Second-Life Mobility: Thematic Insights Into Drivers and Barriers to Used EV Adoption
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This study aims to explore the key factors influencing consumer adoption of used electric vehicles (UEVs), an area that remains under-researched despite its growing relevance in sustainable mobility. Integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), the research employs a qualitative thematic analysis based on the expert interviews to examine constructs such as attitude, subjective norm, perceived behavioral control, perceived usefulness, and perceived ease of use. The analysis reveals that, beyond these established constructs, UEV-specific concerns such as battery reliability, information asymmetry, and residual values influence consumer risk perception. In addition, psychological dimensions like environmental self-identity and trust in institutions are found to mediate behavioral intentions. Compared to new electric vehicles, UEV adoption involves more complex decision-making, with consumers factoring in retrospective evaluations, technological uncertainties, and secondary market dynamics. This study contributes novel insights by extending traditional behavioral models to include post-use transparency, lifecycle information, and ecosystem readiness. The findings offer practical implications for industry stakeholders and policymakers, highlighting the need for transparent pricing, standardized battery diagnostics, and reliable certification systems. Ultimately, this research enhances the understanding of behavioral mechanisms in the UEV market and supports the advancement of more inclusive and sustainable electric mobility solutions.
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[1] IEA. (2022). Global EV Outlook 2022 - Securing supplies for an electric future. Available online: https://iea.blob.core.windows.net/assets/ad8fb04c-4f75-42fc-973a-6e54c8a4449a/GlobalElectricVehicleOutlook2022.pdf (accessed on November 2025).
[2] Lutsey, N., & Nicholas, M. (2019). Update on electric vehicle costs in the United States through 2030. The International Council on Clean Transportation. 12, 1–12. Available online: https://theicct.org/sites/default/files/publications/ EV_cost_2020_2030_20190401.pdf (accessed on November 2025).
[3] Cui, S., & Zhao, N. (2024). A Study on the Current Status and Future Prospects of EV Automotive Market. Journal of Social Science and Cultural Development. 1(2). doi:10.70767/jsscd.v1i2.296.
[4] Li, W., Bao, L., Li, Y., Si, H., & Li, Y. (2022). Assessing the transition to low-carbon urban transport: A global comparison. Resources, Conservation and Recycling, 180, 106179. doi:10.1016/j.resconrec.2022.106179.
[5] Rezvani, Z., Jansson, J., & Bodin, J. (2015). Advances in consumer electric vehicle adoption research: A review and research agenda. In Transportation Research Part D: Transport and Environment. 34, 122–136. doi:10.1016/j.trd.2014.10.010.
[6] Sierzchula, W., Bakker, S., Maat, K., & van Wee, B. (2014). The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68, 183–194. doi:10.1016/j.enpol.2014.01.043.
[7] Tu, J.-C., & Yang, C. (2019). Key Factors Influencing Consumers’ Purchase of Electric Vehicles. Sustainability, 11(14), 3863. doi:10.3390/su11143863.
[8] Kashem, M. A., Shamsuddoha, M., & Nasir, T. (2024). Sustainable Transportation Solutions for Intelligent Mobility: A Focus on Renewable Energy and Technological Advancements for Electric Vehicles (EVs) and Flying Cars. In Future Transportation 4(3), 874–890. doi:10.3390/futuretransp4030042.
[9] Thomas, A., & Mishra, U. (2024). Industry 4.0 and circular economy model for a sustainable electric vehicle battery with controllable wastewater and carbon emission. Energy Reports. 11, 4044–4066. doi:10.1016/j.egyr.2024.03.054.
[10] Liao, F., Molin, E., & van Wee, B. (2017). Consumer preferences for electric vehicles: a literature review. Transport Reviews. 37(3): 252–275). doi:10.1080/01441647.2016.1230794.
[11] Zhang, Y., Yu, Y., & Zou, B. (2011). Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV. Energy Policy. 39(11): 7015–7024. doi:10.1016/j.enpol.2011.07.055.
[12] Wang, N., Tang, L., & Pan, H. (2019). A global comparison and assessment of incentive policy on electric vehicle promotion. Sustainable Cities and Society. 44, 597–603. doi:10.1016/j.scs.2018.10.024.
[13] Breetz, H., Mildenberger, M., & Stokes, L. (2018). The political logics of clean energy transitions. Business and Politics. 20(4): 492–522. doi:10.1017/bap.2018.14.
