Skip to content
#explainable ai Open access

Knowledge Sharing and Organizational Performance: A Systematic Review and Thematic Synthesis of Mechanisms, Contexts and Outcomes (2014-2025)

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Knowledge Management and Sharing

Abstract

Purpose: This review explains how knowledge sharing is converted into organizational performance and identifies the organizational, technological and contextual conditions that enable, constrain or redirect this conversion. Design/methodology/approach: A systematic literature review covering 2014-2025 was conducted in accordance with PRISMA 2020 reporting principles and management-review guidance. The final analytical corpus comprises 92 empirical studies: 46 published during 2014-2024 and 46 published in 2025. The 2014-2024 screening sequence reported in the manuscript began with 1,204 records, of which 912 remained after deduplication; 96 were assessed in full text and 46 were retained. For 2025, 75 records were identified; after removal of duplicates/overlaps, one out-of-period record and two retracted reports, 67 were assessed and 46 retained. Eligibility, extraction and thematic coding were conducted by a single primary reviewer using explicit criteria, an audit trail and repeated coding. The synthesis combined descriptive mapping with iterative thematic analysis; no meta-analysis was conducted because constructs, outcome levels and effect statistics were heterogeneous. Findings: Across the 2014-2025 literature, five interdependent themes emerged: enabling and inhibiting conditions; forms and quality of knowledge sharing; conversion mechanisms; multidimensional performance outcomes; and boundary conditions. The evidence is consistent with a conditional conversion pathway in which culture, trust, leadership, support, structure and digital infrastructure shape exchange, while learning, innovation, improvisation, agility, ambidexterity, knowledge quality and application help explain how sharing is associated with performance. The 2025 subset places stronger emphasis on artificial intelligence, human-machine interaction, knowledge-sharing quality, sustainability and the balance between sharing and protection. This late-period concentration is interpreted as an emerging development rather than a definitive temporal break. Mixed and non-significant findings show that more sharing is not automatically better. Research limitations/implications: The evidence remains dominated by cross-sectional, self-reported SEM studies and emerging-economy contexts. A single-reviewer process and heterogeneous outcome measures limit causal and statistical generalization. Practical implications: Managers should manage the complete conversion chain: identify strategically relevant knowledge, create safe and well-governed sharing channels, validate quality, embed application in work processes and evaluate proximal and distal performance outcomes. Originality/value: The review extends earlier enabler-process-outcome syntheses by isolating the knowledge-sharing-to-performance pathway, separating proximal from distal performance outcomes, incorporating boundary conditions and failure paths, and proposing a dynamic conversion framework that integrates AI, knowledge quality, ambidexterity and governance. It also distinguishes established themes across the review period from emerging emphases concentrated in the 2025 subset.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.