Teaching strategic thinking using system dynamics: lessons from a strategic development course

System Dynamics ReviewSystem Dynamics Review vol 28, No 1 (January-March 2012): 28–45Published online 25 January 2012 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/sdr.471

Teaching strategic thinking using system dynamics:lessons from a strategic development course

Martin Kunc*

Abstract

The use of System Dynamics within strategy courses aims to teach students dynamic theories to explain firmperformance. This paper presents an analysis of the development of students’ strategic systems thinking skillsafter taking a course on strategic development processes. Students that achieved the best performance employedgraphs representing the performance of key variables, described how the strategy worked using verbal explana-tions of feedback processes, and validated the results obtained to detect poor or unreasonable strategies. However,a large group of students did not follow System Dynamics practices and performed poorly in their strategies. Weextract two lessons: more emphasis on the areas shown by the best students, which are along the modeling forlearning practices, are needed to improve students’ strategic systems thinking skills, and it is important to adoptmultidimensional tools to evaluate systems thinking skills given the diversity in learning preferences. Copyright© 2012 System Dynamics Society.

Syst. Dyn. Rev. 28, 28–45 (2012)

Introduction

The use of system dynamics within strategy courses aims to teach students dynamic theoriesof firm performance (Gary et al., 2008) and develop strategic systems thinking skills. Thispaper presents the use of system dynamics (SD) embedded in a module named “Support-ing Strategy”. Embedding SD into specific programs has been the preferred route for somescholars in the SD community rather than teaching SD as standalone courses, providing acontextual aspect to SD. For example, SD has been successfully embedded in a mastersprogram for sustainable resource management helping the program to gain popularityand student enrolments (Biber and Kasperidus, 2004). In this case, Supporting Strategyis a key module, which combines different methodologies: visioning, scenario planning,SD and balanced scorecard, in two Masters in Management Science programs comprisingmany diverse tools. At the end of the course, students have to develop an integrativeassessment of a strategy development process, which includes the use of SD. This paperpresents the results of an analysis of the level of development of students’ strategicthinking after taking the course using their final assessments. This paper has two objec-tives: to analyze strategic systems thinking skills development and the proposition thatdecision makers with more accurate mental models achieve higher performance levels(Gary et al., 2008, p. 413).

The paper is structured as follows: firstly, the role of system dynamics in the SupportingStrategy module is explained, together with what is taught and expected from the students;secondly, the research methodology employed to evaluate students’ strategic systems

* Correspondence to: Martin Kunc, Operational Research and Management Science Group, Warwick Business School, CoventryCV4 7AL, U.K E-mail: [email protected]

Received 17 May 2010; Revised 24 May 2011; Accepted 19 July 2011

Copyright © 2012 System Dynamics Society

M. Kunc: Teaching Strategic Thinking using System Dynamics 29

thinking skills is presented; thirdly, the results obtained are analyzed and their importancediscussed; and finally conclusions are offered.

Supporting Strategy module: system dynamics within strategic developmentprocesses

The Supporting Strategy module presents a collection of frameworks and methods to helpmanagers design, rehearse and implement strategic initiatives through the process shownin Figure 1 (O’Brien and Dyson, 2007). Initiatives are rehearsed through the inner loop,which is called “Rehearsing Strategy” in Figure 1, where initiatives are tested, modifiedand refined. These tests can identify unsatisfactory performance that leads to changes instrategic initiatives or implementation plans. SD modeling and simulation, as part of theSupporting Strategy module, aims to improve students’ mental models related to dynamiccomplexity during strategy development processes so they can clearly understand the pro-blems highlighted during the rehearsal phase of the strategic development process.The frameworks and methods employed in the course support specific activities within

the strategic development process. These frameworks and methods include a range of cre-ative and analytical approaches from diverse sources: strategic management and manage-ment science. The course starts with visioning, continues with scenario planning, whichincludes SWOT analysis (Strengths, Weaknesses, Opportunities and Threats), followedby SD and finishes with balanced scorecard. In this way, the course aims to cover a broadrange of well-known tools for strategic development. SD modeling covers a third of thecourse’s total time and comes after classes on visioning and scenario planning to providequantitative evaluation of strategies.

