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Conceptual models are used to understand information systems application domains and to communicate about them in order to better understand system requirements. Conceptual models are required to represent relevant aspects of the modeled domain faithfully, and be understandable.The use of ontological theories has been proposed to guide the creation of effective models. Specifically, Bunge's ontology has been applied to guide the use of the ERM, the UML, and business process grammars. However, because the choice of ontology reflects beliefs, and model understanding involves human cognition, whether or not ontological guidance results in better models is an empirical question. Several empirical works studied this issue, differing in grammars, modeling aspects, empirical tasks, and measures. We report a meta-analysis of published empirical research about the impact of ontological guidance based on Bunge's model, and user understanding of conceptual models. The results support the proposition that ontological guidance can improve model understandability.

Information systems are intended to be faithful accounts of real-world applications. As an integral part of the development process, analysts create conceptual models in order to understand the application and communicate requirements. Failure to do so has been a prominent reason for IT projects' failure. Hence, improving the quality of models could have a major impact on the information systems' success. To guide the modeling process, researchers use ontology to create more expressive representations of reality. However, improving expressiveness can make the models complicated and cause cognitive hurdles for users. Therefore, the question is whether ontological guidance is worth the trade-off between expressiveness and complexity. This paper describes a meta-analysis of empirical research examining the impact of ontological guidance on users' understandability. The results show that ontological guidance can improve users' understanding of conceptual models, especially those requiring deeper understanding, thus providing support for ontological guidance in conceptual modeling.

Conceptual models visually represent entities and relationships between them in an information system. Effective conceptual models should be simple while communicating sufficient information. This trade-off between model complexity and clarity is crucial to prevent failure of information system development. Past studies have found that more expressive models lead to higher performance on tasks measuring a user's deep understanding of the model and attributed this to lower experience of cognitive workload associated with these models. This study examined this hypothesis by measuring users' EEG brain activity while they completed a task with different conceptual models. 30 participants were divided into two groups: One group used a low ontologically expressive model (LOEM), and the other group used a high ontologically expressive model (HOEM). Cognitive workload during the task was quantified using EEG Engagement Index, which is a ratio of brain activity power in beta as opposed to the sum of alpha and theta frequency bands. No significant difference in cognitive workload was found between the LOEM and HOEM groups indicating equal amounts of cognitive processing required for understanding of both models. The main contribution of this study is the introduction of neurophysiological measures as an objective quantification of cognitive workload in the field of conceptual modeling and information systems.

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