[14] Zou, J., Kamarudin, K. M., Liu, J., & Zhang, J. (2024). Towards Sustainable Mobility: Determinants of Intention to Purchase Used Electric Vehicles in China. Sustainability (Switzerland). 16(19). doi:10.3390/su16198588.
[15] Hardman, S., & Tal, G. (2021). Understanding discontinuance among California’s electric vehicle owners. Nature Energy. 6(5): 538–545. doi:10.1038/s41560-021-00814-9.
[16] Heffner, R. R., Kurani, K. S., & Turrentine, T. S. (2007). Symbolism in California’s early market for hybrid electric vehicles. Transportation Research Part D: Transport and Environment. 12(6): 396–413. doi:10.1016/j.trd.2007.04.003.
[17] Yin, R.K. (2018). Case Study Research and Applications; Sage: Thousand Oaks, CA, USA, Volume 6.
[18] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi:10.1016/0749-5978(91)90020-T.
[19] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems. 13(3): 319–339. doi:10.2307/249008.
[20] Poon, W. C., Sin, K. Y., & Sathasivam, K. (2024). An extended Combined-TAM-TPB to explain the intention to adopt electric vehicles: a multi-group analysis of Generations X, Y, and Z. Research Square. 1(1): 1–31. doi:10.21203/rs.3.rs-4291137/v1.
[21] Rachmawati, I., & Rahardi, R. A. M. (2023). Analysis of Electric Vehicle Purchase Intentions in Indonesia Using the Extension C-TAM-TPB Model. International Journal of Current Science Research and Review. 6(12): 8065–8078. doi:10.47191/ijcsrr/v6-i12-61
[22] Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy 1(4): 139–161. doi:10.1016/0146-6402(78)90002-4
[23] Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences. 39(2): 273–315. doi:10.1111/j.1540-5915.2008.00192.x
[24] Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior. 26(4): 760–767. doi:10.1016/j.chb.2010.01.013.
[25] Feng, B., Ye, Q., & Collins, B. J. (2019). A dynamic model of electric vehicle adoption: The role of social commerce in new transportation. Information and Management. 56(2): 196–212. doi:10.1016/j.im.2018.05.004
[26] Quaglieri, L. (2025). Determinants of electric vehicle adoption: The role of consumer behavior in the transition to sustainable mobility (PhD thesis, Università degli Studi di Roma “La Sapienza”). Available online: https://hdl.handle.net/11573/1742356 (accessed on November 2025).
[27] Nafees, S., & Sujood. (2024). The power of emotions: combining emotional attachment theory (EAT) and the technology acceptance model (TAM) to predict consumers’ intention to use interactive technologies (ITs) at tourism destinations. Tourism Recreation Research. 1–18. doi:10.1080/02508281.2024.2390716.
[28] Haustein, S., & Jensen, A. F. (2018). Factors of electric vehicle adoption: A comparison of conventional and electric car users based on an extended theory of planned behavior. International Journal of Sustainable Transportation. 12(7): 484–496. doi:10.1080/15568318.2017.1398790
[29] Pang, J., Ye, J., & Zhang, X. (2023). Factors influencing users’ willingness to use new energy vehicles. PLoS ONE, 18(). doi:10.1371/journal.pone.0285815.
[30] Schuitema, G., Anable, J., Skippon, S., & Kinnear, N. (2013). The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transportation Research Part A: Policy and Practice. 48, 39–49. doi:10.1016/j.tra.2012.10.004.
[31] Thilina, D., & Gunawardane, N. (2019). The effect of perceived risk on the purchase intention of electric vehicles: An extension to the technology acceptance model. International Journal of Electric and Hybrid Vehicles. 11(1): 73–84. doi:10.1504/ijehv.2019.098717.
[32] Lin, Z., & Filieri, R. (2015). Airline passengers’ continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge. Transportation Research Part E: Logistics and Transportation Review. 81, 158–168. doi:10.1016/j.tre.2015.07.001.
[33] Günther, T., & Lantz, J. (2024). Building Consumer Trust in Pure B2C E-commerce Setting in the Used Electric Car Market: An Exploratory B2C Case Study. Digitala Vetenskapliga Arkivet. Available online: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-347212 (accessed on November 2025).
