Two-stage production modeling of large U.S. banks: A DEA-neural network approach

作者：

Highlights：

• This study proposes a new approach to model a two-stage production process.

• A DEA–BPNN method adds flexibility in two-stage production modeling.

• The proposed model complements two-stage DEA by adding predictive power.

• The adaptive method can support incremental performance improvement.

• The proposed model is empirically supported by using data from large U.S. banks.

摘要

•This study proposes a new approach to model a two-stage production process.•A DEA–BPNN method adds flexibility in two-stage production modeling.•The proposed model complements two-stage DEA by adding predictive power.•The adaptive method can support incremental performance improvement.•The proposed model is empirically supported by using data from large U.S. banks.

论文关键词：Banking,Data envelopment analysis (DEA),Neural network,Two-stage DEA

论文评审过程：Available online 30 April 2015, Version of Record 26 May 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.062