GenAI in Software Engineering: The Role of Technology Acceptance Models

TL;DR

This paper reviews how technology acceptance models, especially UTAUT, can be applied to understand and facilitate the adoption of GenAI in software engineering, emphasizing Bayesian methods for analysis.

Contribution

It highlights key research priorities for applying UTAUT to GenAI acceptance in SE and suggests integrating Bayesian approaches for better data analysis.

Findings

Identifies three research priorities for GenAI acceptance in SE.

Recommends refining constructs to capture GenAI's impact.

Suggests Bayesian analysis for small-sample inference.

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.