Advancing Sustainable Urban Mobility in Oman: Unveiling the Predictors of Electric Vehicle Adoption Intentions

Abstract

The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the key factors influencing consumers’ intentions in the Sultanate of Oman toward adopting electric vehicles. It is based on a mixed methodology combining quantitative data from a questionnaire of 448 participants, analyzed using ordinal logistic regression, with qualitative thematic analysis of in-depth interviews with 18 EV owners. Its results reveal that performance expectations, trust in EV technology, and social influence are the strongest predictors of EV adoption intentions in Oman. These findings suggest that some issues related to charging infrastructure, access to maintenance services, and cost-benefit ratio are key considerations that influence consumers’ intention to accept and use EVs. Conversely, recreational motivation is not a statistically significant factor, which suggests that consumers focus on practical and economic motivations when deciding to adopt EVs rather than on their enjoyment of driving the vehicle. The findings of this study provide valuable insights for decision-makers and practitioners to understand public perceptions of electric vehicles, enabling them to design effective strategies to promote the adoption of these vehicles in the emerging sustainable transportation market of the future.

1. Introduction

The global automotive sector is undergoing a major transformation pushed by the urgent need for effective strategies and the growing challenges of climate change to reduce greenhouse gas (GHG) emissions [1]. In addition, the transportation sector is under increasing investigation as one of the largest contributors to environmental pollution, necessitating a fundamental shift toward sustainable mobility solutions [2,3,4].

Hence, electric vehicles (EVs) have emerged as a pivotal innovation. They offer superior energy efficiency, with the elimination of harmful emissions, making them an alternative to conventional internal combustion engine (ICE) vehicles. In addition to reducing direct emissions, EVs also contribute to reducing total life cycle emissions, especially when sustained by renewable energy sources, enhancing their role in decarbonizing the transportation sector [5,6].

In addition to addressing environmental and energy challenges, governments—especially in emerging economies—are increasingly embracing EV adoption as a strategic initiative to improve their competitive stance within the global automotive value chain. Electrification is currently regarded as a means not only for decarbonization but also for enhancing industrial capabilities, diversifying the economy, and decreasing reliance on fossil fuel industries. Dua emphasizes this trend within the context of a wider “net-zero transport dialogue”, which links climate goals with geopolitical and industrial aspirations [7]. Additionally, New energy vehicles (NEVs) are crucial in addressing environmental pollution and energy shortages, and to promote their development, local governments in China have implemented various policies [8].

The spread of electric vehicles is due to several factors: pressing environmental imperatives, technological advances, strategic policy interventions, and massive infrastructure expansion [9,10]. Their market is experiencing a radical transformation driven by pioneering developments in battery technology, particularly high-voltage density lithium-ion batteries and next-generation solid-state batteries, which have revolutionized the capabilities of electric vehicles by increasing driving range and reducing charging times and production costs [11,12]. These advances, besides increased manufacturing capacity, have made electric vehicles more accessible and less expensive, reinforcing their position as a main component of the global transition to sustainable transportation.

Efforts to expand infrastructure and supportive policies play a significant role in supporting the spread of electric vehicles. Governments around the world are working to enact comprehensive regulatory frameworks, implement tax incentives, direct grants, and impose strict emission reduction policies to boost consumer adoption and stimulate industry transformation [2,13,14]. Moreover, the expansion of charging infrastructure reduces the barriers to deployment, which is range anxiety. The deployment of ultra-fast charging networks, vehicle-to-grid (V2G) integration technologies, and smart grid systems is enhancing access to charging, improving the flexibility of the electricity grid, and increasing consumer confidence in the use of electric vehicles [15,16,17]. With increasing cooperative efforts between policymakers and industry leaders on infrastructure investments and regulatory developments, the EV ecosystem is ready for accelerated growth, enhancing its role in global decarbonization strategies and ensuring the long-term sustainability of the transportation sector.

Despite the fact that rapid technological advances and policy interventions are accelerating the spread of electric vehicles, significant disparities remain across global markets, particularly in developing regions. Economic constraints, lack of charging infrastructure, and inconsistent regulatory frameworks represent significant challenges to the widespread integration of electric vehicles. In many emerging economies, the lack of stable government incentives and low private sector investment hinder the widespread adoption of this technology, underscoring the need for targeted policy measures and strategic infrastructure expansion [18,19]. On the contrary, advanced economies have been able to impose strong regulatory frameworks and extensive charging networks to facilitate the transition to electric mobility [20,21]. Challenges remain, such as high initial cost, gaps in charging infrastructure, consumer hesitation regarding battery life, maintenance costs, and charging reliability [2,3,4,5,6,7,8,9,10], which requires planning to address these challenges.

