positively related to usefulness, learning and change in behavioural competency? How well do experience, age, gender, and educational qualification predict demonstration of managerial competencies? Do students with different learning style preferences differ with regard to enjoyment and usefulness of sessions? H4 Convergent Learners will enjoy the simulation and find it more useful than non-convergent learners.

Results at 5% except where noted at 10% Yes, significant across each reaction variable. Significant in behavioural competencies in 6 factors No, significant only in usefulness of Feedback. Yes, significant in all seven assessed competency factors on a 180 basis. Yes, significant difference with Case Study group lower than wither Simulation or Game group. No significant difference between Game and Simulation group. Yes, the differences are significant with control of prior (pre-test) behavioural competencies. Positive correlations between enjoyment and usefulness in some factors, but not to learning or behaviour change.

Gender is significant in predicting change in Achievement Orientation. Position is significant in predicting changes in Developing Others, Directiveness and Team Leadership. Yes, Enjoyment of Simulation and Lecture are significant at 10%. Usefulness of sessions does not appear to be significant. Yes, significant. Convergent Learners show higher enjoyment and find the game and case study more useful. Non-convergent learners show higher usefulness for the simulation. Multiple linear regression Discriminant analysis 169 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 Table 4.

Summary Table T-Tests Summary Table Differences Standard Deviation Simulation Game Case (n=27) (n=49) (n=24) 0. 688 0. 571 0. 780 0. 555 0. 721 0. 917 0. 764 0. 634 0. 680 0. 550 0. 660 0. 464 0. 542 0. 612 0. 676 0. 557 0. 662 0. 932 0. 832 0. 574 0. 816 0. 730 0. 726 0. 779 0. 701 0. 832 1. 042 3. 594 3. 868 2. 383 3. 488 4. 646 2. 712 3. 498 4. 372 2. 858 3. 413 4. 029 2. 617 2. 735 3. 114 2. 677 3. 225 3. 189 2. 944 5. 249 3. 372 3. 189 Significance of Differences Sim-Game Sim-Case Game-Case 0. 164 0. 000 0. 000 0. 767 0. 000 0. 000 0. 017 0. 656 0. 103 0. 417 0. 000 0. 000 0. 000 0. 000 0. 221 0. 362 0. 02 0. 000 0. 012 0. 013 0. 246 0. 000 0. 000 0. 151 0. 588 0. 000 0. 000 0. 787 0. 028 0. 016 0. 661 0. 237 0. 156 0. 239 0. 114 0. 012 0. 016 0. 445 0. 072 0. 126 0. 644 0. 061 0. 625 0. 215 0. 069 0. 002 0. 474 0. 109 Enjoy Simulation/Case Enjoy Teamwork Enjoy Feedback Enjoy Lecture Useful Simulation/Case Useful Teamwork Useful Feedback Useful Lecture Learning Test Learning Increased Dif Achievement Orientation Dif Developing Others Dif Directiveness Dif Imapct and Influence Dif Interpersonal Understanding Dif Organisational Awareness Dif Team Leadership MCQ Mean Differences pretest to Post-Test Reaction Means

Significant differences at the 5% level are highlighted in bold, those significant at the 10% level are italic. Table 5. Summary Table ANOVA Summary Table Enjoy Simulation/Case Enjoy Teamwork Enjoy Feedback Enjoy Lecture Useful Simulation/Case Useful Teamwork Useful Feedback Useful Lecture Learning Test Learning Increased Dif Achievement Orientation Dif Developing Others Dif Directiveness Dif Impact and Influence Dif Interpersonal Understanding Dif Organisational Awareness Dif Team Leadership MCQ Mean Differences pretest to Post-Test Reaction Means ANOVA (5%) Sim Type F-Ratio Probability 29. 6 0. 0000 11. 85 0. 0000 0. 0489 3. 11 0. 52 0. 5937 34. 53 0. 0000 10. 94 0. 0001 0. 0070 5. 22 2. 62 0. 0777 12. 07 0. 0000 0. 0436 3. 24 1. 11 0. 3345 3. 67 0. 0291 3. 09 0. 0501 2. 21 0. 1152 1. 67 0. 1929 3. 71 0. 0281 Power 1. 000 0. 994 0. 587 0. 134 1. 000 0. 989 0. 820 0. 511 0. 994 0. 604 0. 240 0. 663 0. 583 0. 441 0. 345 0. 668 ANOVA (5%) LSI Pref F-Ratio Probability Power 2. 26 0. 0865 0. 555 0. 55 0. 6464 0. 160 0. 12 0. 9467 0. 072 2. 41 0. 0719 0. 585 1. 68 0. 1757 0. 428 0. 07 0. 9778 0. 061 1. 40 0. 2484 0. 361 0. 97 0. 4092 0. 258 0. 82 0. 4835 0. 223 0. 162 3. 60 0. 778 2. 58 0. 0583 0. 618 0. 02 0. 9973 0. 053 0. 15 0. 9263 0. 078 3. 36 0. 0219 0. 746 1. 43 0. 2377 0. 369 1. 40 0. 2474 0. 361 The chart below shows the mean differences in each competency factor between pre-and post-test for each intervention. The Game group showing greater positive change in each factor except Interpersonal Understanding. Both the simulation and game group show greater positive change than the case group. LSI preference is significant (5%) in change of Achievement Orientation, Interpersonal Understanding and Developing Others (10%).

