Electric Vehicles Sustainability and Adoption Factors
Abstract
1. Introduction
2. Literature Review
2.1. Electric Vehicles (EVs)
2.2. Determinants of Adoption
2.2.1. Governmental Support and Social Proactivity
2.2.2. Symbolic Attributes
2.2.3. Instrumental Attributes
2.2.4. Hedonic Attributes
2.3. Sustainability as a Driver
2.4. Determinants Interaction
3. Research Model
3.1. Model
3.2. Hypotheses
3.2.1. Task-Technology Fit
- H1 a, b: The Task-Technology Fit will have a positive impact on the perceived usefulness of (a) EHEV and (b) EBEV with onboarded CEMG.
- H2 a, b: The Task-Technology Fit will have a positive impact on the perceived ease of use of the feature for (a) EHEV and (b) EBEV with onboarded CEMG.
- H3 a, b: The Task-Technology Fit will have a positive impact on the intention of adoption of (a) EHEV and (b) EBEV with onboarded CEMG.
3.2.2. Perceived Ease of Use
- H4 a, b: The perceived ease of use will have a positive impact on the perceived usefulness of the feature for (a) EHEV and (b) EBEV with onboarded CEMG.
- H5 a, b: The perceived ease of use will have a positive impact on the attitude towards (a) EHEV and (b) EBEV with onboarded CEMG.
3.2.3. Perceived Usefulness
- H6 a, b: The perceived usefulness will have a positive impact on the attitude towards (a) EHEV and (b) EBEV with onboarded CEMG.
- H7 a, b: The perceived usefulness will have a positive impact on the intention of adoption of (a) EHEV and (b) EBEV with onboarded CEMG.
3.2.4. Attitude
- H8 a, b: The attitude towards EV will have a positive impact on the intention of adoption of (a) EHEV and (b) EBEV with onboarded CEMG.
3.2.5. Perceived Behavioral Control
- H9 a, b: The perceived behavioral control will have a positive impact on the intention of adoption of (a) EHEV and (b) EBEV with onboarded CEMG.
3.2.6. Subjective Norms
- H10 a, b: The subjective norm will have a positive impact on the intention of adoption of (a) EHEV and (b) EBEV with onboarded CEMG.
4. Data Collection and Research Methodology
5. Data Analysis and Results
5.1. Measurement Model
5.2. Structural Model and Hypothesis
6. Discussion
6.1. Theoretical and Practical Implications
6.2. Limitations and Future Research
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Questionnaire Items
Construct | Item | Adaption | Source |
Task-Technology Fit | TTF1 | My EHEV/EBEV with an onboarded CEMG is enough for all my trips | [16] |
TTF2 | I reach all my destinations on time with my EHEV/EBEV with an onboarded CEMG | ||
TTF3 | My EHEV/EBEV onboarded CEMG metrics range is enough for my daily needs | ||
TTF4 | An EHEV/EBEV onboarded CEMG is available when needed | [69] | |
TTF5 | An EHEV/EBEV onboarded CEMG can help me deal with unexpected situations | ||
Perceived Ease-of-Use | PEU1 | I (will) find that driving an EHEV/EBEV with an onboarded CEMG is easy | [15] |
PEU2 | My interactions with an EHEV/EBEV with an onboarded CEMG is easy for me to understand | [59] | |
PEU3 | An onboarded CEMG provides helpful guidance in performing tasks | ||
PEU4 | I will find it easy to understand my emission reduction using the onboarded CEMG | [67] | |
PEU5 | Being an onboarded feature, the CEMG makes it easier for me to use the EHEV/EBEV | [68] | |
Perceived Usefulness | PU1 | The advantages of using an EHEV/EBEV with an onboarded CEMG (will) outweigh the disadvantages | [15] |
PU2 | Overall, using an EHEV/EBEV with an onboarded CEMG is useful | [46] | |
PU3 | Using an EHEV/EBEV with an onboarded CEMG would make me safer | ||
