Generative AI (genAI) tools, such as ChatGPT or Copilot, are advertised to improve developer productivity and are being integrated into software development. However, misaligned trust, skepticism, and usability concerns can impede the adoption of such tools. Research also indicates that AI can be exclusionary, failing to support diverse users adequately. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in perspectives and interaction styles. When an individual's cognitive style is unsupported, it creates barriers to technology adoption. Therefore, to understand how to effectively integrate genAI tools into software development, it is first important to model what factors affect developers' trust and intentions to adopt genAI tools in practice? We developed a theoretically grounded statistical model to (1) identify factors that influence developers' trust in genAI tools and (2) examine the relationship between developers' trust, cognitive styles, and their intentions to use these tools in their work. We surveyed software developers (N=238) at two major global tech organizations: GitHub Inc. and Microsoft; and employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate our model. Our findings reveal that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust in these tools. Furthermore, developers' trust and cognitive styles influence their intentions to use these tools in their work. We offer practical suggestions for designing genAI tools for effective use and inclusive user experience.

翻译：生成式人工智能（genAI）工具（如ChatGPT或Copilot）被宣传为能提升开发者生产力，并正被集成到软件开发中。然而，信任错位、怀疑态度和可用性问题可能阻碍此类工具的采用。研究还表明，人工智能可能具有排他性，未能充分支持多样化的用户。多样性的一个方面是认知多样性——用户认知风格的差异——这导致观点和交互方式的分歧。当个体的认知风格得不到支持时，就会造成技术采用的障碍。因此，为了理解如何将genAI工具有效集成到软件开发中，首先需要建模哪些因素在实践中影响开发者对genAI工具的信任和采用意图？我们开发了一个基于理论的统计模型，旨在（1）识别影响开发者对genAI工具信任的因素，以及（2）检验开发者的信任、认知风格与其在工作中使用这些工具的意图之间的关系。我们对两家全球主要科技组织（GitHub Inc.和Microsoft）的软件开发人员（N=238）进行了调查，并采用偏最小二乘结构方程模型（PLS-SEM）来评估我们的模型。我们的研究结果表明，genAI的系统/输出质量、功能价值和目标维持显著影响开发者对这些工具的信任。此外，开发者的信任和认知风格影响他们在工作中使用这些工具的意图。我们为设计genAI工具以促进有效使用和包容性用户体验提供了实用建议。