Analyzing Critical Success Factors of Sustainable Knowledge Management: An Interval-Valued Neutrosophic Approach

Knowledge management (KM) is a structured approach that includes the organized procedures of generating, capturing, arranging, storing, retrieving, distributing, and harnessing an organization’s knowledge resources to attain its goals and improve its effectiveness. Encountering uncertainty and managing imprecise information are fundamental aspects of KM that cannot be avoided. In this context, sustainable KM aims to solve these issues and address prioritizing the long-term sustainability and efficiency of knowledge-related processes within an organization. The aim of this study is to structure a sustainable KM concept for organizations and identify the most common critical success factors (CSFs) with a novel analytical approach. In this context, the Interval-Valued Neutrosophic methodology, which is one of the multi-criteria decision methods (MCDMs), was adopted to evaluate and weight the determined CSFs. Four main headings—KM, environmental, economical, and social criteria—are evaluated along with their subfactors. Our findings show that KM is found to be the most important, and environmental factors followed KM. When the results are examined in terms of subfactors, cleaner production is found to be the most significant, with a global weight value of 11.13.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2024-11-27
DOI
https://doi.org/10.3390/su162310398
Citations
4
Primary Topic
Innovation and Socioeconomic Development
Type
article
Field-Weighted Citation Impact
2.70
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analyzing Critical Success Factors of Sustainable Knowledge Management: An Interval-Valued Neutrosophic Approach

Elif Çaloğlu Büyükselçuk, Hakan TURAN, Muhammet Enis Bulak
4 citations
Sustainability
Innovation and Socioeconomic Development
2.70
article

Analyzing Critical Success Factors of Sustainable Knowledge Management: An Interval-Valued Neutrosophic Approach

Elif Çaloğlu Büyükselçuk, Hakan TURAN, Muhammet Enis Bulak
article en
4 citations

Abstract

Knowledge management (KM) is a structured approach that includes the organized procedures of generating, capturing, arranging, storing, retrieving, distributing, and harnessing an organization’s knowledge resources to attain its goals and improve its effectiveness. Encountering uncertainty and managing imprecise information are fundamental aspects of KM that cannot be avoided. In this context, sustainable KM aims to solve these issues and address prioritizing the long-term sustainability and efficiency of knowledge-related processes within an organization. The aim of this study is to structure a sustainable KM concept for organizations and identify the most common critical success factors (CSFs) with a novel analytical approach. In this context, the Interval-Valued Neutrosophic methodology, which is one of the multi-criteria decision methods (MCDMs), was adopted to evaluate and weight the determined CSFs. Four main headings—KM, environmental, economical, and social criteria—are evaluated along with their subfactors. Our findings show that KM is found to be the most important, and environmental factors followed KM. When the results are examined in terms of subfactors, cleaner production is found to be the most significant, with a global weight value of 11.13.

SustainabilityVol. 16(23)
Üsküdar University (TR), Fenerbahçe University (TR), Kocaeli Üniversitesi (TR)
Responsible consumption and production
Openalex Percentile: Top 8%
Innovation and Socioeconomic Development
2.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.