Data-Driven Governance in Knowledge Institutions: A Multilevel Model for Big Data Analytics Adoption

Abstract

The adoption of Big Data Analytics (BDA) is a critical process for academic libraries to enhance decision-making processes based on evidence-based indicators. BDA techniques enable libraries to analyse vast amounts of structured, unstructured and semi-structured data that is growing at an exponential rate over time. In order to effectively leverage the power of BDA capabilities, policymakers and management have to understand the complex interaction between various personal and institutional factors involved in the adoption decision-making process. The purpose of this study is to explore the factors that affect the adoption of BDA tools in academic libraries in selected universities in the Arab region. To this end, this study combines the extended Unified Theory of Acceptance and Use of Technology, the Technology-Organization-Environment framework, and the Diffusion of Innovations theory. Multinomial logistic regression analysis is performed on survey data collected from 416 librarians, and the results reveal that performance expectancy, effort expectancy, social influence, compatibility, top management support, institutional readiness, knowledge stock, and hedonic motivation are the significant drivers of BDA adoption. These findings indicate that librarians are more inclined to adopt BDA as they find it useful, easy to use and consistent with institutional priorities; strong leadership support and adequate digital infrastructure were also significant contributors to adoption. In contrast, no statistically significant effect was found for observability and regulatory compliance factors. The findings of this study offer practical insights for library managers and decision-makers, assisting them in developing effective implementation strategies to increase BDA adoption and support data-driven library decision-making.