Inhibitors in ridesharing firms from developing Nations: A novel Integrated MCDM – Text Mining approach using Large-Scale data

Abstract

Our study identifies major impediments (or inhibitors) faced by Transportation Network Companies (TNCs) such as Uber, Lyft, and Ola within the context of developing nations. While existing studies on TNCs centered on passenger adoption and drivers’ perspectives, we quantitively assess the inhibitors and provide mitigation strategies. To achieve this, we use machine learning methods, particularly Latent Dirichlet Allocation (LDA) and emotion analysis on large-scale public data, to understand and classify consumer perspectives on TNCs into multiple themes. The latent theme helps experts of different ridesharing firms get a holistic perspective of riders on TNCs, assisting them in identifying the inhibitors. Using the Delphi method, we were able to achieve a consensus in identifying six primary and nineteen secondary inhibitors. We rank the primary inhibitors based on the optimal weight obtained using the Bayesian Best Worst Method. To minimize uncertainty and imprecise judgment in decision-making, we combine the grey theory with the Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL) to identify the interrelationships among the secondary inhibitors. Moreover, we perform sensitivity analysis to show the robustness of our solution. Contrary to conventional perception, our findings indicate that the government is the primary inhibitor for TNCs due to current policy and discrepancies in regulations between central and states. Additionally, our studies introduce five new inhibitors to the literature, which include drivers inciting trip cancellation to avoid commission, internal coalition of drivers, commission miscomprehension among drivers, limited infrastructure for cashless operation, and internal conflict and dysfunction within the department. The findings from large-scale data analysis, coupled with group decision-making, offer various managerial implications that can guide future managers and policymakers to enhance the operational efficiency of firms.