The adoption of electric vehicles represents a transformational step toward environmental sustainability in the Gulf Cooperation Council (GCC) countries. Accordingly, governments strive to accelerate the integration of green technologies into their ambitious climate action and energy efficiency strategies [22,23,24,25]. The Sultanate of Oman is a prominent model because it has adopted sustainability as a national priority through policies aimed at reducing carbon emissions, enhancing energy efficiency, and encouraging environmental conservation [26]. Although adopting electric vehicles is compatible with these environmental goals, expanding their use faces economic, structural, and political challenges, such as the reliance on fossil fuels, subsidized gasoline prices, and emerging charging infrastructure [27]. In addition, gaps in political incentives, inconsistent regulations, and low public awareness hinder widespread adoption of electric vehicles, as consumers are hesitant about cost, efficiency, and long-term benefits. However, recent government initiatives, including investments in charging networks, efforts to diversify energy consumption, and emerging partnerships with the private sector, indicate a growing commitment to sustainable mobility solutions. As Oman seeks to approach the complexities of sustainable transportation, targeted policy interventions, regulatory improvements, and infrastructure expansion will play a critical role in accelerating the adoption of electric vehicles and ensuring long-term economic and environmental sustainability.

Based on the previous discussion, the team of this study believes that conducting a detailed analysis of the determinants of adopting the transition to sustainable transport is necessary to provide targeted interventions to shift toward reducing emissions and achieving sustainable transport. Referring to the UTAUT2 theory, this study uses ordinal logistic regression analysis to assess the main factors that shape consumers’ adoption behavior. These factors include performance expectations, effort expectations, social influence, facilitating conditions, entertainment motivation, price value, and trust. This study provides empirical insights that link theoretical frameworks with actual adoption challenges by identifying the barriers and drivers within the emerging electric vehicle market in Oman. It also contributes to the literature by providing a comprehensive, data-driven analysis of consumer behavior, policy effectiveness, and infrastructure readiness in Oman. In addition, these insights help guide strategic initiatives in enhancing consumer awareness, expanding charging infrastructure, and building confidence in EV technology. Furthermore, the findings of this study provide evidence for decision-makers and stakeholders to develop interventions ensuring an effective transition toward a sustainable transport system.

2. Conceptual Framework and Hypotheses Development

2.1. UTAUT2 Framework for Electric Vehicles Adoption in Oman

A comprehensive understanding of the factors affecting electric vehicle (EV) adoption requires a model that integrates individual attitudes, behavioral drivers, and socio-environmental contexts. The present study adopts the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework to assess the behavioral intention to adopt EVs in Oman, where EV diffusion remains at an early stage.

UTAUT2 extends the original Unified Theory of Acceptance and Use of Technology (UTAUT) model [28], by incorporating both core and consumer-oriented constructs. The original model includes performance expectancy, effort expectancy, social influence, and facilitating conditions—core predictors of behavioral intention and technology use. UTAUT2 builds on this by adding hedonic motivation, price value, and habit, thereby enhancing its explanatory power for consumer adoption contexts [29]. These seven constructs together provide a comprehensive framework for analyzing voluntary adoption behavior, especially in emerging markets like Oman, where infrastructure, economic perceptions, and trust dynamics play a critical role.

Compared to other technology adoption models such as the Technology Acceptance Model (TAM) or Diffusion of Innovation (DOI), UTAUT2 has demonstrated superior explanatory power, accounting for up to 74% of variance in behavioral intention [30,31,32]. This makes it an appropriate framework for the current study, which aims to evaluate EV adoption dynamics within a context characterized by infrastructural challenges and limited consumer exposure. The “habit” construct was excluded from the model due to the early stage of EV adoption in Oman. Habit presumes frequent prior use, which is not yet prevalent in the local context. This approach is consistent with other studies in emerging economies where EV markets are still developing [33,34]. In addition, the “price value” construct is adapted into “perceived price value” to better reflect consumer decision-making in a market where pricing knowledge and real-world EV experience are limited. This refinement captures the trade-off between perceived benefits and expected costs—an important factor in economies where uncertainty about long-term savings remains high [33,35].