[34] Kant, R., Mehta, B., Jaiswal, D., & Kumar, A. (2024). Adoption intention and willingness to pay for electric vehicles: role of social-psychological attributes, fiscal incentives and socio-demographics. Management of Environmental Quality. 35(4): 945–963. doi:10.1108/MEQ-05-2023-0161.
[35] Barbarossa, C., De Pelsmacker, P., & Moons, I. (2017). Personal Values, Green Self-identity and Electric Car Adoption. Ecological Economics. 140, 190–200. doi:10.1016/j.ecolecon.2017.05.015.
[36] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems. 27(3): 425–478. doi:10.2307/30036540.
[37] Liao, Y., Guo, H., & Liu, X. (2023). A Study of Young People’s Intention to Use Shared Autonomous Vehicles: A Quantitative Analysis Model Based on the Extended TPB-TAM. Sustainability (Switzerland). 15(15). doi:10.3390/su151511825.
[38] Chen, M. F., & Tung, P. J. (2014). Developing an extended Theory of Planned Behavior model to predict consumers’ intention to visit green hotels. International Journal of Hospitality Management. 36, 221–230. doi:10.1016/j.ijhm.2013.09.006.
[39] Moons, I., & De Pelsmacker, P. (2015). An extended decomposed theory of planned behaviour to predict the usage intention of the electric car: A multi-group comparison. Sustainability (Switzerland) 7(5): 6212–6245. doi:10.3390/su7056212.
[40] Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers. 21(3): 719–734. doi:10.1007/s10796-017-9774-y.
[41] Abitbol, A., Lee, N. M., & VanDyke, M. S. (2022). Examining the perceived transparency of DTC genetic testing company communication and its impact on consumer trust, attitude and behavioral intentions. Journal of Communication Management. 26(3): 315–330. doi:10.1108/JCOM-01-2022-0006.
[42] Egbue, O., & Long, S. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy Policy, 48, 717–729. doi:10.1016/j.enpol.2012.06.009.
[43] Li, W., Long, R., Chen, H., & Geng, J. (2017). A review of factors influencing consumer intentions to adopt battery electric vehicles. Renewable and Sustainable Energy Reviews. 78, 318–328. doi:10.1016/j.rser.2017.04.076.
[44] Khaleghikarahrodi, M., Santos, J. H., Lynch-Smith, F., & Macht, G. A. (2023). Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior. Proceedings of the Human Factors and Ergonomics Society, 67(1), 1295–1301. doi:10.1177/21695067231192445.
[45] Helveston, J. P., Liu, Y., Feit, E. M. D., Fuchs, E., Klampfl, E., & Michalek, J. J. (2015). Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China. Transportation Research Part A: Policy and Practice. 73, 96–112. doi:10.1016/j.tra.2015.01.002.
[46] Beresteanu, A., & Li, S. (2011). Gasoline Prices, Government Support, and the Demand for Hybrid Vehicles in the United States. International Economic Review. 52(1):161–18). doi:10.1111/j.1468-2354.2010.00623.x.
[47] Mersky, A. C., & Samaras, C. (2016). Fuel economy testing of autonomous vehicles. Transportation Research Part C: Emerging Technologies. 65, 31–48. doi:10.1016/j.trc.2016.01.001.
[48] Zhan, W., Wang, Z., Deng, J., Liu, P., & Cui, D. (2024). Integrating system dynamics and agent-based modeling: A data-driven framework for predicting electric vehicle market penetration and GHG emissions reduction under various incentives scenarios. Applied Energy. 372(123749). doi:10.1016/j.apenergy.2024.123749.
[49] Pei, M., Huang, Z., Zhang, Z., Wang, K., & Ye, X. (2025). Range anxiety and willingness to pay: Psychological insights for electric vehicle. Journal of Renewable and Sustainable Energy. 17(1). doi:10.1063/5.0220237.
[50] Loh, W. S., & Noland, R. B. (2024). Concerns expressed by used electric vehicle owners based on surveying social media. Transportation Research Part D: Transport and Environment. 128(104086). doi:10.1016/j.trd.2024.104086.
[51] Yildiz, B. (2000). Assessment of Policy Alternatives for Mitigation of Barriers to EV Adoption. Portland State University Library. doi:10.15760/etd.6260.
[52] Chu, W., Im, M., Song, M. R., & Park, J. (2019). Psychological and behavioral factors affecting electric vehicle adoption and satisfaction: A comparative study of early adopters in China and Korea. Transportation Research Part D: Transport and Environment. 76, 1–18. doi:10.1016/j.trd.2019.09.009.
