Generative Artificial Intelligence (GenAI) is rapidly reshaping higher education, yet barriers to its adoption across different disciplines and institutional roles remain underexplored. Existing literature frequently attributes adoption barriers to individual-level factors such as perceived usefulness and ease of use. This study instead investigates whether such barriers are structurally produced. Drawing on a multi-method survey analysis of 272 academic and professional services (PSs) staff at a Russell Group university, we examine how disciplinary contexts and institutional roles shape perceived barriers. By integrating multinomial logistic regression (MLR), structural equation modelling (SEM), and semantic clustering of open-ended responses, we move beyond descriptive accounts to provide a multi-level explanation of GenAI adoption. Our findings reveal clear, systematic differences: non-STEM academics primarily report ethical and cultural barriers related to academic integrity, whereas STEM and PSs staff disproportionately emphasize institutional, governance, and infrastructure constraints. We conclude that GenAI adoption barriers are deeply embedded in organizational ecosystems and epistemic norms, suggesting that universities must move beyond generalized training to develop role-specific governance and support frameworks.

翻译：生成式人工智能（GenAI）正迅速重塑高等教育，但不同学科与机构角色间的采纳障碍仍未得到充分探索。现有文献常将采纳障碍归因于感知有用性和易用性等个体层面因素。本研究转而探讨此类障碍是否由结构性因素所致。基于对一所罗素集团大学272名学术与专业服务人员的多方法调查分析，我们考察了学科语境与机构角色如何塑造感知障碍。通过整合多项逻辑回归、结构方程模型及开放式回答的语义聚类，我们超越了描述性分析，提供了GenAI采纳障碍的多层次解释。研究发现揭示了清晰且系统的差异：非STEM学科教师主要报告与学术诚信相关的伦理文化障碍，而STEM学科教师与专业服务人员则更强调制度、治理与基础设施层面的制约。我们得出结论：GenAI采纳障碍深嵌于组织生态系统与认知规范之中，这表明大学必须超越泛化培训，针对不同角色制定专门的治理与支持框架。