A user learning based DSS implementation methodology

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Texas Tech University

The behavioral problem of user resistance to change and its role in systems implementation failure has been raised in MIS and related fields. The opportunity cost of unused DSS technology is substantial, because it cannot improve decision performance. Despite this recognition, extant DSS design methodologies have directed a great deal of their focuses toward the technical issues related to the design of DSS technology. These methodologies are deficient from the perspective of managing the behavioral problem and motivating DSS utilization.

The purpose of this research is to provide a conceptual understanding of the behavioral problem of user resistance to change, and to identify and develop a means of resolving user resistance to change and hence motivating DSS utilization. This research presents a user-learning-based DSS implementation methodology. The methodology development is built upon prior research in MIS and related fields.

A user-learning-based DSS implementation methodology consists of a user-learning model of DSS implementation, a user-learning approach to DSS implementation, a set of implementation steps, and a generic architectural model of knowledge-based user-learning support systems (KULSS). This methodology is applicable to most DSS implementation situations where user resistance to change is observed at the onset of DSS implementation. The methodology facilitates user-cognitive learning to resolve user resistance to change and to develop a felt need for DSS utilization. A set of generic KULSS commands enables the user to identify his actual decision performance, to leam a desired decision performance, and to understand how the actual decision performance differs from the desired decision performance.

Human-computer interaction, User interfaces (Computer systems), Decision support systems