Modes of Learning in the Use of a Computer-Based Business Simulation Game’ International Journal...

Int. J. Learning Technology, Vol. 2, No. 1, 2006 49

Copyright © 2006 Inderscience Enterprises Ltd.

Modes of learning in the use of a computer-based business simulation game

Jonathan Moizer*, Jonathan Lean, Michael Towler and Gordon Smith Plymouth Business School, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK Fax: +44 0 1752 232249 E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: This paper discusses the role of computer based simulations in business education. It examines the learning approaches adopted by students using a simulation game. Results inform the development of causal-loop diagrams capturing representations of zero, single and double-loop learning within the study context. Actions are proposed to maximise the effectiveness of this form of learning technology.

Keywords: computer based simulation; single loop learning; double loop learning; experiential learning; business games.

Reference to this paper should be made as follows: Moizer, J., Lean, J., Towler, M. and Smith, G. (2006) ‘Modes of learning in the use of a computer-based business simulation game’, Int. J. Learning Technology, Vol. 2, No. 1, pp.49–61.

Biographical notes: All authors are based at the University of Plymouth Business School and are actively involved in teaching with business simulations. Dr. Jonathan Moizer is a Senior Lecturer in Business Operations and Strategy. His research interests include the use of computerised simulation for both decision making and learning.

Dr. Jonathan Lean is a Principal Lecturer in Strategic Management and Postgraduate Research Coordinator within the Business School. He has published widely in the field of small business training and development and is actively engaged in research relating to the teaching of business strategy.

Dr. Michael Towler is an Enterprise Management Fellow. His interests range from R&D management to the use of fiction in management education. Along with Drs. Moizer and Lean, Michael was presented with a University Teaching Fellowship Award for work in the field of simulation gaming in 2004.

Gordon Smith is a Senior Lecturer in Business Operations and Strategy. He is involved in the pedagogic aspects of research, particularly in the field of operations strategy.

50 J. Moizer, J. Lean, M. Towler and G. Smith

1 The learning benefits of business simulations

The purpose of this paper is to share an understanding of how to enhance the benefits of computer based learning using simulation games through better appreciating the modes of learning adopted by undergraduate students. A number of studies exist which articulate the potential benefits of using computer based simulations in business education. In his review of studies examining the effectiveness of this type of learning technology, Wolfe (1997) concludes that computer based games produced significant knowledge increases. Furthermore, when compared with case studies, business simulations typically give rise to higher levels of learning. These findings are supported by Faria’s (2001) review of business gaming papers published since the mid-1970’s. He also finds that the majority of studies indicate superior learning over that arising from more traditional teaching modes.

Lane (1995) outlines the key benefits associated with the use of simulation games. He contends that games can provide a learning from experience approach to managerial education, allowing a synthesis of theory and application to practice. The author identifies three critical dimensions of the learning that can be attained from games. First, he identifies that content is self discovered. Within simulations, participants are encouraged to find solutions to problems themselves, thus learning takes the form of a ‘proactive quest for knowledge’. Second, Lane argues that in terms of attaining educational objectives, the simulation experience is richer than more traditional methods of teaching delivery. Assuming appropriate delivery, they enable students to pass through all levels of Bloom’s (1956) ‘hierarchy of thinking skills’ from learning facts, through to synthesis of information. Further, he argues that the emotional and psychological elements involved in competitive simulation games add an additional dimension to this rich experience and help to embed knowledge more firmly. Finally, the author contends that feedback from a simulated environment can be more valuable than even real-world experience, in that, the effects are often more visible and immediate in a simulation game. In the real business context there is greater separation of cause and effect through space and time, meaning that the consequences of actions cannot always be readily observed. Therefore, learning is arguably constrained in the real-world.

