An Outline of Approaches to Analyzing the Behavior of Causal Maps

Analysis of the behavior of casual maps is not as well developed as the analysis of their content and structure. In this chapter, we propose a set of approaches to examine the behavior of causal maps. Simulation approaches that invoke computer simulations, influence diagrams and fuzzy causal maps are eminently suitable to examine the intrinsic behavior of causal maps. Empirical approaches attempt to build a theory of cognition-behavior linkages from the ground up, by unearthing stable linkages between cognition and behavior. Both approaches could be combined in major programs of research.

Causal Mapping for the Investigation of the Adoption of UML in Information Technology Project Development

This research project gathered data about the use of UML and object-oriented analysis and design as the approach to the development of information systems. The data collection method consisted of interviews with information systems application developers with wide ranging differences in background. The authors used causal mapping for analysis of the data gathered. This chapter focuses on the authors’ experiences with causal mapping as a method for exploring issues and relationships. Causal mapping was also used to document tips on its use illustrating these with findings regarding UML and object-oriented analysis and design in particular.

Using Causal Mapping to Uncover Cognitive Diversity within a Top Management Team

The cognitive diversity of top management teams has been shown to affect the performance of a firm. In some cases, cognitive diversity has been shown to improve firm performance, in other cases, it has worsened firm performance. Either way, it is useful to understand the cognitive diversity of a top management team. However, most approaches to measure cognitive diversity never attempt to open the “black box” to understand what makes up the cognitive diversity of the team. This research reports on an approach that identifies diverse belief structures, i.e., cognitive factions, through the use of causal mapping and cluster analysis. The results show that the use of causal mapping provides an efficient and effective way to identify idiosyncratic and shared knowledge among members of a top management team. This approach allows the cognitive diversity of the top management team to not only to be uncovered, but also to be understood.

Belief Function Approach to Evidential Reasoning in Causal Maps

The purpose of this chapter is to demonstrate the use of the evidential reasoning approach under the Dempster-Shafer (D-S) theory of belief functions to analyze revealed causal maps (RCM). The participants from information technology (IT) organizations provided the concepts to describe the target phenomenon of Job Satisfaction. They also identified the associations between the concepts. This chapter discusses the steps necessary to transform a causal map into an evidential diagram. The evidential diagram can then be analyzed using belief functions technique with survey data, thereby extending the research from a discovery and explanation stage to testing and prediction. An example is provided to demonstrate these steps. This chapter also provides the basics of Dempster-Shafer theory of belief functions and a step-by-step description of the propagation process of beliefs in tree-like evidential diagrams.

Using Causal Mapping to Support Information Systems Development

Identifying what different stakeholders in a business need from Information Systems development has always been seen as problematic. There are numerous cases of failure as projects run over time, over budget, and, most significantly, do not meet the needs of the user population. Whilst having a structured design process can go some way towards reducing the potential of failure, these methodologies do not attend sufficiently to clarifying and agreeing objectives or to considering the social and cultural elements inherent in the ownership and adoption of any new system. Instigating an effective, and structured, dialogue between users, developers and, when appropriate, sponsors, is therefore a critical consideration. Linking user needs, as they see them, to the language of IS developers and vice versa is crucial. Both parties need ownership. This chapter focuses upon the use of causal mapping, supported where appropriate by special software, that facilitates the development of a shared understanding (of both business needs and IT opportunities) and through this common platform enables a negotiated and agreed outcome. The nature of the outcome invites translation to structured design processes.

An Empirical Comparison of Collective Causal Mapping Approaches

Recently, capturing and evaluating group causal maps has come to attention of IS researchers (Tegarden and Sheetz, 2003; Lee, Courtney & O’Keefe, 1992; Vennix, 1996; Kwahk and Kim, 1999). This chapter summarizes two studies that formally compare three approaches to building collective maps: aggregate mapping, congregate mapping, and workshop mapping. We first provide a conceptual comparison of the three methods. Then we empirically compare models derived with the three methods using both objective and subjective measures. The results suggest that the aggregate method performs best at the group level, whereas the congregate method performs best at the organizational level. The results also indicate that the workshop method was best at promoting knowledge sharing. These studies suggest that the workshop method can be used in combination with aggregate mapping or congregate mapping methods to improve the collective mapping process.

Revealing Social Structure from Texts

Texts can be coded and analyzed as networks of concepts often referred to as maps or semantic networks. In such networks, for many texts there are elements of social structure — the connections among people, organizations, and events. Within organizational and social network theory an approach called the meta-matrix is used to describe social structure in terms of the network of connections among people, organizations, knowledge, resources, and tasks. We propose a combined approach using the meta-matrix model, as an ontology, to lend a second level of organization to the networks of concepts recovered from texts. We have formalized and operationalized this approach in an automated tool for text analysis. We demonstrate how this approach enables not only meaning but also social structure to be revealed through text analysis. We illustrate this approach by showing how it can be used to discover the social structure of covert networks — the terrorist groups operating in the West Bank.

Causal Mapping

Causal mapping is a technique that can be used to represent cognition because it captures the structure of the causal assertions of an individual or group. As causal mapping becomes more prominent in the IS field, it is important that we understand the method, its strengths and limitations and its place within the spectrum of available research methods. Many researchers have made assumptions (both explicit and implicit) regarding causal mapping, without explicating the steps involved. This chapter details the causal mapping (CM) process and decisions that must be addressed so that researchers and practitioners can utilize this method to understand IS issues from a cognitive perspective, as well as provoke interest in expanding the boundaries of the CM method within the IS field.

Strategic Implications of Causal Mapping in Strategy Analysis and Formulation

The formulation and implementation of effective strategy at every level within an organization requires that those involved in the process have not only a good overall understanding of the present situation, but also an understanding of the underlying cause and effect relationships which underpin strategy at those levels. This includes understanding the interactions, which occur between the levels of strategy in an organization as well as the benefits of a firm’s executive information system. Using various mapping techniques, e.g., concept mapping, cognitive mapping, causal mapping, we investigate the factors that made WorldCom, Inc., a one-time leader in the telecommunications industry, implode and find itself fighting in courts for its very survival.

Reflections on the Interview Process in Evocative Settings

Revealed causal mapping (RCM) represents one of the best ways to study a phenomenon in a discovery or evocative setting. The RCM method provides rich data that facilitates a deeper understanding of the cognitive facets of a phenomenon not available with other methods. In this chapter I will share insights gained from conducting several interactively elicited causal mapping studies in the discovery and evocative research contexts. I address issues a researcher will encounter during in the interview process, the causal statement identification procedure, and the development of the coding scheme. I conclude with some thoughts on lessons learned in the field.