Abstract:

[Purpose/Significance] Open scientific data policies play a pivotal role in promoting the open sharing, unrestricted access to, and reuse of scientific data, thereby enhancing research efficiency and driving innovation. Despite their significance, research on the diffusion of these policies has predominantly focused on policy formulation, often neglecting the critical aspect of policy adoption and implementation at the local government level. This study aims to addres this gap by comprehensively examining the factors that influence the adoption of open scientific data policies by prefecture-level governments in China. The research was motivated by the need to understand how these policies spread across different regions, as well as the underlying mechanisms that facilitate or hinder their adoption. In doing so, the study expands the existing knowledge base by shedding light on the dynamics of policy diffusion in the context of open scientific data, a relatively under-explored area compared to other policy domains. [Method/Process] To achieve its objectives, the study employed an integrated research methodology. First, it utilized a policy diffusion model, adapted from the well-established Berry model, to theoretically frame the research. This model was enhanced by incorporating insights from a comprehensive literature review, which helps identify key internal and external factors influencing policy diffusion. Second, the study employed the event-history analysis to empirically test these factors using data from 286 Chinese cities over the period from 2018 to 2022. This method allows for the examination of the temporal sequence of policy adoption and the identification of causal relationships between the influencing factors and policy diffusion. Finally, a fuzzy-set qualitative comparative analysis (fsQCA) was applied to refine the understanding of multiple causal configurations that lead to successful policy adoption. This approach captures the complexity and interdependence of factors in policy diffusion processes, offering a nuanced perspective that goes beyond traditional statistical methods. [Results/ [Conclusions] The study identified four primary pathways for the diffusion of open scientific data policies in China: resource-driven, organization-and-human-capital-led, multi-stakeholder collaborative, and technology-guided. The resource-driven pathway emphasizes the significance of research funding and the establishment of professional organizations in facilitating policy adoption. The organization-and-human-capital-led pathway highlights the role of government official mobility and a skilled workforce in driving policy diffusion. The multi-stakeholder collaborative pathway underscores the importance of coordinated efforts among various stakeholders, including government agencies, research institutions, and industry partners. Last, the technology-guided pathway focuses on innovation capacity and professional management as key drivers of policy adoption. The findings reveal a heavy reliance on administrative measures in driving policy diffusion, which may lead to unintended consequences such as policy sustainability issues and a lack of alignment with local needs. Therefore, local governments are encouraged to adopt tailored diffusion strategies that consider their specific contexts and resource endowments. Future research should explore the performance of these policies in achieving their intended outcomes and conduct comparative studies across different regions to enhance the generalizability of the findings.

Key words: open scientific data, open scientific data policy, policy diffusion model, qualitative comparative analysis