Abstract
This paper introduces clustering method to traditional Data Envelopment Analysis (DEA) in order to examine the performance of 982 branches of one big Canadian bank nation wide. This improved method can address the effects exerted by different operating environment. K-means, one of the most popular clustering techniques is used to cluster the branches into 8 clusters in order to make a fair comparison. The system differentiated DEA model used in the paper considers the systematic difference among different clusters. The potential management uses of the DEA results were also presented. All the findings are discussed in the context of the Canadian banking market.
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Yang, Z. (2010). Cross System Bank Branch Evaluation Using Clustering and Data Envelopment Analysis. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_30
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DOI: https://doi.org/10.1007/978-3-642-14922-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
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