Exploring the Influence of Thai Government Policy Perceptions on Electric Vehicle Adoption: A Measurement Model and Empirical Analysis

Abstract

This study explores the influence of Thai government policy perceptions on the adoption of electric vehicles (EVs). Transitioning to EVs is vital for reducing greenhouse gas emissions and combating climate change, aligning with global sustainability goals. This study addresses gaps in understanding how multidimensional perceptions of government policies influence EV adoption intentions in emerging markets, particularly in Thailand. A questionnaire was distributed to 3770 respondents across Thailand between January and March 2024. The survey assessed multiple dimensions of government policy, including commitment and efficiency, welfare, communication, policy effectiveness, and tax benefits. Using statistical techniques such as Exploratory Factor Analysis (EFA), second-order confirmatory factor analysis (CFA), and structural equation modeling (SEM), this study validated the constructs of government support perception and examined their influence on EV adoption intentions. The findings highlight that tangible government policies, particularly those improving EV infrastructure and providing clear regulatory support, alongside effective communication about these policies, significantly influence public willingness to adopt EVs. The results also emphasize the critical role of perceived government commitment and fiscal incentives in shaping consumer decisions. Based on these insights, this study recommends prioritizing the expansion of EV infrastructure, enhancing the visibility of government commitment, and improving direct financial incentives to accelerate EV adoption. These findings contribute to the growing body of knowledge on EV adoption in emerging markets and offer practical implications for policymakers seeking to promote sustainable transportation solutions.

1. Introduction

1.1. Research Background

The importance of electric vehicles (EVs) in modern transportation strategies cannot be overstated, particularly in the context of global efforts to mitigate climate change [1]. As a cleaner alternative to internal combustion engine vehicles, EVs offer significant reductions in greenhouse gas emissions and other pollutants [2]. This aligns directly with the Sustainable Development Goals (SDGs) [3], particularly SDG 13 (Climate Action), by contributing to a reduction in carbon footprints and improving air quality in urban environments. Furthermore, the adoption of EVs supports SDG 7 (Affordable and Clean Energy) by fostering the integration of renewable energy sources into the transportation sector. As countries around the world strive to enhance sustainability and reduce environmental impacts, EVs emerge as a crucial technology in the transition towards more sustainable and resilient societies [4,5,6,7].

The perception of government policies related to electric vehicles is equally critical in determining the success of EV adoption. Government policies can significantly influence public attitudes and behaviors towards new technologies. Positive perceptions of these policies can enhance public confidence in EVs, thereby increasing their adoption rates [8]. This includes perceptions of government commitment to sustainable transportation, the efficiency of policy implementation, and the tangible benefits, such as financial incentives and infrastructural support, provided to potential EV buyers [9]. Understanding these perceptions is essential for crafting effective policies that not only promote EV adoption but also align with public expectations and preferences. By exploring how individuals perceive these government efforts, policymakers can tailor their strategies to better support the widespread uptake of EV technology, ensuring that policy interventions are both effective and publicly accepted [6].

1.2. Thai Government Policies for EVs

Thailand’s strategic approach to electric vehicle (EV) adoption and production is encapsulated in its ambitious policy frameworks, specifically the “30@30” policy and the recently introduced EV 3.5 measures. These initiatives are critical components of Thailand’s national strategy to position itself as a leader in the global EV market and transition towards a low-carbon economy.

1.2.1. The 30@30 Policy

Initiated by the National Electric Vehicle Policy Committee, the 30@30 policy targets transforming Thailand into a major production hub for zero-emission vehicles (ZEVs) [10]. The goal is to ensure that by 2030, at least 30% of Thailand’s total automotive production is composed of ZEVs. This policy not only aims to bolster Thailand’s automotive industry but also aligns with the broader objectives of reducing greenhouse gas emissions and advancing towards carbon neutrality by 2050.

Under this policy, the production targets include 725,000 passenger cars and pickups, 675,000 motorcycles, and 34,000 buses and trucks. Additionally, the policy supports the enhancement of infrastructure necessary for EVs, such as the establishment of 12,000 fast-charge public electric vehicle charging stations and 1,450 battery exchange stations for electric motorcycles.

1.2.2. EV 3.5 Measures

The EV 3.5 policy, approved by the Cabinet for implementation between 2024 and 2027, represents a continuation and expansion of Thailand’s commitment to the EV industry. This policy focuses on providing substantial support for both the production and adoption of electric vehicles through various incentives [11]. Key aspects of the EV 3.5 measures include the following:

• 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.

These measures, combined with the strategic development of supportive infrastructure and regulatory frameworks, aim to stimulate the EV market in Thailand. They are expected to facilitate the transition of existing automotive manufacturers to EV production and attract new players to the market, further solidifying Thailand’s position as a regional hub for the electric vehicle industry [12].