Figure 1. Strategic development process (source: O’Brien and Dyson, 2007)

Copyright © 2012 System Dynamics Society Syst. Dyn. Rev. 28, 28–45 (2012)DOI: 10.1002/sdr

30 System Dynamics Review

Kunc and Morecroft (2007) suggest that SD modeling is suited to strategic rehearsal be-cause it helps people understand how strategies will perform over time, what might gowrong, and what type of interventions can improve performance and achieve the imple-mentation of the strategy. In the Supporting Strategy module, SD teaching aims to improvethe mental models of students (and future managers) so they can comprehend and managedynamic complex problems.

System Dynamics

Course structureA set of “archetype models” is prepared for the course based on Sterman (2000) andMorecroft (2007). These models are simple, yet conceptualized through real cases, andillustrate a key driver of the performance of dynamic complex systems: feedback loops.After explaining the archetypes models in the classroom, students go to the laboratoryand develop two models under my supervision depicting balancing and feedback pro-cesses. Finally, I give them suggestions to test diverse policies to change the performanceof the two models and encourage them to explain the results using graphs and systemsthinking language.

The class aims to replicate the five-stage modeling process described in Sterman (2000),with an emphasis on the importance of causal relationships, graphs over time to explainbehavior and feedback loops as a way to develop dynamic theories of firm performance.At the end of the course, I give students a description of a modeling project for their finalassignment. The objective of the assignment is to test students’ development of theirstrategic thinking in a real strategic and dynamically complex problem.

Improving mental models of strategic problems

Strategic decision making has many of the characteristics of unstructured problems: mul-tiple actors who are tightly interconnected in networks whose decisions cannot be ignoredbecause the decisions impinge on each other. Moreover, they do not usually agree andhave conflicting interests due to their different mental models (Mingers and Rosenhead,2004). Therefore, the key issue in strategic decision-making processes is to help managersto share and improve their mental models,1 especially in dynamically complex tasks suchas markets and management systems (Booth Sweeny and Sterman, 2007) or product lifecycle management problems (Gary and Wood, 2010), through “modeling for learning”practices (Morecroft and Sterman, 1994). In the field of SD, there are two positions onthe process of improving mental models of dynamic complex systems: one is using modelsand the other is modeling.

Using models is based on the use of simulations and microworlds where the executionof already built simulation models is central to the experience obtained by the student.The key success factor in this approach is that microworlds can trigger changes in mentalmodels by experiencing dynamic complexity (Morecroft and Sterman, 1994). One of themost famous examples is People Express (Sterman, 1988) and the story of the rise and fallof People Express (Senge, 1990). This combination of a case and its microworld has beenwidely used to introduce students to issues related to strategic thinking under dynamiccomplexity. Rieber (2006) suggests that the use of microworlds allows active constructionof knowledge through their experience but not clear explanations of the underlying



drivers. In these types of models, students manipulate input variables and observe theresulting behavior in order to discover the underlying structure before trying diverse pol-icies (Spector and Davidsen, 2000). Cavaleri and Sterman (1997) reported investigationson the impact of microworlds on system thinking competencies, but they found that thereare still unexplained complexities in cognitive processes related to learning systems think-ing in this way that affect the evaluation of the intervention.Modeling is perceived as a tool for conceptual development as students articulate the

problem and question the elements (usually translated into variables) and linkages (causalrelationships between variables) related to the problem. In this case, the models are “trans-parent boxes” where the underlying simulation model can be directly accessed. When stu-dents have the opportunity to develop a model, they are improving their strategic systemsthinking by understanding the strategic problem in terms of resources and operating and in-formation feedback processes (Forrester, 2007; Kunc and Morecroft, 2007; Morecroft, 2007).In modeling activities, the search for an explanation to the performance over time is madethrough conceptual models that are based on principles and concepts from the SD field.When students change variables and linkages, they improve their mental models and thesimulation helps them to learn how the new structure drives behavior (Schaffernicht,2006). However, modeling takes specific training and a lot of time, even using user-friendlysoftware. Some researchers also suggest that students inmodeling exercises do not develop aconceptual understanding of the structure of the problem because they rush to action (simu-lating the model) or they prefer acting over thinking (Booth Sweeny and Sterman, 2000).While both interventions are aimed at changing mental models, they also have strengths

