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Static models for a dynamic world: why TAM and UTAUT fail for AI—and what we need instead
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DOI：10.1007/s00146-026-03268-3.png)
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Abstract
En 中文
Dominant technology adoption frameworks—the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Diffusion of Innovations (DOI) model—share a common theoretical architecture designed for static technological artifacts. Generative artificial intelligence systems fundamentally violate these assumptions: their affordances and constraints change through user interaction, users function as active actualizers of affordances whose practices co-constitute the effective technology, and adoption proceeds iteratively rather than linearly. This paper makes three contributions. First, we identify three shared assumptions underlying dominant adoption frameworks and demonstrate that all three fail when applied to generative AI. Second, we propose a theoretical definition of dynamic technology as a technology whose affordances and constraints change through interaction with the user, and distinguish three mechanistically distinct forms of constraint dynamics (true capability evolution, constraint perception revision, and workaround discovery) that classical models conflate. Third, we outline the integrated model of dynamic adoption (IMDA), a dual-cycle framework grounded in affordance actualization theory (AAT) that extends AAT in three respects: by adding a temporal-developmental dimension to affordance actualization, by treating technology evolution as an endogenous variable, and by specifying a co-evolutionary macro-level loop between users, technology, and organizational context. The IMDA is developed specifically for the case of generative AI adoption; its generalizability to other dynamic technologies is proposed as a hypothesis for future empirical work.
Keywords:
Technology adoption
Generative AI
TAM
UTAUT
Affordance actualization theory
Dynamic technology
IMDA
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