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On the Identification of Modeler Communities

On the Identification of Modeler Communities

Dirk van der Linden, Stijn J.B.A. Hoppenbrouwers, Henderik A. Proper
Copyright: © 2014 |Volume: 5 |Issue: 2 |Pages: 19
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781466654969|DOI: 10.4018/ijismd.2014040102
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MLA

van der Linden, Dirk, et al. "On the Identification of Modeler Communities." IJISMD vol.5, no.2 2014: pp.22-40. http://doi.org/10.4018/ijismd.2014040102

APA

van der Linden, D., Hoppenbrouwers, S. J., & Proper, H. A. (2014). On the Identification of Modeler Communities. International Journal of Information System Modeling and Design (IJISMD), 5(2), 22-40. http://doi.org/10.4018/ijismd.2014040102

Chicago

van der Linden, Dirk, Stijn J.B.A. Hoppenbrouwers, and Henderik A. Proper. "On the Identification of Modeler Communities," International Journal of Information System Modeling and Design (IJISMD) 5, no.2: 22-40. http://doi.org/10.4018/ijismd.2014040102

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Abstract

The authors discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling (EM). People tend to understand modeling meta-concepts (i.e., a modeling language's constructs or types) in a certain way and can be grouped by this conceptual understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute such a semantic community) can make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts s/he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. The authors have observed that in practice decisions to group people based on certain shared properties are often made, but are rarely backed up by empirical data demonstrating their supposed efficacy. The authors demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities. The authors also discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that there is no empirical support for commonly used (and often implicit) grouping properties such as similar background, focus and modeling language.

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