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Analysis of public acceptance of electric vehicle charging scheduling based on the technology acceptance model

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  • Wang, Ning
  • Tian, Hangqi
  • Zhu, Shunbo
  • Li, Yuan

Abstract

As a form of vehicle-grid integration technology, electric vehicle charging scheduling (EVCS) brings benefits to the operation of power grids and the consumption of renewable energies. Exploring users’ acceptance of EVCS helps to understand the formation mechanisms of acceptance and improve user adoption. Based on 426 questionnaires from Hangzhou, China, technology acceptance model and mean difference test were applied in this paper to explore the impact factors of willingness to accept and the differences in EVCS acceptance among groups with different social attributes. Results showed that 81.2% of the respondents were willing to participate in EVCS and were more inclined to accept charging time scheduling compared to charging location scheduling. Perceived risk had a significantly direct and negative effect on user acceptance, while initial trust, social value, and social influence had a significantly indirect and positive effect. There were no statistically significant differences in the acceptance of EVCS between males and females, or among groups with different education and income levels, with the married group and the 31–40 age group being more willing to accept than the unmarried group and the 18–30 age group respectively. Finally, the results were discussed and recommendations were presented accordingly.

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  • Wang, Ning & Tian, Hangqi & Zhu, Shunbo & Li, Yuan, 2022. "Analysis of public acceptance of electric vehicle charging scheduling based on the technology acceptance model," Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:energy:v:258:y:2022:i:c:s0360544222017078
    DOI: 10.1016/j.energy.2022.124804
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    2. Cudjoe, Dan & Zhu, Bangzhu & Wang, Hong, 2024. "The role of incentive policies and personal innovativeness in consumers' carbon footprint tracking apps adoption in China," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
    4. Sprei, Frances & Kempton, Willett, 2024. "Mental models guide electric vehicle charging," Energy, Elsevier, vol. 292(C).
    5. Ion Popa & Marian Mihai Cioc & Stefan Catalin Pop & Daniel Botez & Marius-Ioan Pantea, 2023. "Aligning Public Policy with REPowerEU Program Objectives by Adopting EESS Solutions: A Technology Acceptance Model Approach," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(64), pages 660-660, August.

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