Comprehensive evaluation of passenger road transportation through dynamic network data envelopment analysis: A case study of state road transport undertakings in India

State road transport undertakings (STUs) in India are public utility services that link diverse terrains and remote and hilly areas throughout the country and play a vital role in enhancing public mobility. These organizations are motivated to work through a sense of social responsibility. For example, they operate in low‐revenue‐generating regions to serve the public. Therefore, they require regular performance monitoring and constant efforts toward course corrections. Existing literature on the performance evaluation of STUs ignores the internal structures of the passenger transportation process and treats the process as a “black‐box.” Consequently, it fails to identify the precise cause of inefficiencies, making course corrections in a multistage transportation process difficult. To close this research gap and comprehensively evaluate the performance of STUs, considering their internal structure and based on long‐term optimization, this study employs a slacks‐based dynamic network data envelopment analysis (DNDEA) approach to deconstruct India's publicly owned passenger road transportation process into operations and revenue divisions, subjected to a multiperiod performance evaluation from 2014–2015 to 2017–2018. Moreover, we utilize panel data regression to explore other influential factors not included in the DNDEA, explaining the efficiency variation across STUs. This study is the first to explore the internal structure of STUs in India and perform a comprehensive evaluation of STUs, considering their internal divisions and multiple‐period long‐term optimization. The DNDEA results highlight evidence of capital misallocation in India's public road transport sector. While operational inefficiency can be attributed to improper utilization of resources, price regulation is the major source of inefficiency in the revenue division. Fixed effects panel data regression revealed that STUs with higher bus utilization and passenger lead are more efficient. The contribution of this study lies in its innovative approach of using DNDEA to analyze India's passenger road transportation sector and its inner constructs, providing insights into the efficiency scores of STUs and their dynamic changes in one model. Managers and policymakers can use the findings of this study to revise and frame policies targeted at improving STU efficiency.