Exploring the Influence of Thai Government Policy Perceptions on Electric Vehicle Adoption: A Measurement Model and Empirical Analysis
Abstract
:1. Introduction
1.1. Research Background
1.2. Thai Government Policies for EVs
1.2.1. The 30@30 Policy
1.2.2. EV 3.5 Measures
- Financial Incentives: These include subsidies based on vehicle type and battery size. For example, electric cars priced under THB 2 million with a battery size of 50 kWh or more will receive a subsidy starting at THB 100,000 in the first year, decreasing over four years. Similarly, incentives for electric motorcycles and pickup trucks are designed to make EVs more accessible and affordable for the public.
- Tax Reductions: Significant reductions in import duties and excise tax rates for electric vehicles are also part of the EV 3.5 measures. The first two years will see a reduction in import duties by up to 40% for ready-made electric vehicles priced under THB 2 million, while excise taxes for electric cars priced under THB 7 million will be reduced from 8% to 2%.
- Support for Domestic Production: The policy encourages local manufacturing of electric vehicles and parts, with stipulations for compensatory production ratios to balance imports and local production, thereby fostering a robust domestic EV industry.
1.3. Research Gap and Objectives
1.4. Research Questions and Contributions
- Theoretical Contributions: The Refinement of Measurement Models develops a nuanced measurement model to assess public perceptions of government EV policies, enhancing the theoretical understanding of how policy influences technology adoption. Methodological Advancements utilize advanced statistical methods, such as second-order confirmatory factor analysis and structural equation modeling, to analyze complex relationships within policy perception studies.
- Practical Contributions: Policymaker Insights provide empirical data to policymakers on effective strategies to enhance public acceptance and adoption of EVs, supporting the creation of more targeted and impactful policies. Strategic Business Information offers valuable insights for automotive businesses on leveraging government policy perceptions in marketing strategies to boost consumer engagement and adoption rates. Enhanced Sustainability Practices support societal transitions towards sustainability by informing policies that encourage environmentally friendly transportation options, aligning with global goals for reduced emissions and energy efficiency.
2. Literature Review
2.1. Reviewing Governments Supporting EV Adoptions
2.2. Hypotheses
3. Methods
3.1. Questionnaire Design and Data Collection
3.2. Data Analysis
3.2.1. Data Analysis Procedure
3.2.2. Measure of Internal Consistency
3.2.3. Exploratory Factor Analysis (EFA)
3.2.4. Second-Order Confirmatory Factor Analysis (CFA)
3.2.5. Structural Equation Modeling (SEM)
3.2.6. Validity of the Statistical Models
4. Results
5. Discussion
5.1. Measurement Model of Government Policies in EV Adoptions
5.2. Factors Influencing Perceptions of Government Policies to EV Adoptions
6. Conclusions and Policy Recommendations
- Enhance and Expand Government Policies: Given the strong influence of tangible government policies (highest factor loading in SEM), it is recommended that the Thai government intensifies its efforts in expanding EV infrastructure, such as increasing the number of charging stations. Additionally, clear and beneficial regulatory frameworks should be established to support both users and manufacturers of EVs. Examples of actions that the government can undertake include the following: (1) Increasing the Number of Charging Stations: Develop a national plan with specific targets for charging infrastructure that includes not only public areas like shopping centers and parking lots but also residential and workplace installations. The government could offer incentives to private businesses and property developers to install charging stations. (2) Facilitating Faster Permit Processes: Simplify and expedite the permit process for the installation of EV charging stations to encourage rapid deployment across the country.
- Increase Government Commitment and Efficiency: As this factor also showed high loading and significant impact, policies should focus on increasing the visibility of government commitment. This can be achieved by setting and publicizing clear targets for EV adoption, dedicating funding to EV technology development, and ensuring efficient implementation of all EV-related initiatives. Examples of actions that the government can undertake include the following: (1) Public Commitment Declarations: Regularly update and publicly share progress on government commitments to electrify public transportation and government fleets. This transparency will reinforce the government’s commitment to sustainable transportation. (2) Efficiency in Incentive Distribution: Ensure that any incentives, such as rebates or grants for purchasing EVs, are processed quickly and efficiently, reducing bureaucracy to improve public trust and participation.
