IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v165y2025icp150-163.html

Exploring the determinants of demand-responsive transit acceptance in China

Author

Listed:
  • Hu, Sangen
  • Li, Chun
  • Wu, Weitiao
  • Yang, Ying

Abstract

Demand-responsive transit (DRT) is gaining prominence in urban public transportation research, especially in rapidly modernizing transit systems of developing countries such as China. Despite DRT's advantages, challenges such as low market demand and utilization persist. To ensure DRT's successful integration and promotion, understanding public acceptance and its determinants is vital. This study expands the technology acceptance model (TAM) by incorporating trust, personal innovativeness, subjective norms, service quality, and perceived risk as pivotal factors influencing DRT acceptance. An online survey was conducted where a total of 627 valid responses were collected via snowball sampling. Structural equation modeling and path analysis were employed to dissect the factors influencing DRT adoption intentions. The results reveal that the proposed extended model accounts for 78.1% of the variance in DRT usage intentions. Trust exerts the most substantial influence on the usage intention of DRT, directly shaping user intentions and indirectly influencing them through various associated constructs. Service quality indirectly impacts intentions through perceived usefulness and personal innovativeness. Personal innovativeness and subjective norms have both direct and indirect impacts, whereas perceived risk solely indirectly affects intentions negatively. The research highlights the critical role of trust and service quality in shaping public DRT intentions and the importance of personal innovativeness and subjective norms in driving adoption. It also emphasizes the necessity of addressing perceived risk for acceptance. Theoretical and practical implications guide policymakers and operators in enhancing DRT services in China's evolving transit environment.

