A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies-I/S success-has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success-SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed a total of 180 articles are cited and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.

Information Systems Success: The Quest for the Dependent Variable

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Getting to YES. Negotiating Agreement Without Giving In

The area of consumer cocreation is in its infancy and many aspects are not well understood. In this article, we outline and discuss a conceptual framework that focuses on the degree of consumer cocreation in new product development (NPD). The authors examine (a) the major stimulators and impediments to this process, (b) the impact of cocreation at each stage of the NPD process, and (c) the various firm-related and consumer-related outcomes. A number of areas for future research are suggested.

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Consumer Cocreation in New Product Development

Hawaii International Conference on System Sciences

Given the significant, sustained growth in services experienced worldwide, Arizona State University’s Center for Services Leadership embarked on an 18-month effort to identify and articulate a set ...

https://journals.sagepub.com/doi/pdf/10.1177/1094670509357611

Moving Forward and Making a Difference: Research Priorities for the Science of Service

In December 2012, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congress 3 and conducted surveys to assess academia’s response to the growing market need for students with Business Intelligence (BI) and Business Analytics (BA) skill sets. This panel report describes the key findings and best practices that were identified, with an emphasis on what has changed since the BI Congress efforts in 2009 and 2010. The article also serves as a “call to action” for universities regarding the need to respond to emerging market needs in BI/BA, including “Big Data.” The IS field continues to be well positioned to be the leader in creating the next generation BI/BA workforce. To do so, we believe that IS leaders need to continuously refine BI/BA curriculum to keep pace with the turbulent BI/BA marketplace.

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The Current State of Business Intelligence in Academia: The Arrival of Big Data

D.E. O'Leary (1987) noted the inappropriate nature of traditional software validation techniques for expert system validation and proposed a research methods approach. R.M. O'Keefe et al. (1987) established validation as an important concern in expert system research and development and presented general descriptive methodologies for qualitative and quantitative methods of formal validation. A specific formal paradigm for prototype validation that is consistent with O'Leary's as well as O'Keefe's general guidelines for expert system development is presented. The paradigm explicitly incorporates validation into the prototype development life cycle under constrained resources, primarily time and money. The validation approach is demonstrated and evaluated relative to an expert system prototype named Signal Control at Isolated Intersections. The paradigm's strengths and weaknesses as well as its potential use with other types of expert system development projects are examined. >

Validating expert systems

/pdf/the-current-state-of-business-intelligence-in-academia-2g2tx7qbut.pdf

The Current State of Business Intelligence in Academia

/pdf/business-intelligence-and-big-data-in-higher-education-2y12fjem46.pdf

Business intelligence and big data in higher education: Status of a multi-year model curriculum development effort for business school undergraduates, MS graduates, and MBAs

Michael Goul

Papers

Moving Forward and Making a Difference: Research Priorities for the Science of Service

The Current State of Business Intelligence in Academia: The Arrival of Big Data

Validating expert systems

The Current State of Business Intelligence in Academia

Business intelligence and big data in higher education: Status of a multi-year model curriculum development effort for business school undergraduates, MS graduates, and MBAs