The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Understanding Business Domain Models: The Effect of Recognizing Resource-Event-Agent Conceptual Modeling Structures
Abstract
In this paper, the author investigates the effect on understanding of using business domain models that are constructed with Resource-Event-Agent (REA) modeling patterns. First, the author analyzes REA modeling structures to identify the enabling factors and the mechanisms by means of which users recognize these structures in a conceptual model and description of an information retrieval and interpretation task. Based on this understanding, the author hypothesizes positive effects on model understanding for situations where REA patterns can be recognized in both task and model. An experiment is then conducted to demonstrate a better understanding of models with REA patterns compared to information equivalent models without REA patterns. The results of this experiment indicate that REA patterns can be recognized with minimal prior patterns training and that the use of REA patterns leads to models that are easier to understand for novice model users.
Related Content
|
Renjith V. Ravi, Mangesh M. Ghonge, P. Febina Beevi, Rafael Kunst.
© 2022.
24 pages.
|
|
Manimaran A., Chandramohan Dhasarathan, Arulkumar N., Naveen Kumar N..
© 2022.
20 pages.
|
|
Ram Singh, Rohit Bansal, Sachin Chauhan.
© 2022.
19 pages.
|
|
Subhodeep Mukherjee, Manish Mohan Baral, Venkataiah Chittipaka.
© 2022.
17 pages.
|
|
Vladimir Nikolaevich Kustov, Ekaterina Sergeevna Selanteva.
© 2022.
23 pages.
|
|
Krati Reja, Gaurav Choudhary, Shishir Kumar Shandilya, Durgesh M. Sharma, Ashish K. Sharma.
© 2022.
18 pages.
|
|
Nwosu Anthony Ugochukwu, S. B. Goyal.
© 2022.
23 pages.
|
|
|