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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.
History
Journal
European journal of information systemsVolume
21Pagination
6 - 21Publisher
Palgrave Macmillan Ltd.Location
Basingstoke, EnglandPublisher DOI
ISSN
0960-085XeISSN
1476-9344Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2011, Operational Research Society Ltd. All rights reservedUsage metrics
Keywords
research methodologyother-settings generalizationexternal validitysampleP-valueBayesian statisticsScience & TechnologySocial SciencesTechnologyComputer Science, Information SystemsInformation Science & Library ScienceManagementComputer ScienceBusiness & EconomicsMEDICAL STATISTICSINFERENCEVALIDITYFISHERNEYMANLOGICInformation Systems
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