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Developing and prioritizing a research data management capability framework for China's research institutions: a Delphi-AHP approach
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DOI：10.1108/EL-08-2025-0337.png)
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Abstract
En 中文
PurposeThis study aims to construct a research data management (RDM) capability framework tailored to the specific context of China's research institutions. It addresses the limited contextual relevance of existing international models by developing a structure that reflects local needs and institutional practices.Design/methodology/approachThis study first used the Delphi method to systematically refine and validate the initial RDM capability framework for China's research institutions. Subsequently, the analytic hierarchy process was applied to assign weights to each area and element, thereby quantifying their relative importance and priority.FindingsThis study ultimately developed an RDM capability framework comprising five key areas and 23 specific practice elements. Among these, institutional policies and procedures received the highest weight, followed by information technology infrastructure and metadata management, while financial support and support services were emphasized as complementary enabling areas. At the practice element level, institutional RDM policies, metadata standards, metadata creation and maintenance, resource allocation and institutional RDM strategy were identified as the most critical elements. These findings underscore the central role of policy and strategy formulation, metadata standardization and operational implementation in shaping the development of RDM capabilities.Originality/valueThis study proposes a context-specific RDM capability framework for China's research institutions, advancing the localization of RDM. It offers a structured conceptual framework for adapting international models to regional contexts and provides clear theoretical and practical guidance for capacity development and resource optimization.
Keywords:
Research data management (RDM)
Capability framework
Delphi
Analytic hierarchy process (AHP)
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1.2K
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