A Theory of Information Compression: When Judgments Are Costly

Richard Thomas Watson (), Kirk Plangger (), Leyland Pitt () and Amrit Tiwana ()
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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

Information Systems Research, 2023, vol. 34, issue 3, 1089-1108

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.

Keywords: information compression; judgment networks; judgment costs; information entropy; social brain; agency theory; type of good; Shannon–Weaver communication model; punctuated equilibrium (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:34:y:2023:i:3:p:1089-1108

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