Causal Mapping for Research in Information Technology by V.K. Narayanan, Deborah J. Armstrong

By V.K. Narayanan, Deborah J. Armstrong

Causal Mapping for study in details expertise offers an creation to causal mapping for IS researchers and practitioners and is going extra to supply IS researchers and practitioners every little thing they should use causal mapping for either study and alertness.

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Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 26 Armstrong Text-Based Method (TBCM) Text-Based Causal Maps rely on non-invasive data collection techniques that avoid the recall biases of interviews (Axelrod, 1976). The researcher’s goal is still to gather knowledge or beliefs and cast it into cognitive structure pertaining to a specific domain. The task with TBCMs is to determine the appropriate source of information and gather the data from that source.

This chapter seeks to explicate the causal mapping (CM) process so that researchers and practitioners can utilize this method to address IS issues within organizations using a cognitive lens. The objectives of the chapter are two-fold: • To demonstrate in detail how CM can be used to understand IS issues from a cognitive perspective • To provoke interest in expanding the boundaries of the CM method within the IS field as we present advances and issues related to CM In the remainder of the chapter, I provide the motivation behind causal mapping research and detail the causal mapping approach for both capturing individual maps and deriving collective causal maps.

Several software packages have been developed to aid the process (discussed in Chapter III), but there is still a significant amount of labor involved. On the positive side, causal mapping is a versatile method on several fronts. As seen in this chapter, CM can be effectively used in several research contexts (discovery, evocative, hypotheses testing, and intervention). The CM method is also versatile with respect to the theories that can be used. With CM you can use multiple theories (lenses) to interpret the data collected (causal statements).

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