  In this paper, the adoption patterns of Generative Artificial Intelligence
(AI) tools within software engineering are investigated. Influencing factors at
the individual, technological, and societal levels are analyzed using a
mixed-methods approach for an extensive comprehension of AI adoption. An
initial structured interview was conducted with 100 software engineers,
employing the Technology Acceptance Model (TAM), the Diffusion of Innovations
theory (DOI), and the Social Cognitive Theory (SCT) as guiding theories. A
theoretical model named the Human-AI Collaboration and Adaptation Framework
(HACAF) was deduced using the Gioia Methodology, characterizing AI adoption in
software engineering. This model's validity was subsequently tested through
Partial Least Squares - Structural Equation Modeling (PLS-SEM), using data
collected from 183 software professionals. The results indicate that the
adoption of AI tools in these early integration stages is primarily driven by
their compatibility with existing development workflows. This finding counters
the traditional theories of technology acceptance. Contrary to expectations,
the influence of perceived usefulness, social aspects, and personal
innovativeness on adoption appeared to be less significant. This paper yields
significant insights for the design of future AI tools and supplies a structure
for devising effective strategies for organizational implementation.
