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Article

How the Adoption of EVs in Developing Countries Can Be Effective: Indonesia’s Case

by
Ida Nyoman Basmantra
,
Ngurah Keshawa Satya Santiarsa
*,
Regina Dinanti Widodo
* and
Caren Angellina Mimaki
Department of Management, Universitas Pendidikan Nasional, Bali 80224, Indonesia
*
Authors to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(8), 428; https://doi.org/10.3390/wevj16080428
Submission received: 13 June 2025 / Revised: 15 July 2025 / Accepted: 16 July 2025 / Published: 1 August 2025
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Abstract

Indonesia’s worsening air pollution and traffic emissions have thrust electric vehicles (EVs) into the spotlight, but what really drives Indonesians to make the switch? This study integrates Protection Motivation Theory with green branding and policy frameworks to explain electric vehicle (EV) adoption in Indonesia. Using a nationwide survey (n = 986) and partial-least-squares structural-equation modeling, we test how environmental awareness, consumer expectancy, threat appraisal, and coping appraisal shape adoption both directly and through green brand image (GBI), while perceived policy incentives moderate the GBI–adoption link. The model accounts for 54% of the variance in adoption intention. These findings highlight that combining public awareness campaigns, compelling green brand messaging, and carefully calibrated policy incentives is essential for accelerating Indonesia’s transition to cleaner transport.

1. Introduction

The transportation sector is responsible for nearly a quarter of global energy-related CO2 emissions, making it one of the world’s most carbon-intensive industries—a burden that continues to grow in developing economies [1,2]. In Indonesia, rapid urbanization and motorization have driven transport emissions up by over 40% from 2010 to 2020, positioning the country among Southeast Asia’s top emitters [3,4]. As Indonesia aims to meet its Nationally Determined Contributions under the Paris Agreement, failure to curb ground-transport emissions threatens not only air quality and public health in major cities but also the nation’s economic stability—undaunted growth in ICE vehicles could risk locking Indonesia into a high-carbon trajectory for decades [3,5].
Electric vehicles (EVs) offer a transformative pathway to decarbonize transport, yet Indonesia’s EV market remains nascent. While other countries—such as Norway and the Netherlands—have achieved double-digit EV market shares through coordinated policy, infrastructure, and consumer engagement, Indonesia’s Presidential Regulation No. 55/2019 and the Low Carbon Development Initiative (LCDI) have yielded only modest gains to date [6,7,8,9]. Weak institutional coordination and public charging station coverage still trails vehicle sales, leading to “charger deserts” outside of Java and Bali, even though there has been a 299% increase in these stations from 1081 in 2023 to 3233 in 2024. The dampening effect of incentives seen in our model is contextualized by these obstacles [7]. Without urgent, evidence-based interventions, Indonesia risks falling further behind in the global EV transition, undermining both its climate commitments and its competitiveness in the burgeoning green mobility sector.
Emerging research suggests that financial incentives and infrastructure alone cannot fully explain consumer adoption patterns in emerging markets [10,11,12,13,14,15,16]. Behavioral and psychological drivers—such as threat appraisal toward environmental risks and consumer expectancy of EV benefits—play a pivotal role in shaping green purchasing decisions [17,18]. Yet, in Indonesia, these cognitive factors have seldom been examined in concert with brand-level perceptions and policy incentives. Green brand image (GBI)—a heuristic reflecting a brand’s environmental integrity and innovation—can reduce perceived risk and foster emotional bonds, particularly in low-information settings [19,20]. Similarly, the uneven distribution and awareness of fiscal and non-fiscal incentives (subsidies, tax breaks, priority lanes) create pockets of policy efficacy that may amplify—or dampen—their impact on consumer attitudes [3,21,22,23,24].
In just two years, Indonesia’s EV landscape has progressed from pilot to early mainstream market. In 2024, national wholesales sold 43,188 vehicles, gaining 153% year over year and accounting for 5% of all auto sales. Recent policy incentives have been crucial: the government reduced VAT to just 1%, eliminated the luxury tax on battery-electric vehicles for the fiscal year 2024, and waived import taxes through 2025. Due to these incentives, interest in online EV searches increased by 104% in Q2 2024 alone, with locally built Hyundai and Wuling models at the top of the lists [22].
Just as quickly, infrastructure is catching up. As of March 2025, the state-utility PLN claims 3772 public fast-charging stations (SPKLU) for cars and around 10,000 slow chargers (SPLU) for two-wheelers, a network that is three times as large as it was the previous year. With a nine-fold increase since 2022, Indonesia now has about one-third of the 24,000 public chargers placed in Southeast Asia [7]. Regarding technology, the 2023 IEEE Transportation Electrification study highlighted torque-vector control and battery-swapping improvements that promise to reduce charging time and increase range, thus reducing psychological hurdles to adoption [1,2,25].
However, the human element is not as good as the hardware. Consumer surveys frequently identify perceived danger, a lack of product awareness, and skepticism about green claims as the main reasons why EVs currently make up less than 7% of Indonesia’s car fleet, despite considerable financial incentives and a quick roll-out of infrastructure [7]. These behavioral frictions highlight why a solely engineering or economic perspective is inadequate; in order to unlock widespread adoption, it is still essential to comprehend how cognitive evaluations and green branding interact under changing governmental incentives.
The global EV movement is picking up speed: according to [1], approximately 14 million electric vehicles were sold in 2023, a 35% increase over 2022 sales, accounting for nearly one in five new cars globally. Distributed-drive EVs can currently reduce drivetrain losses by 7% thanks to energy-oriented torque-vector control frameworks at the cutting edge of technology [22]. These quick developments highlight how crucial it is to comprehend the factors influencing consumer adoption in developing nations like Indonesia, whose infrastructure and regulatory environments diverge significantly from those of early-adopting nations.
To date, studies in Indonesia have largely addressed these variables in isolation, leaving a critical gap in our understanding of how psychological appraisals, green branding, and policy incentives interact to drive EV adoption. This fragmentation hampers the design of truly integrative, high-impact interventions at both corporate and governmental levels [26,27].
Beyond psychology and branding, EV adoption is still fundamentally shaped by a handful of practical factors: up-front price parity with internal-combustion models, the density and perceived reliability of public charging networks, battery-range anxiety, total cost of ownership, and the social cues consumers receive from peers and the media. Recent work in emerging markets shows that the combined effect of sticker price and charger coverage can explain up to two-thirds of first-time EV purchases [28]. In Indonesia, fast chargers remain heavily clustered in Java–Bali, and purchase-price subsidies are often poorly socialized, further complicating consumer decisions [7]. By explicitly juxtaposing these hardware and economics considerations with Protection Motivation Theory, green branding, and policy incentives, the present study offers a more rounded account of why Indonesians do—or do not—make the leap to electric mobility.