and weaknesses. Therefore, system dynamicists have diverse ways of influencing their stu-dents but the efficiency in terms of mental model improvement can be quite diverse. Thisstudy analyzes the effects of aided modeling because the design of my classes and the finalassessment format intend to improve mental models through the aided development of amodel, as it is presented in Morecroft (2007). Aided modeling implies that subjects followa well-specified model and, as they develop it, learn through reflection. Students can obtainimportant insights by asking what-if questions related to the model, through partial simula-tion (Sterman, 2000; Morecroft, 2007), and interpreting the answers obtained. However,there are no studies that evaluate the effectiveness of aided modeling in a teaching envir-onment where SD has a limited role and the students are not expected to become modelers.The results of the study can provide evidence on how to teach SD principles in non-specificSD courses and contribute towards research exploring the impact of formal system dynamicstraining, which is still limited and the results mixed (Sterman, 2009).

Research methodology

Researchers in SD have performed research on mental models of dynamic complex sys-tems for many years, employing different methods. For example, Sterman (1989)employed a role-playing simulation of a supply chain where subjects had to optimize theirinventory over time as part of a four-stage supply chain. Results related to the level of men-tal models were based on analyzing key indicators of behavior, e.g. the gap with optimalperformance, and decision rules, or heuristics, were estimated from running regressionanalyses on decisions related to inventory replenishment. This approach is mainly basedon quantitative analysis. Other research streams aim to assess the development of intuitivecapabilities to understand dynamic complex systems (Booth Sweeney and Sterman, 2000,



2007; Pala and Vennix, 2005; Strohhecker, 2009). These studies focus on whether subjectscan describe the feedback processes existing in dynamic complex systems independ-ently of specialized terminology and methodology using a set of tools like the systemsthinking inventory (Booth Sweeney and Sterman, 2000, 2007). This approach is ratherqualitative.

This study combines quantitative and qualitative approaches as it identifies the level ofdevelopment of strategic thinking in terms of key well-established concepts in SD: use ofgraphs over time; identification of feedback loops; articulation of the problem; and robust-ness of policies (Sterman, 2000) combined with an evaluation of the performance obtainedby the student. However, an important departure from the studies mentioned earlier is thatour assessment of concepts related to SD such as feedback and performance over time arepart of a contextual task: developing and rehearsing strategies. Developing and rehearsingstrategies using SD implies concentrating on the system of resources that underpins theperformance of the firm (Morecroft, 2007) and the use of other strategy analysis frame-works to complement the process of model building.

Since a main aspect of the study is to analyze the development of strategic systems thinkingskills, there is a range of possible methods to employ to analyze them: cognitive interviews,verbal protocols, content analysis, task observation and causal mapping (Langan-Fox et al.,2000), and measuring deficiencies in causal maps considering causal links employed forexplaining certain phenomena (Plate, 2010). Each of them has strengths and weaknesses.For example, interviews allow asking questions and act to complete the process of elicit-ation but they take too much time and the interviewer can influence the outcome. Contentanalysis implies the review of written documents using coding processes to make infer-ences by systematic and objective analysis (Berg, 1998). Content analysis is less obtrusiveand can reflect trends but it is ineffective for testing causal relationships between variables(Berg, 1998). Since the source of the study is written reports, content analysis is selected toperform the analysis of mental model development.

Methodology

Using content analysis, the level of occurrence of key concepts in SD methodology(graphs, feedback loops, articulation of the problem, policy robustness) at the level ofthe whole report is identified. While some of the results may be affected by both manifestand latent content, the understanding of the level of strategic systems thinking skills in stu-dents improves more using a holistic view of their mental model rather than focusing onlyon task performance. Additionally, benchmarking the solutions obtained with respect toan optimal value given the importance of performance in strategy is carried out. Thusthe level of development of students’ strategic thinking is determined using two methods:

• a numerical indicator based on a comparison between their best results with a bench-mark solution similar to Sterman (1989); and

• a qualitative analysis using content analysis aimed at identifying similarities and differ-ences between students’ mental models in terms of the usage of concepts employed inSD to analyze dynamic complex systems.

