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The data envelopment analysis (DEA) treats decision-making units (DMUs) as black boxes: there is an unknown internal structure and transformation mechanism of input to output. Two-stage models have been proposed to resolve this problem by considering the internal structure of DMUs. However, each DMU has a different structure, and in two-stage models, the poor estimation of sub-models causes conflicts in the intermediate layer. Therefore, it is necessary to use additional tools to extract insight into opportunities to enhance the performance of DMUs. This paper presents a three-stage model employing DEA to evaluate efficiency, a Tobit regression model to identify the determinants, and a neural network (NN) to improve those determinants. Improvement in the determinants of a DMU enhances its efficiency. The developed model is applied to the empirical dataset of commercial banks from the countries that have joined the belt and road initiative (BRI), grouping them based on their economist intelligence unit (EIU) rating. The results provide valuable information on the efficiency enhancement process for banks to benefit from the BRI.

Do Commercial Banks Benefited From the Belt and Road Initiative? A Three-Stage DEA-Tobit-NN Analysis