Narayanan V.K., Armstrong D.J. - Causal Mapping for Research in Information Technology (2005)(en)

370 Narayanan and Liao

Review of Approaches for Studying Behavior

Broadly, we may identify two approaches to the behavioral analysis of causal maps:

1.The first approach relies on computer simulation of causal maps. In both cases — prediction and analysis — it is assumed that since a causal map represents a system of cause-effect linkages, once the values of the causes change, logically the effects should change. Here the focus is on logical connections, i.e., what can the intrinsic patterns of relationships within a causal map tell us about future behavior of the unit (e.g., a competitor), without recourse to additional observations.

2.The second approach, which we will call empirical, tries to link the causal maps of any social unit to the actual behavior of the unit itself. Advocated primarily by students of the organization science school, this approach seeks to isolate empirically the behaviors that can be linked to causal maps. The key assumption is that under many conditions these linkages are stable. Hence, once the linkages are empirically established, the behaviors can be predicted from the knowledge of causal maps.

Figure 1 sketches the plan of our review of the approaches to the study of the behavior of causal maps.

Figure 1. A schematic of approaches

Behavior of Causal Maps

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authors chose some concepts for their policy relevance, and once the concepts were established, they employed a six-step process of deriving the predictions (See Box 1).

Bonham and Shapiro argued that this mode of analysis of behavior is very useful in inductive efforts to build a theoretical model of decision making in specific domains. We may add that this approach may also have great value in predicting behavior (e.g., competitors) as well.

Influence Diagrams

In the Roos and Hall (1980) example cited in Chapter I, the authors analyzed the causal map they derived qualitatively to discover the cycles of cause-effect links that explained the behavior of the director of the emergency care unit they studied. The authors acknowledged the limitations of their approach:

It ignores the more complex dynamic properties of feedback, such as dampened or sustained oscillations arising from multi-order negative feedback loops.

To identify the critical feedback loops, Roos and Hall redrew the complex causal map they obtained, by identifying as a starting point variables characterized by a large number of inflows and outflows and by tracing paths through the original causal map that were recursive, i.e., led back to the starting point. This was repeated until every possible path through the system was accounted for. The loops thus identified were analyzed by summing the signs of correlation around each loop in the direction of causality. Roos and Hall noted that they could infer the polarity of a loop since an odd number of negative signs results in a negative feedback loop while no negative signs or an even number of negative signs generates a positive feedback loop. Of course, a negative feedback loop will tend to restore the system to some equilibrium by constraining changes, whereas a positive feedback loop will amplify the changes in the variables in that loop.

Roos and Hall thus illustrated that a complex causal map may not only incorporate direct linkages between variables, but also a set of indirect linkages to both virtuous and vicious cycles as represented by the feedback loops. According to them, each loop presented policy choices to accelerate or dampen changes in the emergency care unit under study, and the director of the unit could enact some, but not all of these policy choices.

Roos and Hall (1980) noted that the use of influence diagrams derived from larger causal maps may be a particularly valuable tool for consultants in conflict-laden situations. Stated in our terms, this approach may be useful primarily for intervention contexts.

Fuzzy Causal Map

The connection between Axelrod’s causal mapping and fuzzy logic was originally made by Bart Kosko. Kosko’s Ph.D. advisor, Lofti Zadeh, then a professor at the University

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An Outline of Approaches to Analyzing the Behavior of Causal Maps 373

of California Berkeley, had introduced the term “Fuzzy Set,” to a set or groups of objects whose elements belonged to the set to different degrees. A technical treatment of fuzzy logic and its application is beyond the scope of this chapter. The interested reader is urged to consult the references given in this chapter as a starting point.

When introduced, fuzzy logic was a controversial idea, but Kosko applied it to the study of causal maps, calling them fuzzy cognitive maps. In his words, “A fuzzy cognitive map or FCM draws a causal picture. It ties facts and things and processes to values and policies and objectives. And it lets you predict how complex events interact and play out.”

The connection of the causal maps to fuzzy logic occurs in two ways. First, causal arrows in the maps can be weighted with any number between 0 and 1, and with a s + or – sign specified. Second, each node can be fuzzy also by “firing” to some degree from 0% to 100%.

In Fuzzy Thinking, Kosko illustrated his ideas with three examples:

1.An example of the first kind was based on an article by Henry Kissinger, “Starting Out in the Direction of Middle East Peace,” that appeared in Los Angeles Times in 1982. Kosko represented Kissinger’s reasoning by means of an FCM, and showed that this FCM had no feedback loops.

2.A second example was an FCM that showed how bad weather could affect the speed with which someone drives on a Los Angeles highway. This had two feedback loops built into them, which made the FCM more complex than the earlier one.

3.A third example was the economic logic behind Walter Williams’ article, “South Africa is Changing,” that appeared in the San Diego Union, which detailed the relationship between foreign investment and apartheid in South Africa.

Kosko made the intriguing connection between the behavior of fuzzy causal maps and dynamic systems, thus opening up the possibility of empirically examining the behavior of causal maps, with predictions grounded in complexity theory. Thus when simulated, FCMs may settle down on one of the three attractors: a fixed-point attractor, a limit-cycle attractor, or chaotic attractor. Kosko argued that FCMs can be simulated by neural nets to discover the behavior of the dynamical system represented by the FCM.