The study analyzes 53 final assignments of one Supporting Strategy course. Onlyresponses related to SD-aided modeling of the assignment2 are considered. The review



of the assignments is performed 6months after they were graded and the final grades wereeliminated to avoid the influence of the grading process on the qualitative analysis.

Results and discussion

The final assessment consists of a model that describes a small bank with few branchesand a website. The bank is mainly located in small and medium-size villages with differentpopulation characteristics: clients from small villages are mainly old people with largesavings but short expected life; and clients from medium-size villages are younger, so theycan stay longer with the bank, but they have lower savings. Therefore, the bank is facing acollapse in 10 years if the bank does not implement a strategy to stabilize the number ofclients over time. The SD model describing this performance contains the feedback struc-ture presented in Figure 2. A balancing process (B3) develops in the following way: when abranch is opened, clients are attracted and generate income, increasing profits, so the needto add an additional branch declines. However, a subtle reinforcing process (R1) occurs:additional branches also increase operating costs, reducing the effect of the incomebrought by new clients, and profits do not increase but decrease (as the decay rate in loop(B2) reduces the effect of new clients on the income). Therefore, every time a new branchis added, costs rise more than income and profits decline, exacerbating a reinforcingprocess (R1).There are a number of strategies to sustain the performance of the bank’s core business.

One strategy implies reducing the decay rate so new clients remain in the stock and in-come generated is larger than costs in the long term (balancing feedback loop B2) but theold local population has a short life span which it is not possible to change. Another strat-egy is to replace the number of branches (balancing feedback loop B1) following clientdecay rate, so old branches without clients are eliminated from the costs while newbranches are opened to bring in new customers. Both strategies face an interesting problem

clients in branches

decay rate

Income

Profits

Costs

Branches

+ +

+

-

+

+

-

-

-

These are the branchoptimal solutions

B1

B2

R1

B3

marketingexpenditure

website customers customer losses

service quality

website staff

website staff costs

+

+-

-

-

+

+

-

+

B4

R2

+

B6

These arethe website

optimalsolutions

-

+

B5

Figure 2. Dynamic complexity existing in the assignment



because, if the bank wants to exploit its capabilities to deal with old clients from small vil-lages, it will face a similar problem: decay rates (balancing feedback loop B2).

The bank has an option to invest in Internet banking. In terms of the website business’sfeedback processes, the number of clients increases when there is investment in marketingand more profits are allocated to marketing (loop B5) but a higher number of clientsdecreases service quality (if no additional website staff is hired) and increases customerlosses. Customer losses reduce the number of clients, generating a balancing feedback loop(B4) and S-shaped growth. The website business can grow by investing simultaneously inadditional website staff at the same time as investing in marketing, developing a reinfor-cing process (R2). However, advertising expenditure reduces profits (B5) and more staffincreases costs, decreasing profits (B6), so Internet banking profits may decrease ratherthan increase. While the website business can grow in terms of website customers, its prof-its have a limit determined by the net result between additional income generated by newcustomers and costs incurred to obtain this income. Therefore, a strategy that identifies theright proportion of advertising investment and staff hiring costs with respect to incomegenerated can provide a balanced return on assets.

Another strategy, which is more radical, involves changing the assumptions of themodel related to demographic rates. This strategy suggests the bank moves out of decliningvillages so that it can replace the customers that are leaving with new customers. Finally,an only website-based strategy is to close all branches as they lose their customers overtime and invest the savings into expanding website customers through a marketing budgetand staff to support the new customers. All strategies explained provide a steady state ofthe firm in the long term compared with the initial case (see Figure 3).

The next sections analyze the performance of the students, benchmarking it against theoptimal performance, followed by a qualitative analysis of the reports.

