Yes / The current study contributes to the literature in efficiency analysis in two ways: 1) we build on the existing 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 efficiency 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.