How Do Individuals Understand Multiple Conceptual Modeling Scripts?

Author:

Jabbari Mohammad, ,Recker Jan,Green Peter,Werder Karl, , ,

Abstract

Because most real-world domains intended to be supported by an information system are complex, practitioners often use multiple different types of conceptual modeling scripts to understand them. We performed two experiments to examine how two theoretical factors of multiple scripts— combined ontological completeness and ontological overlap—influence how users develop an understanding of a real-world domain from multiple scripts. Results of the first experiment show that to some degree, ontological overlap improves participants’ understanding of a domain, more so than combined ontological completeness. In the second experiment, we tracked the eye movement data of participants to understand how ontological overlap between scripts impacts users’ information search and cognitive integration processes. We found that some occurrence of semantically similar constructs between scripts helps individuals to identify and relate constructs presented in different scripts. Users, therefore, can identify and focus on script areas that are relevant to their problem tasks. However, a high level of ontological overlap decreases the attention paid by participants to relevant task-specific areas because they spend more time searching for relevant information. Together, our findings both refine and extend existing conceptual modeling theory. We clarify the dialectics between the full and parsimonious real-world representations offered through multiple scripts and the individual’s understanding of the domain that is represented by those scripts.

Publisher

Association for Information Systems

Subject

Computer Science Applications,Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Semantically consistent transformation of behavioral and structural models;2023 IEEE 25th Conference on Business Informatics (CBI);2023-06-21

2. Modeling 4.0: Conceptual Modeling in a Digital Era;Communications of the Association for Information Systems;2023

3. System: A core conceptual modeling construct for capturing complexity;Data & Knowledge Engineering;2022-09

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