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A Theory of Information Compression: When Judgments are Costly

Research output: Contribution to journalArticlepeer-review

Richard T. Watson, Kirk Plangger, Leyland Pitt, Amrit Tiwana

Original languageEnglish
JournalInformation Systems Research
Accepted/In press24 Jul 2022

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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 non-financial 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 the 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 IS research.

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