A Citation Analysis of the Evolution and State of Information Systems within a Constellation of Reference Disciplines

For the past two decades notions of cumulative tradition and reference disciplines have been a significant part of the introspective debates on the IS field. We provide an exploratory test on these notions using sociometric analysis. In doing so, we extend the work of Culnan and Swanson originally carried out about 25 years ago. By using the concept of a work point and reference points to identify where an IS article is published and the extent to which it draws from or contributes to other disciplines, we can position research in the IS field. First, a quantitative analysis of over 72,600 citations spread across 1406 IS articles in 16 journals over the period 1990-2003 reveals a distinct trend toward a cumulative tradition, a changing mix of reference disciplines, and a two-way relationship between IS and some of the more mature disciplines. Second, post-hoc content analysis provides a glimpse of how IS work is being utilized by other disciplines. Overall, our analysis indicates that IS is taking up a more socio-technical persona, building upon its own knowledge base, and repaying its debts by contributing to other disciplines. We interpret the movement towards building a cumulative tradition, and informing work in other disciplines as positive, as we strive toward being part of an intellectual network and establish centrality in areas that matter to us most

Where Information Systems Research Meets Artificial Intelligence Practice: Towards the Development of an AI Capability Framework

Information systems (IS) research has always been one of the leading applied research areas in the investigation of technology-related phenomena. Meanwhile, for the past 10 years, artificial intelligence (AI) has transformed every aspect of society more than any other technological innovation. Thus, this is the right time for IS research to foster more quality and high-impact research on AI starting by organizing the cumulated body of knowledge on AI in IS research. We propose a framework called AI capability framework that would provide pertinent and relevant guidance for conducting IS research on AI. Since AI is a fast-evolving phenomenon, this framework is founded on the main AI capabilities that shape today’s fast-moving AI ecosystem. Thus, it is crucial that such a framework engages both AI research and practice into a continuous and evolving dialogue