The Renewal Trap: When Performance Adequacy Masks Renewal Failure

Ankur Chavda
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Ankur Chavda: HEC Paris - Strategy & Business Policy

No 1634, HEC Research Papers Series from HEC Paris

Abstract: When can satisfactory performance become a misleading signal of future performance? I theorize a renewal trap that arises when current performance is sustained by accumulated resource stocks while future performance depends on renewal flows that cannot be inferred from current performance alone. I develop the argument in data-intensive settings, where useful data are stored observations whose decision value depends on whether they continue to represent the conditions to which they are applied. As environments change, representational depreciation erodes that value. Performance feedback is a level signal: the organization observes whether current performance is above or below aspiration. Renewal adequacy, by contrast, is a flow condition: whether current renewal is sufficient to sustain future performance. Accumulated useful-data stock can keep performance above aspiration and conceal a renewal shortfall that performance feedback alone does not reveal. The formal model shows when this masking becomes a renewal trap: intensified renewal begins only after performance falls below aspiration, by which time useful-data stock has eroded too far for immediate recovery. Adaptive aspirations and diagnostic observability attenuate the mechanism by shortening the period in which renewal insufficiency remains hidden; larger accumulated-stock cushions can delay intensified renewal. Boundary evidence from display advertising illustrates why accumulated data should not be equated with useful data: incremental predictive content for current decisions is concentrated in recent observations. The article contributes to behavioral performance-feedback theory by showing how performance adequacy can conceal renewal insufficiency.

Keywords: Performance feedback; aspiration levels; problemistic search; behavioral theory of the firm; asset stocks; data-enabled learning (search for similar items in EconPapers)
JEL-codes: D83 L25 M10 O32 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2026-06-26, Revised 2026-07-09
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1634

DOI: 10.2139/ssrn.6885558

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