It is the high level of realism attached to simulation games that enables experiential learning to be achieved. The making of mistakes throughout a simulation develops the decision making skills of the gamer or participant. Students learn through seeing the effects of their decisions traced out by the simulation in to measurable data outputs (Anonymous, 2000). This in itself can often have a positive motivating effect upon students (Lean et al., 1999). Throughout the course of the simulation, they can reflect on their gaming experiences, reconceptualise and make new more informed decisions. A higher level of understanding is achieved through numerous iterations of decisions being played out and the consequences of those decisions being observed (Moizer et al., 2004). Analysis and discussion of cases and texts often fails to capture these dynamic aspects of strategic decision making. This is because they do not allow for the implementation of decisions and the consequent feedback of hard data outputs.

Despite the espoused benefits of simulations, a number of studies (Dowsey, 1977; McCain, 1989) indicate that the empirical evidence concerning the contribution of this form of learning technology is still limited and controversial. Further, the results of many empirical studies relating to gaming have been generalised across different types of students, instructional methods and games (Herz and Merz, 1998). A number of factors

Modes of learning in the use of a computer-based business simulation game 51

govern the effectiveness of simulations in achieving learning objectives. These include the complexity of a game (Cannon, 1995) and its acceptance as a valid representation of the real-world. Achieving an appropriate balance between realism and ‘playability’ is pivotal to attaining successful learning outcomes. Feinstein and Cannon (2001) note that the effectiveness of a simulation depends in part on its purpose. They are critical of the fact that most research has focused upon simulation development rather than evaluation. An important aspect of this paper is its attempt to redress this imbalance by evaluating how a cohort of undergraduate business students learns through playing a simulation game.

This paper investigates the way in which students learn business strategy through participating in a simulation game. Through the collection of qualitative data from debriefing sessions, the study aims to articulate the modes of learning adopted by students. In examining these modes of learning, it is hoped to gain a clearer view of the efficacy of simulation games in teaching and make recommendations aimed at improving the impact of this type of learning technology.

It should be noted that although Lane (1995) distinguishes between simulations and games, other authors (e.g., Keys and Wolfe, 1996; Klein and Fleck, 1990; Maier and Größler, 2000) use the terms interchangeably. Often differences in terminology are influenced primarily by the scientific tradition of the author (Maier and Größler, 2000). In this paper, the terms ‘game’ and ‘simulation’ are used interchangeably to reflect the diversity of language used by authors writing in the field.

2 Methodology

The findings of this study emerged from a debriefing exercise undertaken after using The Business Strategy Game (BSG) simulation (Thompson and Stappenbeck, 1998) as a component of a final year undergraduate business strategy module.

The BSG is based upon a global firm engaged in the manufacture and sale of athletic footwear. The student teams (or companies) compete against each other for a predetermined level of market demand. An administrator (academic) oversees the game and sets the externalities which will guide, constrain and inform team decision making. The simulation requires the input of yearly business decisions into a PC. The decisions are processed on an administrator’s spreadsheet, and the simulation game then rolls on to another year’s play. A score based on a number of performance metrics (profit, market share, capitalisation, sales volume, etc) is determined. This results in the groups moving up or down a business league table. The BSG was run for eight decision periods, allowing clear differentiation to emerge in the performance of firms. It also allowed the students to witness, respond to and learn from a longer term industry dynamic. For example, significant time delays between the deployment of strategies and the emerging consequences became more evident to students.

Debriefing sessions served as the source of primary data for this study. These sessions took the form of meetings with each of 12 playing teams. The primary research aim of these meetings was to stimulate a student led discussion relating to how the teams’ learnt through experiencing the simulation. The study was conceived as an inductive process, whereby the qualitative data collected through debriefing sessions might inform the development of a model to describe the process of student learning through simulation gaming. The data collection approach was therefore qualitative in nature and was based


around a semistructured interviewing approach (see Thorpe et al., 2002). Two academics undertook the series of debriefing interviews with the teams of four students each. Although the questions were kept essentially open, a short interview plan was employed so as to ensure that all the relevant topics were covered in each interview. Each discussion lasted for approximately 30 minutes and was later transcribed for qualitative analysis. The approach adopted for data analysis broadly followed the guidelines of Marshall and Rossman (1989) and Miles and Huberman (1994). The latter authors identify three key processes within qualitative data analysis: data reduction, data display and drawing, and verifying conclusions. In this study, data reduction involved the coding and categorising of interview transcripts and also the production of interview summaries. Data display was achieved through the construction of a ‘partially ordered meta-matrix’ (see Miles and Huberman, 1994). This tabularisation of reduced data facilitated the identification of common issues concerned with the approaches to learning adopted across the student teams. Having identified key themes and patterns from the analysis, the validity of the conclusions was assessed through an iterative process. This involved reviewing the original interview data and searching for confirmatory evidence (or alternatively, evidence that might support different conclusions).

