Effects of conceptual data modeling formalisms on user validation and analyst modeling of information requirements

Data modeling has become a popular method for representing organizational information requirements. Recently, it is being incorporated as a core component of integrated systems development tools, called Computer-Aided Systems Engineering (CASE) tools. CASE tools utilize data modeling as a communication vehicle for IS analysts and end users. Involving end users in the validation of data models will lead to a higher quality representation of user and organizational information requirements. This research makes a clear distinction between how users and analysts utilize data modeling. Previous research presumed that end users would create organizational data models themselves. This research maintains that large scale data models will be developed by analysts and validated by users. The objective of the study was to examine the effects of different data modeling formalisms on analyst performance in developing a data model and on user performance in validating a data model. An Extended Entity-Relationship modeling formalism was compared with a Binary modeling formalism. The study found that the effects of alternative conceptual modeling formalisms vary depending on the types of problem solvers (users or analysts) and their tasks (validation for users and modeling for analysts). For user validation tasks, there was no significant difference in performance between the two treatment groups each utilizing one of the two modeling formalisms. For analyst modeling task, the analysts using the extended E-R modeling formalism had a significantly better semantic modeling performance than the analysts using the binary modeling formalism. There was no significant difference in the syntactic qualities of the data models produced by the two analyst groups.