一句话：基于 442 名开发者的混合方法实证研究，揭示生成式 AI 采纳通过抬高工作需求而加剧倦怠，工作资源与正面认知可缓解该效应。
既有研究多强调生成式 AI（GenAI）带来的生产力提升，却忽视了其采纳过程可能给开发者带来新的压力。本文以工作要求—资源（JD–R）模型为分析透镜，采用并行嵌套式混合方法研究，考察 GenAI 采纳与开发者倦怠之间的关系。作者首先对来自不同组织、岗位与经验水平的 442 名开发者进行问卷调查，随后使用偏最小二乘结构方程模型（PLS-SEM）与回归分析，对工作要求、工作资源与倦怠之间的路径关系建模，并对开放式回答做定性分析以解释定量发现。结果显示：GenAI 采纳会提高工作要求，从而加剧倦怠；而工作资源以及对 GenAI 的正面感知能够削弱这一负面影响，从而将 GenAI 采纳重新框定为一种机会而非单纯的负担。研究提示组织在推广 GenAI 时需同步配置资源与支持，以保护开发者福祉。
Generative AI (GenAI) is rapidly reshaping software development workflows. While prior studies emphasize productivity gains, the adoption of GenAI also introduces new pressures that may harm developers’ well-being. In this paper, we investigate the relationship between the adoption of GenAI and developers’ burnout. We utilized the Job Demands–Resources (JD–R) model as the analytic lens in our empirical study. We employed a concurrent embedded mixed-methods research design, integrating quantitative and qualitative evidence. We first surveyed 442 developers across diverse organizations, roles, and levels of experience. We then employed Partial Least Squares–Structural Equation Modeling (PLS-SEM) and regression to model the relationships among job demands, job resources, and burnout, complemented by a qualitative analysis of open-ended responses to contextualize the quantitative findings. Our results show that GenAI adoption heightens burnout by increasing job demands, while job resources and positive perceptions of GenAI mitigate these effects, reframing adoption as an opportunity.