Kosko emphasized that his approach dealt with the intrinsic logic of the causal map, i.e., it can not establish if the predictions are correct but can give insight into the dynamics if the map were accurate. Nonetheless, in all the above examples, he argued that FCMs yielded predictions that on a common sense basis were acceptable.

In recent years, many have advocated the use of fuzzy causal logic for the analysis of causal maps. An illustrative set of papers is listed in Table 1. Yet empirical works using FCMs are still rare, both in organization sciences and in IT. This may partly be due to the lack of awareness of the technique by the empirically minded research community. Given the increasing interest in complexity theory on the part of organizational science scholars, this technique may provide a valuable avenue to move the empirical research onto a solid theoretical foundation.

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374 Narayanan and Liao

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

Empirical Approaches

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 themes:

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-action- outcome linkage as a system of variables, which incorporates both strategic behavior (i.e., behavior as the consequence of thought) to realize outcomes, and learning

Figure 2. A framework for analyzing behavior

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An Outline of Approaches to Analyzing the Behavior of Causal Maps 375

(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 behaviors.

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 predictions.

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.

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376 Narayanan and Liao

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.

References

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:// wawisr01.uwa.edu.au/1999/KhanChongQuaddus.pdf

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 Transac-

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An Outline of Approaches to Analyzing the Behavior of Causal Maps 377

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), 48-80.

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/ QR2002%20-%20Vazquez.pdf

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.

Endnotes

1The authors thank Paige Rutner, University of Arkansas for her comments on an earlier draft of this chapter.

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378 About the Authors

About the Authors

V.K. Narayanan is currently the Stubbs professor of strategy and entrepreneurship at Drexel University, Philadelphia, Pennsylvania (USA) and Associate Dean for Research in the LeBow College of Business. He holds a Ph.D. in business from the Graduate School of Business at the University of Pittsburgh, Pennsylvania. Since 1988, he has been on the editorial board of Organization Science. He has authored (or co-authored) more than 60 papers and four books. His articles have appeared in leading professional journals such as Academy of Management Journal, Academy of Management Review, Management Information Systems Quarterly, R&D Management and Strategic Management Journal.

Deborah J. Armstrong is an assistant professor of information systems at the University of Arkansas (USA). She received her Ph.D. from the University of Kansas (2001) with a concentration in information systems and supporting emphasis in organizational communications. Dr. Armstrong’s research interests cover a variety of issues at the intersection of IS personnel and mental models involving the human aspects of technology, change, learning and cognition.

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Fran Ackermann is a professor of strategy and information systems. Her main research areas include investigating how information systems can enhance the process of modeling complex qualitative data (in areas such as strategy making, problem solving and project failure). Through using a combination of cause mapping and information systems she is interested in exploring how the processes of eliciting, structuring, analyzing and enabling the group to directly interact with the resultant model can be

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About the Authors 379

supported and enhanced. With Colin Eden she has developed both Decision Explorer and Group Explorer, group decision support packages enabling groups to manage such complexity. She has written widely in the fields of management science, strategic management and information systems.

Mari W. Buche is an assistant professor of information systems at Michigan Technological University (USA). She earned her Ph.D. in business administration/management information systems from the University of Kansas. She investigates issues related to the impact and management of technology change on employees within the business environment. Her research interests include software engineering, change management, information security, and work force issues in IS. Her current work focuses on the changing work identity of information technology professionals.

Kathleen M. Carley is a professor of computation, organizations and society at the Institute for Software Research International at Carnegie Mellon University (USA). She received her Ph.D. from Harvard University. Her research combines cognitive science, social networks and computer science. Her specific research areas are computational social and organization theory, group, organizational and social adaptation and evolution, dynamic network analysis, computational text analysis, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She has co-edited several books including: Computational Organization Theory, Simulating Organizations, and Dynamic Network Analysis.

Gail P. Clarkson earned her Ph.D. at Leeds University Business School, The University of Leeds (UK) where she is currently employed as a post-doctoral researcher, under the auspices of the UK Advanced Institute of Management Research (AIM). A recent entrant to the academic profession, following a successful career as a university administrator, Dr. Clarkson has particular expertise in the application of causal mapping techniques in organizational field settings. In collaboration with Gerard P. Hodgkinson, she is currently investigating sensemaking and other socio-cognitive processes among frontline workers, with a view to developing new insights that will ultimately enhance employee effectiveness and well being.

James F. Courtney is a professor of management information systems at the University of Central Florida in Orlando (USA). He received his Ph.D. in Business Administration with a major in management science from the University of Texas at Austin. His papers have appeared in several journals, including Management Science, MIS Quarterly, Communications of the ACM, IEEE Transactions on Systems, Man and Cybernetics, Decision Sciences, Decision Support Systems, the Journal of Management Information Systems, Database, Interfaces, the Journal of Applied Systems Analysis, and the Journal of Experiential Learning and Simulation. His present research interests are knowledgebased decision support systems, knowledge management, inquiring (learning) organizations and sustainable economic systems.

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