Understanding Conceptual Schemas: Exploring the Role of Application and IS Domain Knowledge

Abstract

Although information systems (IS) problem solving involves knowledge of both the IS and application domains, little attention has been paid to the role of application domain knowledge. In this study, which is set in the context of conceptual modeling, we examine the effects of both IS and application domain knowledge on different types of schema understanding tasks: syntactic and semantic comprehension tasks and schema-based problem-solving tasks. Our thesis was that while IS domain knowledge is important in solving all such tasks, the role of application domain knowledge is contingent upon the type of understanding task under investigation. We use the theory of cognitive fit to establish theoretical differences in the role of application domain knowledge among the different types of schema understanding tasks. We hypothesize that application domain knowledge does not influence the solution of syntactic and semantic comprehension tasks for which cognitive fit exists, but does influence the solution of schema-based problem-solving tasks for which cognitive fit does not exist. To assess performance on different types of conceptual schema understanding tasks, we conducted a laboratory experiment in which participants with high- and low-IS domain knowledge responded to two equivalent conceptual schemas that represented high and low levels of application knowledge (familiar and unfamiliar application domains). As expected, we found that IS domain knowledge is important in the solution of all types of conceptual schema understanding tasks in both familiar and unfamiliar applications domains, and that the effect of application domain knowledge is contingent on task type. Our findings for the EER model were similar to those for the ER model. Given the differential effects of application domain knowledge on different types of tasks, this study highlights the importance of considering more than one application domain in designing future studies on conceptual modeling.

An Empirical Review of the Connection Between Model Viewer Characteristics and the Comprehension of Conceptual Process Models

“…A third stream of research, which is most relevant to this paper, is examining the conditions which determine how well a conceptual model is understood by those using it. This research, to date, has largely examined semantics (i.e., the meaning of constructs in a model, e.g., Weber (1997)), syntax (i.e., the rules about how a model can be constructed with a grammar, e.g., Reijers et al (2011a) and Mendling et al (2010)) and to a much lesser extent pragmatics (i.e., how existing user knowledge may influence how a model is understood, e.g., Khatri et al (2006) and Khatri and Vessey (2016)). Some studies, finally, have attempted to review the relevant works in these areas, e.g., Burton-Jones et al (2009), with the aim to provide guidelines for future research.…”

“…Various aspects of MVCs have been discussed in the literature, partially relating to theoretical knowledge (Khatri et al 2006;Mendling et al 2012;Reijers and Mendling 2011), duration of practice (Recker 2010a;Reijers et al 2011b;Recker and Dreiling 2011), education (Recker 2010a), or familiarity (BurtonJones and Meso 2008).…”

Exploring cognitive style and task-specific preferences for process representations

“…The process was based on the Nobel Prize process scenario published in the context of the BPMN standard [63]. We selected this process because it is both from a domain that most people would have heard of (the Nobel Prize) but also describe a procedure largely unknown to the wider public, in turn reducing potential bias stemming from existing domain knowledge or process familiarity [64]. The process contains 16 activities executed by four actors and includes control flow divergences such as an exclusive split.…”

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.…”