- Communicate Effectively About EV Benefits and Policies: Effective communication has a crucial role in enhancing public perception and confidence. The government should implement comprehensive communication strategies that educate the public about the benefits of EVs and detailed information on available government support and incentives. Examples of actions that the government can undertake include the following: (1) Educational Campaigns: Launch extensive multimedia campaigns that highlight the environmental, economic, and practical benefits of EVs. Use local testimonials and case studies to make the benefits more relatable. (2) Regular Updates on Policy Developments: Use social media, dedicated websites, and public service announcements to keep the public informed about new policies, changes in existing policies, and how these can benefit potential and current EV owners.
- Improve Tax Incentives and Financial Benefits: While tax benefits and direct financial incentives like subsidies have a slightly lower impact compared to direct policies and commitment, they are still crucial. Enhancing these incentives to be more attractive and directly beneficial to potential EV buyers can significantly boost adoption rates. Ensuring these benefits are well publicized and easily accessible will increase their effectiveness. Examples of actions that the government can undertake include the following: (1) Adjusting Tax Incentives: Increase the attractiveness of EVs by offering graduated tax incentives that provide greater benefits to early adopters and those choosing higher-efficiency models. (2) Direct Subsidies for Buyers: Provide direct subsidies for the purchase of EVs that reduce the upfront cost. Consider additional subsidies for trade-ins of older, less efficient vehicles for new electric models.
- Focus on Government Welfare Programs: Although it had the lowest impact, improving welfare programs that directly support potential and current EV users can help in lowering the barriers to entry for many potential adopters. This includes increasing subsidies for purchase, reducing taxes, and offering grants for old vehicle trade-ins for an EV. Examples of actions that the government can undertake include the following: (1) Subsidized Loans for EV Purchases: Offer low-interest financing options for buyers of EVs to make them financially accessible to a broader audience. (2) Grants for Infrastructure Development: Provide grants or tax breaks to businesses developing EV-related infrastructure, such as battery manufacturing facilities or recycling plants, to boost the EV ecosystem.
- Enhancing Infrastructure and Regulatory Support: The “30@30 Policy” emphasizes making Thailand a hub for electric vehicle production and includes the development of infrastructure. However, our findings suggest a need for an even more aggressive expansion of this infrastructure. There is a significant demand for increased numbers and more strategically located EV charging stations to reduce range anxiety and make EVs a viable option for a broader segment of the population.
- Improving Communication and Transparency: While the “EV 3.5 Measures” introduce substantial incentives, our study indicates that the effectiveness of these incentives could be maximized through better communication strategies. Clear, frequent, and transparent communication about how these policies directly benefit potential EV buyers could enhance public understanding and trust, thereby increasing adoption rates.
- Optimizing Financial Incentives: The study also highlights the impactful role of direct financial incentives, such as tax benefits and subsidies. While current measures under “EV 3.5” already include various subsidies and tax reductions, there may be room to increase these incentives or restructure them to ensure they are more accessible and appealing to a larger number of consumers, especially in lower-income brackets.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Philip, T.; Whitehead, J.; Prato, C.G. Adoption of electric vehicles in a laggard, car-dependent nation: Investigating the potential influence of V2G and broader energy benefits on adoption. Transp. Res. Part A 2023, 167, 103555. [Google Scholar] [CrossRef]
- Paudel, A.; Pinthurat, W.; Marungsri, B. Impact of Large-Scale Electric Vehicles’ Promotion in Thailand Considering Energy Mix, Peak Load, and Greenhouse Gas Emissions. Smart Cities 2023, 6, 2619–2638. [Google Scholar] [CrossRef]
- UN. Sustainable Development Goals. Available online: https://www.un.org/sustainabledevelopment/cities/ (accessed on 1 October 2021).
- Majhi, R.C.; Ranjitkar, P.; Sheng, M.S. Analyzing electric vehicle users’ intention to use dynamic wireless charging facilities: A study from New Zealand. Transp. Res. Part F 2024, 102, 125–141. [Google Scholar] [CrossRef]
- Lodhia, S.K.; Rice, J.; Rice, B.; Martin, N. Assessment of electric vehicle adoption policies and practices in Australia: Stakeholder perspectives. J. Clean. Product. 2024, 446, 141300. [Google Scholar] [CrossRef]
- Srivastava, A.; Kumar, R.R.; Chakraborty, A.; Mateen, A.; Narayanamurthy, G. Design and selection of government policies for electric vehicles adoption: A global perspective. Transp. Res. Part E 2022, 161, 102726. [Google Scholar] [CrossRef]
- Liu, Y.; Zhao, X.; Lu, D.; Li, X. Impact of policy incentives on the adoption of electric vehicle in China. Transp. Res. Part A 2023, 176, 103801. [Google Scholar] [CrossRef]
- Jain, N.K.; Bhaskar, K.; Jain, S. What drives adoption intention of electric vehicles in India? An integrated UTAUT model with environmental concerns, perceived risk and government support. Res. Transp. Bussi. Manag. 2022, 42, 100730. [Google Scholar] [CrossRef]
- Li, L.; Wang, Z.; Xie, X. From government to market? A discrete choice analysis of policy instruments for electric vehicle adoption. Transp. Res. Part A 2022, 160, 143–159. [Google Scholar] [CrossRef]
- Energy Policy and Planning Office. Guidelines for Promoting Electric Vehicles (EV) in Thailand. Available online: https://www.eppo.go.th/index.php/en/component/k2/item/17415-ev-charging-221064-04 (accessed on 4 May 2024).