2.2. Hypotheses Development

2.2.1. Performance Expectancy

Performance expectations refer to the perception of improved efficiency and effectiveness from the use of electric vehicles (EVs) that is recognized as one of the most important factors of behavioral intention within technology adoption research [29,36]. Hence, its benefits include reducing environmental impact, lowering fuel costs, etc. These advantages are important in GCC countries, where sustainability and energy efficiency are national priorities. The results of previous studies, such as the results of previous studies [34,37], confirm that perceived benefits, such as cost savings, environmental conservation, and improved productivity, are primary motives for EV adoption. In the Sultanate of Oman and the GCC countries, these benefits support the efforts to promote sustainable transportation and achieve climate goals. Based on the previous discussion, this study proposes the following hypothesis:

H1. 

Performance expectancy significantly influences individuals’ intentions to adopt and use EVs.

2.2.2. Effort Expectancy

Effort expectations indicate how easy it is to use EVs, including operation, charging infrastructure, and ease of use. They are main factors in understanding behavioral intention in technology adoption research [29,38]. Simplifying the use of EVs contributes to reducing cognitive barriers, especially for new users, as indicated by Nastjuk et al. [39] and Lee et al. [40]. EV adoption is in its early stages in Oman; thus, the efforts to encounter the related barriers are essential. Hence, improving infrastructure accessibility and ease of use influences consumers’ intention to adopt EVs. Accordingly, the following research hypothesis is proposed:

H2. 

Effort expectancy significantly influences individuals’ intentions to adopt and use EVs.

2.2.3. Social Influence

Social influence, in this study, is defined as “the extent to which consumers perceive those important others (e.g., family and friends) believe they should use a particular technology” [29] (p. 159). Before deciding to adopt new technologies, individuals rely greatly on the opinions of their social networks. The positive perceptions from these networks are a powerful motivating factor for adoption, while negative opinions can be important barriers [41,42].

Regarding EV adoption, the role of social influence emerges clearly because social network recommendations contribute to shaping individuals’ intentions and decisions. This effect is noticeable in study areas such as the Sultanate of Oman and the Gulf Cooperation Council (GCC) countries, where social values intersect with environmental priorities due to the link between enhancing social status and environmental responsibility. Many studies confirm that social influence influences consumer behavior in markets where social consideration is crucial [33,43]. In light of the above discussion, this study proposes the following hypothesis:

H3. 

Social influence significantly influences individuals’ intentions to adopt and use EVs.

2.2.4. Facilitating Conditions

Facilitating conditions include the availability of resources and support needed to enable the adoption of EVs, including charging infrastructure, maintenance services, and technical support. Venkatesh et al. argue that facilitating conditions play an important role in reducing perceived barriers to adoption of new technology by ensuring that consumers have easy access to the basic resources needed for operating EVs. Infrastructure readiness is essential in forming consumers’ intentions to adopt EVs [29]. Previous studies have confirmed the importance of reliable charging networks and related support systems in encouraging consumers to adopt EVs. For instance, studies by Singh et al. [44], Tarei et al. [45], and Verma et al. [46] found that the availability of robust and easily accessible charging infrastructure supports users’ willingness to turn to EVs, confirming the importance of providing infrastructure to enhance adoption. Thus, the following hypothesis can be proposed:

H4. 

Facilitating conditions significantly influence individuals’ intentions to adopt and use EVs.

2.2.5. Hedonic Motivation

Entertainment motivation (HM), as defined by the UTAUT2 framework, is a major factor influencing the adoption of new technologies [29] that reflect the degree of enjoyment and satisfaction resulting from using the new technology. For EVs, this includes ease of operation and innovative features such as autonomous driving and modern aesthetic appeal. In addition to these features, the emotional satisfaction and experiential value that EVs offer to users are also highlighted. Some previous studies suggest that entertainment motivation is a main factor in determining consumers’ adoption intention [34,44,47,48]. The excitement associated with advanced technologies and unique driving experiences enormously influences the adoption decision. The adoption of EVs in the Sultanate of Oman is still in its early stages; therefore, the entertainment motive is expected to be influential in attracting new users, especially young people with technological interests. That is why the mental image of adopting EVs as symbols of luxury and social status should be taken into account. In light of the previous discussion, this study proposes the following hypothesis:

H5. 

