Empirical Approaches

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A conceptual framework for the empirical approach was suggested by Walsh (1995) in

his review of work on managerial cognition. The framework incorporated three major


1. Under many conditions, we should expect direct linkages between cognition and

behaviors, however,

2. Behaviors mediate the relationship between cognition and outcomes such as

grades or profits, and

3. Cognition, in turn, may change due to the feedback of outcomes from behaviors.

The framework is sketched in Figure 2. The figure summarizes the thought-actionoutcome

linkage as a system of variables, which incorporates both strategic behavior

(i.e., behavior as the consequence of thought) to realize outcomes, and learning

Author Focus Type Technique Software

David Brubaker Introduction of Fuzzy

Cognitive Maps

Theoretical N/A N/A

Alex Chong Establishing and evaluating

a framework for DSSG

based on FCM

Empirical Expert opinions FuzzyGen




Application of FCM in

Strategic Planning of IS

Applied Simulation Not mentioned


Vazquez Huerga

Balanced Differential

Algorithm to learn Fuzzy

Conceptual Maps from data

Theoretical N/A N/A

Zahir Irani,

Amir Sharif

Use FCM as a technique to

model each IT/IS evaluation


Applied N/A N/A

Table 1. Illustrative examples of papers in IS using fuzzy causal maps (FCM)

Cognition Behavior Outcomes

Figure 2. A framework for analyzing behavior

 (outcomes leading to change in thought).

Walsh was interested in managerial cognition, broadly conceived. Others, building upon

his work, have built up more complex frameworks, appropriate to their disciplinary

domains. For example, Rajagopalan and Spreitzer (1997), in their review of research in

strategic management, constructed a model that incorporated antecedent conditions,

strategies, and both economic and non-economic outcomes. It is outside the purview of

this chapter to review different models. Suffice to say, as the work in IS progresses, we

expect researchers to develop more complex models in their specific domains.

For our purpose, the utility of the framework is to highlight the empirical approach to

establishing the relationship between cognition and behavior. Stable relationships, once

established, could become the basis of predictions. We illustrate this approach with a

couple of examples:

1. Calori, Johnson and Sarnin (1994) linked the degree of diversification and the

complexity of the causal maps of decision makers. They argued that complex maps

are needed to manage diversified corporations, since these companies are more

complex than non-diversified corporations.

2. Nadkarni and Narayanan (2004) argued that both complexity and centrality of

causal maps are drivers of strategic flexibility. Complexity will be reflected in a broad

strategic repertoire (resources and competitive actions) and more frequent shifts

in both resources and competitive actions, whereas centrality constrains both


These relationships are among the easier to hypothesize, and hence it is not surprising

that researchers initially paid attention to them. Nonetheless, both these approaches

suggest stable relationships between causal maps and certain behaviors. The empirical

approach focuses on accumulating these predictions to generate a theory of cognitioninduced

behavior. Once accomplished, such a theory could become the basis of


Concluding Thoughts

Although many of the above listed approaches have multiple uses, there may be

differential advantages:

1. Computer-based simulations and influence diagrams appear to be eminently suited

for intervention contexts, sine they require judgment about the specific alternatives

to explore.

2. Fuzzy causal maps appear useful for investigations that attempt to link causal maps

and complexity theory.

3. Domain specific empirical approaches may be useful in hypothesis testing studies

or in studies attempting to build an empirically grounded theory.

Also there are advantages to combining simulation and behavioral approaches. Simulation

approaches that explore the intrinsic behavior of causal maps, may be used to predict

behavior which then may be compared to actual behavior (as in Shapiro and Bonham

study). In this sense, unexpected behaviors or counter examples can be unearthed which

become the foci of theory expansion or modification.

As we have noted, the analysis of behavior of causal maps is in its infancy. We urge

researchers interested in advancing causal mapping methodology to give serious

attention to this facet of causal maps.


Axelrod, R. (1976). Structure of decision: The cognitive maps of political elites.

Princeton, NJ: Princeton University Press.

Brubaker, D. (1996a). Fuzzy cognitive maps, EDN Magazine, 41(8), 209-211.

Brubaker, D. (1996b). More on fuzzy cognitive maps, EDN Magazine, 41(9), 213-215.

Calori, R., Johnson, G., & Sarnin, P. (1994). CEOs’ cognitive maps and the scope of the

organization. Strategic Management Journal, 15(6), 437-457.

Chong, A. (2001). Development of a fuzzy cognitive map based decision support system

generator, Department of Information Technology, Murdoch University, Fourth

Western Australian Workshop on Information Systems Research. Retrieved from

the World Wide Web at: http://wawisr01.uwa.edu.au/2001/Chong2.pdf

Irani, Z. Sharif, A. Love P.E., & Kahraman, C. (2002). Applying concepts of fuzzy

cognitive mapping to model: The IT/IS investment evaluation process. International

Journal of Production Economics, 75, 199-211.

Kardaras, D., & Karakostas, B. (1999). The use of fuzzy cognitive maps to simulate

information systems strategic planning process. Information and Software Technology,

41(4), 197-210.

Khan, M.S. Chong, A., & Quaddus, M. (1999). Fuzzy cognitive maps and intelligent

decision support-a review, Proceedings of the 2nd Western Australian Workshop

on Information Systems Research, WAWISR 1999, Murdoch University, Murdoch,

Western Australia. Retrieved from the World Wide Web at: http://


Kosko, B. (1993). Fuzzy thinking: The new science of fuzzy logic. New York: Hyperion.

Liu, Z. (1999). Contextual fuzzy cognitive map for decision support in geographic

information systems. IEEE Transactions on Fuzzy Systems, 7(5), 495 -507.

Marchant, T. (1999). Cognitive maps and fuzzy implications, European Journal of

Operational Research, 114(3), 626-637.

Miao, Y., & Liu, Z.Q. (2000). On causal inference in fuzzy cognitive map, IEEE TransacAn

tions on Fuzzy Systems, 8, 107-119.

Nadkarni, S., & Narayanan, V.K. (2004). Strategy frames, strategic flexibility and firm

performance: The moderating role of industry clockspeed. Best Paper Proceedings

of the Academy of Management Conference 2004, New Orleans, LA (in press).

Nozcika, G.J., Bonham, G.M., & Shapiro, M.J. (1976). Simulation techniques. In R. Axelrod

(Ed.), Structure of decision: the cognitive maps of political elites (pp. 349-359).

Princeton, NJ: Princeton University Press.

Rajagopalan, N., & Spreitzer, G. (1997). Toward a theory of strategic change: A multi-lens

perspective and integrative framework. Academy of Management Review, 22(1),


Roos, L.L., & Hall, R.I. (1980). Influence diagrams and organizational power. Administrative

Science Quarterly, 25(1), 57–71.

Vazquez, A. (2002). A balanced differential learning algorithm in fuzzy cognitive maps.

Technical Report, Departament de Llenguatges i Sistemes Informatics, Universitat

Politecnica de Catalunya (UPC), C\Jordi Girona 1–3, E0834, Barcelona, Spain.

Retrieved from the World Wide Web at: http://www.upc.es/web/QR2002/Papers/


Walsh, J.P. (1995). Managerial and organizational cognition: Notes from a trip down

memory lane. Organization Science, 6, 280-321.

Zhang, J.Y., Liu, Q., & Zhou, S. (2003). Quotient FCMs-A decomposition theory for fuzzy

cognitive maps. IEEE Transactions on Fuzzy Systems, 11(5), 593-604.


1 The authors thank Paige Rutner, University of Arkansas for her comments on an

earlier draft of this chapter.