Attitude | AT1 | I would feel satisfied about myself if I bought an EHEV/EBEV with an onboarded CEMG | [46] |
AT2 | I take pride in owning an EHEV/EBEV with an onboarded CEMG | ||
AT3 | I like the idea to own an EHEV/EBEV with an onboarded CEMG | ||
AT4 | All things considered, using an EHEV/EBEV with an onboarded CEMG is a good idea | [69] | |
AT5 | All things considered, using an EHEV/EBEV with an onboarded CEMG is advisable | ||
Subjective Norms | SN1 | Most people that are important to me own an EHEV/EBEV with an onboarded CEMG | [35] |
SN2 | I believe that many people who are important to me expect me to own/choose an EHEV/EBEV with an onboarded CEMG | ||
SN3 | People who are important to me have suggested that I switch to an EHEV/EBEV with an onboarded CEMG | ||
Perceived Behavioral Control | PBC1 | I am in full control of using an EHEV/EBEV with an onboarded CEMG to understand my emission reduction | [83] |
PBC2 | I have enough knowledge to use an EHEV/EBEV with an onboarded CEMG to understand my emission reduction | ||
Intention toward EV Adoption | IA1 | I expect to drive an EHEV/EBEV with an onboarded CEMG in the near future | [46] |
IA2 | I have the intention to drive an EHEV/EBEV with an onboarded CEMG in the near future | ||
IA3 | Assuming I had the opportunity, I would intend to buy an EHEV/EBEV with an onboarded CEMG | [10] | |
IA4 | Given that I had the opportunity, I predict that I would buy an EHEV/EBEV with an onboarded CEMG |
Appendix B. EHEV Loadings and Cross-Loadings (From the Authors)
AT | IA | PBC | PEU | PU | SN | TTF | |
AT1-A | 0.918 | 0.759 | 0.489 | 0.735 | 0.791 | 0.351 | 0.658 |
AT2-A | 0.900 | 0.777 | 0.499 | 0.661 | 0.675 | 0.415 | 0.581 |
AT3-A | 0.952 | 0.797 | 0.463 | 0.652 | 0.722 | 0.344 | 0.606 |
AT4-A | 0.968 | 0.798 | 0.478 | 0.677 | 0.766 | 0.354 | 0.641 |
AT5-A | 0.934 | 0.750 | 0.485 | 0.664 | 0.764 | 0.396 | 0.583 |
IA1-A | 0.753 | 0.929 | 0.444 | 0.654 | 0.649 | 0.394 | 0.536 |
IA2-A | 0.753 | 0.955 | 0.417 | 0.618 | 0.650 | 0.391 | 0.521 |
IA3-A | 0.821 | 0.950 | 0.412 | 0.625 | 0.707 | 0.355 | 0.544 |
IA4-A | 0.834 | 0.944 | 0.454 | 0.612 | 0.709 | 0.410 | 0.562 |
IA5-A | 0.659 | 0.834 | 0.484 | 0.501 | 0.543 | 0.479 | 0.409 |
PBC1-A | 0.544 | 0.491 | 0.953 | 0.654 | 0.621 | 0.380 | 0.560 |
PBC2-A | 0.415 | 0.397 | 0.927 | 0.540 | 0.499 | 0.448 | 0.500 |
PEU1-A | 0.576 | 0.497 | 0.572 | 0.859 | 0.667 | 0.144 | 0.661 |
PEU2-A | 0.543 | 0.491 | 0.617 | 0.855 | 0.607 | 0.245 | 0.620 |
PEU3-A | 0.551 | 0.482 | 0.559 | 0.872 | 0.628 | 0.350 | 0.601 |
PEU4-A | 0.685 | 0.636 | 0.463 | 0.800 | 0.660 | 0.332 | 0.596 |
PEU5-A | 0.682 | 0.628 | 0.495 | 0.827 | 0.760 | 0.305 | 0.605 |
PU1-A | 0.739 | 0.656 | 0.598 | 0.793 | 0.941 | 0.304 | 0.689 |
PU2-A | 0.754 | 0.655 | 0.564 | 0.770 | 0.951 | 0.278 | 0.695 |
PU3-A | 0.714 | 0.654 | 0.502 | 0.626 | 0.877 | 0.456 | 0.529 |
SN1-A | 0.247 | 0.270 | 0.385 | 0.227 | 0.273 | 0.845 | 0.180 |
SN2-A | 0.390 | 0.459 | 0.460 | 0.346 | 0.364 | 0.945 | 0.300 |
SN3-A | 0.411 | 0.418 | 0.342 | 0.296 | 0.355 | 0.931 | 0.282 |
TTF1-A | 0.508 | 0.409 | 0.412 | 0.597 | 0.527 | 0.197 | 0.862 |
TTF2-A | 0.536 | 0.509 | 0.428 | 0.582 | 0.571 | 0.210 | 0.849 |
TTF3-A | 0.578 | 0.449 | 0.418 | 0.594 | 0.595 | 0.153 | 0.830 |
TTF4-A | 0.503 | 0.481 | 0.544 | 0.592 | 0.527 | 0.400 | 0.789 |
TTF5-A | 0.557 | 0.436 | 0.511 | 0.628 | 0.608 | 0.225 | 0.759 |
Appendix C. EBEV Loadings and Cross-Loadings (From the Authors)
AT | IA | PBC | PEU | PU | SN | TTF | |
AT1-B | 0.962 | 0.799 | 0.529 | 0.610 | 0.795 | 0.419 | 0.700 |