Hedonic motivation significantly influences individuals’ intentions to adopt and use EVs.

2.2.6. Perceived Price Value

The concept of price value in EV adoption refers to assessing the balance between the initial financial investment and long-term savings, such as lower fuel and maintenance costs. Empirical data suggest that price value plays are significant in EV adoption decisions, influencing consumer behavior by focusing on economic feasibility [33,35,49]. Regarding the emerging EV market in Oman, the prices are a major challenge to adoption. To address this, the concept of price value is adapted to the perceived price value (PPV) that reflects consumers’ initial willingness to pay based on their cognitive assessment of the comparison between potential benefits and associated costs. Therefore, the uncertainty of economic feasibility determines the decision to adopt EVs. Based on the previous point, this study proposes the following hypothesis:

H6. 

Perceived price value significantly influences individuals’ intentions to adopt and use EVs.

2.2.7. Trust

Consumer trust in the new technology of (EVs) is one of the most important factors in making the adoption decision regarding the safety, reliability, and performance of these vehicles, which reduces uncertainty and creates positive orientations and, thus, directly affects the individuals’ intentions of adoption [50,51]. Trust factors include system transparency, technical competence, and operational management that together enhance the users’ trust [52]. Empirical data confirm that the level of trust is a decisive factor in the adoption of these new technologies [49,53,54]. In emerging markets such as Oman, trust levels are expected to be significant in the adoption decision. In light of the above, this study proposes the following hypothesis:

H7. 

Trust significantly influences individuals’ intentions to adopt and use EVs.

3. Methods

3.1. Research Design

This study relies on the adoption of a mixed-methods research design, which combines qualitative approaches (first stage) and quantitative approaches (second stage). This methodology helps to integrate qualitative insights to understand the context and verify the observed connections [55,56]. Creswell [57] (p. 16) points out that the goal of the mixed-methods design is to ‘assess the validity of the conclusions drawn from qualitative research.’ The qualitative phase includes semi-structured interviews with EV owners, exploring their experiences, perceptions, and adoption challenges. These findings help refine the quantitative questionnaire tool to ensure its suitability to the local market. The quantitative phase relies on a structured questionnaire that is analyzed using descriptive statistics and ordinal logistic regression (OLR) to identify the primary factors that influence the EV adoption decision.

3.2. Data Collection and Participants

3.2.1. Qualitative Phase: Semi-Structured Interviews

Semi-structured interviews are conducted with EV owners in Oman. They indicate that participants have first-hand experience of owning EVs, providing valuable insights into the perceived benefits of EVs, such as cost savings and performance efficiency. On the other hand, they also indicate the challenges they face, charging infrastructure limitations and range anxiety, in addition to the factors influencing adoption decisions. Purposive sampling is adopted to ensure representation of various demographic groups, including original adopters from different regions of the Sultanate of Oman. Interviews are recorded, transcribed, and analyzed using thematic analysis. The qualitative phase reflects the design of the quantitative questionnaire, ensuring that this study indicates the most relevant local factors influencing the decision to adopt EVs.

3.2.2. Quantitative Phase: Survey Questionnaire

A structured questionnaire is developed and distributed to analyze the factors that form EV adoption in Oman, ensuring its alignment with the expanded UTAUT2 framework. It is designed to provide comprehensive insights into consumers’ perceptions and adoption intentions through three main sections: The first section is demographic data that provides a basic image to understand the socio-economic backgrounds of the participants and assess the potential impact of these variables on EV adoption. The second section measures the dependent variable: The individuals’ intention to adopt EVs, based on key indicators of technology adoption that allow for a comprehensive analysis of consumer behaviors and expectations. The third section analyzes seven independent variables inspired by the UTAUT2 framework: performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, perceived price value, and trust. Each variable is evaluated using a 10-point Likert scale that allows for an accurate analysis of the extent to which each factor affects the decision to adopt EVs.

3.2.3. Dependent Variable Dimensions and Measurement Items

The dependent variable “intention to adopt electric vehicles” is measured through five main dimensions; each dimension represents a specific aspect of consumer behavior and decision-making processes regarding adopting this technology, as shown in

Table 1. Measurement items for the dependent variable.