2. Theoretical Review

Environmental Awareness (X1)
Environmental awareness is defined as the understanding and appreciation of the natural world and the challenges associated with its protection. It involves a set of principles, opinions, and morals that guide individuals in contributing to environmental well-being. It includes knowledge about environmental issues, attitudes towards the environment, and the skills necessary to address these issues effectively [29,30,31,32]. Environmental awareness also implies a sense of responsibility and the development of behaviors that support environmental conservation and protection [32]. In the context of electric vehicle (EV) adoption, environmental awareness signifies a consumer’s recognition of the environmental harms associated with internal combustion engine vehicles—such as CO2 emissions, air pollution, and climate change—and the belief that switching to EVs can mitigate these impacts [33]. According to [34], there are several indicators in environmental awareness: affective indicators, which explain the level of importance of environmental issues in the eyes of consumers; cognitive indicators, which show how high the level of consumer knowledge of environmental issues; and dispositional indicators, which show the level of consumer responsibility for environmental issues.
Consumer Expectancy (X2)
Consumer expectancy is a multifaceted concept that encompasses both rational and emotional components, and it plays a crucial role in shaping consumer behavior and decision-making processes. It refers to the anticipations or beliefs that consumers hold regarding future events, products, or services. Consumer expectations are shaped by past purchasing experiences, social networks, and information from both public and private sources. These factors contribute to the formation of expectations that serve both individual decision-making needs and reflect broader macroeconomic influences [35]. Consumer expectancy in electric vehicles (EVs) is shaped by a variety of factors, including economic, technological, social, and environmental considerations. These factors influence consumer perceptions and decisions regarding the adoption of EVs. Understanding these expectations is crucial for stakeholders aiming to increase EV adoption [36]. According to [37], the followings are the key aspects of consumer expectancy: overall benefits of EV for user, overall benefits of EV for the environment and society, transportation efficiency, convenient and economical operation (supporting EV’s infrastructure), the easiness to learn to use, the convenience and cost effectiveness for maintenance, and convenience charging system.
Protection Motivation Theory (X3 and X4)
Protection Motivation Theory (PMT) is a psychological framework designed to explain how individuals are motivated to protect themselves from perceived threats. It is widely applied in health behavior research to predict and influence protective behaviors across various contexts. PMT posits that individuals assess threats and their coping abilities, which in turn influences their motivation to engage in protective behaviors. This theory has been utilized in diverse fields, including consumer behavior, health communication, information systems security, and behavioral interventions for older adults [38,39]. In the context of electric vehicle (EV) adoption, PMT can be used to understand the motivations and barriers that influence individuals’ decisions to adopt environmentally friendly technologies. The theory posits that individuals assess threats and their coping abilities, which in turn influence their motivation to engage in protective behaviors. There are two main dimensions in PMT according to [10], namely, Threat Appraisal (X3) and Coping Appraisal (X4). Threat appraisal involves assessing the severity and vulnerability of environmental risks associated with conventional vehicles. Its indicators involve extrinsic reward, intrinsic reward, severity, and vulnerability. Meanwhile, coping appraisal involves evaluating the response efficacy, self-efficacy, and response cost related to adopting EVs.
Green Brand Image (M)
A green brand image is defined as the perception of a brand as environmentally friendly and committed to sustainable practices. It is a critical component of a company’s overall brand image and can significantly influence consumer behavior and brand loyalty. The importance of a green brand image lies in its ability to differentiate a brand in a competitive market, appeal to environmentally conscious consumers, and enhance brand equity by building trust and loyalty among consumers [40]. The concept of a green brand image in the electric vehicle (EV) market is pivotal for companies aiming to attract environmentally conscious consumers and enhance their market position. Green branding in EVs involves promoting the environmental benefits of these vehicles, such as reduced emissions and sustainable energy use, to build a positive brand image [41,42]. According to [20], there are several indicators in green brand image: types of green brand association, the favorability of green brand association, strength of green brand association, and uniqueness of green brand association.
Policy Maker Incentives (Z)
Policy maker incentives are a critical aspect of public policy design and implementation, influencing how effectively policies are developed and executed. The incentives can be financial, informational, or structural, and they play a significant role in shaping policy outcomes. Misaligned financial incentives can lead to unintended consequences, such as promoting behaviors that deviate from optimal outcomes. This highlights the need for careful design and implementation of incentive structures to avoid negative impacts [43,44]. These incentives are designed to address various barriers to EV adoption, such as high upfront costs, limited infrastructure, and consumer awareness. These include purchase subsidies, tax deductions, exemptions from import duties and sales tax and incentives for domestically produced electric vehicles.
Adoption of EVs (Y)
The adoption of electric vehicles (EVs) refers to the process by which consumers, organizations, and societies transition from conventional internal combustion engine vehicles to electric-powered vehicles. This transition is driven by various factors, including environmental concerns, technological advancements, and policy incentives. The adoption process is complex and involves multiple dimensions such as market dynamics, consumer behavior, and government policies. Some of the key elements of EV adoption include financial benefits, social influence, charging infrastructure, and range [43,45,46,47,48]. Understanding these dimensions is crucial for promoting sustainable transportation and achieving global environmental goals. Ultimately, EV adoption represents not just a technological shift, but a transformation in consumer mindset and societal infrastructure.
Therefore, EV adoption serves as the focal construct examined in this research. In order to preserve model parsimony and reduce responder fatigue, charging-infrastructure quality was taken into account during instrument design but ultimately disregarded. The construct loaded well onto coping appraisal questions in early pilots (average loading = 0.72) and conceptually coincided with perceived behavioral control. In line with earlier PMT-based mobility studies, we thus consider infrastructure perceptions as a component of coping appraisal [49].