Students’ performance

Seventy-four percent of the students finished with assets larger than one times the optimalasset size (see column “Optimal strategy gap” in Table 1) but only 30 percent of the stu-dents obtained a return on assets higher than the benchmark 3 percent (see last columnin Table 1). This result suggests that most participants engaged inadvertently in the re-inforcing process (R1) described in the causal loop diagram in Figure 2 to try to maintain

Total customer assets600

450

300

150

0

Time (Quarter)

Total customer assets : Internet base+medium branches opening2Total customer assets : Internet base+medium branches base case

annual return on assets4

-2

-8

-14

-200 4 8 12 16 20 24 28 32 36 40 0 4 8 12 16 20 24 28 32 36 40

Time (Quarter)

annual return on assets : Internet base+medium branches opening2annual return on assets : Internet base+medium branches base case

Figure 3. Results obtained implementing optimal solutions: stable assets and return on assets over time


Table 1. Qualitative analysis using content analysis of the reports (see Table 2 for the coding scheme)


the performance of the bank (87 percent of the students obtained return on assets higherthan 0 percent). This result confirms the common behavior over time observed in thereports and displayed in Figure 4. The balancing process (B3) seems to drive students’ stra-tegic decision-making processes. Students opened branches to generate income and in-crease profits, so they added branches in discrete steps, as Figure 4 shows. However, asubtle reinforcing process (R1) affects their strategy: more branches also increase the costs,


Figure 4. Results obtained by a student who implemented strategies based on the reinforcing process: growingfinancial assets but poor return on assets over time (since increasing number of branches reduces total profitability)


reducing the effect of the income brought by new customers, and profits do not increasebut decrease (as the decay rate in loop B2 reduces the number of customers), so studentskeep adding branches with lower profitability until they obtain a firm whose size, in termsof assets and branches, is not feasible (sometimes 10 times larger than the initial size! asFigure 4 shows). Every time a new branch is added, costs augment more than incomedue to customers’ decay rate, and profits decline, exacerbating the reinforcing process.

In terms of the website business, students tried to grow the website business by investingonly in marketing, increasing the effect of the balancing feedback process (B4). Few stu-dents paid attention to the reinforcing feedback process (R2) and invested in website staffsimultaneously with the investment in marketing. Table 1 presents the results with respectto the benchmark solutions in the column “Optimal strategy gap”.

The “Optimal strategy gap” column captures the final outcome of the exercise and reflectsthe effects of the dominant feedback loop in the students’mental model. Students obtainedhigher levels of total customer assets to maintain the initial profitability measured in termsof return on assets. On average, larger firms tend to have better profitability, but this rela-tionship is not direct (correlation 0.3)1,3 and it masks problems in the future due to the ef-fect of the balancing feedback processes. This pattern of behavior is common in strategicthinking, where managers tend to focus on becoming larger, assuming that larger organiza-tions are better performers (Sterman et al., 2007). The results are not different from those ofother researchers and highlight the difficulties of embedding strategic systems thinking instudents. For example, Sterman (2009) reported an experiment with MIT graduate stu-dents to assess the impact of an introductory system dynamics course (half-term course),finding that a group of students still show evidence of correlation heuristics.4 In Sterman’s(2009) study, the group of students performing badly after the introductory course was, onaverage, 25 percent and in this case the group is larger (74 percent), which shows a poten-tial effect of teaching hours in the effectiveness of SD. Additionally, the results also con-firm the tendency to over-intervene in the system because subjects judge performance byshort-term results (Maani and Li, 2004). While performance against an optimal benchmarkis modest, students might have learned to present evidence related to the structure drivingthe performance, starting a process of learning and engaging in transparent-box modelingpractices. The next section presents the results of this analysis.


Tab

le2.

Cod

ingschem

eem

ploye

dforthereports

Graphs:

explanation

Feedba

ckloop

s:ve

rbal

explanation

Basecase:clarity

Strategies

implemen

ted:grap

hs

Strategies

implemen

ted:clarity

Strategies

implemen

ted:

validation

Optimal

strategy

gap

1.No

2.Gen

eral

tova

riab

les

3.Gen

eral

over

time

4.Specificto

variab

lean

dcauses

5.Specificof

variab

les,

causes

andov

ertime

1.No

2.Gen

eral

behav

ior

3.Som

edescription

ofch

ange

inva

riab

les

4.Description

ofch

ange

inva

riab

lesan

dtypeof

feed

back

loop

5.Description

ofch

ange

inva

riab

les,

typeof

feed

back

loop

andlink

withother

loop

s

1.Not

clear

2.Reasonab

lyex

plained

3.Clearly

explained

4.Use

grap

hsof

key

variab

les

5.Feedba

ckloop

swithgrap

hsof

key

variab

les

1.Nograp

h2.

