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Towards a fairer manager performance measure: a DEA application in the retail industry

Author

Listed:
  • Dany Vyt

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

  • Gérard Cliquet

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

Retailers need to determine the performance of their individual stores and, beyond the stores, the managers in charge of running them. The aim of this paper is to develop performance standards that can be applied to fairly distribute rewards to managers regarding their performances by taking into account store neighbourhood characteristics. In order to propose a fairer measure of store performance, the authors introduce and explore the concepts of organisational justice and store efficiency. They use real data from a French supermarket chain and a geomarketing approach. The twostep Data Envelopment Analysis (DEA) model results are compared with a retailer's ranking. The retailer tends to favour points of sale having a more important sales area, with more employees and operating in a more densely populated area with a higher buying power. The ranking stemming from the DEA model links store performance to other geo-demographic variables.

Suggested Citation

  • Dany Vyt & Gérard Cliquet, 2017. "Towards a fairer manager performance measure: a DEA application in the retail industry," Post-Print halshs-01806424, HAL.
  • Handle: RePEc:hal:journl:halshs-01806424
    DOI: 10.1080/09593969.2017.1383293
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    Cited by:

    1. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.

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