Electric Vehicle Adoption as a Strategy for Air Pollution Mitigation: An Integrated Environmental Behavior Study in Vietnam

INTRODUCTION

Air pollution has become a critical global environmental challenge, posing serious threats to ecosystems and human health (Hobbs et al., 2025). In response, Vietnam has actively promoted electric vehicles (EVs) as a key solution to mitigate emissions and advance sustainable development. EVs are widely recognized not only as a technological innovation for reducing greenhouse gas emissions but also as a central component of green mobility transitions (Lieven, 2015). Similar policy-driven efforts have been observed in other pollution-affected countries such as China, Malaysia, Turkey, and Pakistan, where governments employ regulatory and financial incentives to stimulate EV purchase intention (Jiang et al., 2025). In Vietnam, recent initiatives, including extended registration tax exemptions, further demonstrate strong governmental commitment and provide a relevant context for examining EV consumer behavior.

Despite the growing body of research on EV adoption, most prior studies have relied on either the Technology Acceptance Model (TAM; Davis, 1989) or the Theory of Planned Behavior (TPB; Ajzen, 1991), typically applying these frameworks independently. To address this limitation, Taylor and Todd (1995) proposed the combined C-TAM-TPB model, integrating key constructs such as perceived usefulness (PU), perceived ease of use (PEOU), subjective norms (SN), and perceived behavioral control (PBC). Recent studies have extended this integrated framework. For instance, Vafaei-Zadeh et al. (2022) incorporated additional factors such as price value, perceived risk, environmental self-image, and infrastructure barriers to explain behavioral intention. Similarly, Wang et al. (2024) highlighted the influence of consumer ethnocentrism (CE) on preferences for domestic EV brands, while Hu et al. (2023) emphasized the motivational role of financial incentives.

However, existing studies often overlook the integration of value-based and psychological process perspectives. Specifically, while environmental concern (EC) has been identified as a key driver of green consumption, the limited incorporation of the Value–Attitude–Behavior (VAB) framework and the neglect of the Stimulus–Organism–Response (SOR) model limit a comprehensive understanding of EV purchase intention (Tang et al., 2019). The SOR framework is particularly relevant in policy-driven contexts, as it explains how external stimuli—such as incentive policies (IP)—influence internal cognitive and emotional states, which in turn shape behavioral responses. Integrating SOR with C-TAM-TPB and VAB thus enables a more holistic explanation of consumer behavior by capturing utilitarian, normative, value-driven, and policy-induced effects.

Accordingly, this study develops an integrative research model that combines C-TAM-TPB, VAB, and SOR to examine EV purchase intention in Vietnam. The model incorporates key determinants including PU, PEOU, SN, PBC, CE, EC, and IP, while also emphasizing the mediating role of attitude in translating these factors into behavioral intention. By doing so, the study provides a more comprehensive framework for understanding how financial incentive policies and individual perceptions jointly shape EV adoption in an emerging market.

Methodologically, this study adopts a quantitative approach using partial least squares structural equation modeling (PLS-SEM), which is suitable for analyzing complex models with latent constructs and exploratory theory development (Shakeel, 2022). Based on data collected from 259 respondents, the study empirically tests the proposed relationships and offers both theoretical contributions and practical implications for policymakers and marketers seeking to promote sustainable transportation.

Theorical background and hypotheses

Theorical background of C-TAM-TPB, VAB & SOR theory

In the context of increasing environmental degradation and air pollution, sustainable consumption behavior has emerged as a central topic in environmental and bioscience research. Transportation is a major contributor to greenhouse gas emissions and urban air pollution, making the transition toward cleaner mobility solutions, such as electric vehicles (EVs), essential for environmental protection and public health improvement. From an environmental standpoint, EV adoption is not only a technological shift but also a behavioral response that reflects individuals’ awareness of environmental sustainability and their willingness to reduce carbon footprints.

Drawing on theories of environmental behavior, environmental concern (EC) is widely recognized as a key driver of pro-environmental actions. Individuals with higher levels of environmental concern are more likely to support the adoption of green products and eco-friendly technologies. This is consistent with the Value–Attitude–Behavior (VAB) framework, which explains how environmental values shape attitudes and subsequently influence behavioral intentions. In the EV context, environmental concern enhances positive attitudes toward EVs by emphasizing their role in reducing emissions and mitigating environmental risks.