3 Study results

In analysing the content of the debriefing interviews, the first stage was to attempt to identify common patterns of behaviour across the student teams. It was evident from an early point that there was considerable behavioural variation across the student cohort. Modes of behaviour ranged from almost total disengagement through to deep strategic thinking. Students demonstrating the latter often showed an ability to use feedback from the game to formulate new approaches to strategising and decision making. This evidence suggested parity with the work of Argyris and Schön (1974, 1996), in that, behaviours appeared to correlate with concepts of single and double loop learning. Hence, the relevance of the single and double loop learning theory in the context of simulation gaming became apparent, in that, student responses to performance outcomes varied considerably across the cohort.

3.1 Single loop learning attributes

Single loop learning can be regarded as learning that occurs through operating within existing goals, values, decision rules and plans, without questioning these governing variables. In the context of this study, associated characteristics include optimising game decisions within the bounds of existing business assumptions, or simply reacting to events so as to maintain target seeking behaviour. Additional behaviours that might also be associated with single loop learning were identified through analysis of the interview transcripts. It was noted that a number of teams maintained a fixed view of their business and its operation within the competitive environment. Any feedback that was received through the results of the game were utilised to achieve set goals within the context of this rigid business model. Further, some teams demonstrated a trait of adjusting their business decisions in response to the actions of the leading team in the industry. In that this shows an acceptance of current industry norms and a reluctance to challenge existing


assumptions about how to succeed within the industry, this can be regarded as evidence of single loop learning.

3.2 Double loop learning attributes

In contrast to single loop learners, double loop learners question the governing variables within a given situation and subsequently reframe their strategies. Thus, whilst single loop learning aims at making existing strategies more effective, double loop learning involves “questioning the role of the framing and learning systems that underlie actual goals and strategies” (Usher and Bryant, 1989, p.87). In the context of this study, the interview evidence indicated behaviours whereby some student groups recognised the limitations of their existing business model and used feedback from the game to adapt and change their goals and strategies. It was also observed that in contrast to many teams, some focused upon creative strategising above optimisation within an existing strategic framework. Additionally, a number of teams demonstrated an holistic view of interdependencies within the gaming environment which allowed them to consider alternative strategies and approaches. Finally, whilst teams did not always demonstrate these double loop learning characteristics during the playing of the game, some did demonstrate deeper reflective consideration of these issues within the debriefing sessions.

3.3 Zero loop learning attributes

In addition to the single and double loop learning theories identified by Argyris and Schön (1974), Snell and Man-Kuen Chak (1998) use the term zero loop to describe individuals who fail to receive feedback and consequently, absorb no new information. In this study, a small number of teams exhibited characteristics associated with zero loop learning. Indicators included a high level of disengagement from the game and extremely limited analysis of feedback, resulting in an absence of any clear strategy and the use of guesswork within the decision making process. It was also noted that some teams displaying these modes of behaviour tended to externalise the problems that they faced or criticised the BSG’s validity as a learning tool. This is a further demonstration of their disengagement from the learning process.