1.3. Research Gap and Objectives

While previous studies have examined various aspects of EV adoption and policy impacts, there remains a significant gap in understanding how multidimensional perceptions of government policies influence adoption intentions, particularly in emerging markets like Thailand. For instance, Kongklaew et al. [13] identified barriers to EV adoption in Thailand but did not explore how policy perceptions shape these barriers. Similarly, Wattana et al. [12] analyzed the implications of EV promotion policies on the Thai transport and electricity sectors but did not investigate public perceptions of these policies. This gap is notable given that Philip et al. [1] emphasized the importance of understanding public perceptions in laggard EV markets. This study addresses this gap by developing a novel measurement model that integrates multiple facets of government policy perception, including commitment, efficiency, welfare, communication, and tax benefits. Unlike previous research that often focused on individual policy measures (e.g., Li et al. [9] and Zhou et al. [14]), this approach allows for a more nuanced understanding of the complex interplay between various policy perceptions and EV adoption intentions. This comprehensive approach builds on the work of Jain et al. [8], who highlighted the need for integrated models in understanding EV adoption. Furthermore, by focusing on Thailand—a key player in the Southeast Asian automotive market—this research provides valuable insights into the unique challenges and opportunities for EV adoption in emerging economies. This geographical focus extends the work of researchers like Srivastava et al. [6] and Liu et al. [7], who called for more context-specific studies in diverse markets. It also complements the work of Paudel et al. [2], who examined the impact of large-scale EV promotion in Thailand from an energy perspective. The use of advanced statistical techniques, such as second-order confirmatory factor analysis and structural equation modeling, sets this study apart by enabling a more sophisticated analysis of the relationships between latent constructs. This methodological approach allows for uncovering both direct and indirect effects of policy perceptions on adoption intentions, providing a more comprehensive view than previous studies such as Hasan [15] and Zhang et al. [16], which primarily focused on direct effects. This advanced approach responds to calls from researchers like Lodhia et al. [5] for more rigorous methodologies in assessing EV adoption policies. By addressing these knowledge gaps, this research makes significant contributions to both the theoretical understanding of EV adoption dynamics and the practical implementation of effective policy strategies. The findings from this study have important implications for policymakers, automotive industry stakeholders, and researchers working towards sustainable transportation solutions in Thailand and similar emerging markets.

Objective 1: Construct a measurement model to assess perceptions of government policies regarding electric vehicle adoption. The first objective of this study is to develop a robust measurement model that can effectively assess the perceptions of Thai government policies related to electric vehicle adoption. This model aims to encapsulate various dimensions of policy perception, including perceived government commitment, efficiency, and tangible benefits such as subsidies and infrastructural support. The methodological approach for this objective involves employing second-order confirmatory factor analysis (CFA). This statistical technique will allow us to confirm the hypothesized structure of the perceptions model and ensure that the scale used is both reliable and valid for capturing the complex interrelations between perceived policy attributes.

Objective 2: Investigate the factors influencing perceptions of government policies and their impact on electric vehicle adoption. The second objective seeks to explore the factors influencing public perceptions of government policies and to determine how these perceptions ultimately impact the adoption of electric vehicles. This investigation will consider a range of factors, including demographic variables, prior exposure to EVs, and the specific content of government communication regarding EVs. The methodology for this analysis will be structural equation modeling (SEM), which is suited for examining the relationships between latent constructs. This approach will help in understanding both the direct and indirect effects of perceived government policies on the intention to use EVs, providing a comprehensive view of the causal pathways involved.

To achieve these objectives, this study employs CFA and SEM as the primary analytical methods. The choice of these statistical techniques is grounded in their ability to handle complex, multidimensional constructs and test hypothesized relationships simultaneously [17]. Second-order CFA is particularly suitable for this study as it allows for the examination of higher-order latent constructs (such as overall government support perception) that are composed of multiple first-order factors (e.g., perceived commitment, welfare, and communication) [18]. This approach provides a more nuanced understanding of the complex nature of policy perceptions. SEM, on the other hand, offers the advantage of testing multiple hypothesized relationships in a single, comprehensive model while accounting for measurement error [19]. This is crucial for this study, which aims to investigate the interplay between various dimensions of policy perception and their collective impact on EV adoption intentions. The use of SEM also allows for the assessment of both direct and indirect effects, providing a more complete picture of the factors influencing EV adoption [20]. The robustness of these methods is confirmed by their widespread use in similar studies examining technology adoption and policy perceptions. For instance, Theerathitichaipa et al. [21] employed second-order CFA to validate a measurement model for transportation factors, while Watthanaklang et al. [22] used SEM to analyze perceptions of public transportation quality. These studies demonstrate the effectiveness of these methods in capturing complex relationships in transportation research. Moreover, the combination of second-order CFA and SEM provides a robust framework for addressing potential issues such as multicollinearity and common method bias, which are common concerns in survey-based research [17]. By employing these methods, this study ensures a rigorous and comprehensive analysis of the data, leading to reliable and valid conclusions about the influence of government policy perceptions on EV adoption intentions in Thailand.

1.4. Research Questions and Contributions

To address these objectives, this study seeks to answer the following research questions: RQ1: How do different dimensions of government policy perceptions influence the intention to adopt electric vehicles in Thailand? RQ2: Which aspects of government support are most influential in shaping EV adoption intentions? These research questions are designed to address key gaps in the current literature on EV adoption, particularly in the context of emerging markets like Thailand.

This research makes several key contributions to both theoretical frameworks and practical applications in the field of electric vehicle (EV) adoption:

• 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

The adoption of electric vehicles (EVs) is heavily influenced by the public’s perception of government support, which encompasses a range of activities from policy formulation to communication and the implementation of benefits. Understanding these perceptions is crucial for assessing how government actions affect individual decisions regarding EV adoption [23].

Perceptions of Government Commitment and Efficiency: The perception of government commitment and efficiency is crucial in fostering public trust and confidence in EV policies. Questionnaire items like “I perceive the government’s efforts to promote electric cars as effective” and “The level of commitment demonstrated by the government affects my confidence in using electric cars” assess public sentiment towards the government’s dedication to promoting EVs. High scores on these items suggest that when the government is seen as committed and efficient, it positively impacts individuals’ willingness to adopt EV technology [9].

Perceptions of Government Welfare: Government welfare related to EV adoption includes financial incentives and support programs that directly benefit potential EV users. Items such as “I am aware of government welfare or financial assistance available for purchasing or using electric cars” reflect the awareness levels among the public, which can significantly influence their purchasing decisions. Effective communication and implementation of these welfare programs can lead to higher adoption rates as they lower the financial barriers to EV ownership [24].

