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.

翻译：本文研究了生成式人工智能工具在软件工程中的应用模式。采用混合方法分析了个人、技术和社会层面的影响因素，以全面理解人工智能的应用情况。首先对100名软件工程师进行结构化访谈，以技术接受模型、创新扩散理论和社会认知理论为指导理论。运用吉奥方法论推导出一个名为人机协作与适应框架的理论模型，用以表征软件工程中的人工智能应用。随后通过偏最小二乘结构方程模型，使用从183名软件专业人员收集的数据验证了该模型的有效性。结果表明，在早期的整合阶段，人工智能工具的应用主要受其与现有开发工作流程兼容性的驱动。这一发现与传统技术接受理论相悖。与预期相反，感知有用性、社会因素和个人创新性对应用的影响似乎不太显著。本文为未来人工智能工具的设计提供了重要见解，并为制定有效的组织实施方案提供了框架。