3.4 Categorisation of student learning characteristics

Table 1 summarises 16 characteristics, classified into the three levels of loop learning. The table output is derived from an analysis of the team debriefing interviews. Themes and issues relating to student experiences and behaviours were identified, coded and categorised following the guidelines of Marshall and Rossman (1989). Following this process, the coded transcripts were examined for evidence of the gaming characteristics previously identified as indicators of the different modes of learning. In the case of each team, the presence of these characteristics is indicated by the shaded areas. Overall, the results table indicates that single loop learning was the dominant mode of learning. All teams, to a lesser or greater extent exhibited some characteristics associated with single loop learning. However, in only two cases was single loop learning the only approach surfacing from the interviews. Seven teams also showed characteristics associated with double loop learning. In some cases, double loop learning appeared to be more dominant. In others, single loop learning was more apparent. This is to be


expected, of course, because there is a need to develop strategies and decision rules for organisations, and use these to optimise decisions within the newly configured framework. In the context of this game, one would not expect to see a reframing of business models through every decision cycle but rather, periods during which new strategies are developed, implemented and the results evaluated. It should be noted that in the case of team 10, whilst there was no evidence of double loop learning during the playing of the game, the debriefing interviews highlighted some significant reflective thinking relating to the limitations of the team’s gaming strategy and possible changes that could have been deployed. As a result, for this team there was evidence of zero, single and double loop learning. The three remaining teams (Teams 1, 5 and 11) were characterised by a mixture of both zero and single loop learning. In each of these cases, there appeared to have been some attempt to make decisions aimed at improving the performance of the team, but these attempts were not sustained. Clearly these teams were unable to maintain their engagement with the BSG. This may have been as a result of poor performance attainment within the simulation game or issues concerned with group dynamics. A significant challenge for educators is to develop strategies to better integrate such teams into the learning experience.

Table 1 Indicators of zero, single and double-loop learning in student teams

Zero-loop learning

indicators Single-loop learning

indicators Double-loop learning

indicators

Team

s

Dis

enga

gem

ent

Exte

rnal

isat

ion

Gue

ssw

ork

Abse

nce

of st

rate

gy

Abse

nce

of fe

edba

ck a

naly

sis

Not

acc

eptin

g ga

me

valid

ity

Opt

imis

atio

n

Rigi

d bu

sine

ss m

odel

Reac

tive

shor

t ter

m ta

ctic

s

Follo

w in

dust

ry le

ader

Use

feed

back

to a

chie

ve se

t goa

l

Reco

gnis

e gr

oup

men

tal m

odel

lim

itatio

ns

Use

feed

back

to c

hang

e m

odel

Hol

istic

vie

w o

f in

terd

epen

denc

ies

Stra

tegi

sing

not

opt

imis

ing

Refle

ctiv

e st

rate

gic

thin

king

1 2 3 4 5 6 7 8 9 10 11 12


Overall, the findings from the interviews demonstrated that learning concepts are not static. At different times in the playing cycle, most teams traversed from one mode of learning to another. Some teams progressed from single to double loop learning, whilst others regressed towards zero loop learning. This suggests that our understanding of student learning might be aided through the development of a dynamic representation of game playing. In order to achieve this, a series of causal loop diagrams (CLD’s) were constructed (see Figures 1(a)–1(c) and 2).

Figure 1 Causal representations of zero, single and double loop learning in student teams


Figure 2 Design and delivery interventions to encourage double loop learning

4 Discussion

A simple decision cycle is witnessed in Figure 1(b). Management decisions are instituted and after a delay, the results of the decisions emerge. The performance of the business is compared against the target or desired performance. A fresh set of decisions is made with the intention of closing the performance gap, i.e., optimising performance within the bounds of an existing strategy and decision rules. This behaviour, which was evident across many teams, resonates with Argyris and Schön’s concept of single loop learning.

