Data sharing is increasingly essential for digital government and data-driven innovation, yet many public organizations remain reluctant to make their data openly available. While prior research has examined factors influencing open data adoption, little theoretical work explores why resistance persists within public agencies. This study develops an Innovation Resistance Theory (IRT) model tailored to government data sharing to identify predictors of organizational resistance. An initial model was derived from literature and refined through interviews with 21 public organizations across six European countries. The resulting IRT4DS model identifies 39 barriers spanning usage, value, risk, tradition, and image dimensions, and 23 countermeasures mapped to the most critical barriers and the actors responsible for addressing them. By extending IRT into the context of governmental data sharing, the study advances theoretical understanding of why public data often remains closed and provides actionable guidance for policymakers seeking to design enabling data ecosystems and reduce structural and cultural barriers to OGD adoption.

翻译：数据共享对于数字政府建设与数据驱动创新日益关键，然而众多公共组织仍对开放自身数据持保留态度。尽管已有研究探讨了影响开放数据采纳的因素，但鲜有理论工作深入解释公共机构中持续存在的抵制现象。本研究构建了面向政府数据共享的创新抵制理论（IRT）模型，旨在识别组织性抵制的预测因子。初始模型基于文献推导，并通过与欧洲六国21个公共组织的访谈加以完善。由此形成的IRT4DS模型识别出覆盖使用、价值、风险、传统和形象维度的39项障碍，并针对最关键的障碍及责任主体提出了23项应对措施。通过将IRT扩展至政府数据共享领域，本研究深化了对公共数据为何常处于封闭状态的理论认知，同时为政策制定者设计赋能型数据生态系统、降低开放政府数据（OGD）采纳过程中的结构性及文化性障碍提供了具有实践指导意义的路径。