Dimension	Measurement Item
Adoption intentions	I plan to purchase an electric vehicle within the next few years.
Future interest	I am highly interested in owning and using an electric vehicle in the future.
Purchase likelihood	When buying a new car, I will likely prioritize an electric vehicle over a gasoline-powered one.
Excitement and consideration	I am excited about the idea of driving an electric vehicle and am seriously considering purchasing one.
Primary choice preference	Once EVs become widely available in Oman, they will be my first choice for personal transportation.

.

Each dimension is measured using a 10-point Likert scale. The average scores in these dimensions are divided into three levels of intention: Low intention (1.0–4.00), medium (4.10–7.00), and high (7.10–10.0). This compound approach provides a comprehensive measure of consumers’ behavioral intentions and willingness to adopt EVs in Oman.

3.2.4. The Independent Variables

The independent variables are analyzed based on the UTAUT2 framework with some modifications to suit the context of EV adoption in Oman. These variables include the main psychological, social, and contextual factors that influence adoption decisions. A summary of these variables and sample measurement items is shown in

Table 2. Sample measurement items for independent variables.

Construct	Sample Measurement Item
1. Performance Expectancy	EVs enhance driving efficiency in the long run.
2. Effort Expectancy	Driving an electric vehicle is easier than driving a conventional vehicle.
3. Social Influence	I take my family’s opinion into account when deciding to buy an EV.
4. Facilitating Conditions	Inadequate infrastructure does not encourage citizens to purchase EVs.
5. Hedonic Motivation	I believe that driving an EV will be enjoyable and exciting.
6. Perceived Price Value	The long-term economic benefits of EVs outweigh their initial costs.
7. Trust	I trust that EVs are safe and reliable for daily use.

; the full set of measurement items is presented in Appendix A.

3.2.5. Sample Size and Participants

The size of the study sample is calculated based on Cochran’s formula that ensures a high statistical percentage with a 95% trust level and a 5% margin of error. The minimum required sample size is calculated to be 385 respondents [58]. To enhance the reliability of the study results and address the possibility of incomplete responses, the final sample size is increased to 448 participants. Data are collected from December 2024 to January 2025, including individuals from different governorates of the Sultanate of Oman. Participants are selected in an intentional manner to ensure that the sample represents the research population. The demographic characteristics of the study participants are presented in

Table 3. Demographic characteristics of the study sample.

Variable	Category	Frequency	Percentage
Gender	Male	213	47.5
	Female	235	52.5
Age	Under 20 years	66	14.7
	20–29 years	314	70.1
	30–39 years	38	8.5
	40–49 years	19	4.2
	50 years and above	11	2.5
Monthly Income	Less than 500 OMR	41	9.2
	500–999 OMR	118	26.3
	1000–1499 OMR	144	32.2
	1500–1999 OMR	87	19.4
	2000–2999 OMR	39	8.7
	3000 OMR and above	19	4.2
Residence	Urban	367	81.9
	Rural	81	18.1

.

3.3. Method of Statistical Data Analysis

3.3.1. Qualitative Phase: Thematic Analysis of Interview Data

The first phase of this study adopts a qualitative approach through semi-structured interviews. Participants are selected from among current EV owners in Oman. Thus, this study is based on direct experiences of individuals who have already experienced the adoption process. The interview protocol focuses on essential factors derived from the extended UTAUT2 framework, which include performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, perceived price value, and trust. The analysis is carried out according to the six-step framework developed by Braun and Clarke [59]. This approach includes identifying the data, generating primary codes, identifying main themes, reviewing and refining these themes, defining them, and producing the final report. This approach helps to identify repetitive patterns and extract significant insights related to EV adoption, ensuring a strong qualitative analysis.

Participants were recruited through a WhatsApp group of approximately 300 current electric vehicle owners in Oman. An invitation message was posted in the group explaining the objectives and significance of the study and requesting voluntary participation in the interviews. Eighteen individuals agreed to participate, including one female participant. All interviews were conducted individually and lasted approximately 30–35 min. The semi-structured interviews were guided by the expanded UTAUT2 framework and covered eight thematic areas: (1) Participant characteristics, (2) performance expectancy, (3) effort expectancy, (4) social influence, (5) facilitating conditions, (6) hedonic motivation, (7) price value, and (8) trust in electric vehicle technology.