3. Research Framework and Hypothesis Development

The inability of Indonesians to adopt EVs may arise due to specific influences both internal and external to consumers. Variables such as environmental awareness, consumer expectancy, threat appraisal, and coping appraisal may be the reasons behind EV adoption in Indonesia. In addition, external influences such as green brand image and policy maker incentives further influence the level of EV adoption among Indonesians. The lack of research references that thoroughly and comprehensively investigate this matter is the basis for this study. Based on these considerations, the following conceptual framework was developed to illustrate the hypothesized relationships among environmental awareness, consumer expectancy, threat appraisal, coping appraisal, green brand image, policy maker incentives, and EV adoption (see Figure 1).
The cognitive assessment of an environmental threat’s seriousness and perceived vulnerability is known as threat appraisal (TA) [50]. TA, a second-order reflective construct in this study, is made up of perceived vulnerability and perceived severity. Coping appraisal (CA), which comprises response efficacy and self-efficacy indicators, is the process of evaluating the effectiveness of a protective behavior and one’s capacity to carry it out. While policy maker incentives (PMI) reflect the perceived tangible and desirability of fiscal or non-fiscal incentives that lower the cost of EV ownership, green brand image (GBI) refers to the collection of environmentally oriented brand associations that customers hold in their thoughts.
Building on the preceding literature review, we now weave the individual strands into a coherent framework that explains why Indonesian consumers move—or hesitate—to adopt electric vehicles (EVs). Before developing protective intents, PMT suggests that people evaluate both dangers (such as declining air quality) and their coping abilities (such as perceived ease of using an EV). The scope of that danger assessment is encapsulated in our context by environmental awareness (EA), as consumers who are aware of environmental degradation are more inclined to look for sustainable alternatives. The coping side is represented by consumer expectancy (CE), threat appraisal (TA), and coping appraisal (CA), which collectively represent perceived risks, advantages, and self-efficacy with regard to EV usage. PMT forecasts a greater propensity to act when these cognitive evaluations are more positive.
While cognitive evaluations set the stage, emotional and symbolic factors often determine the final purchase decision. Green brand image (GBI) crystallizes multiple cognitive signals—environmental claims, product attributes, and corporate reputation—into a single, intuitive cue: “This brand is genuinely eco-friendly.” We therefore posit GBI as a partial mediator: it transmits the positive energy of EA, CE, TA, and CA into a compelling story that makes EV adoption personally and socially meaningful.
Indonesia’s policy landscape is shifting rapidly: tax breaks, import-duty exemptions, and charging-infrastructure subsidies now lower entry barriers for first-time EV buyers [7]. Such policy maker incentives (PMI) can amplify adoption intentions directly by reducing cost-related frictions. Yet, they may also attenuate the GBI → Adoption link if consumers view purchase decisions as financially driven rather than values driven—a phenomenon known as the “crowding-out” effect [51]. Consequently, we conceptualize PMI as a moderator that can either bolster or blunt the persuasive power of green branding, depending on how well incentives are socialized and matched with supporting infrastructure.
Taken together, the framework suggests a dual-track pathway to EV adoption: (1) cognitive evaluations of environmental threats and personal efficacy, channeled through (2) an emotionally resonant green brand, all within (3) a policy environment that can nudge—or, if poorly communicated, muddle—consumer decisions. The formal hypotheses that follow operationalize these relationships. Anchored in Protection Motivation Theory and green branding logic, we posit that four cognitive evaluations—environmental awareness, consumer expectancy, threat appraisal and coping appraisal—shape electric vehicle (EV) adoption both directly and indirectly through green brand image (GBI). We further contend that policy maker incentives (PMI) act on two fronts: they directly encourage adoption and moderate the persuasive power of GBI (and selected cognitive paths) in contexts where incentives are salient and well socialized.
Building on this integrated reasoning, we propose the following hypotheses (see Table 1).

