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A neuro‐computational intelligence analysis of the US retailers' efficiency

A neuro‐computational intelligence analysis of the US retailers' efficiency Purpose – Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. The purpose of this paper is to assess the market performance of the top retailers in the USA using 2007 operating data. It also aims to benchmark the performance of neuro‐intelligence models against traditional statistical techniques. Design/methodology/approach – This paper uses neuro‐intelligence models to classify the relative efficiency of top USA retailers. Accuracy indices derived from the application of a non‐parametric data envelopment analysis approach are used to assess the classification accuracy of the models. Findings – Results indicate that the neuro‐intelligence models are superior to traditional statistical methods. The paper also shows that the neuro‐intelligence models have a great potential for the classification of retailers' relative efficiency due to their robustness and flexibility of modeling algorithms. Originality/value – The paper contributes practically and methodologically through the comparison of various parametric and non‐parametric techniques, which results in considerable information for business analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

A neuro‐computational intelligence analysis of the US retailers' efficiency

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Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781011028587
Publisher site
See Article on Publisher Site

Abstract

Purpose – Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. The purpose of this paper is to assess the market performance of the top retailers in the USA using 2007 operating data. It also aims to benchmark the performance of neuro‐intelligence models against traditional statistical techniques. Design/methodology/approach – This paper uses neuro‐intelligence models to classify the relative efficiency of top USA retailers. Accuracy indices derived from the application of a non‐parametric data envelopment analysis approach are used to assess the classification accuracy of the models. Findings – Results indicate that the neuro‐intelligence models are superior to traditional statistical methods. The paper also shows that the neuro‐intelligence models have a great potential for the classification of retailers' relative efficiency due to their robustness and flexibility of modeling algorithms. Originality/value – The paper contributes practically and methodologically through the comparison of various parametric and non‐parametric techniques, which results in considerable information for business analysis.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Mar 30, 2010

Keywords: Data analysis; Neural nets; Retail trade; Stores and supermarkets; United States of America

References