Furthermore, the Stimulus–Organism–Response (SOR) framework provides a useful framework for understanding how external environmental and policy-related factors influence individual behavior. Financial incentive policies (IP), such as tax reductions and subsidies, serve as policy instruments that encourage sustainable consumption. These policies act as external stimuli that shape individuals’ internal evaluations (e.g., perceived usefulness, perceived ease of use, and attitudes), ultimately leading to behavioral responses such as EV purchase intention. This perspective is particularly relevant in emerging economies, where policy interventions play a crucial role in promoting environmentally responsible behavior.

In addition, social and contextual factors, including subjective norms (SN) and perceived behavioral control (PBC), reflect the broader socio-environmental system in which individuals operate. Social influence can encourage environmentally responsible choices by reinforcing collective norms around sustainability, while perceived behavioral control reflects the availability of infrastructure, such as charging stations, and accessible resources necessary for EV adoption. These factors are critical in translating environmental awareness into actual behavioral outcomes.

The integration of the TAM and TPB, known as the C-TAM-TPB, offers a robust theoretical foundation for understanding consumer adoption of innovative technologies, such as electric vehicles (Vafaei-Zadeh et al., 2022). Originally proposed by Davis (1989), TAM posits that individuals’ behavioral intentions toward new technologies are primarily driven by perceived usefulness and perceived ease of use. However, TAM’s limitation lies in its exclusion of social and behavioral control factors. To overcome this, Taylor and Todd (1995) extended TAM by incorporating Ajzen (1991) TPB elements SN and PB, thus enabling a more comprehensive analysis of external social influence and personal volition in adoption decisions. Despite the strength of this integration, it still lacks a value-based explanatory mechanism, which is addressed by incorporating the VAB model.

VAB complements C-TAM-TPB by linking enduring values to attitudes shaping sustainable behavioral intentions consumer contexts. Previous studies have validated the VAB model in diverse domains, including green car adoption and pro-environmental behaviors (Tamar et al., 2021). Therefore, the integration of C-TAM-TPB and VAB provides a more holistic framework for exploring EV purchase intention by capturing both utilitarian and value-driven motivations behind consumer decisions.

SOR explains how external stimuli shape internal states, influencing attitudes and behavioral responses, consumer decision-making (Tang et al., 2019). While widely applied in green consumer behavior research, the SOR framework has rarely been used to examine EV purchase intention, especially in emerging markets like Vietnam, where government incentives and environmental issues co-exist as potent triggers of behavioral change.

SOR, C-TAM-TPB, and VAB explain EV intentions via attitudes  (Jaiswal et al., 2021). In the EV context, PU refers to functional benefits such as cost savings, lower emissions, and improved driving efficiency. Empirical evidence supports a positive relationship between PU and consumer attitude. For instance, Vafaei-Zadeh et al. (2022) showed that Malaysian consumers who found EVs useful were more likely to develop favourable attitudes. When perceived utility aligns with values like environmental sustainability and economic efficiency, the likelihood of a positive attitude increases. Perceived usefulness not only reflects functional benefits but also environmental utility, such as emission reduction and energy efficiency. When individuals recognize EVs as effective solutions to environmental problems, they are more likely to develop positive attitudes.

H1: Perceived usefulness has a positive effect on attitude towards EV.

Perceived ease of use (PE) refers to users' subjective perception of how easy and understandable a particular technology is (Sharma, 2019). This cognitive judgment significantly contributes to users' attitudes and adoption intentions since technologies perceived to require little physical and mental effort will be adopted more (Wu et al., 2019). In the context of electric vehicles, PE plays a vital role, as customers will tend to adopt new products that are simple to use and simple to learn (Wang et al., 2020). PE was further found to enhance the indirect effect on behavioral intention through attitude formation, as it had a positive effect on perceived usefulness. In environmental contexts, simplifying access to sustainable solutions increases the likelihood of pro-environmental behavior.

H2: Perceived ease of use has a positive effect on attitude toward EV.