4. Methodology

4.1. Participants

This study employed a nationwide online survey to capture psychological, branding, and policy factors that drive electric vehicle (EV) adoption in Indonesia. Data were gathered between February and April 2025 via WhatsApp, Instagram, and online community channels. After eliminating multivariate outliers (Mahalanobis p < 0.001), speeders (<180 s), and straight-liners (identical answers > 6 items), 986 usable responses remained, representing all 38 provinces and an almost even gender split (51% male).
This study focused on the individual respondent and considered maximizing sample availability. Therefore, subjects were recruited online through WhatsApp, Instagram, and various online communities, and 1.283 questionnaires were gathered. Next, 125 cases with Mahalanobis distance p < 0.001 (multivariate outliers), 60 cases that took less than 180 s (below the 5th percentile of pilot timings), and 112 examples that showed straight-lining behavior (longest identical-scale run > 6 items) were eliminated. As a result, the final usable sample consists of 986 replies that are representative of all 38 provinces and fairly balanced by gender.

4.2. Measurement Instruments

Data collection was carried out between February and April 2025, using an online questionnaire distributed through Google Forms. The questionnaire was shared via WhatsApp groups, Instagram stories, and various online communities to reach a wide range of demographics. The survey consisted of closed-ended questions based on a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Respondents were informed that their participation was voluntary, and all responses were treated anonymously and confidentially.
A multi-group analysis split the sample at the national median disposable income (≤IDR 7 million vs. >IDR 7 million per month). A permutation test (5.000 permutations) found no significant path-coefficient differences (p > 0.10) except for Coping Appraisal → GBI, which was slightly stronger in the high-income group (Δβ = 0.08, p = 0.03). Hence, the core psychological mechanisms appear income-invariant, with only coping resources exhibiting modest heterogeneity.
The questionnaire data that was collected was processed using SEM-PLS version 4.0. Following standard SEM notation [52], the latent structural relations are specified as follows:
GBI = β1EA + β2CE + β3TA + β4CA + ζ1
EV ADOPTION = γ1EA + γ2CE + γ3TA + γ4CA + γ5GBI + γ6INC + γ7(GBI × INC) + ζ2
where ζ1,2 are disturbance terms. The indirect (mediation) effects are captured by βkγ5 (k = 1–4); γ7 models the moderation of the GBI–adoption link by incentives.
To ensure the validity of the instrument, all indicators underwent content validation by academic experts. A pilot test was also conducted to confirm the clarity and reliability of the items before the full-scale distribution. The collected data were analyzed using a two-step process. The first stage involved evaluating the measurement model to confirm construct reliability, convergent validity, and discriminant validity. In the second stage, the structural model was assessed to test the proposed hypotheses. Bootstrapping with 5000 resamples was conducted to examine the significance of path coefficients and mediation effects, while moderation was assessed through interaction terms. This methodological framework ensures a rigorous analysis of how internal motivations, brand perceptions, and policy interventions influence EV adoption behavior in Indonesia. The approach is expected to yield comprehensive insights into the psychological and contextual factors that drive sustainable mobility transitions.

5. Results

5.1. Respondent Characteristics

Based on the results of research conducted on 986 people in 38 provinces of Indonesia, the characteristics of respondents include gender, age, origin of domicile, education level, and average income per month, as presented in [Table 2, Table 3, Table 4, Table 5 and Table 6].
Respondents were young and well-educated: 42% were 18–25 years old, 50% held at least a bachelor’s degree, and the median monthly income band was IDR 3–10 million. Geographic spread mirrors the national population, though Western Indonesia remains slightly over-represented (65.7%).

5.2. Results of Research Model Analysis with PLS Method (Partial Least Squares)

The following outer (measurement) model, which depicts the relationships between latent constructs and their indicators, was developed for this study (see Figure 2).
Next is the convergent validity test, where the reflective measure is said to be high if correlates more than 0.70. The results of the correlation between indicators and variables can be seen in the following table (see Table 7).
All constructs showed excellent psychometric quality (outer loadings 0.83–0.93; AVE 0.70–0.85; CR > 0.90) (see Table 8).
After it is known that all indicators, variables, and constructs are valid and reliable, it is necessary to conduct an R-Square test to explain the effect of certain exogenous latent variables on endogenous latent variables and whether they have a substantive effect. An R2 of 0.75, 0.50 or 0.25 is typically interpreted as strong, moderate or weak, respectively [53] (see Table 9).
The model explains 54% of the variance in EV adoption (R2 = 0.539) and 39% in green brand image (R2 = 0.386).
The next test is the Q-Square test to represent the synthesis of cross validation and fitting functions with predictions of observed variables and estimates of construct parameters. The Q-Square predictive relevance values of 0.02, 0.15, or 0.35 can be said to be weak, moderate, or strong. From test results, it is known that the value of Q-Square is 71.6%, so the level of goodness of fit in this study is strong. Most of the factors affecting the adoption of EVs are well represented by all research variables although there are still other factors (28.4%) outside the model that affect EV adoption, such as economic factors, technology, and market competition.
The next test is the F-Square test to determine the goodness of the model. The F-Square value has a range of 0.02, 0.15, or 0.35, which indicates whether the latent variable indicator has a weak, moderate, or strong influence at the structural level (see Table 10).
From the results of the F-Square test, it can be seen that the variables of environmental awareness, consumer expectancy, threat appraisal, and coping appraisal have a moderate correlation with green brand image. It is also known that the green brand awareness and policy maker incentives variables have a strong correlation with the adoption of EVs, in contrast to the environmental awareness, consumer expectancy, threat appraisal, and coping appraisal variables which have a moderate correlation with the adoption of EVs.
The last test is a hypothesis test where the hypothesis is said to be significant if the p-Value < 0.05 and T-Count > T-Table, and vice versa. There are two hypothesis testing results to determine the direct effect and indirect effect of each exogenous variable, as follows (see Table 11).
The following are the results of indirect hypothesis testing (see Table 12).
From the test results in the two tables above, it is known that the direct effects—EA, CE, TA, and CA—all positively influence both GBI (β = 0.26–0.37, p < 0.001) and adoption intention (β = 0.11–0.17, p < 0.001). Mediation: GBI partially transmits each cognitive driver’s impact on adoption (indirect β ≈ 0.10–0.14, p < 0.001). Moderation: PMI raises adoption directly (β = 0.39, p < 0.001) but weakens the GBI → adoption link (interaction β = –0.15, p < 0.001)—suggesting incentives that are poorly socialized can undermine green branding signals.
This research reveals a clear hierarchy of influences: psychological appraisals—especially coping appraisal—and green brand image jointly explain more than half of the variance in adoption intention, while policy incentives exert a strong but partly crowding-out effect. In practical terms, “mind-share” still outperforms “wallet-share” unless incentives are paired with visible infrastructure and trusted branding. When combined, our findings show that the mind, not the wallet, is the most dependable way to accelerate EV adoption in Indonesia. Enhancing consumers’ environmental consciousness and expectations has two benefits: it immediately raises adoption intent and strengthens perceptions of green brands, which further increases interest in EVs. Financial incentives are still beneficial, but they may unintentionally weaken the persuasiveness of green branding if they surpass public awareness or the installation of charging infrastructure. Therefore, in order to generate national momentum, policymakers and businesses should prioritize infrastructural and socialization initiatives outside of the Java–Bali corridor, and use financial incentives as an addition to, not a replacement for, consistent branding and educational initiatives.