- Royal Thai Government. EV 3.5 Policy. Available online: https://www.pdlegal.com.sg/thailands-ev-3-5-policy-boosting-locally-produced-bevs-with-subsidies/ (accessed on 4 May 2024).
- Wattana, B.; Wattana, S. Implications of electric vehicle promotion policy on the road transport and electricity sectors for Thailand. Energy Strat. Rev. 2022, 42, 100901. [Google Scholar] [CrossRef]
- Kongklaew, C.; Phoungthong, K.; Prabpayak, C.; Chowdhury, M.S.; Khan, I.; Yuangyai, N.; Yuangyai, C.; Techato, K. Barriers to Electric Vehicle Adoption in Thailand. Sustainability 2021, 13, 12839. [Google Scholar] [CrossRef]
- Zhou, M.; Long, P.; Kong, N.; Zhao, L.; Jia, F.; Campy, K.S. Characterizing the motivational mechanism behind taxi driver’s adoption of electric vehicles for living: Insights from China. Transp. Res. Part A 2021, 144, 134–152. [Google Scholar] [CrossRef]
- Hasan, S. Assessment of electric vehicle repurchase intention: A survey-based study on the Norwegian EV market. Transp. Res. Interd. Perspect. 2021, 11, 100439. [Google Scholar] [CrossRef]
- Zhang, L.; Tong, H.; Liang, Y.; Qin, Q. Consumer purchase intention of new energy vehicles with an extended technology acceptance model: The role of attitudinal ambivalence. Transp. Res. Part A 2023, 174, 103742. [Google Scholar] [CrossRef]
- Hair, J.; Black, B.; Babin, B.; Anderson, R. Multivariate Data Analysis, 7th ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
- Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
- Wong, S.D.; Shaheen, S.A.; Martin, E.; Uyeki, R. Do incentives make a difference? Understanding smart charging program adoption for electric vehicles. Transp. Res. Part C 2023, 151, 104123. [Google Scholar] [CrossRef]
- Murugan, M.; Marisamynathan, S. Policy analysis for sustainable EV charging facility adoption using SEM-ANN approach. Transp. Res. Part A 2024, 182, 104036. [Google Scholar] [CrossRef]
- Theerathitichaipa, K.; Wisutwattanasak, P.; Banyong, C.; Seefong, M.; Jomnonkwao, S.; Champahom, T.; Ratanavaraha, V.; Kasemsri, R. Measurement Model for Determining the Disparity Factors of Intercity Railway Transportation. Civ. Eng. J. 2024, 10, 668–688. [Google Scholar] [CrossRef]
- Watthanaklang, D.; Jomnonkwao, S.; Champahom, T.; Wisutwattanasak, P. Exploring accessibility and service quality perceptions on local public transportation in Thailand. Case Studi. Transp. Policy 2024, 15, 101144. [Google Scholar] [CrossRef]
- Ruoso, A.C.; Ribeiro, J.L.D. The influence of countries’ socioeconomic characteristics on the adoption of electric vehicle. Energy Sust. Develop. 2022, 71, 251–262. [Google Scholar] [CrossRef]
- Selena Sheng, M.; Wen, L.; Sharp, B.; Du, B.; Ranjitkar, P.; Wilson, D. A spatio-temporal approach to electric vehicle uptake: Evidence from New Zealand. Transp. Res. Part D 2022, 105, 103256. [Google Scholar] [CrossRef]
- Peng, Y.; Bai, X. What EV users say about policy efficacy: Evidence from Shanghai. Transp. Policy 2023, 132, 16–26. [Google Scholar] [CrossRef]
- Suresh Kumar, P.; Shriram, R.G.; Rajesh, R.; Rammohan, A. Causal analysis of the challenges to electric vehicles’ adoption using GINA: Implications to emerging economies. Case Studi. Transp. Policy 2024, 15, 101160. [Google Scholar] [CrossRef]
- Huang, X.; Lin, Y.; Lim, M.K.; Tseng, M.-L.; Zhou, F. The influence of knowledge management on adoption intention of electric vehicles: Perspective on technological knowledge. Indust. Manag. Data Syst. 2021, 121, 1481–1495. [Google Scholar] [CrossRef]
