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Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management

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  • Weng-Kun Liu

    (Department of International Business, Feng Chia University, Taichung 40724, Taiwan)

  • Chia-Chun Yen

    (Ph.D. Program in Civil and Hydraulic Engineering, Feng Chia University, Taichung 40724, Taiwan)

Abstract

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply–demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan, was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector–transportation company–passenger supply chain.

Suggested Citation

  • Weng-Kun Liu & Chia-Chun Yen, 2016. "Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1319-:d:85145
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    References listed on IDEAS

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    Cited by:

    1. Chunting Liu & Shanshan Wang & Guozhu Jia, 2020. "Exploring E-Commerce Big Data and Customer-Perceived Value: An Empirical Study on Chinese Online Customers," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    2. Yona, Moran & Birfir, Genadi & Kaplan, Sigal, 2021. "Data science and GIS-based system analysis of transit passenger complaints to improve operations and planning," Transport Policy, Elsevier, vol. 101(C), pages 133-144.
    3. Alžbeta Kucharčíková & Martin Mičiak, 2018. "Human Capital Management in Transport Enterprises with the Acceptance of Sustainable Development in the Slovak Republic," Sustainability, MDPI, vol. 10(7), pages 1-18, July.
    4. Shobhana Chandra & Sanjeev Verma, 2023. "Big Data and Sustainable Consumption: A Review and Research Agenda," Vision, , vol. 27(1), pages 11-23, February.
    5. Kyungtae Kim & Sungjoo Lee, 2018. "How Can Big Data Complement Expert Analysis? A Value Chain Case Study," Sustainability, MDPI, vol. 10(3), pages 1-21, March.
    6. Ricardo Chalmeta & Nestor J. Santos-deLeón, 2020. "Sustainable Supply Chain in the Era of Industry 4.0 and Big Data: A Systematic Analysis of Literature and Research," Sustainability, MDPI, vol. 12(10), pages 1-24, May.

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