Previous research has proposed different types for and contingency factors affecting information technology governance. Yet, in spite of this valuable work, it is still unclear through what mechanisms IT governance affects organizational performance. We make a detailed argument for the mediation of strategic alignment in this process. Strategic alignment remains a top priority for business and IT executives, but theory-based empirical research on the relative importance of the factors affecting strategic alignment is still lagging. By consolidating strategic alignment and IT governance models, this research proposes a nomological model showing how organizational value is created through IT governance mechanisms. Our research model draws upon the resource-based view of the firm and provides guidance on how strategic alignment can mediate the effectiveness of IT governance on organizational performance. As such, it contributes to the knowledge bases of both alignment and IT governance literatures. Using dyadic data collected from 131 Taiwanese companies (cross-validated with archival data from 72 firms), we uncover a positive, significant, and impactful linkage between IT governance mechanisms and strategic alignment and, further, between strategic alignment and organizational performance. We also show that the effect of IT governance mechanisms on organizational performance is fully mediated by strategic alignment. Besides making contributions to construct and measure items in this domain, this research contributes to the theory base by integrating and extending the literature on IT governance and strategic alignment, both of which have long been recognized as critical for achieving organizational goals.
Neuroscience provides a new lens through which to study information systems (IS). These NeuroIS studies investigate the neurophysiological effects related to the design, use, and impact of IS. A major advantage of this new methodology is its ability to examine human behavior at the underlying neurophysiological level, which was not possible before, and to reduce self-reporting bias in behavior research. Previous studies that have revisited important IS concepts such as trust and distrust have challenged and extended our knowledge. An increasing number of neuroscience studies in IS have given researchers, editors, reviewers, and readers new challenges in terms of determining what makes a good NeuroIS study. While earlier papers focused on how to apply specific methods (e.g., functional magnetic resonance imaging), this paper takes an IS perspective in deriving six phases for conducting NeuroIS research and offers five guidelines for planning and evaluating NeuroIS studies: to advance IS research, to apply the standards of neuroscience, to justify the choice of a neuroscience strategy of inquiry, to map IS concepts to bio-data, and to relate the experimental setting to IS-authentic situations. The guidelines provide guidance for authors, reviewers, and readers of NeuroIS studies, and thus help to capitalize on the potential of neuroscience in IS research.
This article reports on the changing nature of Information Systems (IS) with regards to the internet. The authors suggest that the internet revolution upended tractional models of the customer/producer relationship. The internet has forced organizations to be more customer-centric, in particular, the way organizations disseminate information. The authors regard customer-centric IS as increasingly important and one that factors in customers, process, technology and product/service to maximize customer satisfaction.
Personalized services are increasingly popular in the Internet world. This study identifies theories related to the use of personalized content services and their effect on user satisfaction. Three major theories have been identified--information overload, uses and gratifications, and user involvement. The information overload theory implies that user satisfaction increases when the recommended content fits user interests (i.e., the recommendation accuracy increases). The uses and gratifications theory indicates that motivations for information access affect user satisfaction. The user involvement theory implies that users prefer content recommended by a process in which they have explicit involvement. In this research, a research model was proposed to integrate these theories and two experiments were conducted to examine the theoretical relationships. Our findings indicate that information overload and uses and gratifications are two major theories for explaining user satisfaction with personalized services. Personalized services can reduce information overload and, hence, increase user satisfaction, but their effects may be moderated by the motivation for information access. The effect is stronger for users whose motivation is in searching for a specific target. This implies that content recommendation would be more useful for knowledge management systems, where users are often looking for specific knowledge, rather than for general purpose Web sites, whose customers often come for scanning. Explicit user involvement in the personalization process may affect a user's perception of customization, but has no significant effect on overall satisfaction.
Since models play a critical role in human decision processes, model management is considered a very important function for decision support. This article examines how model management systems can be designed to support group problem-solving. First, basic concepts of model management and functional requirements for group model management systems are described. Then, an architecture for group model management systems design is presented. Finally, major implementation issues are discussed.
The paper presents a self-evolving approach to decision support systems (DSS) design. The basic premise of this approach is that a DSS should be aware of how it is being used and, then, automatically adapt to the evolution of its users. With self-evolving capabilities, a DSS will be able to provide a flexible menu hierarchy and a dynamic user interface. The major difference between the self-evolving design and a DSS developed by current approaches such as system development life cycle and user-involved evolutionary design is that the former has an extra component—the evolutionary mechanism—to control the evolution of the system. In order to develop self-evolving capabilities, the following three components must be developed: (1) a database of user profiles to keep track of related system usage data, (2) a knowledge base to store rules for determining appropriate system default policy, and (3) a control mechanism to control the evolution of the system.