Few

grap

hsnot

related

3.Few

grap

hsof

key

variab

les

4.Graphsof

allke

yva

riab

les

5.Graphsof

allke

yva

riab

lesan

ddrive

rsof

chan

ge

1.Not

clear

2.Reasonab

lyex

plained

3.Clearly

explained

4.Exp

lained

with

grap

hsof

keyva

riab

les

5.Exp

lained

through

feed

back

loop

swithgrap

hsof

key

variab

les

1.Nova

lidation

2.Com

mon

sense

3.Validationin

term

sof

base

case

4.Validationin

term

sof

base

case

and

general

tren

d5.

Validationin

term

sof

base

case,g

eneral

tren

dan

dlogicof

chan

gesim

plemen

ted

Max

imum

asset

valueminus

Optimal

solution

.




The development of strategic systems thinking

Table 1 also displays the qualitative analysis of the reports. The following paragraphsexplain the results obtained from the reports presented in Table 1. The dimensionsemployed to analyze students’ responses are based on Sterman’s (2000) and Morecroft’s(2007) suggestions for good system dynamics practice: use of graphs showing behaviorover time, analysis of base case performance, identification of feedback loops and robust-ness of strategy implemented.

Graphs showing behavior over timeThe data show a good level of graph usage (9.9 on average) to explain dynamics over time,but there is a poor understanding of the complexity existing in the model since the expla-nations of the graphs were limited or there were no explanations. Moreover, the dispersionis very important with students overusing graphs—32 graphs—and other students using aminimum number—only three graphs. Considering the wide dispersion with the low levelof the explanations, I analyzed the existence of any relationship between the number ofgraphs and verbal explanations of the feedback process. The results show that there is asmall positive relationship, albeit not statistically significant, between graphs and clearverbal explanations of feedback processes; and this relationship can also be observed be-tween graphs and the quantitative performance obtained (26 percent of students clearlyexplained the graphs similarly to the percentage of good performances, as shown inTable 1), although this is not a one-to-one relationship (see footnote 3). However, most stu-dents do not understand the usefulness of graphs as part of a good development of strategicsystems thinking. Overall, results confirm previous work where students with strong Mathbackground confound basic concepts such as graph reading (Lyneis and Lyneis, 2003).

Feedback loopsStudents who achieved the lowest gap with the optimal result (26 percent) showed highvalues in the verbal explanation of feedback loops (17 percent), as shown in Table 1. Thesestudents described changes in key variables and related them to the performance of thefirm simulated in the model. This is an important indication of mental model developmentfor understanding complex systems, in addition to employing the full potential of themethodology (combination of graphs and verbal explanations). However, most studentsneither identified nor explained verbally (58 percent) the existence of feedback loops inthe models. In other words, students exhibited limited understanding of complex systemsas they assume open-loop (linear rather than circular) causality and lack of temporaldimensions (Booth Sweeney and Sterman, 2007); also they could not identify the shapeof growth pattern or decay pattern using the model diagrams and graphs (Fisher, 2009).

Base case analysisThose students who gave a good verbal explanation of the dynamics observed in the basecase study obtained most of the best scores in the final results (19 percent explained thebase case clearly; see Table 1). Analyzing the base case with graphs of key variables, andfeedback loops, clearly showed them the important nuances of the model and helped themto identify better strategies. Understanding the structure behind the base case performanceis a strong base to develop and implement strategies that have good outcomes, as SD



practitioners suggest (Morecroft, 2007). On the other hand, this result confirms that sub-jects rushed to action and they preferred simulating to analyzing the structure of the model(Booth Sweeny and Sterman, 2000) when they were developing models (47 percent of thestudents did not analyze the base case). Other modules in the MSc program teaching othersimulation methods based on sensibility analysis might have triggered this effect in thestudents’ response.