Environmental concern is a core construct in environmental and bioscience research, reflecting individuals’ awareness of ecological issues such as pollution and climate change. Consumer ethnocentrism (CE), conceptualized as a belief in the appropriateness and morality of purchasing domestic over foreign products , encompasses cognitive, normative, and affective evaluations that reinforce group identity and national loyalty (Bizumic, 2019). Ethnocentrism encourages domestic EV preference, strengthening attitudes via moral patriotism (Tomić Maksan et al., 2019). Despite variations in CE’s effect across product categories and consumer evaluations , existing evidence supports its positive role in shaping attitudes toward EV, particularly in developing economies where local consumption is linked with national development goals.

H3: Consumer ethnocentrism has a positive effect on attitude towards EV.

Environmental concern strongly drives electric vehicle adoption through ecological benefits (Peters & Dütschke, 2014). Moreover, pro-environmental attitudes, including sensitivity to climate change and energy conservation, are strongly associated with favorable perceptions and purchase intentions toward EV . Attitude has also been identified as a key mediating factor between environmental concern and behavioral intention.

H4: Environmental concern positively affects the consumer’s attitude towards EV.

Subjective norms reflect social pressure and collective environmental awareness. In environmental contexts, pro-environmental behaviors are often socially reinforced, meaning individuals are more likely to adopt EVs when such behavior is perceived as socially desirable and environmentally responsible. Subjective norms reflect social pressure from significant others influencing behavior (Han et al., 2020; Shalender & Sharma, 2021). Subjective norms reflect perceived social expectations encouraging electric vehicle adoption (Asadi et al., 2021). Subjective norms, together with attitudes and perceived control, combine to constitute the intention to buy (Shakeel, 2022). Subsequently, the phrase subjective norms refers to the extent to which social and relational processes inspire a consumer to recognize EV as a viable purchase option (Sun et al., 2022); further, when people feel that their significant others support their purchase of an EV, the probability of their intention to buy such a vehicle is likely to increase.

H5. Subjective norm has a positive effect on EV purchase intention.

Perceived behavioral control is closely linked to environmental infrastructure, such as charging stations and energy systems. Perceived behavioral control (PB) reflects an individual’s perception of the ease or difficulty in performing a specific behavior, influenced by available resources, time, and prior experiences  (Ajzen & Fishbein, 2005). Higher perceived behavioral control significantly strengthens electric vehicle adoption intentions (Xu et al., 2020). Furthermore, the interplay between TPB and value-based frameworks suggests that internalized personal norms, influenced by subjective norms, can also reinforce perceived control by aligning individual values with behavioral goals (Dong & Ge, 2022).

H6. Perceived behavioral control positively affects EV purchase intention.

Attitude (AT), defined as an individual’s latent disposition to respond with favour or disfavour toward a particular object or behaviour, plays a central role in shaping behavioural intentions. Within the Theory of Planned Behavior (Ajzen, 1991), attitude refers to one’s overall evaluation of a behaviour and is widely recognized as a key predictor of intention. In the context of EV, a favourable attitude reflects consumers’ positive assessments of EV’s environmental benefits, ease of use, and value. Empirical studies consistently confirm the significant and positive link between attitude and EV adoption intention (Yang et al., 2020). For example, those who perceive EVs as environmentally beneficial and cost-effective are more likely to purchase them, while negative attitudes hinder intention. Similar patterns are found in other areas like renewable energy (Proudlove et al., 2020), cybersecurity (Alanazi et al., 2022), and energy-saving behaviours (Xie et al., 2021), supporting the robustness of the attitude-intention relationship.

H7: Attitude towards EV positively affects EV purchase intention.

Electric vehicles, as innovative transportation technologies, are often priced significantly higher than traditional fuel vehicles, largely due to research, development, and manufacturing cost pressures (Lin & Wu, 2018). This cost disparity has been consistently identified as a critical barrier to EV market diffusion. Financial incentives reduce EV ownership costs and significantly increase purchase intentions across diverse national contexts  (Zhuge et al., 2020).

H8: Financial incentive policy positively affects EV purchase intentions.

Based on the above arguments, Figure 1 presents the proposed research model.