Multiple Linear Regression of independent variables of age, gender, position and academic achievement against the differences in competencies scored. Table 6 below shows the significant factors and the predictive power of the associated competencies. Female participants showed a significantly higher increase in Achievement Orientation than Males. Senior Managers showed significantly higher competency increase in: Developing Others, Directiveness and Team Leadership than Managers. 170

Developments in Business Simulations and Experiential Learning, Volume 32, 2005 Table 6. Multiple Linear Regression competency difference Multiple Linear Regression Competencies Achievement Orientation Developing Others Directiveness Team Leadership Variable Gender = Female Position = Senior Manager Position = Senior Manager Position = Senior Manager Std Error 0. 7448 0. 8681 0. 8502 0. 8790 Probability 0. 0026 0. 0044 0. 0087 0. 0049 Power at 5% 0. 8649 0. 8244 0. 7561 0. 8132 Table 7. ANCOVA Analysis

ANCOVA & MANCOVA 5% Achievement Orientation Developing Others Directiveness Team Leadership Variable Simulation Type X Gender Simulation Type X Position Simulation Type X Position Simulation Type X Position F-Ratio 3. 81 5. 38 0. 03 3. 40 0. 71 1. 69 1. 05 0. 98 Probability 0. 025761 0. 022570 0. 972417 0. 068374 0. 495046 0. 196139 0. 352763 0. 323657 Power 0. 6795 0. 6313 0. 0541 0. 4463 0. 1666 0. 2516 0. 2296 0. 1657 ANCOVA and MANCOVA Analysis (Table 7) show that Achievement Orientation is significantly different between Simulation type and Gender.

Change in Developing Others is significant by position but not seemingly affected by Simulation Type. Directiveness and Team Leadership show that Simulation Type may not be the significant factor – when Position is covariate. The charts below suggest why the simulation type, whilst significant using One Way ANOVA for Developing Others, Directiveness and Team Leadership are influenced by the covariate of position – the latter being a significant predictor using multiple regression for the change in these factors.

We cannot therefore, accept that Simulation Type is alone a significant factor in the change in demonstration of these competencies. The data do suggest that Simulation Type is the most significant factor in change in competency change for Impact and Influence (figure 6) and a significant factor, along with gender (particularly Female) for change in Achievement Orientation (Figure 7). The data on Learning Style Preference do suggest difference in enjoyment and usefulness.

Selecting only to compare Convergent Learners against other preferences across all factors of enjoyment and usefulness show significant differences in Enjoyment and Usefulness of the Simulation/Case, which after Kolb, we would expect to be the situation (Table 8). The charts (figure 8)(figure 9) above show that there is little difference in enjoyment and usefulness between the Simulation and Game, and lower ratings for Case Study by both Convergent and other Learning Style preferences. Significant ifferences have been found for age and position by simulation type, suggesting that younger managers do have a higher rating fro enjoyment and usefulness of the simulation or game. Interestingly, older senior managers (over 40) significantly preferred the simulation to the game. However, the sample size of under 30’s and over 40’s is too small at this stage of the research to be definitive. Table 8. Convergent and Other LSI Preferences Reaction Enjoy Simulation Useful Simulation Convergent or Other LSI Convergent Convergent Std Error Probability Power at 5% . 1834 0. 1827 0. 0112 0. 0447 0. 7261 0. 5214 171 Developments in Business Simulations and Experiential Learning, Volume 32, 2005 noted drawbacks of previous research with a rigorous CONCLUSIONS This study indicates that there are differences between management development programmes using a management simulation, a management game and case studies. All programmes impacted behaviour change and learning and there are strong indications that the choice of simulation, game or case study does make a difference to the extent of the impact.

There is little substantive difference between the management simulation and the management game though both show greater positive behaviour change and greater learning than the case study group. The results show that using a simulation or game in a programme significantly increases participant enjoyment and perceived usefulness – suggesting that engagement in the learning activity is higher and that practice in using skills in a realistic (simulated) setting is fundamental in transferring the learning to the workplace.

The comparison across learning styles provides weak evidence that enjoyment of method of delivery is correlated with learning style preference, which may be due to the instrument, or that a blended programme caters to all learning styles. As this study continues, the Kolb LSI will be supplemented with the more rigorous Myers Briggs assessment to test the correlation between personality types and establish if this should be considered when designing programmes to improve effectiveness.

The results highlight particularly interesting aspects to consider with regard to the differences in behaviour change between males and females and between managers and senior managers. Female participants showed a significantly greater behaviour change than males in Achievement Orientation following the simulation and game programmes – this may be due to the training environment providing an opportunity for the female managers to demonstrate this competency (in a traditionally patriarchal society) and develop the self-confidence to continue to do so in the workplace.

It can also be seen that Senior Managers showed a greater increase in competency behaviours than those in lower positions in the organisation, counter to the result one would speculate – this may be due to simulation or game use in training provided their first opportunity to practice the competencies of Developing Others, Team Leadership and Directiveness and the self-confidence to transfer the new behaviours to the workplace.

It would be interesting to investigate these apparent anomalies qualitatively to establish any underlying potential benefits of using simulations and games that may help educators target the use of particular methods to certain groups. The research is on-going and it is expected that future groups will allow the researchers to analyse a sufficient spread of data, particularly with younger managers to establish if there is a trend, as suggested above, that younger managers prefer and benefit more from computer-based, immersive technology-based training methods.

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