Interview data were manually coded using open coding, allowing for the extraction of primary concepts from the data. Codes were then grouped into broader categories to form key themes through axial coding. To ensure the validity of the analysis, two independent researchers reviewed the coding process. Any discrepancies were discussed, and agreement was reached through consensus, enhancing the reliability of the analysis and reducing subjective bias. Furthermore, qualitative themes were not only used to provide deep contextual understanding but were also supported by quantitative findings for interpretation. For example, participants’ narratives about trust in electric vehicle technology and charging infrastructure supported significant statistical findings obtained through regression analysis, enhancing the integration of qualitative and quantitative approaches.

3.3.2. Quantitative Phase: Statistical Analysis Using OLR

This study relies on Ordered Logistic Regression (OLR) in investigating the determinants of EV adoption thanks to the ordered nature of the dependent variable (low, medium, or high adoption intention). OLR is suitable for inferring categorical outcomes with inherent ordering while considering the influence of multiple predictor variables [60]. Before conducting OLR, the data are examined for missing values, irregularities, and high correlation between variables to ensure the strength of the model. Descriptive statistics are also calculated to summarize demographic characteristics and assess the distribution of main independent variables.

3.3.3. Model Specification

The OLR model estimates the probability of an individual to be classified into an EV adoption intention category regarding the influence of predictor variables taken from the Extended UTAUT2 framework. The model follows the cumulative logit formulation, as shown in Formula (1):

$$P\left(Y\ge y_j | \mathit{X}\right)={\displaystyle \frac{1}{1+\exp \left(-{\alpha }_j-x_i^T\beta \right)}},\mathrm{j}=\mathrm{1,2},3$$

(1)

where the corresponding log-odds form is represented in Formula (2):

$$\mathrm{l}\mathrm{o}\mathrm{g}\left({\displaystyle \frac{P\left(Y\ge y_j | \mathit{X}\right)}{1-P\left(Y\ge y_j | \mathit{X}\right)}}\right)={\alpha }_j+x_i^T\beta ,for,j=\mathrm{1,2},3$$

(2)

In Formula (1), $P\left(Y\ge y_j\right)$ cumulative probability that an individual i falls into the adoption intention category j or higher. ${\alpha }_j$ Is the constant term. X is the vector of independent variables derived from the extended UTAUT2 framework (e.g., performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, perceived price value, and trust). $\beta$ Represents the vector of regression coefficients, which measure the impact of each predictor variable on the probability of EV adoption.

3.3.4. Model Assumptions and Goodness-of-Fit Testing

Several diagnostic tests are performed to ensure the validity and robustness of the OLR model, such as

• Proportional Odds Assumption is tested to verify that the relationship between the predictor variables and the dependent variable remains constant across all categories.
• Model Fit Measures:
  • Likelihood Ratio Tests
  • Nagelkerke R² & McFadden R²
  • Pseudo R-squared values to assess the explanatory power of the model

These measures ensure the statistical validity and robustness of the regression model in explaining EV adoption intention [61,62].

4. Results

4.1. Thematic Analysis Results on EV Adoption in Oman: Insights from Owner Experiences

According to the Braun and Clarke approach, the analysis is conducted to identify the main motives and challenges affecting the adoption of EVs in the Sultanate of Oman through a sample of (n = 18) electric vehicle owners. The sample includes 17 men and one woman, indicating a gender imbalance in EV ownership. Regarding the age, it ranges from 21 to 70 years, with an average of 42.9 years, indicating that middle-aged consumers are most likely to turn to EVs in Oman. In terms of work, it is found that all participants are working except for one participant who is retired. In addition, 50% of participants hold postgraduate degrees. The geographical distribution also shows that most of the participants live in urban areas, which confirms the existing orientation that EV adoption is still concentrated in urban areas due to the availability of maintenance facilities and electric charging stations [10]. These characteristics indicate that the participants—mostly young; urban; and well-educated—likely represent a segment of early adopters of electric vehicles; who may differ from the general population of car owners in Oman; particularly those using internal combustion engine vehicles. This distinction reflects global patterns, where electric vehicle users tend to have higher levels of education, income, and environmental awareness. Accordingly, this demographic trend is considered a limitation of study and an area for future comparative research. The duration of ownership varies among participants, with 50% owning an electric vehicle for less than one year, while 27.8% used electric vehicles for two years, and 22.2% for three years or more. This distribution reflects a mix of new adopters and experienced owners, indicating diverse insights into the adoption experience.

