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Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs.

Qingxian AnXuyang LiuYongli LiBeibei Xiong
Published in: PloS one (2019)
This paper develops two-stage inverse data envelopment analysis models with undesirable outputs to formulate resource plans for 16 Chinese listed commercial banks whose outputs are increased and overall efficiency is kept unchanged in the short term. We use these models to meet three different output targets, namely, increasing both the desirable and undesirable outputs by the same percentage, increasing these outputs by different percentages, and increasing only the desirable outputs while keeping the undesirable outputs unchanged. We find that operation cost and interest expense are more flexible than labor in the adjustment process and that deposits have no obvious law of change. The findings of this work provide some suggestions for bank managers.
Keyphrases
  • electronic health record
  • machine learning