Other-settings generalization in IS research

Seddon, Peter B. and Scheepers, Rens 2006, Other-settings generalization in IS research, in IT for Under-Served Communities; ICIS 27th International Conference on Information Systems, Association for Information Systems, [Milwaukee, Wis.], pp. 1141-1158.

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Title Other-settings generalization in IS research
Author(s) Seddon, Peter B.
Scheepers, Rens
Conference name International Conference on Information Systems (27th : 2006 : Milwaukee, Wis.)
Conference location Milwaukee, Wis.
Conference dates 10-13 Dec. 2006
Title of proceedings IT for Under-Served Communities; ICIS 27th International Conference on Information Systems
Editor(s) [Unknown]
Publication date 2006
Conference series International Conference on Information Systems
Start page 1141
End page 1158
Publisher Association for Information Systems
Place of publication [Milwaukee, Wis.]
Keyword(s) research methodology
other-settings generalization
OSG
external validity
Summary This paper presents a simple conceptualization of generalization, called other-settings generalization, that is valid for any IS researcher who claims that his or her results have applicability beyond the sample where data were collected. An other-settings generalization is the researcher’s act of arguing, based on the representativeness of the sample, that there is a reasonable expectation that a knowledge claim already believed to be true in one or more settings is also true in other clearly defined settings. Features associated with this conceptualization of generalization include (a) recognition that all human knowledge is bounded, (b) recognition that all knowledge claims—including generalizations—are subject to revision, (c) an ontological assumption that objective reality exists, (d) a scientific-realist definition of truth, and (e) identification of the following three essential characteristics of sound other-settings generalizations: (1) the researcher must clearly define the larger set of things to which the generalization applies; (2) the justification for making other-settings generalizations ultimately depends on the representativeness of the sample, not statistical inference; (3) representativeness is judged by comparing key characteristics of the proposition being generalized in the sample and target population. The paper concludes with the recommendation that future empirical IS research should include an explicit discussion of the other-settings generalizability of research findings.
Language eng
Field of Research 080699 Information Systems not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2006, AIS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30036296

Document type: Conference Paper
Collection: School of Information and Business Analytics
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