- Li, K.; Wang, L. Optimal electric vehicle subsidy and pricing decisions with consideration of EV anxiety and EV preference in green and non-green consumers. Transp. Res. Part E 2023, 170, 103010. [Google Scholar] [CrossRef]
- Figenbaum, E.; Wangsness, P.B.; Amundsen, A.H.; Milch, V. Empirical Analysis of the User Needs and the Business Models in the Norwegian Charging Infrastructure Ecosystem. World Electr. Veh. J. 2022, 13, 185. [Google Scholar] [CrossRef]
- Broadbent, G.H.; Allen, C.I.; Wiedmann, T.; Metternicht, G.I. Accelerating electric vehicle uptake: Modelling public policy options on prices and infrastructure. Transp. Res. Part A 2022, 162, 155–174. [Google Scholar] [CrossRef]
- Rye, J.; Sintov, N.D. Predictors of electric vehicle adoption intent in rideshare drivers relative to commuters. Transp. Res. Part A 2024, 179, 103943. [Google Scholar] [CrossRef]
- Filippini, M.; Kumar, N.; Srinivasan, S. Nudging adoption of electric vehicles: Evidence from an information-based intervention in Nepal. Transp. Res. Part D 2021, 97, 102951. [Google Scholar] [CrossRef]
- Deka, C.; Dutta, M.K.; Yazdanpanah, M.; Komendantova, N. Can gain motivation induce Indians to adopt electric vehicles? Application of an extended theory of Planned Behavior to map EV adoption intention. Energy Policy 2023, 182, 113724. [Google Scholar] [CrossRef]
- Kuppusamy, S.; Magazine, M.J.; Rao, U. Impact of downstream emissions cap-and-trade policy on electric vehicle and clean utility adoption. Transp. Res. Part E 2023, 180, 103353. [Google Scholar] [CrossRef]
- White, L.V.; Carrel, A.L.; Shi, W.; Sintov, N.D. Why are charging stations associated with electric vehicle adoption? Untangling effects in three United States metropolitan areas. Energy Res. Social Sci. 2022, 89, 102663. [Google Scholar] [CrossRef]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. A data-driven approach to managing electric vehicle charging infrastructure in parking lots. Transp. Res. Part D 2022, 105, 103198. [Google Scholar] [CrossRef]
- Qian, X.; Gkritza, K. Spatial and temporal variance in public perception of electric vehicles: A comparative analysis of adoption pioneers and laggards using twitter data. Transp. Policy 2024, 149, 150–162. [Google Scholar] [CrossRef]
- Albatayneh, A.; Juaidi, A.; Abdallah, R.; Jeguirim, M. Preparing for the EV revolution: Petrol stations profitability in Jordan. Energy Sust. Dev. 2024, 79, 101412. [Google Scholar] [CrossRef]
- Lee, R.; Brown, S. Evaluating the role of behavior and social class in electric vehicle adoption and charging demands. iScience 2021, 24, 102914. [Google Scholar] [CrossRef] [PubMed]
- Pamidimukkala, A.; Kermanshachi, S.; Rosenberger, J.M.; Hladik, G. Barriers and motivators to the adoption of electric vehicles: A global review. Green Energy Int. Transp. 2024, 3, 100153. [Google Scholar] [CrossRef]
- Asadi, S.; Nilashi, M.; Iranmanesh, M.; Ghobakhloo, M.; Samad, S.; Alghamdi, A.; Almulihi, A.; Mohd, S. Drivers and barriers of electric vehicle usage in Malaysia: A DEMATEL approach. Resourc. Conver. Recyc. 2022, 177, 105965. [Google Scholar] [CrossRef]
- Li, L.; Wang, Z.; Wang, Q. Do policy mix characteristics matter for electric vehicle adoption? A survey-based exploration. Transp. Res. Part D 2020, 87, 102488. [Google Scholar] [CrossRef]
- Belgiawan, P.F.; Dharmowijoyo, D.B.E.; Novizayanti, D.; Farda, M.; Prasetio, E.A.; Dirgahayani, P. Does range or fiscal policies matter on EV adoption in Jakarta Metropolitan Area? Transp. Res. Interd. Perspect. 2024, 23, 101027. [Google Scholar] [CrossRef]