AT2-B | 0.942 | 0.801 | 0.573 | 0.594 | 0.815 | 0.463 | 0.677 |
AT3-B | 0.975 | 0.832 | 0.519 | 0.582 | 0.803 | 0.409 | 0.710 |
AT4-B | 0.96 | 0.819 | 0.512 | 0.585 | 0.819 | 0.417 | 0.705 |
AT5-B | 0.948 | 0.821 | 0.536 | 0.580 | 0.815 | 0.478 | 0.688 |
IA1-B | 0.776 | 0.955 | 0.517 | 0.518 | 0.721 | 0.505 | 0.633 |
IA2-B | 0.854 | 0.979 | 0.508 | 0.531 | 0.737 | 0.453 | 0.704 |
IA3-B | 0.844 | 0.976 | 0.489 | 0.560 | 0.751 | 0.436 | 0.713 |
PBC1-B | 0.589 | 0.537 | 0.954 | 0.588 | 0.600 | 0.441 | 0.467 |
PBC2-B | 0.447 | 0.431 | 0.928 | 0.478 | 0.458 | 0.452 | 0.376 |
PEU1-B | 0.478 | 0.429 | 0.445 | 0.813 | 0.529 | 0.123 | 0.456 |
PEU2-B | 0.481 | 0.405 | 0.542 | 0.866 | 0.534 | 0.245 | 0.442 |
PEU3-B | 0.492 | 0.445 | 0.529 | 0.867 | 0.577 | 0.418 | 0.460 |
PEU4-B | 0.586 | 0.547 | 0.520 | 0.866 | 0.604 | 0.335 | 0.508 |
PEU5-B | 0.589 | 0.527 | 0.425 | 0.876 | 0.639 | 0.259 | 0.539 |
PU1-B | 0.820 | 0.749 | 0.594 | 0.690 | 0.947 | 0.395 | 0.657 |
PU2-B | 0.811 | 0.712 | 0.500 | 0.660 | 0.948 | 0.313 | 0.660 |
PU3-B | 0.740 | 0.667 | 0.501 | 0.538 | 0.913 | 0.450 | 0.631 |
SN1-B | 0.335 | 0.366 | 0.364 | 0.230 | 0.317 | 0.864 | 0.341 |
SN2-B | 0.473 | 0.485 | 0.485 | 0.360 | 0.417 | 0.960 | 0.337 |
SN3-B | 0.448 | 0.468 | 0.456 | 0.300 | 0.399 | 0.959 | 0.286 |
TTF1-B | 0.614 | 0.602 | 0.328 | 0.410 | 0.605 | 0.270 | 0.861 |
TTF2-B | 0.647 | 0.668 | 0.340 | 0.453 | 0.632 | 0.268 | 0.906 |
TTF3-B | 0.603 | 0.553 | 0.452 | 0.563 | 0.559 | 0.144 | 0.845 |
TTF4-B | 0.658 | 0.619 | 0.474 | 0.551 | 0.627 | 0.439 | 0.870 |
TTF5-B | 0.618 | 0.602 | 0.356 | 0.453 | 0.571 | 0.351 | 0.837 |
References
- European Commission. Study on the Financing Needs in the Area of Sustainable Urban Mobility; MOVE/B4/457-1 2010 SI2.585084; European Comission: London, UK, 2012; p. 141. Available online: https://transport.ec.europa.eu/system/files/2016-09/2012-03-study-on-the-financing-needs-in-the-area-of-sustainable-urban-mobility-final-report.pdf (accessed on 7 January 2025).
- Egbue, O.; Long, S. Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy Policy 2012, 48, 717–729. [Google Scholar] [CrossRef]
- Hoen, K.M.R.; Tan, T.; Fransoo, J.C.; Van Houtum, G.-J. Switching Transport Modes to Meet Voluntary Carbon Emission Targets. Transp. Sci. 2014, 48, 592–608. [Google Scholar] [CrossRef]
- Anowar, S.; Eluru, N.; Hatzopoulou, M. Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution? Transp. Res. Part A Policy Pract. 2017, 105, 66–78. [Google Scholar] [CrossRef]
- Thiel, C.; Perujo, A.; Mercier, A. Cost and CO2 aspects of future vehicle options in Europe under new energy policy scenarios. Energy Policy 2010, 38, 7142–7151. [Google Scholar] [CrossRef]
- Borén, S.; Nurhadi, L.; Ny, H.; Robèrt, K.-H.; Broman, G.; Trygg, L. A strategic approach to sustainable transport system development—Part 2: The case of a vision for electric vehicle systems in southeast Sweden. J. Clean. Prod. 2017, 140, 62–71. [Google Scholar] [CrossRef]
- Singer, M.; Johnson, C.; Wilson, A.; Reichelt, L.; Abdullah, M.; Dybas, N. Clean Cities and Communities Partnership 2023 Activity Report; NREL/TP-5400-92098; National Renewable Energy Laboratory: Golden, CO, USA, 2025. Available online: https://www.nrel.gov/docs/fy25osti/92098.pdf (accessed on 2 December 2024).
- International Energy Agency. Global EV Outlook 2024; CC BY 4.0; International Energy Agency: Paris, France, 2024; Available online: https://www.iea.org/reports/global-ev-outlook-2024 (accessed on 10 January 2025).
