How Information Asymmetry Affects Higher Education Teachers' Adoption of Generative AI: An Extended TAM Approach

Abstract

This study investigates how information asymmetry affects higher education teachers' adoption of generative AI through an extended Technology Acceptance Model approach. Generative AI creates information asymmetry where teachers lack visibility into AI decision-making processes. We integrated TAM with the Diffusion of Innovation theory, and positioning transparency as a key mechanism for reducing information asymmetry. Results support all seven hypotheses and demonstrate that transparency influences all TAM and DOI constructs. The results also highlight educators' pedagogical orientation in technology adoption decisions. This research contributes by extending TAM with transparency as an antecedent variable, addressing AI-specific information asymmetry concerns, and providing insight for facilitating informed generative AI adoption in educational contexts.

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