Towards the improved treatment of generalization of knowledge claims in IS research: drawing general conclusions from samples

This paper presents a framework for justifying generalization in information systems (IS) research. First, using evidence from an analysis of two leading IS journals, we show that the treatment of generalization in many empirical papers in leading IS research journals is unsatisfactory. Many quantitative studies need clearer definition of populations and more discussion of the extent to which ‘significant’ statistics and use of non-probability sampling affect support for their knowledge claims. Many qualitative studies need more discussion of boundary conditions for their sample-based general knowledge claims. Second, the proposed new framework is presented. It defines eight alternative logical pathways for justifying generalizations in IS research. Three key concepts underpinning the framework are the need for researcher judgment when making any claim about the likely truth of sample-based knowledge claims in other settings; the importance of sample representativeness and its assessment in terms of the knowledge claim of interest; and the desirability of integrating a study's general knowledge claims with those from prior research. Finally, we show how the framework may be applied by researchers and reviewers. Observing the pathways in the framework has potential to improve both research rigour and practical relevance for IS research.