Ethical Maturity Framework for Big Data Governance in Academic Libraries: A Multi-Method Study

Background In an age of widespread adoption of big data technologies in academic libraries, ethical governance is a high priority in professional discourse. Yet many have no means to effect normative principles as they are translated into effective and auditable measures. This study synthesizes and validates an Ethical Maturity Framework (EMF) which takes into account the professional and operational realities of academic librarianship.Methods Guided by a concurrent triangulation design, the study synthesizes three evidence streams: (1) A PRISMA-ScR scoping review of 48 peer-reviewed articles (Scopus, Web of Science and Library and Information Science Abstracts (LISA) 2015–2025), guided by a set of search strings for Boolean values and inclusion/exclusion; (2) Content analysis of 47 publicly accessible governance documents from four Saudi research universities, coded on a five-level maturity scale (0 to 4) with inter-rater reliability κ = 0.78; and (3) a survey of N = 252 library practitioners. Hybrid analytical methods, exploratory factor analyses (EFA)/Confirmatory Factor Analysis (CFA) with systematic documentary analysis, were applied as the means through which to underpin both the theoretical design and its empirical verification.Results Exploratory and confirmatory factor analysis provided evidence of a consistent six-dimensional construct (CFI = 0.945, RMSEA = 0.059, Cronbach’s α = 0.79–0.88). The perception–practice gap was statistically significant on the six dimensions (MANOVA: Wilks’ λ = 0.499, p < .001), with the greatest divide on Transparency and Explainability and Social Value and Public Good (gap = +1.90 on a normative 0–4 scale).Conclusion Ethical maturity requires moving away from symbolic compliance and toward institutional governance. The EMF presents a scalable diagnostic tool that is consistent with the General Data Protection Regulation (GDPR), the Saudi Personal Data Protection Law (PDPL), and the UNESCO AI Ethics Recommendations (2024) and is suitable across a wide range of regulatory and cultural contexts.