- Rietmann, N.; Hügler, B.; Lieven, T. Forecasting the trajectory of electric vehicle sales and the consequences for worldwide CO2 emissions. J. Clean. Prod. 2020, 261, 121038. [Google Scholar] [CrossRef]
- Degirmenci, K.; Breitner, M.H. Consumer purchase intentions for electric vehicles: Is green more important than price and range? Transp. Res. Part D Transp. Environ. 2017, 51, 250–260. [Google Scholar] [CrossRef]
- Encarnação, S.; Santos, F.P.; Santos, F.C.; Blass, V.; Pacheco, J.M.; Portugali, J. Paths to the adoption of electric vehicles: An evolutionary game theoretical approach. Transp. Res. Part B Methodol. 2018, 113, 24–33. [Google Scholar] [CrossRef]
- Gärling, A.; Thøgersen, J. Marketing of electric vehicles. Bus. Strategy Environ. 2001, 10, 53–65. [Google Scholar] [CrossRef]
- Schuitema, G.; Anable, J.; Skippon, S.; Kinnear, N. The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transp. Res. Part A Policy Pract. 2013, 48, 39–49. [Google Scholar] [CrossRef]
- Kautish, P.; Lavuri, R.; Roubaud, D.; Grebinevych, O. Electric vehicles’ choice behaviour: An emerging market scenario. J. Environ. Manag. 2024, 354, 120250. [Google Scholar] [CrossRef]
- Roemer, E.; Henseler, J. The dynamics of electric vehicle acceptance in corporate fleets: Evidence from Germany. Technol. Soc. 2022, 68, 101938. [Google Scholar] [CrossRef]
- Cruz-Jesus, F.; Figueira-Alves, H.; Tam, C.; Pinto, D.C.; Oliveira, T.; Venkatesh, V. Pragmatic and idealistic reasons: What drives electric vehicle drivers’ satisfaction and continuance intention? Transp. Res. Part A Policy Pract. 2023, 170, 103626. [Google Scholar] [CrossRef]
- Casals, L.; Martinez-Laserna, E.; Amante García, B.; Nieto, N. Sustainability analysis of the electric vehicle use in Europe for CO2 emissions reduction. J. Clean. Prod. 2016, 127, 425–437. [Google Scholar] [CrossRef]
- Hawkins, T.R.; Gausen, O.M.; Strømman, A.H. Environmental impacts of hybrid and electric vehicles—A review. Int. J. Life Cycle Assess. 2012, 17, 997–1014. [Google Scholar] [CrossRef]
- Lubecki, A.; Szczurowski, J.; Zarębska, K. The importance of uncertainty sources in LCA for the reliability of environmental comparisons: A case study on public bus fleet electrification. Appl. Energy 2025, 377, 124593. [Google Scholar] [CrossRef]
- Maselli, M.; Pelegrina, J.; Marotti De Mello, A.; Ribeiro Souza, J.V.; Marx, R.; Priarone, P.C. Electric or internal combustion vehicles? A Life Cycle Assessment in São Paulo. Renew. Sustain. Energy Rev. 2025, 212, 115431. [Google Scholar] [CrossRef]
- Picatoste, A.; Justel, D.; Mendoza, J.M.F. Circularity and life cycle environmental impact assessment of batteries for electric vehicles: Industrial challenges, best practices and research guidelines. Renew. Sustain. Energy Rev. 2022, 169, 112941. [Google Scholar] [CrossRef]
- Jannesar Niri, A.; Poelzer, G.A.; Zhang, S.E.; Rosenkranz, J.; Pettersson, M.; Ghorbani, Y. Sustainability challenges throughout the electric vehicle battery value chain. Renew. Sustain. Energy Rev. 2024, 191, 114176. [Google Scholar] [CrossRef]
- Ehsani, M.; Gao, Y.; Longo, S.; Ebrahimi, K. Modern Eletric, Hybrid Eletric, and Fuel Cell Vehicles, 3rd ed.; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar] [CrossRef]
- Kakkar, R.; Agrawal, S.; Tanwar, S. A systematic survey on demand response management schemes for electric vehicles. Renew. Sustain. Energy Rev. 2024, 203, 114748. [Google Scholar] [CrossRef]
- Clement, K.; Haesen, E.; Driesen, J. Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids. In Proceedings of the 2009 IEEE/PES Power Systems Conference and Exposition 2009, Seattle, WA, USA, 15–18 March 2009; pp. 1–7. [Google Scholar] [CrossRef]
- Hajimiragha, A.; Canizares, C.A.; Fowler, M.W.; Elkamel, A. Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid Limitations. IEEE Trans. Ind. Electron. 2010, 57, 690–701. [Google Scholar] [CrossRef]
- Sierzchula, W.; Bakker, S.; Maat, K.; Van Wee, B. Technological diversity of emerging eco-innovations: A case study of the automobile industry. J. Clean. Prod. 2012, 37, 211–220. [Google Scholar] [CrossRef]
- Daziano, R.A.; Chiew, E. Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation. Energy Policy 2012, 51, 876–894. [Google Scholar] [CrossRef]
- Koprubasi, K. Modeling and Control of a Hybrid-Electric Vehicle for Drivability and Fuel Economy Improvements. Ph.D. Thesis, Ohio State University, Columbus, OH, USA, 2008. Available online: https://ui.adsabs.harvard.edu/abs/2008PhDT.......125K (accessed on 8 December 2024).