MATERIALS AND METHODS

Sampling and data collection

A convenience sampling method was employed for data collection. Following Hair (2011), the recommended minimum sample size is five times the number of observed variables; thus, with 29 items, at least 145 responses were required. This study analyzed 259 valid responses out of 294 collected (88% valid rate), with a high response rate (20%) achieved through prior contact with participants. The online survey was conducted in Vietnam via Google Forms from May 15 to May 30, 2025. Respondents were screened with the question “Have you ever purchased an electric vehicle?”, and only those who had never purchased one continued. To minimize common method bias (Kock et al., 2021), multiple procedural remedies were applied, including the use of reverse-coded items as suggested by Weijters and Baumgartner (2012).

The survey followed the principles of “back-translation” because it was originally constructed in English (Brislin, 1980). The scales were adapted and translated into English by two separate business English students, and then the collected results were back-translated into English (Csep et al., 2024; Jin et al., 2024; Osluf et al., 2024; Rypel et al., 2024; Clark & Foster, 2025; Joungtrakul & Smith, 2025; Kebe et al., 2025; Musa et al., 2025; Njoroge & Odhiambo, 2025; Raza et al., 2025; Anh et al., 2026).

Table 1. Demographic analysis of the subjects (N=259).

The sample (N=259) shows balanced gender distribution, strong driving experience, high educational attainment, preference for domestic EV brands, and middle-income dominance, providing a robust basis for empirical analysis overall analyses (Table 1).

Measurement and analysis

The study uses a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The scale is completely inherited from previous studies; some terms are adjusted to suit the context (eg, CE items used by Wang et al. (2024) to survey Chinese people will be adjusted). PLS-SEM was chosen due to its suitability for analyzing models with high complexity and multiple latent constructs (Hair et al., 2017). Unlike covariance-based approaches, PLS relies on a component-based algorithm that is well-suited for predictive analysis and theory development, particularly in contexts involving intricate structural relationships (Sarstedt et al., 2016). Given the multidimensional nature of this study with constructs such as PU, PE, SN, PB, EC, CE, IP, AT, and PI, PLS-SEM was deemed appropriate. Following the two-step analytical strategy recommended by Anderson and Gerbing (1988), the analysis was conducted in two stages: assessment of the measurement model followed by the structural model evaluation.

RESULTS AND DISCUSSION

Measurement model evaluation

The study conducted tests for reliability, convergent validity, and discriminant validity. Firstly, Cronbach’s alpha (α) > 0.6 is considered acceptable in exploratory research, indicating that the reliability of the scales is ensured (Hair et al., 2022). Besides, all measurement scales have an AVE > 0.5, thus meeting the criteria for convergent validity (Hair et al., 2022), and composite reliability (CR) > 0.5 is considered to meet the minimum requirements (Hair et al., 2014). Discriminant validity is established when a construct’s AVE square root exceeds its correlations with other constructs, indicating theoretical distinctiveness (Fornell & Larcker, 1981). The results show that all α > 0.6, AVE ranges from 0.570 to 0.719, and CR > 0.8 (Table 2).

Table 2. Scale evaluation.

Table 3 presents the assessment of discriminant validity. According to, discriminant validity is considered inadequate when the correlation between factors falls below 0.5. Furthermore, the square root of the AVE for each construct exceeds the corresponding inter-construct correlations, confirming the validity of the measurement model (Chin et al., 1997). These findings indicate that the theoretical model is appropriate for subsequent analysis.

Table 3. Discriminant validity - Fornel-Lacker criterion.

Structural model evaluation

Table 4. Variables relationship testing.

The R² values indicate that the model explains 72.4% of the variance in attitude and 21.4% of the variance in purchase intention, suggesting a substantial explanatory power for internal beliefs but only a weak to moderate influence on behavioral outcomes. Regarding effect size (f²), financial incentive policy (f² = 0.112), consumer ethnocentrism (f² = 0.109), and perceived ease of use (f² = 0.085) showed notable impacts on attitude, whereas attitude (f² = 0.072) and perceived behavioral control (f² = 0.034) had small effects on purchase intention. According to Cohen (2013) guidelines, these results reflect small to moderate effect sizes (Table 4).

All hypotheses are supported; attitude strongly predicts purchase intention (β = 0.247), while incentives most strongly shape attitude (β = 0.278), with attitude mediating effects of key antecedents through statistically significant indirect pathways effects (Table 5).

Table 5. Test results of the mediating effect.

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