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A Theory of Information Compression: When Judgments Are Costly

Author

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  • Richard Thomas Watson

    (University of Georgia, Athens, Georgia 30602)

  • Kirk Plangger

    (King’s College London, London WC2B 4BG, United Kingdom)

  • Leyland Pitt

    (Simon Fraser University, Vancouver, British Columbia V6C 1W6, Canada)

  • Amrit Tiwana

    (University of Georgia, Athens, Georgia 30602)

Abstract

A theory of information compression (TIC) conceptualizes how anticipated judgment costs can affect decision quality. We theorize—inductively from decision making in medicine, energy pricing, auditing, and financial analytics—how judgment networks can exacerbate financial and nonfinancial judgment costs that compress information. Information compression occurs when a process intended to inform decision making generates information that has little variation. This can reduce decision quality and market efficiency. We offer potential remedies to mitigate its adverse societal consequences. We use complementary theoretical perspectives to nomologically contextualize how information compression arises. We introduce an information compression measure based on information entropy. TIC’s theoretical crux is that the expansion of a judgment network’s publicness exacerbates information compression by increasing judgment costs for some entities in a judgment network. We close with future research ideas on TIC’s core propositions and its broader theoretical implications for information systems research.

Suggested Citation

  • Richard Thomas Watson & Kirk Plangger & Leyland Pitt & Amrit Tiwana, 2023. "A Theory of Information Compression: When Judgments Are Costly," Information Systems Research, INFORMS, vol. 34(3), pages 1089-1108, September.
  • Handle: RePEc:inm:orisre:v:34:y:2023:i:3:p:1089-1108
    DOI: 10.1287/isre.2022.1163
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