Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry

The current study contributes to the literature in efficiency analysis in two ways: 1) we build on the exist-ing studies in Dynamic Network Data Envelopment Analysis (DNDEA) by proposing a sequential structure incorporating dual-role characteristics of the production factors; 2) we initiate the efforts to complement the proposal of our innovative sequential DNDEA through a behavioural-causal analysis. The proposal of this statistical analysis is very important considering it does not only validate the proposal of the ef-ficiency analysis but also our practice can be generalized to the future studies dealing with designing innovative production process. Finally, we apply these two different analyses to the banking industry. Using a sample of 43 Chinese commercial banks including five different ownership types (state-owned, joint-stock, city, rural, and foreign banks) between 2010 and 2018, we find that the inefficiency level is around 0.14, although slight volatility has been observed. We find that the highest efficiency is dominated by state-owned banks, and although foreign banks are less efficient than joint-stock banks, they are more efficient than city banks. Finally, we find that rural banks have the highest inefficiency.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

This study makes two contributions to efficiency analysis literature. First, it proposes a sequential structure in Dynamic Network Data Envelopment Analysis (DNDEA) by incorporating the dual-role characteristics of production factors. Second, it introduces a behavioral-causal analysis to validate the proposal and generalize it to future studies in innovative production process design. The application of these analyses to the banking industry reveals an overall inefficiency level of 0.14, with state-owned banks exhibiting the highest efficiency and rural banks showing the highest inefficiency.