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Modeling User Intentions for Electric Vehicle Adoption in Thailand: Incorporating Multilayer Preference Heterogeneity

Author

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  • Thanapong Champahom

    (Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Chamroeun Se

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Wimon Laphrom

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Sajjakaj Jomnonkwao

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Ampol Karoonsoontawong

    (Department of Civil Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand)

  • Vatanavongs Ratanavaraha

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

Abstract

Background : The automotive industry is pivotal in advancing sustainability, with electric vehicles (EVs) essential for reducing emissions and promoting cleaner transport. This study examines the determinants of EV adoption intentions in Thailand, integrating demographic and psychographic factors from Environmental psychology and innovation diffusion theory; Methods : Data from a structured questionnaire, administered to 4003 respondents at gas stations with EV charging facilities across Thailand, were analyzed using a Correlated Mixed-Ordered Probit Model with Heterogeneity in Means (CMOPMHM); Results : Findings indicate that younger adults, particularly those aged 25–34 years old and 45–54 years old, are more likely to adopt EVs, whereas conventional or hybrid vehicle owners are less inclined. Rural residency or travel also hinders adoption. Individuals with strong environmental values and openness to new technologies are more likely to adopt EVs; Conclusions : The proposed model quantified the relative importance of these factors and uncovered heterogeneity in user preferences, offering reliable and valuable insights for policymakers, EV manufacturers, and researchers. The study suggests targeted policies and enhanced charging infrastructure, especially in rural areas, and recommends leveraging environmental values and trialability through communication campaigns and test drive events. These insights can guide the development of targeted incentives, infrastructure expansion, communication strategies, and trialability programs to effectively promote wider EV adoption in Thailand and similar markets.

Suggested Citation

  • Thanapong Champahom & Chamroeun Se & Wimon Laphrom & Sajjakaj Jomnonkwao & Ampol Karoonsoontawong & Vatanavongs Ratanavaraha, 2024. "Modeling User Intentions for Electric Vehicle Adoption in Thailand: Incorporating Multilayer Preference Heterogeneity," Logistics, MDPI, vol. 8(3), pages 1-21, August.
  • Handle: RePEc:gam:jlogis:v:8:y:2024:i:3:p:83-:d:1459332
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    References listed on IDEAS

    as
    1. Salari, Nasir, 2022. "Electric vehicles adoption behaviour: Synthesising the technology readiness index with environmentalism values and instrumental attributes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 60-81.
    2. Lorenzo Cappellari & Stephen P. Jenkins, 2006. "Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation," Stata Journal, StataCorp LP, vol. 6(2), pages 156-189, June.
    3. Liu, Yiran & Zhao, Xiaolei & Lu, Dan & Li, Xiaomin, 2023. "Impact of policy incentives on the adoption of electric vehicle in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    4. Peter Haan & Arne Uhlendorff, 2006. "Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 6(2), pages 229-245, June.
    5. Liu, Junbei & Zhuge, Chengxiang & Tang, Justin Hayse Chiwing G. & Meng, Meng & Zhang, Jie, 2022. "A spatial agent-based joint model of electric vehicle and vehicle-to-grid adoption: A case of Beijing," Applied Energy, Elsevier, vol. 310(C).
    6. Rye, Jamie & Sintov, Nicole D., 2024. "Predictors of electric vehicle adoption intent in rideshare drivers relative to commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    7. Jui-Che Tu & Chun Yang, 2019. "Key Factors Influencing Consumers’ Purchase of Electric Vehicles," Sustainability, MDPI, vol. 11(14), pages 1-22, July.
    8. Langbroek, Joram H.M. & Cebecauer, Matej & Malmsten, Jon & Franklin, Joel P. & Susilo, Yusak O. & Georén, Peter, 2019. "Electric vehicle rental and electric vehicle adoption," Research in Transportation Economics, Elsevier, vol. 73(C), pages 72-82.
    9. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    10. Deka, Chayasmita & Dutta, Mrinal Kanti & Yazdanpanah, Masoud & Komendantova, Nadejda, 2023. "Can gain motivation induce Indians to adopt electric vehicles? Application of an extended theory of Planned Behavior to map EV adoption intention," Energy Policy, Elsevier, vol. 182(C).
    11. Breschi, Valentina & Ravazzi, Chiara & Strada, Silvia & Dabbene, Fabrizio & Tanelli, Mara, 2023. "Driving electric vehicles’ mass adoption: An architecture for the design of human-centric policies to meet climate and societal goals," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    12. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, November.
    13. White, Lee V. & Sintov, Nicole D., 2017. "You are what you drive: Environmentalist and social innovator symbolism drives electric vehicle adoption intentions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 94-113.
    14. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    15. Iogansen, Xiatian & Wang, Kailai & Bunch, David & Matson, Grant & Circella, Giovanni, 2023. "Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    16. Srivastava, Abhishek & Kumar, Rajeev Ranjan & Chakraborty, Abhishek & Mateen, Arqum & Narayanamurthy, Gopalakrishnan, 2022. "Design and selection of government policies for electric vehicles adoption: A global perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    17. Kangda Chen & Fuquan Zhao & Han Hao & Zongwei Liu, 2018. "Synergistic Impacts of China’s Subsidy Policy and New Energy Vehicle Credit Regulation on the Technological Development of Battery Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-19, November.
    18. Barbarossa, Camilla & De Pelsmacker, Patrick & Moons, Ingrid, 2017. "Personal Values, Green Self-identity and Electric Car Adoption," Ecological Economics, Elsevier, vol. 140(C), pages 190-200.
    19. Roemer, Ellen & Henseler, Jörg, 2022. "The dynamics of electric vehicle acceptance in corporate fleets: Evidence from Germany," Technology in Society, Elsevier, vol. 68(C).
    20. Peng, Ruoqing & Tang, Justin Hayse Chiwing G. & Yang, Xiong & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2024. "Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States," Applied Energy, Elsevier, vol. 355(C).
    21. 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).
    22. Ziegler, Andreas, 2012. "Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles: A discrete choice analysis for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1372-1385.
    23. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
    24. Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
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