Related papers: What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI

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

URL: http://arxiv.org/abs/2505.17418v2
Date: Wed, 28 May 2025 19:36:52 GMT
Title: What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI
Authors: Rudrajit Choudhuri, Bianca Trinkenreich, Rahul Pandita, Eirini Kalliamvakou, Igor Steinmacher, Marco Gerosa, Christopher Sanchez, Anita Sarma,
Abstract summary: We developed a theoretical model of factors influencing trust and adoption intentions towards genAI.<n>We found that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust.<n>We provide suggestions to guide future genAI tool design for effective, trustworthy, and inclusive human-genAI interactions.
Score: 18.1243411839447
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Generative AI (genAI) tools are advertised as productivity aids. Yet, issues related to miscalibrated trust and usage friction continue to hinder their adoption. Additionally, AI can be exclusionary, failing to support diverse users adequately, further exacerbating these concerns. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in interaction styles. When an individual's cognitive styles are unsupported, it creates additional barriers to technology adoption. Thus, to design tools that developers trust, we must first understand what factors affect their trust and intentions to use these tools in practice? We developed a theoretical model of factors influencing trust and adoption intentions towards genAI through a large-scale survey with developers (N=238) at GitHub and Microsoft. Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), we found that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust, which along with their cognitive styles, affects their intentions to use these tools in work. An Importance-Performance Matrix Analysis (IPMA) identified factors that, despite their strong influence, underperform, revealing specific genAI aspects that need design prioritization. We bolster these findings by qualitatively analyzing developers' perceived challenges and risks of genAI usage to uncover why these gaps persist in development contexts. For genAI to indeed be a true productivity aid rather than a disguised productivity sink, it must align with developers' goals, maintain contextual transparency, reduce cognitive burden, and provide equitable interaction support. We provide practical suggestions to guide future genAI tool design for effective, trustworthy, and inclusive human-genAI interactions.

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