- Munshi, T.; Dhar, S.; Painuly, J. Understanding barriers to electric vehicle adoption for personal mobility: A case study of middle income in-service residents in Hyderabad city, India. Energy Policy 2022, 167, 112956. [Google Scholar] [CrossRef]
- Muthén, L.K.; Muthén, B. Mplus User’s Guide: Statistical Analysis with Latent Variables, User’s Guide; Muthén & Muthén: Los Angeles, CA, USA, 2017. [Google Scholar]
- Lopez-Arboleda, E.; Sarmiento, A.T.; Cardenas, L.M. Policy assessment for electromobility promotion in Colombia: A system dynamics approach. Transp. Res. Part D 2023, 121, 103799. [Google Scholar] [CrossRef]
- Feng, B.; Ye, Q.; Collins, B.J. A dynamic model of electric vehicle adoption: The role of social commerce in new transportation. Infor. Manag. 2019, 56, 196–212. [Google Scholar] [CrossRef]
- Thananusak, T.; Punnakitikashem, P.; Tanthasith, S.; Kongarchapatara, B. The Development of Electric Vehicle Charging Stations in Thailand: Policies, Players, and Key Issues (2015–2020). World Electr. Veh. J. 2020, 12, 2. [Google Scholar] [CrossRef]
- Bjerkan, K.Y.; Nørbech, T.E.; Nordtømme, M.E. Incentives for promoting Battery Electric Vehicle (BEV) adoption in Norway. Transp. Res. Part D 2016, 43, 169–180. [Google Scholar] [CrossRef]
- Balla, S.N.; Pani, A.; Sahu, P.K.; González-Feliu, J. Examining shifts in public discourse on electric mobility adoption through Twitter data. Transp. Res. Part D 2023, 121, 103843. [Google Scholar] [CrossRef]
- Jiang, H.; Xu, H.; Liu, Q.; Ma, L.; Song, J. An urban planning perspective on enhancing electric vehicle (EV) adoption: Evidence from Beijing. Travel Behav. Soc. 2024, 34, 100712. [Google Scholar] [CrossRef]
- Chandra, M. Investigating the impact of policies, socio-demography and national commitments on electric-vehicle demand: Cross-country study. J. Transp. Geo. 2022, 103, 103410. [Google Scholar] [CrossRef]
- Parker, N.; Breetz, H.L.; Salon, D.; Conway, M.W.; Williams, J.; Patterson, M. Who saves money buying electric vehicles? Heterogeneity in total cost of ownership. Transp. Res. Part D 2021, 96, 102893. [Google Scholar] [CrossRef]
- Liu, X.; Sun, X.; Zheng, H.; Huang, D. Do policy incentives drive electric vehicle adoption? Evidence from China. Transp. Res. Part A 2021, 150, 49–62. [Google Scholar] [CrossRef]
- White, L.V.; Sintov, N.D. You are what you drive: Environmentalist and social innovator symbolism drives electric vehicle adoption intentions. Transp. Res. Part A 2017, 99, 94–113. [Google Scholar] [CrossRef]
- Alyamani, R.; Pappelis, D.; Kamargianni, M. Modelling the determinants of electrical vehicles adoption in Riyadh, Saudi Arabia. Energy Policy 2024, 188, 114072. [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]
- Zhang, B.S.; Ali, K.; Kanesan, T. A model of extended technology acceptance for behavioral intention toward EVs with gender as a moderator. Front. Psychol. 2022, 13, 1080414. [Google Scholar] [CrossRef] [PubMed]
- Brand, C.; Anable, J.; Tran, M. Accelerating the transformation to a low carbon passenger transport system: The role of car purchase taxes, feebates, road taxes and scrappage incentives in the UK. Transp. Res. Part A 2013, 49, 132–148. [Google Scholar] [CrossRef]
- Li, L.; Wang, Z.; Gong, Y.; Liu, S. Self-image motives for electric vehicle adoption: Evidence from China. Transp. Res. Part D 2022, 109, 103383. [Google Scholar] [CrossRef]