6. Discussion

This study integrated theory with empirical research to explore the impact mechanism of the alignment between environmental awareness, consumer expectancy, threat appraisal, and coping appraisal on the adoption of EVs with green brand image as a mediator and policy maker incentives as a moderator, and arrived at the following conclusions.
First, the adoption of EVs is significantly impacted by all independent factors, including environmental awareness, consumer expectancy, threat appraisal, and coping appraisal, both directly and indirectly through the mediation of green brand image. This demonstrates how the four aforementioned factors directly affect Indonesia’s EV adoption. The four factors listed above have a greater impact on influencing customer behavior to adopt EVs if a favorable green brand image component is included. The findings of this study are consistent with those of [51,53] who demonstrated that perceptions of environmental threats play a significant role in EV purchase intention in Indonesia. This supports our findings that environmental awareness influences both green brand image and EV adoption directly. In their study, [54] also estimated EV adoption rates in various Indonesian regions using zero-shot learning, but they disregarded branding and psychological factors. By including cognitive factors like threat appraisal and the mediating function of green brand image, our findings bridge this gap and provide a more comprehensive explanation of consumer decision mechanisms. Also, EV technology and climate-change mitigation modules should be embedded in the national Kurikulum Merdeka for grades 10–12. Early exposure fosters pro-environment habits that pay off when students reach vehicle-buying age.
Although (Cheng et al., 2020) developed a model of consumer readiness that highlights the significance of psychological factors, our research goes beyond these findings by demonstrating that green brand image acts as a bridge that enhances the influence of psychological variables like coping appraisal and consumer expectancy on EV adoption intention [54]. Globally, policy maker incentives have been shown to be effective in many nations [55] However, in the Indonesian context, we demonstrate that these incentives actually cause fear, which lowers EV adoption, in the absence of socialization and fair infrastructure support. In support of these findings, research by [54,55,56,57,58,59,60] has confirmed the importance of infrastructure readiness and green marketing strategies. We also reiterate that in order for incentives to function as effectively as possible, green brand image, a component of green marketing, must be combined with psychological approaches and well-socialized policies. Any new subsidy, tax holiday, or import-duty waiver should be launched concurrently with a public education campaign—e.g., EV experience days at malls and universities—which frames the incentive as a promotion of sustainable living rather than as a low-cost rebate.
PLS-SEM results show that policy-maker incentives have a strong, direct positive effect on EV adoption (β = 0.388; T = 18.155; p < 0.001). However, the interaction between policy-maker incentives and green brand image negatively and significantly affects EV adoption (β = −0.148; T = 5.759; p < 0.001). This suggests that, in the absence of thorough pre-implementation socialization, incentives can actually undermine the credibility of a green brand image and dampen adoption. Concerns that the incentives are short-term or merely a political ploy are raised by the fact that up to 65% of respondents said they had never received comprehensive information on the EV subsidy or tax rebate program [61,62,63,64]. Furthermore, 60% of respondents outside of Java and Bali reported extremely low infrastructure accessibility, which leads to the belief that incentives will not have much of an impact because they cannot readily charge their cars. Additionally, the distribution of charging stations is still heavily concentrated in Java and Bali. The government should prioritize fast-charging corridors along Sumatra’s Trans-Sumatra Highway and Sulawesi’s backbone route. Public dashboards that track charger uptime in real time can further enhance perceived ease of use. In contrast to [65,66,67], who found that fiscal incentives directly boost EV adoption in developed nations, the Indonesian setting demonstrates that the moderating effect of incentives may reverse in the absence of infrastructure growth and awareness-raising (R2 = 0.539; Q2 = 0.716). In order for consumers to witness firsthand the dependability of the technology and comprehend the specifics of incentives down to the administrative level, EV brands should integrate green branding strategies with on-the-ground education. Examples of this include putting up “mobile chargers” at public events in collaboration with local governments and hosting EV demos in shopping malls [68]. According to Majeed et al. [67,68], the government is also expected to create a digital map of charging stations that is updated on a regular basis and run a multi-channel campaign that includes radio ads and village roadshow programs to explain each subsidy component, priority channels, and the process for applying for tax rebates. Overly generous, long-running subsidies risk substituting for—not complementing—green branding. The government can also implement a sunset ladder: high rebates in Phase I (≤10% market share), tapering to zero once EVs reach their 25% share, replaced by non-financial nudges (e.g., HOV-lane access, preferential parking). This keeps incentives catalytic rather than permanent crutches. Our findings arrive against a backdrop of rapid technical progress in EV control architectures. For instance, Ref. [69] demonstrate that hierarchical torque-vector control can simultaneously improve handling stability and energy efficiency, accelerating the very learning curve that Indonesian consumers are weighing in their purchase calculus.
According to the negative γ7 coefficient, incentives that are seen as generous but have inadequate charging infrastructure support undermine the persuasiveness of green branding. From a practical standpoint, this means that infrastructure roll-outs that are visible should be followed by financial incentives. It is advised to use a two-step approach: (1) tie purchase incentives to regions that already have “clusters” of fast-charging stations (at least three public chargers within ten kilometers), and (2) combine subsidies with experience initiatives, such as free roadshows for test drives at new charging stations. This strategy restores the balance between incentives and brand image by transforming the existing “price-only” nudge into a price-plus-convenience package.
To summarize, this study confirms that cognitive evaluations grounded in Protection Motivation Theory—environmental awareness, consumer expectancy, threat appraisal, and coping appraisal—are powerful antecedents of both green brand image (GBI) and electric vehicle (EV) adoption intent. GBI partially mediates each cognitive path, underscoring the pivotal role of branding in translating rational concerns into emotionally resonant purchase motives. Policy maker incentives (PMI) exert a dual influence: they raise adoption intentions directly, yet when consumers perceive the incentive as the chief reason to purchase, the motivational strength of GBI is diluted (negative moderation). Collectively these mechanisms explain more than half of the variance in adoption, signaling that psychological levers and branding remain critical even in a generous fiscal environment.

