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Application of Data Envelopment Analysis (DEA) in the Selection of Sustainable Suppliers: A Review and Bibliometric Analysis

Author

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  • Katerina Fotova Čiković

    (Department of Economy, University North, Trg dr. Žarka Dolinara 1, 48000 Koprivnica, Croatia)

  • Ivana Martinčević

    (Department of Logistics and Sustainable Mobility, University North, Trg dr. Žarka Dolinara 1, 48000 Koprivnica, Croatia)

  • Joško Lozić

    (Department of Economy, University North, Trg dr. Žarka Dolinara 1, 48000 Koprivnica, Croatia)

Abstract

The supplier selection process is a strategic decision-making process that influences the company’s sustainability. Lately, the sustainability concept has been highlighted as an organization’s source of success and profitability. Therefore, the selection of a sustainable supplier has become an imperative for organizations and is the focus of this manuscript. Suppliers are key stakeholders in the supply chain, and their proper selection is a key factor in a successful and sustainable supply chain. For this reason, it is crucial to determine how and which methods are mostly used by companies when choosing sustainable suppliers with the aim of examining whether the Data Envelopment Analysis (DEA) contributes to the same. This article is the first to present a comprehensive bibliometric analysis of 87 articles dealing with the application of DEA in the sustainable supplier selection in the period 2010–2022, with the application of the keywords “Data Envelopment Analysis”, “Supplier”, and “Sustainable” in Scopus and Web of Science databases. The main goal of this manuscript is to explore the applications of DEA in a sustainable supplier selection and to provide an analysis and visualization of bibliometric data to reveal the annual trends of published articles in this area, the top contributing journals, the most cited papers, the most contributing authors, citations, affiliations, and countries’ analysis, and an in-depth keyword visualization analysis. The findings of this study provide valuable insights and emphasize the ever-growing trend toward the selection of sustainable partners and suppliers in business using DEA methodology. Notably, this work shows the applicability and efficacy of DEA in specialized areas of supply chain management and should contribute to the construction of an overview of the existing literature on DEA studies regarding the process of selection of sustainable suppliers in supply chain management as well as stimulate the interest in the topic. This article gives an overview of a research field that is actually insufficiently explored through the scientific literature and presents a wide area and guidelines for future work.

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  • Katerina Fotova Čiković & Ivana Martinčević & Joško Lozić, 2022. "Application of Data Envelopment Analysis (DEA) in the Selection of Sustainable Suppliers: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(11), pages 1-30, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6672-:d:827625
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    1. Małgorzata Janicka & Artur Sajnóg, 2023. "Do environmental and economic performance go hand in hand? An industrial analysis of European Union companies with the non‐parametric data envelopment analysis method," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(5), pages 2590-2605, September.
    2. Michał Nowakowski & Dariusz Porębski, 2022. "Application of the DEA Method for Evaluation of Information Usefulness Efficiency on Websites," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    3. Emre Cakmak, 2023. "Supplier Selection for a Power Generator Sustainable Supplier Park: Interval-Valued Neutrosophic SWARA and EDAS Application," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    4. Ömer Karakoç & Samet Memiş & Bahar Sennaroglu, 2023. "A Review of Sustainable Supplier Selection with Decision-Making Methods from 2018 to 2022," Sustainability, MDPI, vol. 16(1), pages 1-21, December.
    5. Pei Fun Lee & Weng Siew Lam & Weng Hoe Lam, 2023. "Performance Evaluation of the Efficiency of Logistics Companies with Data Envelopment Analysis Model," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

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