An Exploratory Pilot Study of Cognitive Factors Influencing the Adoption of AI Coding Agents in Agile Development Teams

Abstract

This study considers the cognitive factors influencing the adoption of artificial intelligence (AI) coding agents among software engineers in Agile development environments. In this context, Transparency refers to the degree to which an AI system’s decision processes are clear or understandable to its users, while Trust denotes the confidence developers place in the reliability and correctness of AI-generated code. Specifically, it analyzes the roles of Transparency and Trust as predictors of developers’ Behavioral Intention to deploy AI-generated code. Using a quantitative, cross-sectional survey design administered to 19 Agile software professionals, this study uses descriptive statistics, Pearson correlation analysis, and simple and multiple linear regression to test a mediation model based on the Technology Acceptance Model (TAM). In this exploratory pilot sample, Trust emerged as the strongest predictor of Behavioral Intention (β = 0.64, p = 0.007), though findings should be interpreted cautiously given the limited sample size. Trust demonstrated the strongest association with Behavioral Intention, while Transparency showed a non-significant positive association with Trust. The observed pattern is consistent with a possible indirect relationship, although mediation was not confirmed in this sample. This evidence suggests that organizations seeking to improve AI adoption in development teams should prioritize the reliability and accuracy of AI tools, conditions that encourage trust, over transparency features alone. This study adds to the cognitive engineering literature on human–AI interaction in high-cognitive-demand work environments.