GenAI in Software Engineering: The Role of Technology Acceptance Models

• URL: http://arxiv.org/abs/2604.27642v1
• Date: Thu, 30 Apr 2026 09:34:46 GMT
• Title: GenAI in Software Engineering: The Role of Technology Acceptance Models
• Abstract summary: We use the Unified Theory of Acceptance and Use of Technology (UTAUT) as an example to identify key areas for future research on GenAI acceptance.<n>We identify three priorities for future research: (1) identifying and refining constructs to account for GenAI's nature and transformational impact; (2) improving operationalization practices to strengthen construct validity and cross-study comparability; and (3) incorporating Bayesian analysis to support small-sample inference.
• Score: 3.3288694736505193
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Context: Many organizations are keen to incorporate generative~AI (GenAI) into their software development processes. Technology acceptance models, such as the Unified Theory of Acceptance and Use of Technology (UTAUT), are traditionally used to identify individual-level barriers to the acceptance of new technologies and can facilitate the transition to GenAI. However, UTAUT has seen limited use within software engineering (SE) research. Objective: Using UTAUT as an example, to identify key areas for future research on GenAI acceptance, including the role of Bayesian approaches for data analysis. Method: We review foundational and SE-specific literature on UTAUT and analyze its emerging applications for GenAI in SE. Results: We identify three priorities for future research: (1) identifying and refining constructs to account for GenAI's nature and transformational impact; (2) improving operationalization practices to strengthen construct validity and cross-study comparability; and (3) incorporating Bayesian analysis to support small-sample inference by integrating prior knowledge, iterative model updating, and simulation of scenarios. Conclusion: UTAUT is a suitable candidate to combine with Bayesian analysis for practical insights on individual-level barriers to GenAI use in SE, but additional theories should be considered.

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