7. Conclusions

Based on the findings that environmental awareness (EA) and consumer expectancy (CE) significantly enhance green brand image (GB), and threat appraisal (TA) and coping appraisal (CA) also strengthen a brand’s “green” perception (Y), and practical implications for stakeholders can be formulated as follows.
First, in order to ensure that the “green” image of the brand is not merely a claim but is backed up by concrete data and compelling storytelling, EV brands must first conduct integrated educational campaigns that combine information on the detrimental effects of conventional vehicle emissions with technical and economic advantages, such as CO2 emission reductions or operating cost comparisons. In order to effectively address TA and CA, field demonstrations, test drives, or “EV on Wheels” events will immediately showcase EV performance, lower perceived response costs, and boost customer self-efficacy in adopting EVs. Additionally, it has been demonstrated that GB is the primary mediator that moves the effects of these cognitive variables to EV adoption (e.g., EA → GB → Adoption: β = 0.138; p < 0.001). Therefore, any promotional materials should include data-driven narratives, such as the “Carbon Emission Free” label with specific emission savings figures, and invite customers to join EV user communities in order to foster a sense of community and trust.
Second, while policy maker incentives directly influence EV adoption (β = 0.388; p < 0.001), their interaction with GB can have a negative impact if socialization and proper infrastructure are not in place. For this reason, the government must create comprehensive policy incentives that work in tandem with infrastructure expansion. In reality, the government should make sure that at least 50% of EV owners in cities have access to charging stations within a 5–10-km radius before introducing subsidies or tax breaks [69,70]. It should also give a digital map of charging stations that is updated on a regular basis [71,72]. In order to give consumers assurance that the incentives are genuine and tangible, socialization of the incentives should be carried out through a program known as “EV Awareness Week” via workshops, radio commercials, regional social media, or other media, that explains the registration process and the timeline for subsidy disbursement [73,74]. Additionally, the policy commitment will be equally promoted throughout Indonesia’s 38 provinces through partnerships with EV brands in “EV on Wheels” roadshows in small towns.
At last, the model only explains 53.9% of the variance in EV adoption (R2 = 0.539) and 38.6% of the variance in green brand image (R2 = 0.386), meaning that about 46% of external factors (technology, macroeconomics, battery readiness) have not been taken into account. This suggests that long-term collaboration between industry and government is required. As a result, future research must include new variables like Perceived Financing Support or Level 3 Charging Infrastructure, as well as longitudinal research that tracks changes in consumer attitudes and technological advancements like locally produced, inexpensive EVs. As a result, the ecosystem for EV adoption will be constructed beginning with consumer cognitive understanding, brand trust, infrastructural assurance, and comprehensive policies [48,74].
This study has a number of limitations. There are a few things to be aware of. First, the high percentage of participants (42%) who were between the ages of 18 and 25—many of whom may not yet be potential car buyers—could inflate stated adoption intentions because the study did not compare EV purchase prices to comparable internal combustion engine (ICE) models or account for respondents’ actual ability to purchase a vehicle [73,74]. Second, despite the fact that hybrid, plug-in hybrid, and completely battery-electric drivetrains varied significantly in terms of cost, infrastructure requirements, and environmental performance—differences that could influence customer assessments in different ways—we classified “electric vehicles” as a single category. Third, Indonesia’s electrical mix is primarily based on fossil fuels, and the life-cycle CO2 released during battery production was not included in the environmental analysis, which concentrated on tail-pipe emissions. These variables potentially reduce the overall ecological benefit of EV adoption. To provide a more comprehensive evaluation of adoption determinants and environmental implications, future research should separate EV drivetrain types, stratify samples by purchasing power, and take grid-mix and complete life-cycle factors into account.
Due to the cross-sectional design of the study and the fact that data was only gathered once, the results only show how EVs were perceived and intended to be adopted during that time frame, they do not account for the dynamics of shifting consumer attitudes, infrastructure development, or policy socialization. Despite the fact that 986 people from 38 provinces participated in the poll, the majority of respondents (65.72%) came from Western Indonesia, with only 10.24% coming from Eastern Indonesia, including Papua and Maluku. Given the significant regional variations in demographics, socioeconomics, and infrastructure availability, this may restrict how far the findings may be applied. Also, future research can do longitudinal trajectories. Because Indonesia’s coal-dominated grid (≈66% generation share) fand charging network are both in flux, panel studies are needed to track how the GBI–incentive dynamic evolves as infrastructure density and grid cleanliness improve.
A further risk of self-selection bias is that responders may be more environmentally conscious and digitally literate than the overall population due to the online data collection method (Google Forms distributed via Instagram, WhatsApp, and online communities). Also, digitally marginalized consumers may be underrepresented in data collected just online. To ensure a more demographically balanced sample, future research should include online panels with on-site intercepts at dealership events or provincial licensing offices.
The model only includes four cognitive variables (environmental awareness, consumer expectancy, threat appraisal, coping appraisal), one mediator (green brand image), and one moderator (policy maker incentives), while the R-Square test shows that about 46.1% of the variance in the adoption of EVs remains unexplained, indicating the presence of other external factors such as battery technology availability, after-sales service quality, or unmeasured social preferences. Furthermore, despite research showing that perceived charging accessibility has a significant impact on coping appraisal and EV adoption intention, the Policy Maker Incentives variable only measures general policy perceptions without quantitatively measuring the quality of charging infrastructure (e.g., “number of charging stations per 10,000 population” or “average charging duration”).
In conclusion, it is expected that the findings of this study will provide a basis for future researchers, industry participants, and policy makers to create more efficient plans to promote the use of EVs in Indonesia and create an environment that promotes the sustainability of the country’s transportation system.

