Complexity and clarity in conceptual modeling: Comparison of mandatory and optional properties

Ontology-driven conceptual modeling: A systematic literature mapping and review

“…However, probably the first step towards finding the adequate balance between an increased understanding of the semantics of a model and its increased complexity is first identifying how learning, interpretation and understanding of these models takes place (research opportunity 3). Finally, we agree with Gemino & Wand (2005), that the issue of understanding versus complexity "can be studied by combining theoretical considerations and empirical methods". Theoretical contributions and artifacts should be validated and evaluated by empirical studies that assess the perceived usefulness and perceived ease of these theoretical contributions (research opportunity 1).…”

“…We have noticed in the articles of this literature study, especially those papers situated in the knowledge layer and learning layer of the SLR, that many of these empirical results often encounter the issue of complexity in the process of ontology-driven conceptual modeling (Gemino & Wand, 2005;Guizzardi et al, 2011). In order to tackle this ill-favored effect of complexity, we agree with (Guizzardi & Halpin, 2008) that research in ontology-driven conceptual modeling on the one hand needs to provide theoretically sound conceptual tools with precisely defined semantics but on the other hand must hide as much as possible the complexity that arise of these ontological theories.…”

“…However, as noted by (Gemino & Wand, 2005), we cannot solely focus on the complexity and comprehension of models without considering the domain understanding obtained through these models. For example, their study indicated that the use of mandatory properties with subtypes added to the overall complexity of the model but did provide a better understanding and comprehension of semantics of the model.…”

“…Parallel to research of the physical layer that focused on the semantics and ontological deficiencies of modeling constructs and grammars, there was a similar stream of research in the knowledge layer that aimed at assessing the perceptions of users and modelers of these modeling constructs and grammars. To assess these perceptions, experimental studies were performed to observe "how users and modelers perceive ontological constructs and more specifically to determine their perception of the clarity and comprehensibility of these grammars and constructs" (Evermann & Halimi, 2008;Gemino & Wand, 2005;Parsons, 2011;Shanks, Tansley, Nuredini, & Tobin, 2008). Another line of research in the knowledge layer focused more on the shortcomings of conceptual modeling languages for representing certain domains.…”

How collaborative technology supports cognitive processes in collaborative process modeling: A capabilities-gains-outcome model

“…Increased perceptions of usefulness will depend on the modeling capacities supplied by the technology (as captured in the ease of modeling construct and as hypothesizes above), but also through the levels of knowledge development enabled through the use of the technology. This is because process modeling is essentially a cognitive information processing task in which individuals apply, and increase, two types of knowledge: knowledge about the act of modeling (method knowledge) as well as knowledge about the process domain being modeled (domain knowledge) [31,39]. Technology that allows group members to increase knowledge development, therefore, contributes directly to performance gains in process modeling, which will manifest in elevated usefulness perceptions.…”