- Rezvani, Z.; Jansson, J.; Bodin, J. Advances in consumer electric vehicle adoption research: A review and research agenda. Transp. Res. Part D Transp. Environ. 2015, 34, 122–136. [Google Scholar] [CrossRef]
- Guozhi, P.; Tao, Z.; Guanhong, D.; Zekang, H.; Feifan, Y.; Shaobo, X. Battery sizing for plug-in hybrid electric vehicles considering social cost of carbon. J. Energy Storage 2025, 131, 117556. [Google Scholar] [CrossRef]
- Jiang, F.; Yuan, X.; Hu, L.; Xie, G.; Zhang, Z.; Li, X.; Hu, J.; Wang, C.; Wang, H. A comprehensive review of energy storage technology development and application for pure electric vehicles. J. Energy Storage 2024, 86, 111159. [Google Scholar] [CrossRef]
- Chan, C.C. An overview of electric vehicle technology. Proc. IEEE 1993, 81, 1202–1213. [Google Scholar] [CrossRef]
- Ding, N.; Prasad, K.; Lie, T.T. The electric vehicle: A review. Int. J. Electr. Hybrid Veh. 2017, 9, 49. [Google Scholar] [CrossRef]
- Jansson, J.; Nordlund, A.; Westin, K. Examining drivers of sustainable consumption: The influence of norms and opinion leadership on electric vehicle adoption in Sweden. J. Clean. Prod. 2017, 154, 176–187. [Google Scholar] [CrossRef]
- Huijts, N.M.A.; Molin, E.J.E.; Steg, L. Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. Renew. Sustain. Energy Rev. 2012, 16, 525–531. [Google Scholar] [CrossRef]
- Shang, W.-L.; Zhang, J.; Wang, K.; Yang, H.; Ochieng, W. Can financial subsidy increase electric vehicle (EV) penetration—Evidence from a quasi-natural experiment. Renew. Sustain. Energy Rev. 2024, 190, 114021. [Google Scholar] [CrossRef]
- Graham-Rowe, E.; Gardner, B.; Abraham, C.; Skippon, S.; Dittmar, H.; Hutchins, R.; Stannard, J. Mainstream consumers driving plug-in battery-electric and plug-in hybrid electric cars: A qualitative analysis of responses and evaluations. Transp. Res. Part A: Policy Pract. 2012, 46, 140–153. [Google Scholar] [CrossRef]
- Venkatraman, M.P. Opinion leaders, adopters, and communicative adopters: A role analysis. Psychol. Mark. 1989, 6, 51–68. [Google Scholar] [CrossRef]
- Weimann, G.; Tustin, D.H.; Van Vuuren, D.; Joubert, J.P.R. Looking for Opinion Leaders: Traditional vs. Modern Measures in Traditional Societies. Int. J. Public Opin. Res. 2007, 19, 173–190. [Google Scholar] [CrossRef]
- Rogers, E. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003. [Google Scholar]
- Abbott, K.W.; Genschel, P.; Snidal, D.; Zangl, B. International Organizations as Orchestrators; Cambridge University Press: Cambridge, UK, 2015. [Google Scholar]
- Abbott, K.W. Engaging the public and the private in global sustainability governance. Int. Aff. 2012, 88, 543–564. [Google Scholar] [CrossRef]
- Pattberg, P. Public–private partnerships in global climate governance. WIREs Clim. Chang. 2010, 1, 279–287. [Google Scholar] [CrossRef]
- Sirgy, M.J. Self-Concept in Consumer Behavior: A Critical Review. J. Consum. Res. 1982, 9, 287. [Google Scholar] [CrossRef]
- Barbarossa, C.; Beckmann, S.C.; De Pelsmacker, P.; Moons, I.; Gwozdz, W. A self-identity based model of electric car adoption intention: A cross-cultural comparative study. J. Environ. Psychol. 2015, 42, 149–160. [Google Scholar] [CrossRef]
- Axsen, J.; Kurani, K.S. Interpersonal Influence Within Car Buyers’ Social Networks: Five Perspectives on Plug-In Hybrid Electric Vehicle Demonstration Participant; Institute of Transportation Studies: Davis, CA, USA, 2009; Available online: https://escholarship.org/uc/item/35w7s3jp (accessed on 17 January 2025).