Effects of Government Policy: The direct impact of government policies on individual attitudes and behaviors is captured through items like “Government policies promoting electric car usage have a positive impact on my worldview” and “I believe government policies supporting electric cars are crucial factors in their adoption”. These items measure the perceived effectiveness of governmental regulations and initiatives aimed at encouraging the use of EVs, indicating that supportive policies can enhance the attractiveness of EVs among consumers [5].

Effects of Government Communication: Effective communication from the government regarding EV benefits, incentives, and policies plays a pivotal role in shaping public perceptions. The clarity, frequency, and transparency of government communication, as assessed by items such as “Government communication and campaigns about electric cars affect my perspective” and “The clarity and transparency of government communication affect my confidence in using electric cars”, are essential for increasing public awareness and understanding, thereby fostering a supportive environment for EV adoption [5].

Effects of Tax Benefits: Tax benefits are a significant component of government support for EVs, directly impacting the cost-effectiveness of purchasing and owning an EV. Items like “Tax benefits provided by the government positively influence my decision to choose electric cars” evaluate how fiscal incentives affect the decision-making process for potential EV buyers. Positive perceptions of these benefits can substantially encourage individuals to opt for EVs over traditional vehicles [14,25,26].

Intention to Use Electric Vehicles: Finally, the overall intention to use electric vehicles is a direct outcome of the above factors. It is measured by items such as “Knowing that the government supports electric cars increases my intention to use this technology” and “I am inclined to consider electric cars as a suitable option because of government support”. These reflect the cumulative effect of government actions on individual behavioral intentions, highlighting the pivotal role of comprehensive and integrated government support in achieving higher EV adoption rates [15,27].

Recent policy changes in various countries highlight the dynamic nature of government support for EV adoption. In China, the world’s largest EV market, the government has shifted from direct subsidies to a dual-credit policy that mandates automakers to produce a certain percentage of new energy vehicles [28]. Norway, a global leader in EV adoption, has begun phasing out some of its generous incentives, such as exemptions from value-added tax, as EV market shares surpass 50% [29]. These examples illustrate the evolving nature of EV policies globally, as governments adjust their strategies to match market maturity and national priorities. Thailand’s policies, as discussed earlier, reflect a similar trend of adapting support mechanisms to drive EV adoption while fostering a domestic EV industry.

2.2. Hypotheses

The research hypotheses explore the relationships between different perceptions of government support for electric vehicles (EVs) and the intention to use EVs. These hypotheses are fundamental to understanding how various aspects of government policy and communication affect consumer decisions to adopt EV technology.

Hypothesis 1.

Perceptions of Government Commitment and Efficiency → Intention to Use EVs.

H1 posits that positive perceptions of the government’s commitment and efficiency in promoting electric vehicles positively influence the intention to use EVs [30]. This hypothesis suggests that when the government is perceived as committed and effective in its EV initiatives, such as implementing regulations and promoting EV adoption actively, it increases the likelihood that individuals will consider and ultimately choose to use EVs [31].

Hypothesis 2.

Perceptions of Government Welfare → Intention to Use EVs.

H2 examines the impact of the public perceptions of government welfare, including subsidies, tax benefits, and other financial incentives on the intention to use EVs [32]. It asserts that awareness and favorable perceptions of these incentives are critical drivers in motivating potential buyers to opt for EVs, assuming these incentives make EVs more accessible and financially attractive [33,34].

Hypothesis 3.

Effects of Government Policy → Intention to Use EVs.

H3 focuses on the broader effects of government policies (beyond financial incentives) on the intention to use EVs. This includes policies related to the development of infrastructure like charging stations, as well as broader regulatory support for EVs [28]. The hypothesis suggests that supportive and clear government policies can significantly sway individuals towards choosing EVs by enhancing the perceived ease and feasibility of EV usage [35,36].

Hypothesis 4.

Effects of Government Communication → Intention to Use EVs.

H4 explores how effective communication from the government regarding EVs impacts individuals’ intentions to adopt such vehicles. This involves the government’s role in educating the public about the benefits of EVs, the availability of incentives, and information on EV technology advancements [37]. Effective communication is expected to increase trust and reduce uncertainties associated with EV adoption, thereby increasing the likelihood of adoption [38,39].

Hypothesis 5.

Effects of Tax Benefits → Intention to Use EVs.

H5 addresses the direct impact of tax benefits on the intention to use EVs [40,41]. This hypothesis posits that tax benefits, such as reductions in purchase taxes, annual road taxes, or other related fiscal incentives, make EVs more economically appealing to potential users. The financial savings from these benefits are believed to play a significant role in the decision-making process for potential EV adopters [42,43].

3. Methods

3.1. Questionnaire Design and Data Collection

The survey instrument was designed to capture comprehensive data on perceptions of government policy and intentions regarding EV adoption. The questionnaire consisted of three main sections: demographic information, government policy perceptions, and intention to use EVs. The demographic section collected data on age, gender, education level, occupation, and residence location. The government policy perceptions section included 18 items measuring five constructs: government commitment and efficiency, government welfare, effects of government policy, government communication, and tax benefits. Example questions included “I perceive the government’s efforts to promote electric cars as effective” (government commitment) and “I am aware of government financial assistance available for purchasing electric cars” (government welfare). The intention to use EVs section included 3 items, such as “I am inclined to consider electric cars as a suitable option because of government support”. All items in the policy perceptions and intention sections were measured using a 7-point Likert scale, where 1 represented ‘strongly disagree’ and 7 represented ‘strongly agree’ (

Table 1. Statistical summary.

