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Digital payment adoption in public transportation: Mediating role of mode choice segments in developing cities

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  • Wani, Shahiq Ahmad
  • Pani, Agnivesh
  • Mohan, Ranju
  • Bhowmik, Basuraj

Abstract

The emergence of digital payments, propelled by technology advancements, is revolutionising how we transact and pay for goods and services, ushering in the era of convenience and efficiency. The numerous benefits of these systems have prompted public transportation agencies to implement them. Understanding the factors influencing such technology adoption in the early stages is necessary. While limited research in the transportation sector exists, it is mainly focused on mobile ticketing or conducted before the adoption of digital payment technology. The former is not yet widespread in developing countries as they still have a human-driven system. This study presents a two-step methodology for analysing the population using a segmentation approach done using Latent class cluster analysis (LCCA) and thereby analysing each cluster and total data to evaluate the factors influencing the adoption of digital payments through ML classification algorithms – Decision trees, Random Forest and XGBoost. Trip parameters as model indicators and socio-demographic constructs as covariates are input to LCCA, resulting in seven clusters labelled Satisfied P.T. Users (24.48 %), Access-Concerned IPT Users (20.22 %), Satisfied MTW Users (12.91 %), Information-Concerned MTW Users (12.16 %), Comfort-Concerned Car Users (11.21 %), Safety-Concerned Captive P.T. Users (10.09 %) and Comfort-Concerned MTW Users (8.93 %). A comparison of the model predictions, accuracies and important features highlights that XGBoost is best performing for most of the clusters. Results indicated that the different segments exhibit different properties. Prior use of travel apps, type of phone owned, internet availability, and age significantly influence the adoption of digital payment. The study highlights the importance of understanding the different user segments and tailoring the digital payment technologies accordingly to promote their adoption in Public transportation. The study can help policymakers shape technological decisions and encourage technology use. Future studies can explore the attitudinal aspects of commuters and employ advanced analytical methods for further exploration.

Suggested Citation

  • Wani, Shahiq Ahmad & Pani, Agnivesh & Mohan, Ranju & Bhowmik, Basuraj, 2025. "Digital payment adoption in public transportation: Mediating role of mode choice segments in developing cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:transa:v:191:y:2025:i:c:s0965856424003677
    DOI: 10.1016/j.tra.2024.104319
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    as
    1. Huang, Yu & Parker, Dawn & Minaker, Leia, 2021. "Identifying latent demand for transit-oriented development neighbourhoods: Evidence from a mid-sized urban area in Canada," Journal of Transport Geography, Elsevier, vol. 90(C).
    2. , Jefriyanto, 2022. "Fixed Asset Revaluation: Decision Making," OSF Preprints s947a, Center for Open Science.
    3. Al-Okaily, Manaf & Lutfi, Abdalwali & Alsaad, Abdallah & Taamneh, Abdallah & Alsyouf, Adi, 2020. "The Determinants of Digital Payment Systems’ Acceptance under Cultural Orientation Differences: The Case of Uncertainty Avoidance," Technology in Society, Elsevier, vol. 63(C).
    4. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    5. Ethan Ligon & Badal Malick & Ketki Sheth & Carly Trachtman, 2019. "What explains low adoption of digital payment technologies? Evidence from small-scale merchants in Jaipur, India," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-22, July.
    6. Bo Qu & Li Wei & Yujia Zhang, 2022. "Factors affecting consumer acceptance of electronic cash in China: an empirical study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    7. Dash, Ganesh & Sharma, Kiran & Yadav, Neha, 2023. "The diffusion of mobile payments: Profiling the adopters and non-adopters, Roger's way," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    8. ., 2022. "Alternative decisions, consequences and evaluations," Chapters, in: The Economic Analysis of Civil Law, chapter 1, pages 15-20, Edward Elgar Publishing.
    9. Abenoza, Roberto F. & Cats, Oded & Susilo, Yusak O., 2017. "Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 64-84.
    10. Yongsung Lee & Giovanni Circella & Patricia L. Mokhtarian & Subhrajit Guhathakurta, 2020. "Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in California," Transportation, Springer, vol. 47(5), pages 2505-2528, October.
    11. Costa, Álvaro & Fernandes, Ruben, 2012. "Urban public transport in Europe: Technology diffusion and market organisation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 269-284.
    12. Rafiq, Rezwana & McNally, Michael G., 2021. "Heterogeneity in Activity-travel Patterns of Public Transit Users: An Application of Latent Class Analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 1-18.
    13. Eldeeb, Gamal & Mohamed, Moataz, 2020. "Quantifying preference heterogeneity in transit service desired quality using a latent class choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 119-133.
    14. Ton, Danique & Arendsen, Koen & de Bruyn, Menno & Severens, Valerie & van Hagen, Mark & van Oort, Niels & Duives, Dorine, 2022. "Teleworking during COVID-19 in the Netherlands: Understanding behaviour, attitudes, and future intentions of train travellers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 55-73.
    15. Su-Min Yu & Zhi-Jiao Du, 2022. "Large-Scale Group Decision-Making," Springer Books, Springer, number 978-981-16-7889-9, March.
    16. Kim, Sung Hoo & Circella, Giovanni & Mokhtarian, Patricia L., 2019. "Identifying latent mode-use propensity segments in an all-AV era," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 192-207.
    17. Marta Campos Ferreira & Manuel Oliveira & Teresa Galvão Dias, 2022. "To Use or Not to Use? Investigating What Drives Tourists to Use Mobile Ticketing Services in Tourism," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
    18. Hillel, Tim & Bierlaire, Michel & Elshafie, Mohammed Z.E.B. & Jin, Ying, 2021. "A systematic review of machine learning classification methodologies for modelling passenger mode choice," Journal of choice modelling, Elsevier, vol. 38(C).
    19. repec:osf:osfxxx:s947a_v1 is not listed on IDEAS
    20. Jenn, Alan & Lee, Jae Hyun & Hardman, Scott & Tal, Gil, 2020. "An in-depth examination of electric vehicle incentives: Consumer heterogeneity and changing response over time," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 97-109.
    21. Danique Ton & Lara-Britt Zomer & Florian Schneider & Sascha Hoogendoorn-Lanser & Dorine Duives & Oded Cats & Serge Hoogendoorn, 2020. "Latent classes of daily mobility patterns: the relationship with attitudes towards modes," Transportation, Springer, vol. 47(4), pages 1843-1866, August.
    22. Hensher, David A., 2017. "Future bus transport contracts under a mobility as a service (MaaS) regime in the digital age: Are they likely to change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 86-96.
    23. Mr. Alexei P Kireyev, 2017. "The Macroeconomics of De-Cashing," IMF Working Papers 2017/071, International Monetary Fund.
    24. Alkhowaiter, Wassan Abdullah, 2020. "Digital payment and banking adoption research in Gulf countries: A systematic literature review," International Journal of Information Management, Elsevier, vol. 53(C).
    25. Brakewood, Candace & Ziedan, Abubakr & Hendricks, Sara J. & Barbeau, Sean J. & Joslin, Ann, 2020. "An evaluation of the benefits of mobile fare payment technology from the user and operator perspectives," Transport Policy, Elsevier, vol. 93(C), pages 54-66.
    26. Patil, Pushp & Tamilmani, Kuttimani & Rana, Nripendra P. & Raghavan, Vishnupriya, 2020. "Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal," International Journal of Information Management, Elsevier, vol. 54(C).
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