Suggested Citation

  • Hu, Sangen & Li, Chun & Wu, Weitiao & Yang, Ying, 2025. "Exploring the determinants of demand-responsive transit acceptance in China," Transport Policy, Elsevier, vol. 165(C), pages 150-163.
  • Handle: RePEc:eee:trapol:v:165:y:2025:i:c:p:150-163
    DOI: 10.1016/j.tranpol.2025.02.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X2500068X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2025.02.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Chao Wang & Mohammed Quddus & Marcus Enoch & Tim Ryley & Lisa Davison, 2014. "Multilevel modelling of Demand Responsive Transport (DRT) trips in Greater Manchester based on area-wide socio-economic data," Transportation, Springer, vol. 41(3), pages 589-610, May.
    2. Ritu Agarwal & Jayesh Prasad, 1998. "A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology," Information Systems Research, INFORMS, vol. 9(2), pages 204-215, June.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Talwar, Shalini & Dhir, Amandeep & Khalil, Ashraf & Mohan, Geetha & Islam, A.K.M. Najmul, 2020. "Point of adoption and beyond. Initial trust and mobile-payment continuation intention," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    5. Jie Lyu & Jing Zhang, 2021. "An Empirical Study into Consumer Acceptance of Dockless Bikes Sharing System Based on TAM," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    6. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    7. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    8. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    9. Martins, Carolina & Oliveira, Tiago & Popovič, Aleš, 2014. "Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application," International Journal of Information Management, Elsevier, vol. 34(1), pages 1-13.
    10. Globisch, Joachim & Dütschke, Elisabeth & Schleich, Joachim, 2018. "Acceptance of electric passenger cars in commercial fleets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 122-129.
    11. Neeraj Saxena & Taha Rashidi & David Rey, 2020. "Determining the Market Uptake of Demand Responsive Transport Enabled Public Transport Service," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    12. Vij, Akshay & Ryan, Stacey & Sampson, Spring & Harris, Susan, 2020. "Consumer preferences for on-demand transport in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 823-839.
    13. Currie, Graham & Fournier, Nicholas, 2020. "Why most DRT/Micro-Transits fail – What the survivors tell us about progress," Research in Transportation Economics, Elsevier, vol. 83(C).
    14. Kamal, Syeda Ayesha & Shafiq, Muhammad & Kakria, Priyanka, 2020. "Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)," Technology in Society, Elsevier, vol. 60(C).
    15. Peng Liu & Run Yang & Zhigang Xu, 2019. "Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions," Risk Analysis, John Wiley & Sons, vol. 39(2), pages 326-341, February.
    16. Fahimeh Golbabaei & Tan Yigitcanlar & Alexander Paz & Jonathan Bunker, 2023. "Perceived Opportunities and Challenges of Autonomous Demand-Responsive Transit Use: What Are the Socio-Demographic Predictors?," Sustainability, MDPI, vol. 15(15), pages 1-18, August.
    17. Xiaoxia Dong & Matthew DiScenna & Erick Guerra, 2019. "Transit user perceptions of driverless buses," Transportation, Springer, vol. 46(1), pages 35-50, February.
    18. Katarzyna Turoń, 2022. "Selection of Car Models with a Classic and Alternative Drive to the Car-Sharing Services from the System’s Rare Users Perspective," Energies, MDPI, vol. 15(19), pages 1-15, September.
    19. Wu, Weitiao & Zhu, Yanchen & Liu, Ronghui, 2024. "Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
    20. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    21. Chen, Ching-Fu, 2008. "Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(4), pages 709-717, May.
    22. Schasché, Stephanie E. & Sposato, Robert G. & Hampl, Nina, 2022. "The dilemma of demand-responsive transport services in rural areas: Conflicting expectations and weak user acceptance," Transport Policy, Elsevier, vol. 126(C), pages 43-54.
    23. Hu, Beibei & Zhong, Zhenfang & Zhang, Yanli & Sun, Yue & Jiang, Li & Dong, Xianlei & Sun, Huijun, 2022. "Understanding the influencing factors of bicycle-sharing demand based on residents’ trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    24. Zhang, Lei & Tong, Hangyan & Liang, Yuqing & Qin, Quande, 2023. "Consumer purchase intention of new energy vehicles with an extended technology acceptance model: The role of attitudinal ambivalence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    25. Nyga, Andreas & Minnich, Aljoscha & Schlüter, Jan, 2020. "The effects of susceptibility, eco-friendliness and dependence on the Consumers’ Willingness to Pay for a door-to-door DRT system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 540-558.
    26. Salonen, Arto O., 2018. "Passenger's subjective traffic safety, in-vehicle security and emergency management in the driverless shuttle bus in Finland," Transport Policy, Elsevier, vol. 61(C), pages 106-110.
    27. William Jen & Kai-Chieh Hu, 2003. "Application of perceived value model to identify factors affecting passengers' repurchase intentions on city bus: A case of the Taipei metropolitan area," Transportation, Springer, vol. 30(3), pages 307-327, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rajak, Manindra & Shaw, Krishnendu, 2021. "An extension of technology acceptance model for mHealth user adoption," Technology in Society, Elsevier, vol. 67(C).
    2. Chee, Pei Nen Esther & Susilo, Yusak O. & Wong, Yiik Diew, 2020. "Determinants of intention-to-use first-/last-mile automated bus service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 350-375.
    3. Susan A. Brown & Viswanath Venkatesh & Sandeep Goyal, 2012. "Expectation Confirmation in Technology Use," Information Systems Research, INFORMS, vol. 23(2), pages 474-487, June.
    4. Zhen Wang & John Lim & Xiaojia Guo, 2010. "Negotiator Satisfaction in NSS-Facilitated Negotiation," Group Decision and Negotiation, Springer, vol. 19(3), pages 279-300, May.
    5. Niu, Zhipeng & Hu, Xiaowei & Qi, Shouming & Yang, Haihua & Wang, Siqing & An, Shi, 2021. "Determinants to parking mode alternatives: A model integrating technology acceptance model and satisfaction–loyalty model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 216-234.
    6. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    7. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    8. Md. Alamgir Hossain & Ruhul Amin & Abdullah Al Masud & Md. Imran Hossain & Mohammad Awal Hossen & Mohammad Kamal Hossain, 2023. "What Drives People’s Behavioral Intention Toward Telemedicine? An Emerging Economy Perspective," SAGE Open, , vol. 13(3), pages 21582440231, July.
    9. Yaprak, Ümit & Kılıç, Fatih & Okumuş, Abdullah, 2021. "Is the Covid-19 pandemic strong enough to change the online order delivery methods? Changes in the relationship between attitude and behavior towards order delivery by drone," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    10. Ivonne Angelica Castiblanco Jimenez & Laura Cristina Cepeda García & Maria Grazia Violante & Federica Marcolin & Enrico Vezzetti, 2020. "Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications," Future Internet, MDPI, vol. 13(1), pages 1-21, December.
    11. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    12. Hossain, Akram & Quaresma, Rui & Rahman, Habibur, 2019. "Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study," International Journal of Information Management, Elsevier, vol. 44(C), pages 76-87.
    13. Simarpreet Kaur & Sangeeta Arora, 2023. "Understanding customers’ usage behavior towards online banking services: an integrated risk–benefit framework," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 74-98, March.
    14. Taneja, Shilpa & Ali, Liaqat, 2021. "Determinants of customers’ intentions towards environmentally sustainable banking: Testing the structural model," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    15. Adu-Gyamfi, Gibbson & Song, Huaming & Asamoah, Ama Nyarko & Li, Liang & Nketiah, Emmanuel & Obuobi, Bright & Adjei, Mavis & Cudjoe, Dan, 2022. "Towards sustainable vehicular transport: Empirical assessment of battery swap technology adoption in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    16. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 2017. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 19(3), pages 549-568, June.
    17. Peng Jing & Gang Xu & Yuexia Chen & Yuji Shi & Fengping Zhan, 2020. "The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review," Sustainability, MDPI, vol. 12(5), pages 1-26, February.
    18. Iviane Ramos-de-Luna & Francisco Montoro-Ríos & Francisco Liébana-Cabanillas, 2016. "Determinants of the intention to use NFC technology as a payment system: an acceptance model approach," Information Systems and e-Business Management, Springer, vol. 14(2), pages 293-314, May.
    19. Teng Yu & Jian Dai & Chengliang Wang, 2023. "Adoption of blended learning: Chinese university students’ perspectives," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    20. Wang, Yu-Yin & Wang, Yi-Shun & Lin, Tung-Ching, 2018. "Developing and validating a technology upgrade model," International Journal of Information Management, Elsevier, vol. 38(1), pages 7-26.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:trapol:v:165:y:2025:i:c:p:150-163. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.