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

Note: M = mean, SD = standard deviation, SK = skewness, and KU = kurtosis.

).

In this study, we distributed 4000 questionnaires between January and March 2024 across five major regions: Northern, Northeastern, Central, Eastern, and Southern. Following data cleaning procedures, 3770 valid questionnaires were retained for analysis. The distribution of questionnaires reflected the proportionate distribution of EV registrations in each region. For example, in the Central Thailand region, which includes Bangkok and where 28.91% of the country’s charging stations are located, a corresponding 28.91% of the total questionnaires (approximately 1156) were distributed. A similar proportional distribution was applied to the other four regions based on their respective EV infrastructure and registration data.

The respondent selection criteria were designed to capture a diverse and representative sample of potential EV adopters. The age requirement of 18 years or older ensures a legal driving age, while the valid driver’s license criterion targets individuals who are active participants in personal transportation. The regional residence requirement helps maintain geographical representation. The mix of current EV owners and potential adopters was crucial to capturing both experienced and prospective user perspectives, providing a comprehensive view of policy perceptions across different stages of EV adoption. Respondents were approached at gas stations equipped with EV charging facilities to ensure a mix of EV users and potential adopters. This approach allowed for the collection of data from individuals with varying levels of exposure to and interest in electric vehicles, providing a comprehensive view of policy perceptions and adoption intentions across different segments of the population.

The demographic profile of the respondents, as detailed in

Table 2. Demographic data.

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%

Note: N = 3770.

, includes a diverse range of participants categorized by gender, age, education, occupation, and resident zone. Specifically, the distribution is as follows: 61.5% male and 38.5% female; age distribution includes 9.0% under 25 years, 33.4% between 25 and 34 years, 24.4% between 35 and 44 years, 28.4% between 45 and 54 years, and 4.8% over 55 years. The occupation spread includes government employees (16.3%), private employees (30.8%), and business owners (29.8%), among others. Urban residents constitute 64.2% of the sample, reflecting the higher urbanization associated with EV charging infrastructure availability. This demographic diversity supports the study’s aim to generalize its findings across different societal segments, essential for understanding the broad impacts of government policies on EV adoption.

3.2. Data Analysis

3.2.1. Data Analysis Procedure

The data analysis procedure for this research follows a systematic and structured approach. Initially, the collected data from the questionnaires are subjected to preprocessing, which includes cleaning, handling missing values, and ensuring the normality of the data [19]. This is essential for preparing the dataset for further analysis. The data analysis encompasses several statistical techniques to ensure a comprehensive evaluation of the constructs and hypotheses proposed in this study (Figure 1).

3.2.2. Measure of Internal Consistency

To evaluate the reliability of the scales used in the questionnaire, Cronbach’s alpha is calculated for each set of related items. This statistic assesses the internal consistency of the scale, indicating how closely related the items are as a group [44]. A Cronbach’s alpha value greater than 0.7 is typically considered acceptable, reflecting good internal consistency among the items within each factor. This step is crucial to confirm that the questionnaire items reliably measure the intended constructs [17]. Internal consistency is assessed using Cronbach’s alpha (α), a widely accepted measure in social science research. The formula for Cronbach’s alpha is α = (k/(k − 1)) × (1 − (Σσi²/σT²)), where k is the number of items, σi² is the variance of item i, and σT² is the variance of the total score. A Cronbach’s alpha value greater than 0.7 is generally considered acceptable, indicating good internal consistency among the items within each factor [17]. This measure helps ensure that the items reliably represent the constructs they are intended to measure.

3.2.3. Exploratory Factor Analysis (EFA)

EFA is performed to explore the underlying factor structure of the questionnaire items. EFA helps in identifying the number of factors that the measured variables could be grouped into, based on the correlations among the items. This technique is used to ascertain the factor loadings and to verify the grouping of items under each factor, ensuring that each factor represents a specific dimension of the constructs being measured, such as perceptions of government policy effectiveness or communication strategies [18].

EFA is conducted using principal axis factoring with oblique rotation to uncover the underlying structure of the measured variables. The factor model can be expressed as X = ΛF + ε, where X is the vector of observed variables, Λ is the matrix of factor loadings, F is the vector of common factors, and ε is the vector of unique factors. Factor loadings greater than 0.5 are considered significant [17]. The Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity are used to assess sampling adequacy and the appropriateness of factor analysis. This step helps in identifying the number of factors that the measured variables could be grouped into, based on the correlations among the items.

3.2.4. Second-Order Confirmatory Factor Analysis (CFA)

After establishing the initial factor structure through EFA, a second-order CFA is conducted to validate and refine the factor structure. This advanced form of CFA tests the model fit and assesses the relationships between the observed variables and their underlying latent constructs. During this phase, key indicators such as Average Variance Extracted (AVE) and Composite Reliability (CR) are computed to evaluate the construct validity and reliability, ensuring that the factors accurately measure the constructs and are consistent across different sets of data [21].

Second-order CFA is employed to validate and refine the factor structure identified through EFA. The second-order CFA model can be represented as η = Γξ + ζ and y = Λyη + ε, where η represents first-order factors, ξ represents the second-order factor, Γ is the matrix of regression coefficients relating ξ to η, ζ represents disturbance terms, y represents observed variables, Λy is the matrix of factor loadings, and ε represents measurement errors. This advanced form of CFA tests the model fit and assesses the relationships between the observed variables and their underlying latent constructs, providing a more nuanced understanding of the complex structure of policy perceptions.

