Decision Support Systems for Library Policy and Governance

Abstract

Today's libraries encounter ever-growing intricacy with regard to governance and policy issues like budget management, staff organization, ensuring equal access, and technology integration. In resolving these issues, this paper proposes a data-driven framework which helps the administrator’s strategic, and informed, policy decisions, referred to as the Decision Support System for Library Governance and Policy Optimization (DSS-LGPO). The DSS-LGPO model incorporates four core modules: Policy Impact Simulator, Resource Optimization Engine, Stakeholder Sentiment Analyzer, and Regulatory Compliance Tracker. These modules evaluate a mix of governance history, sentiment data, feedback, and resource consumption. The use of decision making with multiple criteria (MCDM) alongside some machine learning algorithms facilitates tailored recommendations to achieve the goals of the institution. From the provided case studies, it is possible to evaluate the anticipated effects and the impact of policies, governance, strategy, and modern library systems. In addition to assisting the information technological evolution of library science, this research also helps design a governance model suitable for a range of institutional scales and geo-spatial contexts.