Industrial management
Hossein Sayyadi Tooranloo; Mohammad Zarei Mahmoudabadi; Reza Norouzi Avargani
Abstract
Purpose: Over the past decade, the development of Network Data Envelopment Analysis (NDEA) models has enabled researchers to capture the internal structures and interrelationships among sub-units of decision-making units (DMUs). Compared to conventional DEA models, this approach provides deeper managerial ...
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Purpose: Over the past decade, the development of Network Data Envelopment Analysis (NDEA) models has enabled researchers to capture the internal structures and interrelationships among sub-units of decision-making units (DMUs). Compared to conventional DEA models, this approach provides deeper managerial and analytical insights into performance evaluation. The purpose of this study is to systematically review and clarify the overall trends in the development and application of NDEA models, with a particular focus on uncertainty-based approaches, over the period 2014–2024.Methodology: This research employs Microsoft Excel and VOSviewer software tools to conduct co-word analysis, visualize scientific networks, and identify research clusters. The reviewed articles were classified into two major dimensions: research domain (application areas) and research logic (deterministic or non-deterministic).Findings: The results indicate that most NDEA applications are concentrated in industrial sectors, and that deterministic logic dominates the existing body of literature. The term network data envelopment analysis was identified as the second most frequent keyword following data envelopment analysis. Based on the synthesis of reviewed studies, this research proposes a conceptual framework for NDEA and outlines potential future research directions centered on the integration of NDEA with uncertainty theories.Originality/Value: To the best of our knowledge, no comprehensive study has simultaneously addressed network data envelopment analysis and uncertainty. By identifying research gaps, mapping the scientific structure of the field, and highlighting emerging themes and future avenues, this study provides a valuable reference for researchers and practitioners interested in performance evaluation under uncertainty.