A smaller number of teams moved beyond a single loop approach to playing the game, exhibiting evidence of a change in their business model (Figure 1(c)). Having recognised the gap between their target and actual business performance from the feedback generated within the game reports, they realised that their existing strategies were not sufficient to allow them to attain a higher level of performance. In effect, they reformulated their mental models, as evidenced by the designed change they made to their strategies intergaming. These teams showed some evidence of double loop learning in that they appeared to challenge the strategic approaches and methods that they set out with, and adapted their decisions accordingly. The causal loops represented in Figures 1(b) and (c), support the work of Sterman (1994, pp.294–296) and Lane (1995, p.611). In his discussion of how people learn in and about complex dynamic systems, Sterman outlines how information feedback about the real-world can result in changes in decisions within existing frameworks and modes of thinking (single loop learning), or can result in transformation of peoples mental models. This leads to changes in strategies and decision rules which govern how subsequent decisions are made (double loop learning). Lane argues that microworlds (i.e., simulated environments) facilitate such double loop learning. However, the current study highlights the existence of zero loop learning in the context of gaming. Zero loop learning is characterised by an effective absence of performance feedback to the decision maker. Therefore, no links are evident between the decision-action-consequence portions of the cycle. Thus, fresh decisions are not informed by any kind of analysis and evaluation of performance (Figure 1(a)).


4.1 Educator interventions to facilitate double-loop learning

Simulations can certainly facilitate an appreciation of the dynamic and capricious nature of business strategy; however, the evidence from this study shows that games are not self teaching. There is a need to consider how the game is employed as part of a wider package of learning. Otherwise, there is a danger that learning remains pedestrian, with students trapped in a single or even zero loop mode of learning. Consequently, the critical question is how can management educators ensure that double loop learning becomes the dominant modus operandi? It is challenging to address this question. Argyris (2002) warns that whilst many people espouse double loop learning, they find it difficult to attain, and instead, fall into an ‘antilearning’ pattern.

Figure 2 contains the addition of module design and delivery as key drivers for the learning and feedback processes. Educators have the means to employ a managed intervention in this synthetic learning environment. This can be an important tool in encouraging a greater prevalence of double loop learning. This figure represents the point at which the gap between target and actual business performance is perceived as a critical juncture. At this point, students may continue along a path of single loop learning, or they may reconfigure their shared mental model of how their simulated business operates, thus changing the strategies and decision rules that govern decision making.

The key role of the educator in designing and delivering a module utilising a business simulation should be to help create the conditions that allow the students to challenge the assumptions behind their existing business models. What then do we need to do to draw the students into this double loop mode of thinking? The study found that zero loop learners typically exhibited symptoms of disengagement from the game. In developing courses which make use of games, educators must ensure that measures are taken to both inspire team motivation and draw out learning through effective integration between game and lectures. A range of considerations exist for module design and delivery.

4.2 Technology selection and complexity

At the expense of stating a maxim, choosing an appropriate game is very important. If too simple, learners may not progress from single to double loop learning, whilst if too complex, learners may regress from single to zero loop learning. As concluded by Amory et al. (1999), understanding the relationship between educational needs and game elements is critical. The simulation game has to not only initiate, but maintain interest over an extended gaming period. For example, with some computer based simulations it is possible to incorporate extra game components so as to emphasise and develop understanding of particular areas of business, e.g., marketing, finance or quality management. The simulation will also need to achieve the desired learning outcomes for the module. The complexity and verisimilitude of the game plays its part. Lane (1995) argues that too many elements in the learning mix can detract from the achievement of learning objectives. Low (1980) suggests that the subject matter of a simulation game and the level of difficulty it presents to the students must be appropriate for the stage of study. Further, Burns and Gentry (1998) conclude that if students perceive mastery of an experiential learning exercise to be unmanageable, they can be demotivated and deterred from learning.


4.3 Assessment issues

A further issue for consideration is how the game lends itself to integration with other taught elements of the module. Assessment, whether formative or summative, will have an impact upon how students perceive the extent to which the game is integrated with the module. In seeking to facilitate double loop learning, different perspectives exist on the potential role of assessment. On the one hand, a lack of formal assessment might diffuse anxieties about the implications of decisions made, enabling students to fully explore possibilities in a riskfree situation. Such a context might facilitate double loop learning. However, evidence from other studies indicates that participants might be more highly motivated to engage with a simulation game if there is an element of summative assessment (see Base et al., 1986; Herz and Merz, 1998). Arguably, by not assessing students one is simply offering the freedom to disengage from the simulation process. Given that engagement is a prerequisite to learning, then assessment is an important measure for exposing students to double loop learning.