- Dittmar, H. The Social Psychology of Material Possessions: To Have Is to Be; Harvester Wheatsheaf: Hemel Hempstead, UK; St. Martin’s Press: New York, NY, USA, 1992. [Google Scholar]
- Skippon, S.; Garwood, M. Responses to battery electric vehicles: UK consumer attitudes and attributions of symbolic meaning following direct experience to reduce psychological distance. Transp. Res. Part D Transp. Environ. 2011, 16, 525–531. [Google Scholar] [CrossRef]
- Vandecasteele, B.; Geuens, M. Motivated Consumer Innovativeness: Concept, measurement, and validation. Int. J. Res. Mark. 2010, 27, 308–318. [Google Scholar] [CrossRef]
- Lave, L.B.; Hendrickson, C.T.; McMichael, F.C. Environmental Implications of Electric Cars. Science 1995, 268, 993–995. [Google Scholar] [CrossRef]
- Baptista, P.; Silva, C.; Gonçalves, G.; Farias, T. Full life cycle analysis of market penetration of electricity based vehicles. World Electr. Veh. J. 2009, 3, 505–510. [Google Scholar] [CrossRef]
- Daniel, J.J.; Rosen, M.A. Exergetic environmental assessment of life cycle emissions for various automobiles and fuels. Exergy Int. J. 2002, 2, 283–294. [Google Scholar] [CrossRef]
- Wang, M.Q.; Plotkin, S.; Santini, D.J.; He, J.; Gaines, L.; Patterson, P. Total Energy-Cycle Energy and Emissions Impacts of Hybrid Electric Vehicles; Argonne National Laboratory, University of Chicago, U.S. Department of Energy: Chicago, IL, USA, 1997. [Google Scholar]
- Burnham, A.; Wang, M.Q.; Wu, Y. Development and Applications of GREET 2.7—The Transportation Vehicle-CycleModel (Nº ANL/ESD/06-5); ANL: Argonne, IL, USA, 2006. [Google Scholar] [CrossRef]
- Dewulf, J.; Van Der Vorst, G.; Denturck, K.; Van Langenhove, H.; Ghyoot, W.; Tytgat, J.; Vandeputte, K. Recycling rechargeable lithium ion batteries: Critical analysis of natural resource savings. Resour. Conserv. Recycl. 2010, 54, 229–234. [Google Scholar] [CrossRef]
- Ajzen, I. From Intentions to Actions: A Theory of Planned Behavior. In Action Control; Kuhl, J., Beckmann, J., Eds.; Springer: Berlin/Heidelberg, Germany, 1985; pp. 11–39. [Google Scholar] [CrossRef]
- Stern, P.C. New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior. J. Soc. Issues 2000, 56, 407–424. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319. [Google Scholar] [CrossRef]
- Goodhue, D.L.; Thompson, R.L. Task-Technology Fit and Individual Performance. MIS Q. 1995, 19, 213. [Google Scholar] [CrossRef]
- Glerum, A.; Stankovikj, L.; Thémans, M.; Bierlaire, M. Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions. Transp. Sci. 2014, 48, 483–499. [Google Scholar] [CrossRef]
- Jensen, A.F.; Cherchi, E.; Mabit, S.L.; Ortúzar, J.D.D. Predicting the Potential Market for Electric Vehicles. Transp. Sci. 2017, 51, 427–440. [Google Scholar] [CrossRef]
- Plötz, P.; Gnann, T.; Wietschel, M. Modelling market diffusion of electric vehicles with real world driving data—Part I: Model structure and validation. Ecol. Econ. 2014, 107, 411–421. [Google Scholar] [CrossRef]
- Giffi, C.; Vitale, J.; Drew, M.; Kuboshima, Y.; Sase, M. Unplugged: Electric Vehicle Realities Versus Consumer Expectations. Deloitte Survey. Deloitte Touche Tohmatsu Limited. 2011. Available online: https://www.juridice.ro/wp-content/uploads/2011/09/raoirt-Delloite.pdf (accessed on 12 November 2024).
- Smith, J.M. Evolution and the Theory of Games; Springer: Boston, MA, USA, 1988. [Google Scholar] [CrossRef]
- Gebauer, J.; Shaw, M.J.; Gribbins, M.L. Task-Technology Fit for Mobile Information Systems. J. Inf. Technol. 2010, 25, 259–272. [Google Scholar] [CrossRef]
- Dishaw, M.T.; Strong, D.M. Extending the technology acceptance model with task–technology fit constructs. Inf. Manag. 1999, 36, 9–21. [Google Scholar] [CrossRef]
- Irawan, M.Z.; Bastarianto, F.F.; Priyanto, S. Using an integrated model of TPB and TAM to analyze the pandemic impacts on the intention to use bicycles in the post-COVID-19 period. IATSS Res. 2022, 46, 380–387. [Google Scholar] [CrossRef]
- Kim, T.T.; Suh, Y.K.; Lee, G.; Choi, B.G. Modelling roles of task-technology fit and self-efficacy in hotel employees’ usage behaviours of hotel information systems. Int. J. Tour. Res. 2010, 12, 709–725. [Google Scholar] [CrossRef]
