{"title":"发展中国家共享乘车公司的阻碍因素:使用大规模数据的新型集成式 MCDM - 文本挖掘方法","authors":"","doi":"10.1016/j.tre.2024.103832","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inhibitors in ridesharing firms from developing Nations: A novel Integrated MCDM – Text Mining approach using Large-Scale data\",\"authors\":\"\",\"doi\":\"10.1016/j.tre.2024.103832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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. 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引用次数: 0
摘要
我们的研究确定了 Uber、Lyft 和 Ola 等运输网络公司(TNC)在发展中国家面临的主要障碍(或抑制因素)。现有的跨国公司研究主要集中在乘客和司机的角度,而我们则对抑制因素进行了量化评估,并提供了缓解策略。为此,我们使用机器学习方法,特别是大规模公共数据的潜在德里希勒分配(LDA)和情感分析,来理解消费者对 TNC 的看法并将其分为多个主题。潜在主题有助于不同共享出行公司的专家从整体角度了解乘客对跨国公司的看法,帮助他们找出抑制因素。利用德尔菲法,我们在确定六个主要抑制因素和十九个次要抑制因素方面达成了共识。我们根据贝叶斯最差法得出的最佳权重对主要抑制因素进行排序。为了尽量减少决策中的不确定性和不精确判断,我们将灰色理论与决策试验和评估实验室(Grey-DEMATEL)相结合,以确定二级抑制剂之间的相互关系。此外,我们还进行了敏感性分析,以显示我们解决方案的稳健性。与传统观念相反,我们的研究结果表明,由于现行政策以及中央和各州之间的法规差异,政府是跨国公司的主要抑制因素。此外,我们的研究还在文献中引入了五个新的抑制因素,包括司机煽动取消行程以规避佣金、司机内部联合、司机对佣金的误解、无现金运营的基础设施有限以及部门内部的冲突和功能失调。大规模数据分析的结果与群体决策相结合,提供了各种管理启示,可指导未来的管理者和政策制定者提高企业的运营效率。
Inhibitors in ridesharing firms from developing Nations: A novel Integrated MCDM – Text Mining approach using Large-Scale data
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.
期刊介绍:
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.