- Avineri, E. On the use and potential of behavioural economics from the perspective of transport and climate change. J. Transp. Geo. 2012, 24, 512–521. [Google Scholar] [CrossRef]
- Solek, A. Behavioral Economics Approaches to public policy. J. Inter. Studi. 2014, 7, 33–45. [Google Scholar] [CrossRef]
- Matjasko, J.L.; Cawley, J.H.; Baker-Goering, M.M.; Yokum, D.V. Applying Behavioral Economics to Public Health Policy: Illustrative Examples and Promising Directions. Am. J. Prev. Med. 2016, 50, S13–S19. [Google Scholar] [CrossRef]
Item | Measures | M | SD | SK | KU |
---|---|---|---|---|---|
Perceptions of government commitment and efficiency (Cronbach’s α = 0.955) | |||||
I1 | I perceive the government’s efforts to promote electric cars as effective. | 4.029 | 1.562 | −0.069 | −0.855 |
I2 | Government initiatives and campaigns encourage me to choose electric cars. | 4.133 | 1.523 | −0.087 | −0.780 |
I3 | I believe the government is committed to promoting sustainable transportation through electric cars. | 4.158 | 1.556 | 0.019 | −0.691 |
I4 | The level of commitment demonstrated by the government affects my confidence in using electric cars. | 4.132 | 1.588 | 0.014 | −0.777 |
I5 | I feel confident in the government’s long-term commitment to supporting electric cars. | 4.129 | 1.582 | 0.032 | −0.776 |
Perceptions of government welfare (Cronbach’s α = 0.925) | |||||
I6 | I am aware of government welfare or financial assistance available for purchasing or using electric cars. | 3.973 | 1.581 | −0.038 | −0.731 |
I7 | Knowledge of financial welfare programs from the government influences my consideration of electric cars. | 4.084 | 1.507 | −0.006 | −0.687 |
I8 | The availability of government incentives stimulates my interest in electric cars as a viable option. | 4.089 | 1.490 | −0.037 | −0.621 |
Effects of government policy (Cronbach’s α = 0.941) | |||||
I9 | Government policies promoting electric car usage have a positive impact on my worldview. | 4.166 | 1.512 | −0.120 | −0.513 |
I10 | I believe government policies supporting electric cars are crucial factors in their adoption. | 4.180 | 1.491 | −0.074 | −0.598 |
I11 | Adjusting to government regulations inspires me to consider electric cars | 4.119 | 1.471 | −0.038 | −0.561 |
I12 | Government initiatives such as charging infrastructure construction are important for the success of electric cars. | 4.148 | 1.489 | −0.089 | −0.675 |
Effects of government communication (Cronbach’s α = 0.826) | |||||
I13 | Government communication and campaigns about electric cars affect my perspective. | 4.562 | 1.526 | −0.113 | −0.553 |
I14 | I rely more on government-provided information to make decisions about electric cars. | 4.482 | 1.619 | −0.194 | −0.618 |
I15 | The clarity and transparency of government communication affect my confidence in using electric cars. | 4.571 | 1.553 | −0.074 | −0.651 |
Effects of Tax Benefits (Cronbach’s α = 0.940) | |||||
I16 | Tax benefits provided by the government positively influence my decision to choose electric cars. | 4.184 | 1.541 | −0.051 | −0.706 |
I17 | I consider tax benefits for electric car owners as significant advantages. | 4.238 | 1.536 | −0.095 | −0.626 |
I18 | Tax incentives for electric car users encourage me to adopt this technology. | 4.246 | 1.550 | −0.059 | −0.678 |
Intention to use electric vehicles (Cronbach’s α = 0.932) | |||||
I19 | Knowing that the government supports electric cars increases my intention to use this tool. | 4.030 | 1.556 | −0.110 | −0.664 |
