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Public’s Intention and Influencing Factors of Dockless Bike-Sharing in Central Urban Areas: A Case Study of Lanzhou City, China

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  • Wei Ji

    (Institute of County Economic Development & Rural Revitalization Strategy, Lanzhou University, Lanzhou 730000, China
    School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Chengpeng Lu

    (Institute of County Economic Development & Rural Revitalization Strategy, Lanzhou University, Lanzhou 730000, China
    School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Jinhuang Mao

    (Institute of County Economic Development & Rural Revitalization Strategy, Lanzhou University, Lanzhou 730000, China
    School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Yiping Liu

    (Institute of County Economic Development & Rural Revitalization Strategy, Lanzhou University, Lanzhou 730000, China)

  • Muchen Hou

    (Institute of County Economic Development & Rural Revitalization Strategy, Lanzhou University, Lanzhou 730000, China
    School of Economics, Lanzhou University, Lanzhou 730000, China)

  • Xiaoli Pan

    (Institute of County Economic Development & Rural Revitalization Strategy, Lanzhou University, Lanzhou 730000, China
    School of Economics, Lanzhou University, Lanzhou 730000, China)

Abstract

Taking the main district in Lanzhou city of China as an example, the questionnaires were designed and distributed, and then the effects of five factors, i.e., behavioral attitude, subjective norm, perceived behavioral control, perceived ease of use and perceived usefulness, on the behavioral intention of dockless bike-sharing (DBS) use were empirically analyzed based on the integrated model of technology acceptance model (TAM) and the theory of planned behavior (TPB) as well as the structural equation model. Results show that the five factors all impose significantly positive effects on the public’s behavioral intention of DBS use but differ in influencing degrees. Behavioral attitude, subjective norm and perceived behavioral control can all directly affect the public’s behavioral intention of DBS use, with direct influence coefficients of 0.691, 0.257 and 0.198, while perceived ease of use and perceived usefulness impose indirectly effects on behavioral intention, with indirect influence coefficients of 0.372 and 0.396. Overall, behavioral attitude imposes the most significant effect, followed by perceived ease of use, perceived usefulness and subjective norm, and finally perceived behavioral control. This indicates that the public’s behavioral intention of DBS use depends heavily on their behavioral attitude towards the shared bikes. In view of the limited open space of the main district in Lanzhou, the explosive growth of shared bikes, oversaturated arrangements, disordered competition, unclear and unscientific divisions of parking regions, and hindrance of traffic, this study proposes a lot of policy suggestions from the research results. A series of supporting service systems related to DBS should be formulated. The shared bikes with different characteristics should be launched for different age groups, gender groups and work groups. The corresponding feedback platform for realtime acquisition, organization, analysis and solution of data information, as well as the adequate platform feedback mechanism, should be established.

Suggested Citation

  • Wei Ji & Chengpeng Lu & Jinhuang Mao & Yiping Liu & Muchen Hou & Xiaoli Pan, 2021. "Public’s Intention and Influencing Factors of Dockless Bike-Sharing in Central Urban Areas: A Case Study of Lanzhou City, China," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9265-:d:616801
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    References listed on IDEAS

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    1. Böcker, Lars & Anderson, Ellinor & Uteng, Tanu Priya & Throndsen, Torstein, 2020. "Bike sharing use in conjunction to public transport: Exploring spatiotemporal, age and gender dimensions in Oslo, Norway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 389-401.
    2. Lin, Jenn-Rong & Yang, Ta-Hui, 2011. "Strategic design of public bicycle sharing systems with service level constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(2), pages 284-294, March.
    3. Médard de Chardon, Cyrille & Caruso, Geoffrey, 2015. "Estimating bike-share trips using station level data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 260-279.
    4. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    5. Felipe González & Carlos Melo-Riquelme & Louis Grange, 2016. "A combined destination and route choice model for a bicycle sharing system," Transportation, Springer, vol. 43(3), pages 407-423, May.
    6. Xiaojia Guo & Chengpeng Lu & Dongqi Sun & Yexin Gao & Bing Xue, 2021. "Comparison of Usage and Influencing Factors between Governmental Public Bicycles and Dockless Bicycles in Linfen City, China," Sustainability, MDPI, vol. 13(12), pages 1-14, June.
    7. Kyung Hwan Lee & Eun Jeong Ko, 2014. "Relationships between neighbourhood environments and residents' bicycle mode choice: a case study of Seoul," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(3), pages 383-395, November.
    8. Kyle Gebhart & Robert Noland, 2014. "The impact of weather conditions on bikeshare trips in Washington, DC," Transportation, Springer, vol. 41(6), pages 1205-1225, November.
    9. Kaplan, Sigal & Manca, Francesco & Nielsen, Thomas Alexander Sick & Prato, Carlo Giacomo, 2015. "Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior," Tourism Management, Elsevier, vol. 47(C), pages 34-46.
    10. Peter Newton & Denny Meyer, 2013. "Exploring the Attitudes-Action Gap in Household Resource Consumption: Does “Environmental Lifestyle” Segmentation Align with Consumer Behaviour?," Sustainability, MDPI, vol. 5(3), pages 1-23, March.
    11. Han, Sun Sheng, 2020. "The spatial spread of dockless bike-sharing programs among Chinese cities," Journal of Transport Geography, Elsevier, vol. 86(C).
    12. Faghih-Imani, Ahmadreza & Eluru, Naveen & El-Geneidy, Ahmed M. & Rabbat, Michael & Haq, Usama, 2014. "How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal," Journal of Transport Geography, Elsevier, vol. 41(C), pages 306-314.
    13. Xing, Yingying & Wang, Ke & Lu, Jian John, 2020. "Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 87(C).
    14. Vogel, Marie & Hamon, Ronan & Lozenguez, Guillaume & Merchez, Luc & Abry, Patrice & Barnier, Julien & Borgnat, Pierre & Flandrin, Patrick & Mallon, Isabelle & Robardet, Céline, 2014. "From bicycle sharing system movements to users: a typology of Vélo’v cyclists in Lyon based on large-scale behavioural dataset," Journal of Transport Geography, Elsevier, vol. 41(C), pages 280-291.
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