Performance measures of retail banking networks: a decision support tool
AbstractIn this paper, we apply a standard model of performance evaluation to the retail banking industry. In this framework, the global economic performance is broken down into technical efficiency related to the optimal use of resources and price efficiency related to the optimal choice of a product-mix. Our main contribution is twofold. First we adapt this traditional framework to the retail banking network by giving a relevant interpretation of the efficiency measures at the branch manager and at the regional top management levels. Second, we relate explicitly the product-mix efficiency to the market environment and to the size of branches. We postulate that branches in different environments could face different production technologies and that optimal product-mixes could vary with the size of the branches. We take a sample size of 1585 branches from a single bank brand breaking down in 17 French regions. We use a nonparametric approach to model the production technologies and to identify optimal benchmarks. Our main objective is to end up with a decision support tool for the top bank management in order to plan product-mix strategies and to give the right incentives to branches’ managers. This tool should prove useful since, in retail banking networks, such tools have to be simple, robust, easy to control, and adapted to the vertical organization of the banking network.
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Bibliographic InfoPaper provided by Université de Bourgogne - CREGO EA7317 Centre de recherches en gestion des organisations in its series Working Papers CREGO with number 1071203.
Length: 28 pages
Date of creation: Dec 2007
Date of revision:
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Postal: 2 Bd Gabriel, BP 26611, 21066 Dijon Cedex, France
Postal: Angèle Renaud, CREGO, 2 Bd Gabriel, BP 26611, 21066 Dijon Cedex, France
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-01-26 (All new papers)
- NEP-BAN-2008-01-26 (Banking)
- NEP-EFF-2008-01-26 (Efficiency & Productivity)
- NEP-NET-2008-01-26 (Network Economics)
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