Reframing interpretivism and positivism as understanding and explanation: Consequences for information systems research

Abstract

This research offers a new perspective by reframing the positivist-interpretive debate as a distinction between the functional outcomes of research: explanation and understanding. Based on an older and well-established literature in philosophy, this distinction can reinvigorate important differences in research outcomes that have been lost. Understanding or “subjective meaning” is connected to the intentionality, thoughts, and motivations of the human subjects under study. From this perspective, understanding is the type of knowledge gained from determining the meanings, categories, and symbols humans attach to actions, knowledge, and systems. In contrast, explanation is achieved by subsuming individual instantiations of the phenomenon under broad general laws, or identifying causal mechanisms that support antecedent-consequent pairs. Researchers can proactively use the understanding-explanation distinction as a heuristic to create new lines of research questions based on what has not been explained or understood rather than on which ontology or methodology has not been used

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References found in this work

The sciences of the artificial.Herbert Alexander Simon - 1969 - [Cambridge,: M.I.T. Press.
Studies in the logic of explanation.Carl Gustav Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.
Causality and explanation.Wesley C. Salmon - 1998 - New York: Oxford University Press.
Statistical explanation & statistical relevance.Wesley C. Salmon - 1971 - [Pittsburgh]: University of Pittsburgh Press. Edited by Richard C. Jeffrey & James G. Greeno.

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