Author Contributions

Conceptualization, I.N.B., N.K.S.S., R.D.W. and C.A.M.; methodology, I.N.B., N.K.S.S. and C.A.M.; analysis and data curation, I.N.B., N.K.S.S., R.D.W. and C.A.M.; writing—original draft preparation, N.K.S.S., I.N.B. and R.D.W.; writing—review and editing, N.K.S.S., I.N.B. and R.D.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Universitas Pendidikan Nasional, Denpasar, Bali, Indonesia (Approval No. 289/RC-II/UND/I/2025, dated 7 January 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

During the preparation of this manuscript, the authors used AI-assisted language tools (e.g., Grammarly, ChatGPT-based grammar suggestions, and DeepL Translate) solely to check grammar, spelling, and improve sentence clarity. The authors reviewed and edited all AI-assisted output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

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Wevj 16 00428 g001
Figure 1. Research framework.
Figure 1. Research framework.
Wevj 16 00428 g001
Wevj 16 00428 g002
Figure 2. Outer model.
Figure 2. Outer model.
Wevj 16 00428 g002
Table 1. Hypotheses.
Table 1. Hypotheses.
Hypothesis CodeStatementRelationship
A.
Cognitive → Green Brand Image
H1Environmental Awareness → GBIDirect
H2Consumer Expectancy → GBIDirect
H3Threat Appraisal → GBIDirect
H4Coping Appraisal → GBIDirect
B.
Cognitive → Adoption of EVs
H5Environmental Awareness → AdoptionDirect
H6Threat Appraisal → AdoptionDirect
H7Coping Appraisal → AdoptionDirect
H8Consumer Expectancy → AdoptionDirect
C.
Brand-mediated Effects
H9EA → GBI → AdoptionMediation
H10CE → GBI → AdoptionMediation
H11TA → GBI → AdoptionMediation
H12CA → GBI → AdoptionMediation
D.
Incentive-based Moderation
H13PMI moderated EA → GBI → AdoptionModeration
H14PMI moderated CE → GBI → AdoptionModeration
H15PMI moderated TA → GBI → AdoptionModeration
H16PMI moderated CA → GBI → AdoptionModeration
H17PMI moderated GBI → AdoptionModeration
E.
Incentive Direct Effect
H18Policy Maker Incentives → AdoptionDirect
Table 2. Respondent characteristics based on gender.
Table 2. Respondent characteristics based on gender.
ClassificationTotal (Person)Percentage (%)
Male50251%
Female48449%
Total986100%
Table 3. The age breakdown of respondents.
Table 3. The age breakdown of respondents.
ClassificationTotal (Person)Percentage (%)
18–25 years old41042%
26–35 years old24024%
36–45 years old17618%
46–55 years old11912%
>56 years old414%
Total986100%
Table 4. Regional origin data.
Table 4. Regional origin data.
ClassificationTotal (Person)Percentage (%)
Western Indonesia64865.72%
Central Indonesia23724.04%
Eastern Indonesia10110.24%
Total986100%
Table 5. Education-level percentages.
Table 5. Education-level percentages.
ClassificationTotal (Person)Percentage (%)
Elementary/Junior High School657%
High School23924%
Bachelor/Diploma Degree49150%
Master Degree16617%
Doctoral Degree253%
Total986100%
Table 6. Respondent characteristics based on average income per month.
Table 6. Respondent characteristics based on average income per month.
ClassificationTotal (Person)Percentage (%)
<IDR 3,000,00019019%
IDR 3,000,000–IDR 10,000,00038539%
IDR 10,000,000–IDR 25,000,00027228%
>IDR 25,000,00013914%
Total986100%
Table 7. Convergent validity and reliability test results.
Table 7. Convergent validity and reliability test results.
IndicatorsLoadingAVECronbach’s AlphaComposite Reliability
Environmental AwarenessEA_10.8970.8010.9170.918
EA_20.888
EA_30.895
EA_40.900
Consumer ExpectancyCE_10.8720.7810.9530.955
CE_20.885
CE_30.888
CE_40.883
CE_50.882
CE_60.885
CE_70.889
Threat
Appraisal		TA_10.8980.8060.9200.920
TA_20.897
TA_30.898
TA_40.898
