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Generalizing Generalizability in Information Systems Research

Published: 01 September 2003 Publication History

Abstract

Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research.

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cover image Information Systems Research
Information Systems Research  Volume 14, Issue 3
September 2003
96 pages

Publisher

INFORMS

Linthicum, MD, United States

Publication History

Published: 01 September 2003

Author Tags

  1. Case studies
  2. Generalizability
  3. Interpretive research
  4. Positivist research
  5. Qualitative research
  6. Quantitative research
  7. Research design
  8. Research methodology

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  • (2024)Using machine learning to create and capture value in the business models of small and medium-sized enterprisesInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2023.10263773:COnline publication date: 5-Jan-2024
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