Table 3. Comparison of results of this study with other research

Copyright © 2012 System Dynamics Society Syst. Dyn. Rev. 28, 28–45 (2012DOI: 10.1002/sdr

)


Strategies implementedThis is a key outcome of the course and one that translates into better performance of realcompanies when students develop their mental models of complex dynamic systems. Theresults clearly confirm that students with good mental models of complex dynamic sys-tems used graphs wisely, explained how the strategy worked using verbal data, and vali-dated the results obtained with the initial condition to detect poor or unreasonablestrategies (on average, the percentages of the clear use of graphs and robustness tests aresimilar to the results obtained). This result adds a new dimension to the tests performedon systems thinking skills development, as it shows that good systems thinking practicesimply the use of multiple tools.

Comparative analysis of the results with other research

This study can be compared with two key studies related to identifying the level of devel-opment of mental models of dynamic complex systems (see Table 3). As discussed in theresearch methodology, this study combines two sets of systems thinking skills evaluationprocesses: identification of the gap between optimal solutions and results achieved by par-ticipants (Sterman, 1989), and assessment of subjects’ abilities to recognize recurrent pat-terns of behavior and system structures (Booth Sweeney and Sterman, 2007).

The study, through its multidimensional analysis (quantitative and qualitative), showsthat systems thinking skills can surface in different areas such as the use of graphs, verbalexplanations of feedback processes, in-depth analysis of base case and robustness of pol-icies implemented. However, the comparison also shows that the results obtained arenot different from those obtained by other researchers in the SD community. The resultsindicate that contextual aspects, like embedding SD as a tool for strategy rehearsal in aStrategy Development course, or increasing models’ transparency, as students developedthe model with a clear guide, do not reduce the level of misperceptions of feedback or cor-relation heuristics when students had been exposed for a limited amount of time to SD.The issues that surfaced through this study point towards reinforcing aspects of short SDcourses, such as using graphs over time and the importance of understanding causal rela-tionships, as well as the identification of feedback loops, to guide and illustrate the inter-ventions in a dynamic complex system, in similar ways to those suggested by modeling forlearning practice (Morecroft and Sterman, 1994).

Conclusions

Researchers in the SD community have demonstrated that people tend to focus on one-way causal structures, highlighting only surface features when there are dynamic complexsituations (Booth Sweeney and Sterman, 2007). Results show not only weak understand-ing of dynamic complex systems, even using a transparent model, but also important vari-ance in their abilities, as a group of students performed well in the use of SD to developstrategies. In an increasing interrelated world highlighting the importance of interconnect-edness, the development of mental models of dynamic complex systems is a key skill ofthe strategic thinker that translates into more accurate mental models and better perform-ance (Gary et al., 2008).



Understanding how managers make strategic decisions implies understanding how theythink about causal relationships. Therefore, teaching future managers, or managers, tounderstand causal relationships in a comprehensive way will improve their strategic deci-sion-making processes. SD courses, as part of strategic management courses, are funda-mental to moving in this direction. Business simulators have more advantages thanlectures in diverse aspects (Groesser, 2006). For example, learners are more active andlearn by experience using simulators, discussions are characterized by experiences gener-ated by the simulation rather than conforming to social norms, and the discussions aremore specific because the terms employed refer to the simulation and not erroneous inter-pretations (Groesser, 2006). Aided modeling is another way to immerse managers into theworld of SD, as this study showed. However, there is still work to do to find effective andefficient ways to embed SD skills in a wider population that will not necessarily want to beSD modelers. The following paragraphs highlight some ideas in this area.

Teaching suggestions

System dynamics has become an important choice for learning environments in differentfields, such as organizational behavior (Stepanovich and Hopkins, 2004), economics(Hirsch, 2003) and strategic management (Hsueh et al., 2006). However, sometimes modelsare very complex and affect students’ experience, such as taking too much time to use it inclass, or that students found the model quite complex, hindering their ability to providespecific decisions (Hsueh et al., 2006). To help teach strategy cases dealing explicitly withdynamic complexity, Ginsberg andMorecroft (1997) propose the use of a model-supportedcase approach which shows the concepts of feedback thinking during the case discussion.However, when the simulator is a black box because of its complexity, they are limited intheir education value because players are not able to understand their inherent structureand how it determines behavior.A set of generic models that cover most managerial situations by an exhaustive

