Abstract
This study presents theory that formalises, and generalises to problems of different levels of structure, the role of the application domain in IS problem solving. It does so by developing a unifying theory to explain the diverse findings from two experiments that focused on the role of the application domain in IS problem solving. The theoretical framework that we use to form the structure for our theory is a dual-task problem-solving model based on the theory of cognitive fit. Cognitive fit applies to problem solving in each of the contributing domains (application and IS), as well as to the interaction between the two. The theory of cognitive fit allows us to distinguish different types of interactions between the tasks that must be conducted in the IS and application domains when the two types of tasks ‘match’ and when they do not. Those interactions may be supportive, neutral, or conflicting, depending on whether the problem under investigation is well- or ill-structured.
Table of Contents
Domain knowledge, which is fundamental to all disciplines (Alexander, 1992), is knowledge of the area to which a set of theoretical concepts is applied. Domain knowledge has long been acknowledged as an important avenue of inquiry in educational research (see, for example, Alexander, 1992; Alexander and Judy, 1988) with studies being conducted in such diverse areas as physics and economics, on the one hand, and history and reading, on the other. Such studies have found that thinking is dominated by content and skills that are domain-specific (McPeck, 1990), and that the lack of domain knowledge results in inelegant problem-solving strategies (Alexander and Judy, 1988).
In the information systems (IS) discipline, the term ‘domain knowledge’ has dual significance. First, IS domain knowledge provides representations, methods, techniques, and tools that form the basis for the development of application systems. Second, those application systems are developed to organise or structure solutions to real-world problems that exist in a given business area, or application domain. IS problem solving therefore applies theoretical concepts from the IS domain to the application domain of interest. Hence, knowledge of the IS and the application domains go hand-in-hand in solving IS problems.
A number of studies argue that application domain knowledge impacts IS problem-solving effectiveness (see, for example, Blum, 1989; Curtis et al., 1988; Glass and Vessey, 1992). Few studies have, however, addressed this relationship empirically. Exceptions are Burton-Jones and Weber (1999), Khatri et al (2006), Purao et al (2002), Shaft and Vessey (1995, 1998, 2006), and Vessey and Conger (1993). Most research has examined processing aspects, with far fewer studies addressing data aspects, such as conceptual modelling. Finally, no studies have presented theory that seeks to explain the role of application domain knowledge in IS problem solving.
Given the pervasiveness of the application domain in IS development, it is important to understand why and how application domain knowledge can aid IS problem solving. In this paper we present theory that explains the roles of both IS and application domain knowledge, and the interactions between the two. We use theory related to dual-task problem solving as the theoretical framework. We then introduce the theory of cognitive fit as the fundamental theory for identifying and explaining different types of interrelationships between IS and application domain knowledge. Finally, we use theory from cognitive psychology in suggesting that the types of interaction between IS and application domain knowledge differ depending on the nature of the problem under investigation. We illustrate our theory with two recently published studies, each addressing different types of problems (Khatri et al., 2006; Shaft and Vessey, 2006).
In the next section, we present the theoretical foundations on which our theory is based. The following two sections present our theory of cognitive fit in dual-task problem solving in well- and ill-structured problems and then we present the implications of our theory for both the IS and cognitive psychology communities. Lastly, we present our conclusions.