Service Quality in Rail Systems: Listen to the Voice of Social Media

被引:3
作者
Guner, Samet [1 ]
Taskin, Kamil [1 ]
Cebeci, Halil Ibrahim [1 ]
Aydemir, Emrah [1 ]
机构
[1] Sakarya Univ, Sakarya Business Sch, Sakarya, Turkiye
关键词
machine learning (artificial intelligence); urban transportation data and information systems; big data; public transportation; customer satisfaction/loyalty; quality; passenger rail transportation; CUSTOMER SATISFACTION; TRANSIT NETWORK; PUBLIC-TRANSIT; TWITTER; TRANSPORTATION; PERCEPTIONS; MANAGEMENT; FRAMEWORK; LOYALTY; TRAINS;
D O I
10.1177/03611981231200225
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Service quality is essential to increase and maintain user loyalty to the railway system. In the literature, surveys have been used to measure user satisfaction, and mathematical methods have been applied to quantify the survey results. In recent years, user-generated content, including comments and complaints shared via social media, has been used to measure the quality of rail services. This content may provide important insights into the quality of the service provided with its dynamic structure. In this study, a SERVQUAL-based social-media analytics approach is used to measure railway service quality, placing special emphasis on the temporal variations in a national rail system. Topic modeling was used to assign each content item to the relevant service dimension and sentiment analysis was applied to measure the level of satisfaction. Importance-performance analysis was employed at the final stage to generate policy suggestions. Gathering more than 2.3 million social-media messages posted from 2011 to 2021, we examined the temporal evolution of service quality of the Turkish rail system. The results reveal the most and least important services and the satisfaction level of each dimension. The differences between the priorities of conventional and high-speed rail passengers are defined, and policy recommendations are presented.
引用
收藏
页码:824 / 847
页数:24
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