3.2.5. Structural Equation Modeling (SEM)

The final analytical technique employed is structural equation modeling (SEM), which is used to test the hypothesized relationships between the constructs [18]. SEM allows for the analysis of complex relationships between observed and latent variables and provides insights into the direct and indirect effects of the constructs on each other [20]. This method is particularly useful for testing the impact of perceived government policies on the intention to use electric vehicles, which involves multiple pathways and potential mediators [15].

The measurement model for each construct can be represented as follows:

X_i = λ_iξ + δ_i

(1)

where Xi is the observed variable, λ_i is the factor loading, ξ is the latent construct, and δ_i is the measurement error.

The structural model can be represented as follows:

η = Βη + Γξ + ζ

(2)

where η is the vector of endogenous variables, Β is the matrix of path coefficients between endogenous variables, Γ is the matrix of path coefficients from exogenous to endogenous variables, ξ is the vector of exogenous variables, and ζ is the vector of residuals.

3.2.6. Validity of the Statistical Models

To assess the validity and fit of the measurement and structural models, several indices are examined. Construct Reliability (CR) is calculated as CR = (Σλi)²/[(Σλi)² + Σ(1 − λi²)], where λi is the standardized factor loading. Average Variance Extracted (AVE) is computed as AVE = Σλi²/n, where n is the number of items. Good model fit is indicated by a ratio of Chi-square to degrees of freedom (χ²/df) less than 3, a Root Mean Square Error of Approximation (RMSEA) less than 0.08, a Comparative Fit Index (CFI) greater than 0.95, a Standardized Root Mean Square Residual (SRMR) less than 0.08, and a Tucker–Lewis Index (TLI) greater than 0.95. These indices collectively provide a comprehensive assessment of model fit and validity [18]. The combination of these measures ensures a rigorous evaluation of the measurement and structural models, supporting the reliability and validity of the study’s findings.

These methods together provide a robust framework for analyzing the data obtained from the questionnaires, ensuring that the findings are reliable and valid. This approach allows for a thorough understanding of the factors that influence the adoption of electric vehicles in Thailand [22]. The methods are widely used in the field of social sciences for analyzing complex data structures and testing theoretical models. Detailed descriptions and guidelines on how to apply these techniques can be found in numerous advanced statistics textbooks and publications dedicated to research methodology in the social sciences [17,18]. The Mplus7 Program is used for analyzing the data [45].

While the use of statistical methods like second-order CFA and SEM provides robust analytical power, it is important to acknowledge their limitations. One potential issue is multicollinearity, which can occur when predictor variables are highly correlated. To address this, variance inflation factor (VIF) tests were conducted, ensuring all VIF values were below the recommended threshold of 5, indicating that multicollinearity was not a significant concern in the model. Sample bias is another potential limitation. Although stratified random sampling was employed to ensure representativeness across Thailand’s regions, there may still be some bias towards individuals who frequent gas stations with EV charging facilities. This could potentially overrepresent early adopters or those more familiar with EV technology. An attempt to mitigate this was made by including a mix of EV owners and non-owners in the sample, but some bias may remain.

4. Results

The descriptive statistics presented in Table 1 provide an overview of the central tendencies and dispersions associated with each item of the questionnaire. For example, the items assessing perceptions of government commitment and efficiency reveal a mean score ranging from 4.029 to 4.158, indicating a generally positive perception towards government efforts in promoting electric vehicles. The standard deviations, which range from 1.471 to 1.619 across different items, suggest a moderate spread in respondents’ perceptions. Skewness and kurtosis values close to zero for most items indicate that the data are fairly normally distributed, with slight deviations in some cases.

Exploratory Factor Analysis (EFA) results, as depicted in

Table 3. Results of exploratory factor analysis.

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

Note: Factor 1: perceptions of government commitment and efficiency (PCEs), Factor 2: perceptions of government welfare (PGWs), Factor 3: effects of government policy (EGPs), Factor 4: effects of government communication (EGCs), and Factor 5: effects of tax benefits (ETBs).

, identified five distinct factors that explain a significant proportion of the variance in respondents’ perceptions and intentions regarding electric vehicles. The factors include perceptions of government commitment and efficiency, government welfare, the effects of government policy, government communication, and tax benefits. The factor loadings are substantial, with all items loading significantly on their respective factors, which supports the construct validity of the measurement model. The percentage of variance explained by these factors ranges from 13.251% to 23.096%, indicating a strong factor structure.

The results of the second-order CFA, shown in

Table 4. Second-order confirmatory factor analysis results.

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

Note: ** significant at α = 0.001.

and Figure 2, further validate the factor structure established through EFA. This analysis provides strong support for the model, with high factor loadings and acceptable fit indices. The Average Variance Extracted (AVE) values range from 0.613 to 0.855, and Composite Reliability (CR) values range from 0.826 to 0.947, all exceeding the commonly accepted thresholds, indicating good construct validity and reliability. The goodness-of-fit statistics, including RMSEA, CFI, TLI, and SRMR, also indicate an excellent fit of the model to the data, confirming the appropriateness of the factor solutions.

The SEM results, illustrated in

Table 5. SEM results.

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 **

Note: ** significant at α = 0.001.

and Figure 3, provide a comprehensive view of the relationships between the constructs. The model testing confirms several hypothesized paths, with significant relationships observed between perceptions of government commitment and efficiency, welfare, policy effects, communication, tax benefits, and the intention to use electric vehicles. The standardized path coefficients indicate the strength and direction of these relationships, with all paths showing positive effects. The goodness-of-fit indices for the SEM model, such as RMSEA, CFI, and TLI, demonstrate a very good fit, suggesting that the hypothesized model adequately represents the underlying data structure and the dynamics of how government support influences public intentions toward EV adoption.

