Causal Mapping | ScienceGate

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.

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

Causal Mapping for Research in Information Technology ◽

10.4018/978-1-59140-396-8.ch009 ◽

2005 ◽

pp. 203-232 ◽

Cited By ~ 4

Author(s):

David P. Tegarden ◽

Linda F. Tegarden ◽

Steven D. Sheetz

Keyword(s):

Firm Performance ◽

Top Management Team ◽

Black Box ◽

Top Management Teams ◽

Management Team ◽

Top Management ◽

Cognitive Diversity ◽

Causal Mapping ◽

And Cluster Analysis ◽

Cognitive Factions

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.

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Building the IT Workforce of the Future

Advances in Information Resources Management - Global, Social, and Organizational Implications of Emerging Information Resources Management ◽

10.4018/978-1-60566-962-5.ch017 ◽

2011 ◽

pp. 323-340

Author(s):

Deborah J. Armstrong ◽

H. James Nelson ◽

Kay M. Nelson ◽

V. K. Narayanan

Keyword(s):

Software Development ◽

Present State ◽

Cognitive Maps ◽

Thought Processes ◽

Causal Mapping ◽

Abstract Thought ◽

It Workforce ◽

Mapping Techniques ◽

Revealed Causal Mapping ◽

Cognitive Transition

The software development process has undergone a considerable amount of change from the early days of spaghetti code to the present state of the art of development using strategic patterns. This has caused not only changes in the toolkits that developers use, but also a change in their mindset—the way that they approach and think about software development. This study uses revealed causal mapping techniques to examine the change in mindset that occurs across the procedural to OO development transition, and lays the foundation for future studies of the OO/ pattern cognitive transition. The results indicate that there is not only increasing complexity in the cognitive maps of the OO developers, but also that there is a need for the developer to shift from routine, assembly line coding to more abstract thought processes.

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Disruptive-Technology Avoidance in Healthcare

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2019040103 ◽

2019 ◽

Vol 14 (2) ◽

pp. 28-48

Author(s):

Bahae Samhan ◽

K.D. Joshi

Keyword(s):

Healthcare Providers ◽

Disruptive Innovation ◽

Mapping Technique ◽

Disruptive Technology ◽

Healthcare Organizations ◽

Health Records ◽

Causal Mapping ◽

Range Theory ◽

Potential Benefits ◽

Revealed Causal Mapping

Disruptive innovation has transformed business activities as well as individuals throughout a variety of industries. In healthcare, the implementation of electronic health records (EHR) innovation has changed the way healthcare organizations handle patient records. Despite the potential benefits EHR can bring to healthcare organizations, there is evidence to show that healthcare providers are avoiding EHR innovations. Little research in information system mainstream research has addressed this phenomenon. To understand EHR avoidance, a mid-range theory is evoked from this textual analysis of responses gathered from healthcare providers at a large international hospital. The data was analyzed by applying a revealed causal mapping technique (RCM). Results of the study revealed not only the key constructs surrounding EHR avoidance, but also the underlying concepts that are shaping each of these constructs. This study demonstrated that the use of the RCM methodology yielded concepts and constructs of EHR avoidance that are not suggested by generalized theory, and revealed main interactions and linkages between these constructs.

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In Silico Generation of Alternative Hypotheses Using Causal Mapping (CMAP)

PLoS ONE ◽

10.1371/journal.pone.0005378 ◽

2009 ◽

Vol 4 (4) ◽

pp. e5378 ◽

Cited By ~ 4

Author(s):

Gabriel E. Weinreb ◽

Maryna T. Kapustina ◽

Ken Jacobson ◽

Timothy C. Elston

Keyword(s):

In Silico ◽

Causal Mapping ◽

Alternative Hypotheses

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ADAPTIVE TRUTH VALUED FLOW INFERENCE

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488594000225 ◽

1994 ◽

Vol 02 (03) ◽

pp. 279-286

Author(s):

HO CHUNG LUI

Keyword(s):

Causal Relationship ◽

Fuzzy Rule ◽

Fuzzy Relation ◽

Experimental Result ◽

Causal Mapping ◽

Fuzzy Point ◽

Adaptation Procedure ◽

Truth Value ◽

Value Flow

Based on the Truth Value Flow Inference(TVFI) theory presented by P.Z.Wang, a fuzzy rule P → Q can be denoted as a fuzzy point in the X × Y domain, when a set of rules are given, the fuzzy points are joined together to become a "fuzzy mountain". Such mountain represents the causal relationship between X and Y. This paper describes an adaptation procedure so that the fuzzy mountain can be refined from past examples. Experimental result shows that after learning, the fuzzy relation approaches to the true but unknown causal mapping between X and Y.

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