Supporting better practice benchmarking: A DEA-ANN approach to bank branch performance assessment
作者：
Highlights：
• We propose a two-stage hybrid model that integrates ANN with DEA.
• We position the problem in view of Design Science Research Methodology.
• We study the model’s ability to classify bank branches into predefined efficiency classes.
• Bank branches may learn not only from best performers but also from better performers.
• Results indicate a satisfactory classification ability especially for efficient bank branches.
摘要
•We propose a two-stage hybrid model that integrates ANN with DEA.•We position the problem in view of Design Science Research Methodology.•We study the model’s ability to classify bank branches into predefined efficiency classes.•Bank branches may learn not only from best performers but also from better performers.•Results indicate a satisfactory classification ability especially for efficient bank branches.
论文关键词：Artificial Neural Network,Data Envelopment Analysis,Banking,Performance,Best Practice,Benchmarking,Artificial Intelligence
论文评审过程：Received 31 October 2019, Revised 14 May 2020, Accepted 25 May 2020, Available online 30 May 2020, Version of Record 30 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113599