I20 | I am inclined to consider electric cars as a suitable option because of government support. | 4.057 | 1.528 | −0.075 | −0.625 |
I21 | Government support is crucial in enhancing my positive attitude towards using electric cars. | 4.109 | 1.543 | −0.059 | −0.643 |
Characteristics | Category | Frequency | Percentage |
---|---|---|---|
Gender | Male | 2320 | 61.5% |
Female | 1450 | 38.5% | |
Age | <25 years old | 338 | 9.0% |
25–34 years old | 1260 | 33.4% | |
35–44 years old | 919 | 24.4% | |
45–54 years old | 1071 | 28.4% | |
Over 55 years old | 182 | 4.8% | |
Education | Primary School | 292 | 7.8% |
High School | 587 | 15.6% | |
Vocational Education | 1004 | 26.6% | |
Bachelor’s Degree | 1458 | 38.7% | |
Master’s Degree | 414 | 11.0% | |
Doctoral Degree | 15 | 0.4% | |
Occupation | Government Employee | 615 | 16.3% |
Private Employee | 1160 | 30.8% | |
Business Owners | 1125 | 29.8% | |
Agriculturist | 250 | 6.6% | |
Student | 176 | 4.7% | |
General Employee | 413 | 11.0% | |
Other | 31 | 0.8% | |
Resident zone | Rural | 1351 | 35.8% |
Urban | 2419 | 64.2% | |
Are you always a driver? | No | 836 | 2.22% |
Yes | 2934 | 77.8% | |
Engine type | Internal Combustion Engine | 1856 | 49.2% |
Hybrid | 450 | 11.9% | |
Plug-in Hybrid | 435 | 11.5% | |
Electric Vehicle | 1029 | 27.3% | |
Vehicle Type | Pick-Up Truck | 601 | 15.9% |
Car | 2078 | 55.1% | |
Sport Utility Vehicle (SUV) | 794 | 21.1% | |
Pick-Up Passenger Vehicle (PPV) | 207 | 5.5% | |
Pick-Up Truck | 90 | 2.4% | |
Most used driving areas | Urban | 2467 | 65.4% |
Rural | 1303 | 34.6% |
Code | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 |
---|---|---|---|---|---|
I1 | 0.683 | ||||
I2 | 0.645 | ||||
I3 | 0.697 | ||||
I4 | 0.725 | ||||
I5 | 0.728 | ||||
I6 | 0.779 | ||||
I7 | 0.739 | ||||
I8 | 0.719 | ||||
I9 | 0.606 | ||||
I10 | 0.601 | ||||
I11 | 0.663 | ||||
I12 | 0.576 | ||||
I13 | 0.744 | ||||
I14 | 0.778 | ||||
I15 | 0.788 | ||||
I16 | 0.639 | ||||
I17 | 0.659 | ||||
I18 | 0.614 | ||||
Eigenvalues | 12.605 | 1.060 | 0.697 | 0.576 | 0.418 |
% of variance explained | 23.096 | 17.386 | 15.819 | 15.762 | 13.251 |
Reliability (Cronbach’s alpha) | 0.955 | 0.925 | 0.941 | 0.826 | 0.940 |
Measure of sampling adequacy (KMO) | 0.969 |
Constructs and Indicators | Standardized Estimates | Standard Error | t-Value | R2 |
---|---|---|---|---|
Perceptions of government commitment and efficiency (AVE = 0.786, CR = 0.948) | ||||
I1 | 0.894 | 0.004 | 223.508 ** | 0.798 |
I2 | 0.891 | 0.004 | 217.440 ** | 0.793 |
I3 | 0.876 | 0.005 | 189.448 ** | 0.768 |
I4 | 0.886 | 0.004 | 205.948 ** | 0.784 |
I5 | 0.885 | 0.004 | 198.721 ** | 0.783 |
Perceptions of government welfare (AVE = 0.816, CR = 0.930) | ||||
I6 | 0.824 | 0.006 | 147.038 ** | 0.679 |
I7 | 0.953 | 0.002 | 383.328 ** | 0.908 |
I8 | 0.927 | 0.003 | 308.096 ** | 0.859 |
Effects of government policy (AVE = 0.794, CR = 0.939) | ||||
I9 | 0.871 | 0.005 | 193.105 ** | 0.759 |
I10 | 0.927 | 0.003 | 284.271 ** | 0.860 |
I11 | 0.904 | 0.004 | 252.139 ** | 0.817 |
I12 | 0.861 | 0.005 | 168.324 ** | 0.741 |
Effects of Government communication (AVE = 0.613, CR = 0.826) | ||||
I13 | 0.798 | 0.008 | 101.003 ** | 0.637 |
I14 | 0.780 | 0.008 | 94.476 ** | 0.608 |
I15 | 0.771 | 0.008 | 91.629 ** | 0.595 |
Effects of Tax Benefits (AVE = 0.855, CR = 0.947) | ||||
I16 | 0.933 | 0.003 | 302.119 ** | 0.871 |
I17 | 0.932 | 0.003 | 335.320 ** | 0.868 |
I18 | 0.909 | 0.004 | 245.528 ** | 0.826 |
