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A comprehensive review of data envelopment analysis (DEA) methodology in port efficiency evaluation

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Maritime Economics & Logistics Aims and scope

Abstract

Given the key role ports play in the trade and economic growth of countries, port managers are looking for ways to increase efficiency and improve port performance. To this end, the first step is to assess port efficiency. Data envelopment analysis (DEA) is one of the tools often used to evaluate firms in different areas. In recent years, DEA has been frequently used in various ways to evaluate port performance as well. Since knowledge of the various applications of DEA is important for researchers as well as port managers and policy makers, a systematic review has been conducted here to screen and analyze 116 papers published in scientific journals between 1993 and October 2021, with most articles, 12, published in Maritime Economics & Logistics. The results of this review article show that DEA is a good assessment tool for analyzing future port performance. However, some applications of DEA models, such as the determination of inefficiency factors and the implementation of inefficiency avoidance methods, have not yet been studied. Based on the analysis conducted, we have identified the existing research gaps in the applications of DEA in the port industry and offer some directions for future research.

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Notes

  1. In an output-oriented model, inputs are assumed fixed and output is maximized to reach the efficiency frontier. In input-oriented models instead, output is assumed fixed and inputs are minimized, defining in this way the efficiency frontier.

  2. In two articles, the place of assessment is not indicated, so these two were not included in Table 3.

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Acknowledgements

We would like to thank MEL reviewers for their valuable comments which have helped us improve our work.

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Correspondence to Evelin Krmac.

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Krmac, E., Mansouri Kaleibar, M. A comprehensive review of data envelopment analysis (DEA) methodology in port efficiency evaluation. Marit Econ Logist 25, 817–881 (2023). https://doi.org/10.1057/s41278-022-00239-5

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  • DOI: https://doi.org/10.1057/s41278-022-00239-5

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