Keynote1: Robert Davison

Robert Davison is a Professor of Information Systems at the City University of Hong Kong. His current research focuses on virtual Knowledge Management and Collaboration in Chinese SMEs. He has published over 200 articles in a variety of international journals (such as MIS Quarterly, the Information Systems Journal, Journal of IT, Journal of the AIS, Journal of the American Society for Information Science & Technology, IEEE Transactions on Engineering Management, Decision Support Systems, Communications of the AIS, and Communications of the ACM) and conferences. Robert is the Editor-in-Chief of the Electronic Journal of Information Systems in Developing Countries and the Information Systems Journal. Home Page:


Topic: The Importance of Context in Research

In this presentation, I will explore the importance of context in research design and reporting. My perspective is that of a journal editor who sees a large number of submissions, many of which are very imprecise in their contextual descriptions and often cavalier in their assumption that context is not important. I start by positioning context with respect to generalization and the boundary conditions that apply to research designs and findings. I apply the dichotomy of universalism and particularism and discuss the interaction of theory and culture with context in order to consider the scope of validity for research findings and conclusions. From reflections on theory, I move to an examination of indigenous theory, in particular where it may be needed and how it can be developed. Finally, I use the metaphor of storytelling as a way of explaining how context can readily be incorporated into theoretical accounts of research thereby ensuring that context is dealt with appropriately. I illustrate the presentation with many examples of papers that display either contextual inadequacies or a wealth of useful contextual detail. My aim is to discourage the conduct of research, and the acceptance of papers, that falsely imply universalism, rely on convenient samples or ignore indigenous constructs.