Understanding Quality in Conceptual Modeling
IEEE Software
Research Commentary: Information Systems and Conceptual Modeling--A Research Agenda
Information Systems Research
Evaluating modeling techniques based on models of learning
Communications of the ACM - Service-oriented computing
Using ontology to validate conceptual models
Communications of the ACM - Service-oriented computing
Complexity and clarity in conceptual modeling: comparison of mandatory and optional properties
Data & Knowledge Engineering - Special issue: Quality in conceptual modeling
Data & Knowledge Engineering - Special issue: Quality in conceptual modeling
A process for generating fitness measures
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
Evaluating quality of conceptual modelling scripts based on user perceptions
Data & Knowledge Engineering
Quality and perceived usefulness of process models
Proceedings of the 2010 ACM Symposium on Applied Computing
Evaluating quality of conceptual models based on user perceptions
ER'06 Proceedings of the 25th international conference on Conceptual Modeling
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Semantic quality expresses the degree of correspondence between the information conveyed by a model and the domain that is modelled. As an early quality indicator of the system that implements the model, semantic quality must be evaluated before proceeding to implementation. Current evaluation approaches are based on ontological or meta-model analysis and/or use objective metrics. They ignore the model user's perception of semantic quality, which also determines whether the benefits of using a faithful model will be achieved. The paper presents the development of a perceived semantic quality measure. It presents a measure pre-test, i.e. a study aimed at refining and validating a new measure before its use in research and practice. The results of the pre-test show that our measure is reliable and that it is sufficiently differentiated from other perception-based measures of information model use like ease of use, usefulness, and user information satisfaction.