Modeling Extended Service Quality for Public Transportation in the Post-Pandemic Period: Differentiating between Urban and Rural Areas: A Case Study of Intercity Railway, Thailand

Panuwat Wisutwattanasak, Thanapong Champahom, Sajjakaj Jomnonkwao (), Manlika Seefong, Kestsirin Theerathitichaipa, Rattanaporn Kasemsri and Vatanavongs Ratanavaraha
Additional contact information
Panuwat Wisutwattanasak: Institute of Research and Development, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Thanapong Champahom: Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand
Sajjakaj Jomnonkwao: School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Manlika Seefong: School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Kestsirin Theerathitichaipa: School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Rattanaporn Kasemsri: School of Civil Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
Vatanavongs Ratanavaraha: School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Logistics, 2023, vol. 7, issue 4, 1-24

Abstract: Background: Scholars have indicated differences in the attitudes of urban and non-urban populations, especially after the COVID-19 outbreak, which extend to their needs and expectations regarding rail transport development. The aim of this study is to enhance the quality of train services in the post-pandemic era, and multigroup analysis will be applied to achieve the difference in area context. Methods: The research data were collected from rail transport users throughout Thailand, consisting of 665 urban and 935 rural users. The questionnaires primarily focused on user expectations regarding rail service quality and travel conditions in the post-pandemic landscape using multigroup confirmatory factor analysis (MCFA). Results: The results unveiled significant variations in user trends and needs across different contexts and areas. In urban settings, there was a notably higher overall service expectation compared to rural areas. Specifically, urban users prioritized factors such as accessibility and service empathy, whereas rural rail users placed greater emphasis on staff quality and reasonable pricing. Conclusions: These findings furnish rail transport service agencies with valuable insights and guidance for comprehending their users’ needs. They can develop appropriate organizational strategies, service quality enhancements, and policy adjustments tailored to the unique demands of urban and rural areas in the post-pandemic era, thereby ensuring sustainability. Additionally, the methodology of multigroup analysis served as a significant scientific contribution; this showed that the statistical analysis of different area contexts in the study should not be ignored.

Keywords: COVID-19; SERVQUAL; confirmatory factor analysis; multigroup analysis; area context (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2305-6290/7/4/93/pdf (application/pdf)
https://www.mdpi.com/2305-6290/7/4/93/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:7:y:2023:i:4:p:93-:d:1294543

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().