4.4 Developing strategic thinkers, not just strategic planners

An additional aspect of integration relates to the way that educators use lecture and tutorial inputs. More particularly, how might these inputs encourage double loop learning? Heracleous (1998) proposes that, whilst strategic planning is associated with single loop learning, it is strategic thinking that is congruent with double loop learning. He argues that both strategic thinking and strategic planning are necessary, as the creative strategies emerging from strategic thinking must be operationalised through a process of convergent and analytical thoughts, i.e., strategic planning. Thus, the focus of teaching inputs must consider how to stimulate strategic vision, abstraction and creativity, alongside the introduction of more traditional analytical strategic frameworks aimed at the operationalisation of strategies. The challenge to educators is to develop students as strategic thinkers as well as simply strategic planners. Using the game as a standalone learning platform in the context of a teaching model that emphasises prescriptive planning is unlikely to support the development of double loop learning.

An approach that develops strategic thinking capabilities can be developed in a number of ways through teaching interventions. At the undergraduate level, a simple introduction to concepts such as scenario analysis (van der Heijden, 1996; de Geus, 2002), mental models and system archetypes (Senge, 1993) and system metaphors (Morgan, 1997) would be helpful in allowing students to frame strategic problems. This would equip them to better appreciate the holistic nature of business strategy. If this can be achieved, then students can understand that developing strategies in firms where dynamic and structural complexities exist cannot be obtained merely by breaking down the business problems and issues into their constituent parts and applying static analytical frameworks.

An holistic understanding of strategy may be further reinforced through revealing the underlying causal feedback structure of a simulation game. It is the structure of a simulation that drives its dynamics. The majority of ready to use business simulation games do not reveal a great deal of information about their structure to the user, i.e., the models are opaque (see Langley and Larsen, 1995). In briefing students, educators have the choice to reveal, or not to reveal aspects of the underlying structure of the simulation, i.e., whether to make the models more transparent. The fact that some student teams in


this study did not appreciate the effect of feedback suggests that their business models failed to develop as the game progressed, and were not sufficiently sophisticated. These teams were still caught in the zero loop decision cycle. Had more of the underlying causal structure of the BSG been revealed to the student teams, and assuming that students were able to understand the link between structure and dynamic performance, then they could have explored a number of possible strategic scenarios before actually selecting a course a action. This intervention might be best delivered in one to one surgeries, rather than to the whole student cohort, otherwise there is a risk of detracting from the learning experience of stronger students who may be able to discover underlying structures for themselves.

4.5 Learning on reflection

Clearly, it is not possible to achieve total concurrency in the linkages between taught inputs and experiential learning through the actual gaming cycle. However, Knotts and Keys (1997) argue that most frequently, learning occurs when participants are forced to reflect on their experiences. Duijn et al. (2003) also recommend that double loop learning can be encouraged by ‘stepping out of the simulation’ to allow reflection to occur. Therefore, there is a significant role for some form of debriefing exercise in drawing together and reinforcing the learning points that emerge from the gaming activity. Indeed, given the challenges involved in facilitating double loop learning during the course of a game, it is likely that a number of students will still continue along a path of single-loop learning. Therefore, a reflective debriefing session might be regarded as an essential safety net to ensure that all the students appreciate the major lessons emerging from the use of a business simulation.

5 Conclusion

The concepts of zero, single and double loop learning provide a helpful framework for understanding the extent of and process by which learning is achieved within a computer based gaming context. Despite the potential advantages of a simulation as an experiential learning environment, there was only partial evidence in this study of double loop learning amongst students. Only a subset of students appeared to experience double loop learning, and whilst the majority might be categorised as single loop learners, a further group of students were disengaged with the game and appeared to learn very little. This demonstrates that simulations are not self teaching. Yet, through careful consideration of module design and delivery issues, the authors propose that double loop learning can be nurtured. However, only through the effective alignment of traditional teaching approaches and computer based simulations can the full benefits of such learning technologies be realised.


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