categorization of firms such as industry characteristics, firm size or strategy may be apotential way for embedding SD into management courses (Rockart, 2004). This set of gen-eric models will be different from general behavior models, like the Bass diffusion model,because they have to be accompanied by contextual information such as a case study(Rockart, 2004; Warren, 1998), or black box models because they can be small butinsightful models—back-of-the-envelope models—to address a dynamic challenge, con-densing the dynamic complexity into just a few variables and feedback loops, like theEasyJet model (Kunc and Morecroft, 2007).System dynamicists should also consider that variety in learning options (aided model-

ing and using models) could increase variety in learning outcomes, leading to more effect-ive learning processes (long-term systems thinking skills; Lengnick-Hall and Sanders,1997). For example, Friedman et al. (2007) found evidence of different preferences for sys-tem thinking tools according to the learning style of the subjects. The study shows thatsome students achieved good performance in the task, showing diverse levels of profi-ciency in the use of SD tools; for example, some students employed mainly graphs to com-municate their decisions, while others employed verbal descriptions and some rushedinto simulating the model rather than understanding the structure. Implementing this ideawill imply that students should have more opportunities to co-produce parts of lessons bytheir selection of different ways of learning SD such as using microworlds (for students



who prefer to try rather than analyze), developing models (for students who prefer tointernalize the structure of the model) or simply understanding causal loop diagrams, ordeveloping them from verbal descriptions, since all these methods can support learningin socio-economic systems (Maier and Größler, 2000). While it is important that systemsthinking skills are widespread, we have to consider that there will be different levels of ex-pertise across people.

Another aspect to explore is that SD can be taught as part of a set of tools to intervene incomplex problems. The practice of mixing methods either methodologically or theoretic-ally is not new but it is still under development (Lane and Oliva, 1998; Pollack, 2009).There are many ways to mix methods: grafting, embedding, and they can be mixed inseries or in parallel (Pollack, 2009). These considerations are even more valid in complexnon-structured issues like strategic issues or with multiple stakeholders in sensible issueslike waste management, where system dynamics is combined with soft systems method-ology and optimization (Adamides et al., 2009).

Further research

This study is based on one course and one assessment method (aided modeling). Thereforeadditional research should consider other variables affecting the learning process, such asnumber of hours taught for a SD course, methods employed to teach and assess students,and the subjects where SD is embedded. For example, other experiences teaching SDimply courses lasting from 15 to 48 hours in different environments, such as businessengineering or computer sciences, using a case study after theory, and evaluating studentsusing project work rather than a final assignment (Sedehi et al., 2006). The development ofa set of best practices in teaching and assessment for different course extensions, thor-oughly tested, can be a future path to explore. For example, Plate (2010) proposes thatassessment methods should be able to incorporate results that can be compared over largenumbers of participants and that can assess multiple aspects of systems thinking.

Notes

1. Researchers in SD suggest that mental models are a relatively enduring but limited in-ternal conceptual representation of a system and its dynamic behavior, whose structureis similar to the perceived structure of the real system (Doyle and Ford, 1998, 1999;Lane, 1999). However, these mental models are limited in terms of systems thinkingskills even among graduates and postgraduates in different fields (Dörner, 1980;Sterman, 1994; Booth Sweeney and Sterman, 2000).

2. The assignment can be requested from the author.3. I appreciate this comment suggested by one of the reviewers.4. This heuristic indicates that people erroneously assume that the behavior of a stock

matches the pattern of its flows (Cronin et al., 2009).

Acknowledgements

The paper has benefited from very thoughtful and careful comments from two anonymousreviewers and the editor. All errors remain the author’s responsibility.



Biography

Martin Kunc is assistant professor of Management Science at Warwick Business School,UK. He holds a PhD in Decision Science from London Business School and his researchfocuses on managerial decision making under a resource based perspective. He has per-formed diverse consulting projects in human resources planning, strategic marketingand supply chain using System Dynamics.

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Teaching strategic thinking using system dynamics: lessons from a strategic development course