- Pamidimukkala, A.; Kermanshachi, S.; Michael Rosenberger, J.; Hladik, G. Utilizing Extended Theory of Planned Behavior to Evaluate Consumers’ Adoption Intention of Electric Vehicles. Green Energy Intell. Transp. 2025, 100258. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, M.G.; Hall, M.; Davis, G.B.; Davis, F.D.; Walton, S.M. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
- Tanujaya, B.; Prahmana, R.C.I.; Mumu, J. Likert scale in Social Sciences research: Problems and difficulties. J. Soc. Sci. 2022, 16, 89–101. [Google Scholar]
- Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook; Springer International Publishing: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
- Kock, N.; Lynn, G.; Stevens Institute of Technology. Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. J. Assoc. Inf. Syst. 2012, 13, 546–580. [Google Scholar] [CrossRef]
- Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
- Ringle, C.M.; Wende, S.; Becker, J.-M. SmartPLS 4; SmartPLS: Oststeinbek, Germany, 2022. [Google Scholar]
- Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
- Chin, W.W. Commentary: Issues and Opinion on Structural Equation Modeling. MIS Q. 1998, 22, vii–xvi. [Google Scholar]
- Gefen, D.; Straub, D. A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example. Commun. Assoc. Inf. Syst. 2005, 16, 91–109. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Götz, O.; Liehr-Gobbers, K.; Krafft, M. Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach. In Handbook of Partial Least Squares; Vinzi, V.E., Chin, W.W., Henseler, J., Wang, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 691–711. [Google Scholar] [CrossRef]
- Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
- Liang, T.P.; Ling, Y.; Yeh, Y.H.; Lin, B. Contextual factors and continuance intention of mobile services. Int. J. Mob. Commun. 2013, 11, 313. [Google Scholar] [CrossRef]
- Ajzen, I.; Fishbein, M. Understanding Attitudes and Predictiing Social Behavior; Prentice Hall: Englewood Cliffs, NJ, USA, 1980. [Google Scholar]
- Ding, N.; Xi, Y.; Jiang, W.; Li, H.; Su, J.; Yang, S.; Lie, T.T. State-of-the-art carbon metering: Continuous emission monitoring systems for industrial applications. Heliyon 2025, 11, e42308. [Google Scholar] [CrossRef] [PubMed]
- Cai, X.; Zheng, S.; Zhang, X.; Ye, Z.; Liu, C.; Tan, Z. The impact of CO2 emission synergy on PM2.5 emissions and a dynamic analysis of health and economic benefits: A case study of China’s transportation industry. J. Clean. Prod. 2024, 471. [Google Scholar] [CrossRef]
- Budnitz, H.; Meelen, T.; Schwanen, T. Electric vehicle adoption intentions among UK residents parking in shared and public spaces. Transportation 2024, 1–24. [Google Scholar] [CrossRef]
- Cordova-Cruzatty, A.C.; King, D.A.; Kuby, M.; Parker, N. Experiences and perceptions of multi-family housing property managers about electric vehicle charging provision. Transp. Res. Interdiscip. Perspect. 2024, 28, 101263. [Google Scholar] [CrossRef]
- Gehrke, S.R.; Reardon, T.G. Patterns and predictors of early electric vehicle adoption in Massachusetts. Int. J. Sustain. Transp. 2022, 16, 514–525. [Google Scholar] [CrossRef]
- Novotny, A.; Szeberin, I.; Kovács, S.; Máté, D. National culture and the market development of battery electric vehicles in 21 countries. Energies 2022, 15, 1539. [Google Scholar] [CrossRef]
- Harichandan, S.; Kar, S.K. An empirical study on consumer attitude and perception towards adoption of hydrogen fuel cell vehicles in India: Policy implications for stakeholders. Energy Policy 2023, 178, 113587. [Google Scholar] [CrossRef]
- Rawat, A.; Garg, C.P.; Sinha, P. Analysis of the key hydrogen fuel vehicles adoption barriers to reduce carbon emissions under net zero target in emerging market. Energy Policy 2024, 184, 113847. [Google Scholar] [CrossRef]
Vehicle Group | Composition |
---|---|
NEEV | FFV; FCV; |
EHEV | HEV; PHEV; |
EBEV | BEV; |
Social Sector | Attribute | Factor |
---|---|---|
Public | Symbolic | Environmental Position |
Support | ||
Social | Symbolic | Environmental Position |
Green self-Identity | ||
Social Influence | ||
Instrumental | Ease of Use | |
