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TRV-2026-0455Certified recordPeer-reviewed

Navigating the Complexity of Generative AI Adoption in Software Engineering

This article explores the adoption of Generative Artificial Intelligence (AI) tools within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a questionnaire survey with 100 software engineers, drawing upon the Technology Acceptance Model, the Diffusion of Innovation Theory, and the Social Cognitive Theory a…

Labor · The Trace — both readings · certified 2026-07-20 · v1 · article view · machine-readable

Current reading — gain

Software engineers adopted generative AI tools when the tools were compatible with existing development workflows, based on surveys of engineers in 2024.

Current reading — problem

For software engineers in early-stage integration, expected adoption drivers such as perceived usefulness, social factors, and personal innovativeness had less pronounced impact than conventional technology acceptance theories predict.

What this doesn’t fix

Findings reflect early stage of AI integration and are based on limited survey samples of 100 and 183 software engineers, limiting generalizability to later stages or broader populations.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-0455, v1: “Navigating the Complexity of Generative AI Adoption in Software Engineering.” Truvace, 2026-07-20. /record/TRV-2026-0455 (accessed at citation time). sha256 13d1e582b4075c90…

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