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Generalizing Generalizability inInformation Systems ResearchAllen S. Lee • Richard L. BaskervilleVirginia Commonwealth University, 1015 Floyd Avenue, Room 4146, Richmond, Virginia 23284-4000Georgia State University, 33 Gilmer Street SE, Atlanta, Georgia 30303-3000allenslee@alum.mit.edu • baskerville@acm.orgGeneralizability is a major concern to those who do, and use, research. Statistical,sampling-based generalizability is well known, but methodologists have long beenaware of conceptions of generalizability beyond the statistical. The purpose of this essay isto clarify the concept of generalizability by critically examining its nature, illustrating its useand misuse, and presenting a framework for classifying its different forms. The frameworkorganizes the different forms into four types, which are deFned by the distinction betweenempirical and theoretical kinds of statements. On the one hand, the framework afFrms thebounds within which statistical, sampling-based generalizability is legitimate. On the otherhand, the framework indicates ways in which researchers in information systems and otherFelds may properly lay claim to generalizability, and thereby broader relevance, even whentheir inquiry falls outside the bounds of sampling-based research.(Research Methodology; Positivist Research; Interpretive Research; Quantitative Research; QualitativeResearch; Case Studies; Research Design; Generalizability)1. IntroductionGeneralizability is a major concern to those who do,and use, research. Among other things, it refers tothe validity of a theory in a setting different from theone where it was empirically tested and conFrmed. Atheory that lacks such generalizability also lacks use-fulness. Because the Feld of information systems (IS)is not just a science but also a profession (and there-fore has professional constituents such as IS execu-tives, managers, and consultants), the generalizabilityof an IS theory to different settings is important notonly for purposes of basic research, but also for pur-poses of managing and solving problems that corpo-rations and other organizations experience in society.Statistical, sampling-based generalizability is a validconcept within its bounds, but its uncritical applica-tion as the norm for all generalizability can lead to animproper assessment of the generalizability of manyresearch studies. The purpose of this essay is to clarifythe concept of generalizability by critically examiningits nature, illustrating its use and misuse, and offeringa framework for classifying its different forms.TheOxford English Dictionary(1989) deFnes gen-eralize as “to form general notions by abstractionfrom particular instances,” generalizable as “capableof being generalized,” and generalizability as “thefact or quality of being generalizable.” Conceptual-ized in this way, generalizability need not have aquantitative or statistical dimension. However, manyIS researchers, both quantitative and qualitative, haverestricted themselves to just one particular notionof generalizability—namely, a statistical, sampling-based notion. ±urthermore, they have imposed thisparticular notion even outside the bounds of statis-tical, sampling-based research. It is as if statistical,sampling-based generalizability has been overgen-eralized, as it were, to nonstatistical, nonsamplingforms of research. Qualitative IS researchers have for-gone claims to generalizability when, in fact, theyhave not yet broached conceptions of generalizabil-ity appropriate to their own research. Ultimately, this1047-7047/03/1403/0221$05.001526-5536 electronic ISSNInformation Systems Research © 2003 INFORMSVol. 14, No. 3, September 2003, pp. 221–243