Government support for electric vehicles (AVE = 0.832, CR = 0.961) | ||||
Perceptions of government commitment and efficiency | 0.947 | 0.003 | 323.464 ** | 0.897 |
Perceptions of government welfare | 0.892 | 0.004 | 207.424 ** | 0.797 |
Effects of government policy | 0.985 | 0.002 | 454.165 ** | 0.971 |
Effects of government communication | 0.778 | 0.009 | 90.656 ** | 0.605 |
Effects of tax benefits | 0.944 | 0.003 | 299.427 ** | 0.890 |
Constructs and Indicators | Standardized Estimates | Standard Error | t-Value | R2 |
---|---|---|---|---|
Measurement model | ||||
Perceptions of government commitment and efficiency (AVE = 0.859, CR = 0.968) | ||||
I1 | 0.926 | 0.006 | 164.740 ** | 0.858 |
I2 | 0.933 | 0.006 | 166.277 ** | 0.871 |
I3 | 0.912 | 0.006 | 161.095 ** | 0.832 |
I4 | 0.938 | 0.003 | 296.852 ** | 0.880 |
I5 | 0.926 | 0.003 | 275.463 ** | 0.858 |
Perceptions of government welfare (AVE = 0.796, CR = 0.921) | ||||
I6 | 0.842 | 0.006 | 150.803 ** | 0.709 |
I7 | 0.933 | 0.004 | 235.124 ** | 0.870 |
I8 | 0.900 | 0.005 | 190.406 ** | 0.811 |
Effects of government policy (AVE = 0.779, CR = 0.934) | ||||
I9 | 0.878 | 0.004 | 203.466 ** | 0.771 |
I10 | 0.904 | 0.004 | 224.939 ** | 0.817 |
I11 | 0.895 | 0.004 | 215.603 ** | 0.801 |
I12 | 0.853 | 0.005 | 162.403 ** | 0.727 |
Effects of government communication (AVE = 0.615, CR = 0.827) | ||||
I13 | 0.795 | 0.008 | 101.728 ** | 0.631 |
I14 | 0.786 | 0.008 | 97.865 ** | 0.618 |
I15 | 0.771 | 0.008 | 93.130 ** | 0.595 |
Effects of Tax Benefits (AVE = 0.854, CR = 0.946) | ||||
I16 | 0.930 | 0.003 | 303.050 ** | 0.865 |
I17 | 0.933 | 0.003 | 345.318 ** | 0.871 |
I18 | 0.909 | 0.004 | 247.639 ** | 0.827 |
Intention to use electric vehicles (AVE = 0.797, CR = 0.922) | ||||
I19 | 0.867 | 0.005 | 174.729 ** | 0.752 |
I20 | 0.898 | 0.004 | 218.375 ** | 0.807 |
I21 | 0.912 | 0.004 | 244.323 ** | 0.832 |
Structural model (hypothesis path) | ||||
H1: Perceptions of government commitment and efficiency → Intention to use EVs | 0.262 | 0.019 | 14.071 ** | |
H2: Perceptions of government welfare → Intention to use EVs | 0.133 | 0.041 | 3.210 ** | |
H3: Effects of government policy → Intention to use EVs | 0.198 | 0.065 | 3.049 ** | |
H4: Effects of government communication → Intention to use EVs | 0.242 | 0.016 | 14.717 ** | |
H5: Effects of tax benefits → Intention to use EVs | 0.172 | 0.034 | 5.090 ** |
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. |
© 2024 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
Chonsalasin, D.; Champahom, T.; Jomnonkwao, S.; Karoonsoontawong, A.; Runkawee, N.; Ratanavaraha, V. Exploring the Influence of Thai Government Policy Perceptions on Electric Vehicle Adoption: A Measurement Model and Empirical Analysis. Smart Cities 2024, 7, 2258-2282. https://doi.org/10.3390/smartcities7040089
Chonsalasin D, Champahom T, Jomnonkwao S, Karoonsoontawong A, Runkawee N, Ratanavaraha V. Exploring the Influence of Thai Government Policy Perceptions on Electric Vehicle Adoption: A Measurement Model and Empirical Analysis. Smart Cities. 2024; 7(4):2258-2282. https://doi.org/10.3390/smartcities7040089
Chicago/Turabian StyleChonsalasin, Dissakoon, Thanapong Champahom, Sajjakaj Jomnonkwao, Ampol Karoonsoontawong, Norarat Runkawee, and Vatanavongs Ratanavaraha. 2024. "Exploring the Influence of Thai Government Policy Perceptions on Electric Vehicle Adoption: A Measurement Model and Empirical Analysis" Smart Cities 7, no. 4: 2258-2282. https://doi.org/10.3390/smartcities7040089