5. Discussion

5.1. Measurement Model of Government Policies in EV Adoptions

Perceptions of Government Commitment and Efficiency: This construct includes items assessing the public’s view of the government’s dedication to fostering the EV market. For instance, the item “I perceive the government’s efforts to promote electric cars as effective” (loading = 0.894) and “The level of commitment demonstrated by the government affects my confidence in using electric cars” (loading = 0.886) both have high loadings, emphasizing that perceived government enthusiasm and efficiency are pivotal in building consumer confidence [4]. These perceptions likely stem from public communications and visible government actions, reinforcing the role of perceived commitment in encouraging EV uptake [5].

Perceptions of Government Welfare: Within this construct, items such as “I am aware of government welfare or financial assistance available for purchasing or using electric cars” (loading = 0.824) and “Knowledge of financial welfare programs from the government influences my consideration of electric cars” (loading = 0.953) demonstrate high loadings. This underscores the importance of not only the existence of government support programs but also the awareness and understanding of these programs among the public [46]. Effective communication about these benefits, therefore, plays a crucial role in enhancing their impact [47].

Effects of Government Policy: The latent variable “Effects of Government Policy” demonstrates strong factor loadings, particularly reflected in items such as “Government policies promoting electric car usage have a positive impact on my worldview” (loading = 0.871) and “I believe government policies supporting electric cars are crucial factors in their adoption” (loading = 0.927). These items, with high loadings, indicate that policies that are directly observable and impact daily life, like the development of charging infrastructure (item: “Government initiatives such as charging infrastructure construction are important for the success of electric cars,” loading = 0.861), are perceived as highly effective [48]. This suggests that tangible, practical policies that directly affect the usability of EVs are most influential in shaping public perceptions and adoption behaviors [49].

Effects of Government Communication: The influence of effective government communication is captured in items like “Government communication and campaigns about electric cars affect my perspective” (loading = 0.798) and “The clarity and transparency of government communication affect my confidence in using electric cars” (loading = 0.771). Although these loadings are slightly lower compared to the more direct policy and welfare benefits, they still show a significant impact. This suggests that while direct benefits may drive immediate decisions, the foundational understanding and perception shaped by government communication cannot be overlooked, as it builds the groundwork for public acceptance and trust [50].

Effects of Tax Benefits: The “Effects of Tax Benefits” construct shows particularly strong loadings, highlighting the significant influence of fiscal policies on EV adoption decisions. The item “Tax benefits provided by the government positively influence my decision to choose electric cars” (loading = 0.933) and “I consider tax benefits for electric car owners as significant advantages” (loading = 0.932) both score high, illustrating the direct impact of financial incentives on consumer choice [51]. These findings suggest that tax incentives are not just noticed but are a major factor in the financial calculus of potential EV buyers, making EVs a more attractive and viable option [13].

In the composite construct “Government Support for Electric Vehicles”, which synthesizes the impact of all underlying latent variables, the factor loadings from the second-order confirmatory factor analysis reveal the relative influence of each dimension of government support on EV adoption. This analysis is crucial as it highlights which aspects of government intervention are perceived as most effective by the public. The loadings for each of these latent variables are as follows: Effects of Government Policy (loading = 0.985), Perceptions of Government Welfare (loading = 0.892), Effects of Tax Benefits (loading = 0.944), Perceptions of Government Commitment and Efficiency (loading = 0.947), Effects of Government Communication (loading = 0.778).

Effects of Government Policy has the highest loading at 0.985, indicating that policies directly promoting EV usage, such as the development of charging infrastructure and regulatory support, are perceived as the most influential by the public [36]. This high loading can be attributed to the tangible and immediate impact these policies have on the practical aspects of using EVs, making them more accessible and convenient. The visibility and direct interaction with such infrastructural developments resonate well with the public, emphasizing the role of tangible benefits in adoption decisions. Following closely are Perceptions of Government Commitment and Efficiency and Effects of Tax Benefits, with loadings of 0.947 and 0.944, respectively. The almost equal importance of these factors suggests that the public places a high value not only on the practical outcomes of policies but also on the economic incentives associated with EV adoption [52]. Tax benefits reduce the financial burden, making EVs a more attractive option, while perceived commitment and efficiency of government actions ensure trust and confidence in the long-term viability of EVs [53]. Perceptions of Government Welfare, with a loading of 0.892, highlights the importance of direct financial support and subsidies. Although slightly lower than policy and tax benefits, it still plays a significant role, likely because it addresses the upfront cost barriers associated with purchasing EVs [44]. This support is crucial in markets like Thailand, where the cost differential between EVs and traditional vehicles can be a significant deterrent. Lastly, Effects of Government Communication has the lowest loading at 0.778. While still important, this suggests that the effectiveness of communication strategies is somewhat less direct compared to the immediate financial and policy-driven incentives [54]. Effective communication is essential for raising awareness and educating the public about the benefits and necessities of EVs, but it acts more as a supportive tool that enhances the impacts of other more direct measures [55]. This order of factor loadings underlines that while all aspects of government support are crucial, the ones that provide direct, tangible benefits or reduce barriers to EV adoption are perceived as most influential [56]. These findings suggest that to maximize the effectiveness of government support for EVs, policies should prioritize direct interventions that modify the practical and economic landscape of EV usage, supplemented by strong communication strategies to enhance public awareness and acceptance [37].

5.2. Factors Influencing Perceptions of Government Policies to EV Adoptions

The measurement model for “Intention to Use Electric Vehicles” clearly indicates that the public’s willingness to adopt EVs is highly influenced by their perceptions of government support. The items related to this construct show high factor loadings, indicating strong influences from various dimensions of government action. For example, the strong influence from the item “Government support is crucial in enhancing my positive attitude towards using electric cars” (loading = 0.912) suggests that the overall perception of supportive government initiatives directly translates into higher readiness and willingness among consumers to adopt EVs [41].

