Modeling Developer Burnout with GenAI Adoption

2025-10-08

AI Summary

This study investigates the mechanisms through which generative artificial intelligence (GenAI) adoption influences software developers’ occupational burnout. Grounded in the Job Demands–Resources (JD-R) theory, it employs a mixed-methods approach: a survey of 442 developers, analyzed via partial least squares structural equation modeling (PLS-SEM), regression analysis, and qualitative text analysis. It is the first to extend the JD-R model to GenAI adoption contexts, revealing that GenAI intensifies burnout primarily by increasing workload demands. Crucially, organizational resource support and individual-perceived value of GenAI serve as significant moderators and buffers. High perceived value transforms technological stress into an empowering experience, thereby alleviating burnout and fostering adaptive engagement. These findings offer a novel theoretical framework and empirical evidence for understanding mental health dynamics among knowledge workers in the AI era.

Technology Category

• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Investigating GenAI adoption's impact on developer burnout
• Analyzing how job demands/resources mediate GenAI-burnout relationship
• Identifying mitigation strategies for GenAI-induced workplace stress

Innovation

• Used mixed-methods design combining surveys and analysis
• Applied PLS-SEM and regression to model burnout factors
• Leveraged JD-R framework to assess GenAI's dual impact