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Performance comparison between retailing stores using a Malmquist-type index

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Detalhes bibliográficos
Resumo:This study develops a methodology that combines different management science methods (Data Envelopment Analysis, Malmquist indices and statistical tests) to provide insights concerning the performance of stores. Firstly, the methodology enables to specificate appropriate targets for stores in a retail network, by using Data Envelopment Analysis. This is performed by comparing similar stores which belong to the same group, i.e., supermarkets and hypermarkets. Secondly, the methodology compares globally the performance between those groups by characterizing their productivity levels. For this, the methodology combines the use of a Malmquist-type index with statistical tests. This index is decomposed into sub-indices for comparing the differences between groups in terms of efficiency spread in each group of stores and the productivity differences between the best-practice frontiers spanned by the benchmark stores from each group. The hypothesis tests are used to verify if the differences between groups captured by the sub-indices are statistically significant.
Autores principais:Vaz, Clara B.
Outros Autores:Camanho, Ana
Assunto:Data envelopment Analysis Malmquist-index
Ano:2009
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
Descrição
Resumo:This study develops a methodology that combines different management science methods (Data Envelopment Analysis, Malmquist indices and statistical tests) to provide insights concerning the performance of stores. Firstly, the methodology enables to specificate appropriate targets for stores in a retail network, by using Data Envelopment Analysis. This is performed by comparing similar stores which belong to the same group, i.e., supermarkets and hypermarkets. Secondly, the methodology compares globally the performance between those groups by characterizing their productivity levels. For this, the methodology combines the use of a Malmquist-type index with statistical tests. This index is decomposed into sub-indices for comparing the differences between groups in terms of efficiency spread in each group of stores and the productivity differences between the best-practice frontiers spanned by the benchmark stores from each group. The hypothesis tests are used to verify if the differences between groups captured by the sub-indices are statistically significant.