Relative Advantage | ||
Perceived Risks | ||
Hedonic | Enjoyment | |
Pleasure |
N | % | |
---|---|---|
Gender | ||
Male | 68 | 49.6 |
Female | 69 | 50.4 |
Other/prefer not to say | 0 | 0.00 |
Age | ||
18–24 | 5 | 3.65 |
25–34 | 29 | 21.16 |
35–44 | 17 | 12.41 |
45–54 | 47 | 34.31 |
55–64 | 34 | 24.82 |
Other/prefer not to say | 5 | 3.65 |
Education Level | ||
Secondary Level Graduate | 7 | 5.11 |
College Graduate | 92 | 67.15 |
Master | 32 | 23.36 |
Doctorate | 4 | 2.92 |
Other/prefer not to say | 2 | 1.46 |
Employment Status | ||
Employed (full time) | 96 | 70.07 |
Employed (part time) | 9 | 6.57 |
Unemployed | 3 | 2.19 |
Retired | 15 | 10.95 |
Student | 6 | 4.38 |
Other/prefer not to say | 8 | 5.84 |
Construct | CR | CA | AVE |
---|---|---|---|
AT | 0.879 | 0.879 | 0.892 |
IA | 0.903 | 0.911 | 0.837 |
PBC | 0.870 | 0.891 | 0.884 |
PEU | 0.898 | 0.899 | 0.710 |
PU | 0.831 | 0.833 | 0.855 |
SN | 0.945 | 0.940 | 0.825 |
TTF | 0.876 | 0.876 | 0.670 |
Construct | CR | CA | AVE |
---|---|---|---|
AT | 1.000 | 1.000 | 1.000 |
IA | 0.943 | 0.941 | 0.944 |
PBC | 0.909 | 0.873 | 0.886 |
PEU | 0.913 | 0.910 | 0.736 |
PU | 0.934 | 0.929 | 0.876 |
SN | 0.896 | 0.845 | 0.863 |
TTF | 0.917 | 0.915 | 0.747 |
Construct | AT | IA | PBC | PEU | PU | SN | TTF |
---|---|---|---|---|---|---|---|
AT | 0.944 | ||||||
IA | 0.801 | 0.914 | |||||
PBC | 0.520 | 0.502 | 0.940 | ||||
PEU | 0.702 | 0.647 | 0.639 | 0.842 | |||
PU | 0.761 | 0.702 | 0.595 | 0.771 | 0.924 | ||
SN | 0.428 | 0.464 | 0.437 | 0.327 | 0.408 | 0.908 | |
TTF | 0.617 | 0.553 | 0.567 | 0.733 | 0.662 | 0.289 | 0.818 |
Construct | AT | IA | PBC | PEU | PU | SN | TTF |
---|---|---|---|---|---|---|---|
AT | 1.000 | ||||||
IA | 0.786 | 0.972 | |||||
PBC | 0.573 | 0.517 | 0.941 | ||||
PEU | 0.593 | 0.555 | 0.573 | 0.858 | |||
PU | 0.816 | 0.758 | 0.570 | 0.675 | 0.936 | ||
SN | 0.445 | 0.472 | 0.464 | 0.327 | 0.402 | 0.929 | |
TTF | 0.677 | 0.694 | 0.453 | 0.563 | 0.694 | 0.363 | 0.864 |
Construct | AT | IA | PBC | PEU | PU | SN |
---|---|---|---|---|---|---|
IA | 0.894 | |||||
PBC | 0.589 | 0.563 | ||||
PEU | 0.784 | 0.710 | 0.720 | |||
PU | 0.891 | 0.806 | 0.693 | 0.883 | ||
SN | 0.471 | 0.503 | 0.497 | 0.354 | 0.471 | |
TTF | 0.701 | 0.616 | 0.644 | 0.824 | 0.770 | 0.316 |
Construct | AT | IA | PBC | PEU | PU | SN |
---|---|---|---|---|---|---|
IA | 0.810 | |||||
PBC | 0.604 | 0.564 | ||||
PEU | 0.620 | 0.595 | 0.637 | |||
PU | 0.844 | 0.809 | 0.622 | 0.727 | ||
SN | 0.473 | 0.520 | 0.533 | 0.359 | 0.448 | |
TTF | 0.706 | 0.746 | 0.499 | 0.613 | 0.752 | 0.412 |
EHEV (a) | EBEV (B) | ||||||
---|---|---|---|---|---|---|---|
Results | Path Coefficient | Significance Level (p) | Results | Path Coefficient | Significance Level (p) | ||
H1a | Supported | 0.210 | 0.026 ** | H1b | Supported | 0.459 | 0.000 *** |
H2a | Supported | 0.733 | 0.000 | H2b | Supported | 0.563 | 0.000 *** |
H3a | Rejected | 0.028 | 0.676 | H3b | Supported | 0.221 | 0.006 *** |
H4a | Supported | 0.617 | 0.000 *** | H4b | Supported | 0.417 | 0.000 *** |
H5a | Supported | 0.285 | 0.000 *** | H5b | Rejected | 0.078 | 0.225 |
H6a | Supported | 0.541 | 0.000 *** | H6b | Supported | 0.763 | 0.000 *** |
H7a | Rejected | 0.170 | 0.147 | H7b | Supported | 0.239 | 0.050 ** |
H8a | Supported | 0.589 | 0.000 *** | H8b | Supported | 0.386 | 0.000 *** |
H9a | Rejected | 0.025 | 0.770 | H9b | Rejected | 0.003 | 0.963 |
H10a | Supported | 0.124 | 0.020 ** | H10b | Supported | 0.122 | 0.009 *** |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Figueiredo, V.; Baptista, G. Electric Vehicles Sustainability and Adoption Factors. Urban Sci. 2025, 9, 311. https://doi.org/10.3390/urbansci9080311
Figueiredo V, Baptista G. Electric Vehicles Sustainability and Adoption Factors. Urban Science. 2025; 9(8):311. https://doi.org/10.3390/urbansci9080311
Chicago/Turabian StyleFigueiredo, Vitor, and Goncalo Baptista. 2025. "Electric Vehicles Sustainability and Adoption Factors" Urban Science 9, no. 8: 311. https://doi.org/10.3390/urbansci9080311
APA StyleFigueiredo, V., & Baptista, G. (2025). Electric Vehicles Sustainability and Adoption Factors. Urban Science, 9(8), 311. https://doi.org/10.3390/urbansci9080311