Strategy and Motivation of Institutional Research Data Management: Integration of SWOT Analysis and Convergence Matrix

Abstract

Research data management (RDM) in institutional repositories requires alignment between stakeholder needs, organizational conditions, and technical readiness for the findable, accessible, interoperable, and reusable (FAIR) principles. This study developed and applied an integrated institutional RDM evaluation framework combining needs analysis, SWOT analysis, and the GO FAIR Convergence Matrix. Design science research guided the framework development. Data were collected through a purposive survey of 105 stakeholders, expert interviews, repository observations, and independent FAIR ratings by two expert evaluators. The survey showed that 86.7% of respondents selected the two highest response categories for easy repository data access and use, while 87.6% did so for improving the current data management system. SWOT analysis identified institutional commitment as the principal strength (89.5%), additional RDM training as the leading weakness-related need (65.7%), emerging-technology adoption as the principal opportunity (81.0%), and government policy changes as the leading threat (73.3%). The FAIR assessment revealed a substantial gap between articles and datasets. Articles achieved normalized scores of 100.0% for Findable, 100.0% for Accessible, 62.5% for Interoperable, and 45.8% for Reusable, whereas datasets achieved 3.1% for Findable and 0.0% for the other domains. The study contributes an integrated framework that connects stakeholder priorities, organizational SWOT factors, and indicator-level FAIR evidence to support phased, evidence-based institutional RDM interventions.