The Renewal Trap: How Perishable Data Stocks Delay Incumbent Adaptation

When is satisfactory performance a misleading signal of organizational capability? I theorize a renewal trap: a form of resource-induced adaptation delay that arises when current performance is sustained by a depreciating inherited resource whose future value depends on renewal capability. I develop the argument in the context of data-driven competition. In datadriven markets, accumulated data can improve prediction and personalization, but its usefulness decays as users, products, technologies, and competitive conditions change. This creates a performance-feedback problem. Current performance can reflect stock health, the remaining value of old data, rather than renewal health, the ability to turn current interactions into useful new data. As a result, satisfactory performance may suppress attention to renewal precisely when future advantage requires it. A simple formal model isolates this mechanism. Inherited stock advantage persists only when renewal flows offset depreciation. When rivals have stronger renewal capability, they can overcome inherited stock disadvantages. Most importantly, incumbent vulnerability is highest not when data depreciate fastest, but when data depreciate slowly enough to sustain current performance yet quickly enough to erode future advantage. I illustrate the argument with public display-advertising data, showing that older campaign data predict current outcomes less well. This pattern gives empirical content to data depreciation, a key boundary condition of the theory. The paper reframes performance feedback as a diagnostic problem: when inherited resources depreciate slowly, success can conceal the erosion of renewal capability.