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TRUVACE RECORD VERSION
record: TRV-2026-0455
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T11:01:54.218993Z
status: published
lens: trace
sector: labor
headline: Navigating the Complexity of Generative AI Adoption in Software Engineering
dek: 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…
gain_title: Software engineers adopted generative AI tools when the tools were compatible with existing development workflows, based on surveys of engineers in 2024.
problem_title: 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.
trace_subject: adoption of generative AI tools by software engineers
gain_reading: Software engineers adopted generative AI tools when the tools were compatible with existing development workflows, based on surveys of engineers in 2024.
gain_evidence: compatibility of AI tools within existing development workflows predominantly drives their adoption
problem_reading: 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.
problem_evidence: The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected
quick_read: In a study published March 28 2024, researchers examined generative AI tool adoption among software engineers using surveys of 100 engineers and validation with 183 engineers, developing and testing the Human-AI Collaboration and Adaptation Framework.

The finding that compatibility with existing workflows drives adoption more than perceived usefulness or social influence matters for labor because it shifts how organizations design tools and implementation strategies, but uncertainty remains about whether this pattern holds beyond the early stage and beyond the sampled engineers.
limitation: 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.
tag: Automated dual reading
key_points: Surveyed 100 software engineers using Technology Acceptance Model, Diffusion of Innovation Theory, and Social Cognitive Theory frameworks | Derived Human-AI Collaboration and Adaptation Framework using Gioia methodology | Validated model with Partial Least Squares-Structural Equation Modeling on data from 183 software engineers | Found compatibility within existing workflows predominantly drives adoption at early stage
rundown: Researchers applied a convergent mixed-methods approach, first surveying 100 software engineers guided by Technology Acceptance Model, Diffusion of Innovation Theory, and Social Cognitive Theory, then deriving the Human-AI Collaboration and Adaptation Framework via Gioia methodology.

The framework was validated using Partial Least Squares-Structural Equation Modeling on data from 183 software engineers, with results showing workflow compatibility as the predominant driver and challenging conventional acceptance theories that emphasize usefulness and social factors.
sources:
- peer_reviewed | ACM Transactions on Software Engineering and Methodology | https://doi.org/10.1145/3652154 | 2024-03-28
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