Service Quality Assessment and Optimization of High-Speed Railway Waiting Halls Using a Kano Model and Multidimensional Questionnaire Analysis

With the rapid development of high-speed railways, the quality of service in the waiting halls of high-speed railway stations has become a subject of great concern. In order to clarify the impact of various service elements on the overall satisfaction associated with high-speed railway passenger stations, this study offers an in-depth exploration of the service quality of the waiting halls of high-speed railway stations by considering the physical environment (such as thermal environment, acoustic environment, light environment, and air quality), environmental design (including architectural design, route design, and hygiene situations), and service facilities (such as rest facilities, information facilities, safety features, commercial facilities, and ticketing facilities). The study uses a combination of an online questionnaire and an on-site questionnaire to collect data, and we ensured the reliability and validity of the research results through reliability and validity analyses. The Kano model was used to accurately identify the demand attributes of passengers for various service elements in the departure hall. Linear regression analysis was used to conduct a detailed study of the quantitative relationship between the influencing factors and overall satisfaction, and the satisfaction level of each dimension was systematically calculated to accurately quantify the impact of different factors on the overall satisfaction. Pearson correlation analysis was used to carefully explore the correlations among the factors and reveal the potential relationships. The study clearly depicts the performance of each service element. According to the demand classification of the Kano model, Must-Have Quality (M) elements include air quality, thermal environment, route design, the hygiene situation, and information facilities; Attractive Quality (A) elements include the acoustic environment, light environment, and architectural design; rest facilities, commercial facilities, and ticketing facilities are classified as One-Dimensional Quality (O); and safety facilities are of Indifferent Quality (I). Combined with regression analysis and correlation analysis, these results were used to further determine the focus of service element optimization. By clarifying the attributes of different service elements and their degree of impact on overall satisfaction, the corresponding optimization direction is proposed.