H1. 

Perceptions of Government Commitment and Efficiency → Intention to Use EVs.

Factor loading is 0.262. This hypothesis confirms that perceived government commitment and operational efficiency significantly impact individuals’ intentions to use EVs. The factor loading, though moderate, underscores the importance of visible and effective government actions. This is supported by research showing that when governments are perceived as proactive and efficient such as through environmental concern [57], it boosts public confidence in the infrastructure and support systems necessary for new technologies like EVs.

H2. 

Perceptions of Government Welfare → Intention to Use EVs.

Factor loading is 0.133. Although lower, this loading indicates that government welfare policies play a role in shaping intentions to use EVs. The relatively lower impact suggests that while important, direct welfare measures such as subsidies might not be as immediate in their effect on adoption as other more direct interventions. This can be due to the public’s delayed recognition or understanding of these benefits unless they are very clearly communicated and understood [41].

H3. 

Effects of Government Policy → Intention to Use EVs.

Factor loading is 0.198. The moderate loading here illustrates that substantive and well-implemented government policies have a tangible effect on EV adoption intentions. This includes clear regulations, support for infrastructure, and incentives that directly remove barriers to EV usage. The effectiveness of policy is likely influenced by its visibility and direct impact on the consumer’s daily experience, such as the availability of charging stations [27].

H4. 

Effects of Government Communication → Intention to Use EVs.

Factor loading is 0.242. The relatively high impact of this factor highlights the critical role of effective communication in enhancing public awareness and shaping perceptions positively towards EVs. This suggests that government communication strategies that effectively convey the benefits and necessities of EV adoption can amplify the impacts of other supportive measures. The clarity, frequency, and transparency of government communication are crucial in reducing uncertainties and enhancing the attractiveness of EVs. [58].

H5. 

Effects of Tax Benefits → Intention to Use EVs.

Factor loading is 0.172. The confirmation of this hypothesis with a moderate loading suggests that tax benefits are crucial but not the sole factor influencing EV adoption. The direct financial advantages provided by tax incentives undoubtedly make EVs more economically attractive. However, the specific value of these benefits in terms of dollar savings might need to be substantial and well publicized to significantly sway purchasing decisions [14,16].

The results of this study reveal that perceptions of government commitment and efficiency emerged as the most influential factor in determining EV adoption intentions in Thailand. This finding carries significant implications for promoting EV adoption. The strong influence of this factor suggests that when the public perceives the government as genuinely committed to and efficient in implementing EV policies, they are more likely to consider adopting EVs themselves. This perception of commitment and efficiency likely creates a sense of long-term stability and support for EV adoption, reducing perceived risks associated with this new technology. For instance, a government perceived as committed is more likely to be trusted to maintain supportive policies over time, addressing concerns about the longevity of incentives or the continued development of necessary infrastructure. Similarly, perceptions of government efficiency may alleviate concerns about practical barriers to EV adoption, such as the speed of charging infrastructure deployment or the ease of accessing incentives. These findings both align with and extend previous research in the field of EV adoption. The importance of government support in EV adoption has been highlighted in several studies. For instance, Zhang et al. [16] found that policy support significantly influenced EV purchase intentions in China. However, while their study focused on the direct effects of policy measures, the current research goes further by examining how perceptions of these policies influence adoption intentions.

The strong influence of perceived government commitment and efficiency aligns with findings from Huang et al. [27], who identified government support as a key factor in EV adoption in Taiwan. However, the current study extends this understanding by breaking down government support into specific dimensions, allowing for a more nuanced analysis of which aspects of government support are most crucial. Interestingly, while previous studies such as Zhou et al. [14] have emphasized the importance of financial incentives in EV adoption, the current study finds that perceptions of government commitment and efficiency have a stronger influence than perceptions of tax benefits. This suggests that in the Thai context, building trust in the government’s EV strategy may be even more important than direct financial incentives. Furthermore, the significant role of government communication in this study supports findings by Qian et al. [37], who highlighted the importance of information provision in shaping public perceptions of EVs. However, the current study extends this by demonstrating how communication efforts contribute to overall perceptions of government support and, consequently, to adoption intentions.

6. Conclusions and Policy Recommendations

This research aimed to explore the influence of Thai government policy perceptions on the adoption of electric vehicles (EVs) through a well-defined measurement model and empirical analysis. Employing a structured questionnaire that captured various dimensions of government policy perception, this study gathered data from 3770 participants across major regions in Thailand. The research utilized advanced statistical techniques including Exploratory Factor Analysis (EFA), second-order confirmatory factor analysis (CFA), and structural equation modeling (SEM) to analyze the data. These methods helped in validating the constructs of government support perception and in examining how these perceptions influence the intention to use EVs.

This study provides several key insights. Regarding RQ1, our findings demonstrate that measurement model findings (Table 4), the second-order CFA, showed that all constructs—Effects of Government Policy, Perceptions of Government Commitment and Efficiency, Effects of Tax Benefits, Perceptions of Government Welfare, and Effects of Government Communication—were valid and reliable. Notably, Effects of Government Policy and Perceptions of Government Commitment and Efficiency had the highest loadings, indicating they were perceived as the most influential by the respondents. As for RQ2, structural equation modeling findings (Table 5), the SEM results confirmed significant positive impacts of all tested government policy aspects on the intention to use EVs. The highest impacts were noted from Perceptions of Government Commitment and Efficiency, demonstrating the crucial role of tangible and direct governmental actions in promoting EV adoption.

