What Needs Attention? Prioritizing Drivers of Developers’ Trust and Adoption of Generative AI

Abstract

Generative AI (genAI) tools promise productivity gains, yet developers still struggle to determine when to trust and effectively integrate these tools into their everyday work. Moreover, genAI can be exclusionary, failing to adequately support developers across individual differences. One such difference is cognitive style , which can shape how developers engage with genAI (e.g., risk-averse developers may gate outputs behind tests, whereas risk-tolerant ones may prototype directly and address issues post hoc). When tools fail to accommodate these differences, they can create additional usability barriers. Thus, to design tools that developers intend to use, we must understand which factors shape developers’ trust in and adoption of genAI at work . We developed a theoretical model of developers’ trust and adoption of genAI using a large-scale survey ( N = 238) conducted at GitHub and Microsoft. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we found aspects related to genAI’s system and output quality (e.g., presentation, safety/security, and performance), functional value (e.g., educational and practical benefits), and goal maintenance (ability to sustain alignment with task goals) to be significantly associated with trust. Trust, alongside developers’ cognitive styles (i.e., risk tolerance, technophilic motivations, and computer self-efficacy), was in turn significantly associated with adoption intentions, which ultimately were associated with reported usage. An Importance-Performance Matrix Analysis (IPMA) identified high-importance factors for which existing genAI tools provided insufficient support, highlighting actionable targets for design improvements. We bolstered these findings through a qualitative analysis of developers’ reported challenges and risks of genAI use, uncovering why these gaps persisted in development contexts. Our study offers practical guidance for designing genAI tools that support trustworthy and inclusive developer–AI interactions.