GenAI in Software Engineering: The Role of Technology Acceptance Models

2026-04-30

AI Summary

This study addresses the limited systematic understanding of barriers to generative AI (GenAI) adoption among software engineers and the constrained applicability of traditional technology acceptance models—such as the Unified Theory of Acceptance and Use of Technology (UTAUT)—in this context. To bridge this gap, the research proposes a novel theoretical model that integrates UTAUT with Bayesian analysis to identify key determinants of GenAI adoption. Its primary contributions include a reconceptualization of UTAUT constructs tailored to GenAI’s unique characteristics, refined operationalization of these constructs to enhance validity, and the application of Bayesian methods to enable robust inference even with small sample sizes. The work establishes a new theoretical and methodological foundation for future investigations into the mechanisms underlying GenAI acceptance in software engineering.

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.

Problem

• Generative AI
• Software Engineering
• Technology Acceptance
• UTAUT
• Individual-level barriers

Innovation

• Generative AI
• UTAUT
• Bayesian analysis
• Technology acceptance
• Software engineering