Based on the findings, the policy recommendations below are prioritized according to their influence (factor loadings) and potential impact on EV adoption. By prioritizing these policies according to their impact and public perception, the Thai government can effectively accelerate EV adoption, contributing to environmental sustainability and reduced urban pollution, aligning with global trends and national goals for energy efficiency and reduced carbon emissions.

• 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.

The findings of this research suggest areas for enhancement in the existing frameworks of the “30@30 Policy” and “EV 3.5 Measures” currently implemented by the Thai government. While these policies lay a solid foundation for promoting EV adoption—targeting 30% of automotive production to be zero-emission vehicles by 2030 and introducing various incentives under the EV 3.5 measures—they could be further optimized based on the study’s insights.

• 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.

By aligning these recommendations with ongoing government initiatives, the Thai government can not only strengthen the existing policy framework but also ensure that it effectively addresses the needs and preferences of potential EV users, thus accelerating the transition towards a more sustainable automotive sector. These enhancements would reinforce the government’s commitment to environmental sustainability and its goal of becoming a regional leader in the EV market.

Enhancing the visibility of government commitment and improving financial incentives for EV adoption requires a multi-faceted approach. For increasing commitment visibility, the government could launch a high-profile “EV Adoption Roadmap” with specific, measurable goals for the next 5–10 years, coupled with a quarterly public reporting system on progress towards these targets. This could be complemented by the creation of an inter-ministerial task force dedicated to coordinating and expediting EV-related initiatives, demonstrating a whole-of-government commitment. Improving financial incentives might involve introducing a tiered incentive system, providing higher benefits for early adopters and gradually decreasing over time to encourage quicker adoption. This could be paired with a “feebate” system, where fees on high-emission vehicles fund rebates for low-emission and electric vehicles [59]. Additionally, the government could establish a low-interest loan program specifically for EV purchases, partnering with local banks for implementation. These strategies would not only make the government’s commitment more visible but also provide tangible financial benefits to potential EV adopters, addressing both key aspects highlighted in this study’s findings.

For policymakers, the findings emphasize the importance of a holistic approach to EV promotion. The strong influence of perceived government commitment and efficiency on adoption intentions suggests that policymakers should focus on clearly communicating their long-term commitment to EV promotion and demonstrating tangible progress in areas such as infrastructure development. The significant impact of government communication highlights the need for clear, consistent, and widespread public awareness campaigns about EV policies and benefits. Moreover, this study underscores the importance of regular policy evaluation and adjustment, potentially using the measurement model developed here, to maximize the impact on adoption intentions. By implementing these recommendations, policymakers can potentially enhance the effectiveness of their EV promotion strategies, leading to increased adoption rates and progress towards sustainable transportation goals.

The findings of this study reveal patterns that are consistent with research conducted in other countries, suggesting some degree of generalizability in the factors influencing EV adoption intentions. For instance, the strong influence of perceived government commitment and efficiency on adoption intentions in Thailand aligns with findings by Li et al. [60] in China, who also found that policy support significantly influenced EV purchase intentions. Similarly, the importance of tax benefits and financial incentives found in this study echoes research by Zhou et al. [14] in China and Filippini et al. [32] in Nepal, both of which highlighted the role of financial incentives in driving EV adoption. The significant impact of government communication on adoption intentions observed in this study is consistent with findings by Qian et al. [37] in the United States, who emphasized the importance of information provision in shaping public perceptions of EVs. These similarities across different national contexts suggest that certain key factors, such as perceived government support, financial incentives, and effective communication, play a consistently important role in influencing EV adoption intentions. While the relative importance of these factors may vary depending on specific national circumstances, the emergence of these common patterns indicates that the findings from this study in Thailand may have broader applicability to other countries’ efforts to promote EV adoption.

While this study provides valuable insights into the influence of government policy perceptions on EV adoption in Thailand, its focus on a single country may limit the generalizability of findings to other nations with different cultural, economic, and political contexts. The unique characteristics of Thailand’s automotive market, policy landscape, and societal norms may shape consumer perceptions and behaviors in ways that differ from other countries. Future research could address this limitation by conducting comparative studies across multiple countries, particularly those with varying levels of EV adoption and different policy approaches. Such cross-cultural comparisons would help identify which aspects of the findings are specific to the Thai context and which may be more universally applicable. Additionally, future studies could explore how social norms, individual values, and cultural factors interact with government policies to influence EV adoption intentions. Investigating these additional factors could provide a more comprehensive understanding of the complex dynamics influencing EV adoption across different cultural and national contexts. Such research could employ mixed-method approaches, including qualitative studies, to capture the nuanced ways in which cultural and societal factors shape responses to government EV policies. Future research in this field could also be significantly enriched by incorporating principles from behavioral economics to explore how cognitive biases and heuristics influence policy perceptions and EV adoption. As Avineri [61] argues, traditional transport planning and policymaking often assume rational decision-making, but research in behavioral sciences indicates that individuals’ choices often deviate from these rational predictions. Applying behavioral economic insights to EV adoption research could provide a more nuanced understanding of decision-making processes. For instance, future studies could explore how loss aversion or present bias affect individuals’ responses to different types of EV incentives. Solek [62] discusses how behavioral economic findings can be used by policymakers to encourage desirable behaviors, which could be particularly relevant in the context of promoting EV adoption. Moreover, as Matjasko et al. [63] highlight, even subtle features of the environment can have meaningful impacts on behavior. Future research could investigate how small changes in the framing or presentation of EV policies might influence their effectiveness. It is also important to note that the model in this study did not include control variables such as age, gender, education level, urban versus rural residence, or income. Previous research (e.g., Huang et al. [27]) has suggested that these demographic factors may influence EV adoption intentions. Future studies could enhance the robustness of these findings by incorporating such control variables into the analysis.