[53] Plötz, P., Funke, S. A., Jochem, P., & Wietschel, M. (2017). CO2 Mitigation Potential of Plug-in Hybrid Electric Vehicles larger than expected. Scientific Reports. 7(1). doi:10.1038/s41598-017-16684-9.
[54] Turrentine, T., Garas, D., Lentz, A., & Woodjack, J. (2011). The UC Davis MINI E Consumer Study; University of California: Davis, CA, USA
[55] Featherman, M., Jia, S. (Jasper), Califf, C. B., & Hajli, N. (2021). The impact of new technologies on consumers beliefs: Reducing the perceived risks of electric vehicle adoption. Technological Forecasting and Social Change, 169(120847). doi:10.1016/j.techfore.2021.120847.
[56] Lim, W. M. (2025). What Is Quantitative Research? An Overview and Guidelines. Australasian Marketing Journal. 33(3): 325–348. doi:10.1177/14413582241264622.
[57] Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research. Administration and Policy in Mental Health and Mental Health Services Research. 42(5): 533–544. doi:10.1007/s10488-013-0528-y.
[58] Noy, C. (2008). Sampling knowledge: The hermeneutics of snowball sampling in qualitative research. International Journal of Social Research Methodology. 11(4): 327–344). doi:10.1080/13645570701401305.
[59] Guest, G., Bunce, A., & Johnson, L. (2006). How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability. Field Methods. 18(1): 59–82. doi:10.1177/1525822X05279903.
[60] Patton, M.Q. (2014). Qualitative Research & Evaluation Methods: Integrating Theory and Practice; Sage Publications: Thousand Oaks, CA, USA.
[61] Denzin, N. K. (2017). The Research Act: A Theoretical Introduction to Sociological Methods. Routledge, New York, USA. doi:10.4324/9781315134543.
[62] Creswell, J. W., & Poth, C. N. (2016). Qualitative Inquiry and Research Design: Choosing among Five Approaches. Sage Publications: Los Angeles, CA, USA.
[63] Patton, M. Q. (1999). Enhancing the quality and credibility of qualitative analysis. Health Services Research. 34(5 Pt 2): 1189–1208.
[64] Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member Checking: A Tool to Enhance Trustworthiness or Merely a Nod to Validation? Qualitative Health Research. 26(13): 1802–1811. doi:10.1177/1049732316654870.
[65] Berger, R. (2015). Now I see it, now I don’t: researcher’s position and reflexivity in qualitative research. Qualitative Research. 15(2): 219–234. doi:10.1177/1468794112468475.
[66] Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis; SAGE: Thousand Oaks, CA, USA.
[67] Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926.
[68] Riverso, R., Altamura, C., & La Barbera, F. (2023). Consumer Intention to Buy Electric Cars: Integrating Uncertainty in the Theory of Planned Behavior. Sustainability (Switzerland). 15(11). doi:10.3390/su15118548.
[69] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems. 13(3): 319–339. doi:10.2307/249008.
[70] Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and tam in online shopping: An integrated model. MIS Quarterly: Management Information Systems, 27(1), 51–90. doi:10.2307/30036519.
[71] McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research. 13(3): 334–359. doi:10.1287/isre.13.3.334.81.
[72] Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology and Health, 26(9), 1113–1127. doi:10.1080/08870446.2011.613995.
[73] Cialdini, R. B., Kallgren, C. A., & Reno, R. R. (1991). A Focus Theory of Normative Conduct: A Theoretical Refinement and Reevaluation of the Role of Norms in Human Behavior. Advances in Experimental Social Psychology, 24(C), 201–234. doi:10.1016/S0065-2601(08)60330-5.
[74] White, K., Habib, R., & Hardisty, D. J. (2019). How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. Journal of Marketing. 83(3):22–49. doi:10.1177/0022242919825649.
[75] Noppers, E. H., Keizer, K., Bockarjova, M., & Steg, L. (2015). The adoption of sustainable innovations: The role of instrumental, environmental, and symbolic attributes for earlier and later adopters. In Journal of Environmental Psychology. 44, 74–84. doi:10.1016/j.jenvp.2015.09.002.
[76] Rogers, E.M. (2003). Diffusion of Innovations, 3rd ed.; Free Press: New York, NY, USA, 1983; ISBN 0-02-926671-8.
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