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Determinants to parking mode alternatives: A model integrating technology acceptance model and satisfaction–loyalty model

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  • Niu, Zhipeng
  • Hu, Xiaowei
  • Qi, Shouming
  • Yang, Haihua
  • Wang, Siqing
  • An, Shi

Abstract

As an emerging technology that does not rely on the construction of several parking facilities and effectively relieves the pressure of urban parking, shared parking is widely promoted. However, users may turn to conventional parking mode because of concerns about the performance of shared parking modes. Therefore, to determine why users change parking mode and to improve the behavior patronage rate of shared parking mode, studying the factors influencing users’ behavior intentions is very important. Technology acceptance model and satisfaction–loyalty model are combined to construct a comprehensive framework for analyzing the key factors that affect the behavioral intentions of shared parking mode and to describe the multidimensional characteristics of the behavioral intentions. The hypotheses were tested and analyzed on the basis of the empirical data of Harbin and the structural equation model (SEM). Eventually, a multigroup SEM analysis, which takes gender as a moderating variable, is used. Results show that the re-patronage of shared parking is significantly affected by its technology acceptance and that the perceived risk is the key variable leading to change parking mode. In addition, the factor invariance of the identified model framework is examined against gender, and few significant differences are found. The framework is expected to contribute to operators in increasing their market share of shared parking services and to governments in promoting new transportation technologies in cities.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:152:y:2021:i:c:p:216-234
    DOI: 10.1016/j.tra.2021.08.010
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    Cited by:

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    2. Jun Guo & Hongzhi Guan & Yan Han & Yunqiang Xue, 2023. "Analysis of the Potential Economic Impact of Parking Space Comprehensive Utilization on Traditional Business District," Sustainability, MDPI, vol. 16(1), pages 1-24, December.
    3. Yunxiang Zhang & Xianmin Song & Pengfei Tao & Haitao Li & Tianshu Zhan & Qian Cao, 2023. "Investigating Factors for Travelers’ Parking Behavior Intentions in Changchun, China, under the Influence of Smart Parking Systems," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    4. Niu, Zhipeng & Hu, Xiaowei & Fatmi, Mahmudur & Qi, Shouming & Wang, Siqing & Yang, Haihua & An, Shi, 2023. "Parking occupancy prediction under COVID-19 anti-pandemic policies: A model based on a policy-aware temporal convolutional network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    5. Jang, Sunghoon & Hong, Doosun & Lee, Chungwon, 2024. "Exploring the behavioral adoption of automated parcel locker systems under COVID-19," Transport Policy, Elsevier, vol. 151(C), pages 1-11.
    6. Sun, Shichao & Zhang, Hui & Zhou, Linlin & Wang, Ke, 2023. "Is the relationship between the perceived service quality and passenger loyalty linear or non-linear? A novel model-independent interpretation method is applied," Transport Policy, Elsevier, vol. 144(C), pages 65-79.
    7. Sunbin YOO & KUMAGAI Junya & KAWABATA Yuta & MANAGI Shunsuke, 2022. "Achieving Inclusive Transportation: Fully Automated Vehicles with Social Support," Discussion papers 22017, Research Institute of Economy, Trade and Industry (RIETI).

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