Editor-in-Chief Lecture

Author

• Rasoul Zavaraqi

Professor, Department of Knowledge and Information Science, University of Tabriz. zavaraqi@tabrizu.ac.ir

Abstract

Purpose: The expansion of universities, research institutions, information databases, statistical systems, and the growing production of journal articles, theses and dissertations, expert reports, and policy documents has not necessarily produced a corresponding improvement in the quality of organizational and national decision-making. By addressing this paradox between the accumulation of knowledge and the persistence of problems, this editorial argues for a transition from unsystematic reliance on intuition, experience, authority, and fragmented data toward Evidence-Based Knowledge Management (EBKM). It further proposes a conceptual and operational framework for translating trustworthy knowledge into decisions that are transparent, evaluable, and open to revision.
Methodology: This editorial adopts a conceptual, analytical, and critical approach, drawing on the literature on evidence-based knowledge management, evidence-based management and decision-making, knowledge translation, evidence-informed policymaking, information and knowledge science, organizational learning and memory, open science, and research assessment. Through a critical integration of these intellectual streams, major barriers to the effective use of knowledge are identified and a proposed architecture for EBKM is developed.
Findings: The analysis identifies person-centered decision-making, the research–practice gap, the absence of an epistemic audit trail, selective use of evidence, asymmetrical organizational memory, the illusion of data-driven decision-making, and uncritical reliance on artificial intelligence as major obstacles to translating knowledge into trustworthy decisions. To address these problems, a nine-stage EBKM architecture is proposed: defining the problem and formulating an answerable question; gathering multisource evidence; critically appraising evidence; synthesizing and contextualizing evidence; creating an evidence dossier; implementing decisions in staged and reversible forms; evaluating outcomes; feeding results back into organizational memory; and reviewing, downgrading, or retiring outdated knowledge.
Conclusion: Evidence-based knowledge management does not imply eliminating intuition, experience, or professional judgment and replacing them with data or algorithms. Rather, it requires these forms of knowledge to be subjected to appraisal, comparison, and accountability. Within this framework, trustworthy scientific research, organizational and local evidence, appraisable professional expertise, and stakeholder values and perspectives are integrated as complementary sources of evidence. Embedding EBKM in scientific, economic, policy, and social systems requires institutional infrastructures capable of translating evidence into decisions and decisions into learning. Universities and scholarly journals can contribute to this transition by strengthening evidence synthesis, transparency, reproducibility, publication of negative findings, and knowledge translation.
Value: The conceptual contribution of this editorial lies in offering an integrated view of EBKM that combines the rigor of evidence appraisal associated with evidence-based medicine, the architecture of knowledge organization and retrieval from information and knowledge science, the institutional sensitivity of public policymaking, the analytical capacity of data analytics and artificial intelligence, and the contextual and learning-oriented perspective of organizational studies. The proposed framework shifts knowledge management from a logic of knowledge accumulation and storage toward the governance of the life cycle of knowledge claims and responsible decision-making.

Keywords

• Evidence-Based Knowledge Management
• EBKM
• Evidence-Informed Decision-Making
• Knowledge Translation
• Evidence-Based Management
• Evidence-Informed Policymaking
• Organizational Memory
• Knowledge Management

Main Subjects

• Philosophical and Theoretical Dimensions of Data, Information and Knowledge Studies