Knowledge that Transforms

IS research has considered the outsourcing decision from the perspective of transaction cost economics (TCE) and institutional theory. In this research, we consider how the appropriation of the logic of transaction cost economics is contingent on decision makers’ institutional context. The institutional contexts contrasted are professional versus political contexts. In a survey of 214 city governments in the United States, we substantiate the existence of these two institutional contexts, a distinction that has been noted to extend into the private sector as well. Subsequent analyses of the moderating effects of institutional context on the application of the TCE heuristic to the outsourcing decision revealed the following: The institutional context moderated the impacts of “human frailty” conditions—of opportunism and bounded rationality—and of transaction frequency on outsourcing decisions. In professional contexts, opportunism reduced outsourcing and frequency increased outsourcing; in political contexts, bounded rationality fostered outsourcing and frequency dissuaded outsourcing. However, no institutional moderation was noted for the situational conditions of asset specificity and uncertainty. Instead, situational conditions were found to increase the incidence of outsourcing across both contexts. Findings about the contingent effects of human frailty conditions augment our understanding of the outsourcing phenomenon by emphasizing that decision makers’ attentiveness to the logic of transaction costs during outsourcing is shaped by their institutional context. Findings with regard to situational conditions suggest a need for future research to consider the role of another contextual factor—resource munificence—in mitigating the effects of situational conditions on responses to transaction costs.

The aim of this research essay is to examine the structural nature of theory in Information Systems. Despite the importance of theory, questions relating to its form and structure are neglected in comparison with questions relating to epistemology. The essay addresses issues of causality, explanation, prediction, and generalization that underlie an understanding of theory. A taxonomy is proposed that classifies information systems theories with respect to the manner in which four central goals are addressed: analysis, explanation, prediction, and prescription. Five interrelated types of theory are distinguished: (1) theory for analyzing, (2) theory for explaining, (3) theory for predicting, (4) theory for explaining and predicting, and (5) theory for design and action. Examples illustrate the nature of each theory type. The applicability of the taxonomy is demonstrated by classifying a sample of journal articles. The paper contributes by showing that multiple views of theory exist and by exposing the assumptions underlying different viewpoints. In addition, it is suggested that the type of theory under development can influence the choice of an epistemological approach. Support is given for the legitimacy and value of each theory type. The building of integrated bodies of theory that encompass all theory types is advocated.

Explanation facilities are considered essential in facilitating user interaction with knowledge-based systems (KBS). Research on explanation provision and the impact on KBS users has shown that the domain expertise affects the type of explanations selected by the user and the basis for seeking such explanations. The prior literature has been limited, however, by the use of simulated KBS that generally provide only feedback explanations (i.e., ex post to the recommendation of the KBS being presented to the user). The purpose of this study is to examine the way users with varying levels of expertise use alternative types of KBS explanations and the impact of that use on decision making. A total of 64 partner/ manager-level and 82 senior/staff-level insolvency professionals participated in an experiment involving the use of a fully functioning KBS to complete a complex judgment task. In addition to feedback explanations, the KBS also provided feedforward explanations (i.e., general explanations during user input about the relationships between information cues in the KBS) and included definition type explanations (i.e., declarative-level knowledge). The results show that users were more likely to adhere to recommendations of the KBS when an explanation facility was available. Choice patterns in using explanations indicated that novices used feedforward explanations more than experts did, while experts were more likely than novices to use feedback explanations. Novices also used more declarative knowledge and initial problem solving type explanations, while experts used more procedural knowledge explanations. Finally, use of feedback explanations led to greater adherence to the KBS recommendation by experts—a condition that was even more prevalent as the use of feedback explanations increased. The results have several implications for the design and use of KBS in a professional decision-making environment.