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Systems analysts create conceptual models in order to understand information system (IS) application domains and communicate system requirements (Mylopoulos, 1992) with stakeholders, analysts, designers, and implementers. Failing to understand the domain requirements is a major cause of failure in IS development projects (Wand & Weber, 2002, p. 363). Correcting an error in understanding user requirements post-implementation of the IS is “100 times more costly than it is to correct it during requirements analysis” (Moody, 2005, p. 245). Thus, by enhancing the quality of conceptual models and one could expect a major impact on the success of IS projects.
Conceptual models are required to provide a faithful representation of the relevant aspects of the domain (Wand & Weber, 2002). Use of ontology, “a branch of philosophy that deals with the order and structure of reality in the broadest sense possible” (Angeles, 1981), has been proposed to guide what ought to be modeled (Wand & Weber, 1989) since they “account for the structure and behavior of the world in general” (Storey, 2017, p. 19). Models that are ontologically valid are considered to be more faithful to the reality and thus more ‘ontologically expressive’1 (Wand & Weber, 1993). However, a more expressive grammar tends to have additional constructs (in order to provide a more complete mapping between the grammar and constructs in the ontology) as well as guidelines to make sure that the created model is clear (Wand & Weber 1993, p. 228). Thus, Wand and Weber (1993) posited that “the goals of expressive adequacy and simplicity are often in conflict. Additional constructs and production rules enhance expressive power at the cost of increased complexity” (p. 234). The increased complexity might interfere with the process of creating conceptual models, and in interpretation of the models by users. Overall, modelers will face a trade-off between expressiveness and simplicity/parsimony (Khatri, Vessey, Ram & Ramesh 2006; Bowen, O’Farrell, & Rohde 20092).
The objective of the current paper is to investigate whether conceptual models that are more ontologically expressive can lead to better user understanding despite the possibility of adding to complexity. We use the term ‘ontological guidance’ to refer to conceptual models where the creators of the model sought guidance from ontology and tried to create conceptual models that are more faithful to reality (i.e., more ontologically expressive). Evaluating the clarity and completeness aspects of a representation (e.g., a conceptual model) is not contingent on using a particular ontological theory.
Systematic investigation of the value of using ontology requires that empirical work to have been previously conducted. Practically, all empirical work has been done using Bunge ontology (Bunge, 1977) (as adapted to information systems by Wand and Weber (1989, 1993, 1995, 2002)). Bunge’s ontology is considered the most widely used ontology in systems analysis and design and in conceptual modeling research (Allen & March, 2006a; Fonseca, 2007). Besides the wide adoption of this ontological theory in the IS discipline, Weber (1997) as well as Tilakaratna and Rajapakse (2017) consider Bunge’s ontology to be the most complete and best formulated ontology to evaluate information systems analysis and design (Weber, 1997 p. 33; Tilakaratna & Rajapakse, 2017, p. 2). The present paper synthesizes past empirical work without making any claims regarding merits of different ontological theories.