Bank branch operational performance: A robust multivariate and clustering approach |
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| Affiliation: | 1. Grado Department of Industrial and Systems Engineering, System Performance Laboratory, Virginia Tech, Falls Church, VA 22043, USA;2. Business Analytics & Statistics Department, Haslam College of Business, The University of Tennessee, Knoxville, TN 37996-0532, USA;3. Centre for Management of Technology and Entrepreneurship, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada;4. Rogers Communications Inc., Toronto, Ontario M4W 1G9, Canada;1. Department of Electronics and Communication Engineering, RCC Institute of Information Technology, Kolkata 700015, India;2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India;3. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India;1. Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Av. Ecuador, 3659 Santiago, Chile;2. Facultad de Ingeniería, Universidad Andres Bello, Universidad de Santiago de Chile, Chile;1. Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States;2. Department of Pediatrics-Division of Pediatric Endocrinology, University of Michigan Health System, Ann Arbor, Michigan 48109, United States;3. Department of Radiology, Section of Pediatric Radiology University of Michigan Health System, Ann Arbor, Michigan 48109, United States;1. Department of Computer science and Mathematics, Lebanese American University, Beirut, Lebanon;2. Department of Electrical & Computer Engineering, Khalifa University of Science, Technology & Research, Abu Dhabi, UAE;3. Concordia Institute for Information Systems Engineering, Montreal, Canada;1. Department of Economics, University of Oradea, Universităţii 1, Oradea 410087, Romania;2. Department of Mathematics and Informatics, University of Oradea, Universităţii 1, Oradea 410087, Romania |
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| Abstract: | This paper proposes a multi-step procedure that integrates robust methods, clustering analysis and data envelopment analysis (DEA) to identify bank branch managerial clusters and to study efficiency performance. By applying robust techniques based on principal component analysis, we look for (1) the detection of influential branches, i.e., exhibiting extreme operating behaviors, and (2) the clustering of branches based on operating characteristics. Our premise is that influential branches affect both the clustering and the determination of efficiency performance. The application of the procedure yields various aggregate influential-based branch profiles along with cluster profiles. These aggregate profiles provide valuable insights on the determinants of branch efficiency performance and operating patterns. Using the profiles as contextual information, DEA input-oriented slack-based models are applied to study branch efficiency performance from meta-frontier and cluster-frontier perspectives. Branch performance is characterized in terms of influential-based and cluster profiles, and efficiency designations. This allows for the understanding of how efficiency and peer selection are affected by influential branches, and how the profiles can be used to inform design decisions. |
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