Coping AppraisalCA_10.9100.8460.9090.913
CA_20.923
CA_30.926
Green Brand
Image		GB_10.8400.6960.8550.855
GB_20.831
GB_30.837
GB_40.830
Policy Maker
Incentives		PM_10.9210.8340.9000.902
PM_20.904
PM_30.914
Adoption of EVAD_10.8740.7680.8990.900
AD_20.879
AD_30.880
AD_40.872
Table 8. Discriminant validity test results.
Table 8. Discriminant validity test results.
ADCACEEAGBPMTAPM × GB
AD
CA0.280
CE0.2930.047
EA0.2850.0280.037
GB0.6430.2720.3840.387
PM0.4460.0190.0250.0180.028
TA0.2450.0230.0210.0220.3070.016
PM × GB0.1890.0160.0230.0280.0400.0450.037
Table 9. R-Square test results.
Table 9. R-Square test results.
VariablesR-Square
Adoption of EVs (AD)0.539
Green Brand Image (GB)0.385
Table 10. F-Square test results.
Table 10. F-Square test results.
VariablesAdoption of EVs (Y)Green Brand Image (M)
Environmental Awareness (EA)0.0300.207
Consumer Expectancy (CE)0.0360.224
Threat Appraisal (TA)0.0230.110
Coping Appraisal (CA)0.0560.110
Green Brand Image (GB)0.201
Policy Maker Incentives (PM)0.326
Policy Maker Incentives (PM) × Green Brand Image (GB)0.043
Adoption of EVs (AD)
Table 11. Direct effect hypothesis test results.
Table 11. Direct effect hypothesis test results.
HypothesesOriginal SampleT Statisticsp Values
Environmental Awareness (EA) —> Green Brand Image (GB)0.35714.4250.000
Consumer Expectancy (CE) —> Green Brand Image (GB)0.37115.6570.000
Threat Appraisal (TA) —> Green Brand Image (GB)0.26010.5280.000
Coping Appraisal (CA) —> Green Brand Image (GB)0.26110.8610.000
Environmental Awareness (EA) —> Adoption of EVs (AD)0.1285.2790.000
Consumer Expectancy (CE) —> Adoption of EVs (AD)0.1425.6210.000
Threat Appraisal (TA) —> Adoption of EVs (AD)0.1094.5940.000
Coping Appraisal (CA) —> Adoption of EVs (AD)0.1697.1380.000
Green Brand Image (GB) —> Adoption of EVs (AD)0.38813.9440.000
Policy Maker Incentives (PM) × Green Brand Image (GB) —> Adoption of EVs (AD)−0.1485.7590.000
Policy Maker Incentives (PM) —> Adoption of EVs (AD)0.38818.1550.000
Table 12. Indirect effect hypothesis test results.
Table 12. Indirect effect hypothesis test results.
HypothesesOriginal Sample (O)T Statistics (|O/STDEV|)p Values
Environmental Awareness (EA) —> Green Brand Image (GB) —> Adoption of EVs (AD)0.1389.6040.000
Consumer Expectancy (CE) —> Green Brand Image (GB) —> Adoption of EVs (AD)0.14410.7430.000
Threat Appraisal (TA) —> Green Brand Image (GB) —> Adoption of EVs (AD)0.1018.3710.000
Coping Appraisal (CA) —> Green Brand Image (GB) —> Adoption of EVs (AD)0.1018.5030.000
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Basmantra, I.N.; Santiarsa, N.K.S.; Widodo, R.D.; Mimaki, C.A. How the Adoption of EVs in Developing Countries Can Be Effective: Indonesia’s Case. World Electr. Veh. J. 2025, 16, 428. https://doi.org/10.3390/wevj16080428

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Basmantra IN, Santiarsa NKS, Widodo RD, Mimaki CA. How the Adoption of EVs in Developing Countries Can Be Effective: Indonesia’s Case. World Electric Vehicle Journal. 2025; 16(8):428. https://doi.org/10.3390/wevj16080428

Chicago/Turabian Style

Basmantra, Ida Nyoman, Ngurah Keshawa Satya Santiarsa, Regina Dinanti Widodo, and Caren Angellina Mimaki. 2025. "How the Adoption of EVs in Developing Countries Can Be Effective: Indonesia’s Case" World Electric Vehicle Journal 16, no. 8: 428. https://doi.org/10.3390/wevj16080428

APA Style

Basmantra, I. N., Santiarsa, N. K. S., Widodo, R. D., & Mimaki, C. A. (2025). How the Adoption of EVs in Developing Countries Can Be Effective: Indonesia’s Case. World Electric Vehicle Journal, 16(8), 428. https://doi.org/10.3390/wevj16080428

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