The thematic analysis is conducted according to seven main axes that influence the decision to adopt EVs, as follows:

• Perceived benefits and functional efficiency,
• Ease of use and driving experience,
• Influence of social networks and adoption decisions,
• Availability of charging infrastructure and support services,
• Driving enjoyment, comfort, and lifestyle appeal,
• Cost considerations and long-term financial benefits,
• Confidence in EV technology, safety, and market reliability.

4.1.1. Perceived Benefits and Functional Efficiency

Regarding the thematic analysis, economic efficiency is a main element that determines the adoption of EVs in Oman. Fifteen participants state that the amount of electricity consumption is less than traditional fuel expenses. One participant points out, “Owning an EV offers long-term financial benefits and decreases concerns of fuel prices.” In addition, eleven owners mention that EVs need lower maintenance costs because they do not have complicated mechanical parts like internal combustion engines (ICE). Another participant highlights this advantage: “Electric cars require minimal maintenance due to the absence of complicated mechanical parts, making them affordable.” Regardless of the cost considerations, participants emphasize the superior driving experience offered by EVs: quick torque, smooth acceleration, and digital integration. Five participants indicate the luxury and advanced technological characteristics of EVs, describing them as “high-tech machines that enhance driving comfort through remote control features and smooth operation.”

Environmental factors are expressed by respondents as having a strong effect on their decisions to buy an electric car. Three of them indicate that EVs have great capability to provide significant environmental benefits by reducing greenhouse gas emissions and reducing the reliance on fossil fuels. Another participant states, “EVs help improve air quality by eliminating harmful emissions such as carbon dioxide (CO₂) and nitrogen oxides.” Three other participants focus on the efficiency and quick response of EVs. They indicate that the instant torque of EVs makes them faster and more flexible than conventional cars. Participants also refer to the combination of AI and ongoing software enhancements that improve the performance and the safety of EVs.

4.1.2. Ease of Use and Driving Experience

Regarding the ease of use and driving experience, the thematic analysis of nine participants’ responses indicates that EVs are easier to drive compared to conventional cars. One of the participants says, ‘driving an EV is much easier like using a touch screen phone compared to a keypad one. In addition, six participants emphasize the smooth acceleration of EVs. One of them confirms, ‘It is easier and lighter because of its instant torque and smooth acceleration. I do not need to press the brake and shift from petrol to brake. The process is much easier.’ Two respondents highlight the absence of a gear shift, describing it as a major convenience factor. Finally, one of the participants believes that advanced technology is the most important factor that attracted him to buy this kind of car.

4.1.3. Influence of Social Networks and Adoption Decisions

EV owners reveal that an individual’s decision to buy an EV is often preceded by social discussions with family, friends, colleagues, and other trusted people. Four participants state that family encouragement influenced their purchase, despite the existence of some doubt about its long-term benefits. Moreover, several participants emphasize the impact of friends and colleagues’ feedback. Another respondent says, ‘I consulted my colleagues who own EVs, and their experiences reassured me that I was making the right decision.’ Finally, two participants report that they were not influenced by others’ opinions because they did not encourage them to make the decision to purchase. The previous findings suggest that social networks greatly influence the consumer’s purchasing decision, although some people remain resistant due to some doubts.

4.1.4. Availability of Charging Infrastructure and Support Services

The analysis of the participants’ responses regarding the availability of charging stations, maintenance services, and spare parts reveals three main issues. The first one is the limited availability of charging stations. While home charging is possible, the limited public charging stations are a serious challenge, as confirmed by eight of the participants. One participant states, ‘charging stations are extremely limited, and we hope to see an increase in their numbers soon.’ Another participant emphasizes this challenge by pointing out that ‘The lack of sufficient charging stations makes long-distance travel difficult.’

The second aspect is the accessibility to maintenance centers. Six participants indicate that although the EV maintenance services are available, the number of specialized centers remains limited. One participant says, ‘maintenance services are available, but only a few specialized centers provide them.’ Another participant emphasizes this challenge, pointing out, ‘there is an urgent need to expand EV maintenance services to meet the increasing demand.’

The third aspect is the availability of spare parts. Eight participants emphasize that spare parts for EVs are either not available or very expensive. One participant notes, ‘spare parts are available, but their prices are very high.’ Some EV owners have to import parts from abroad, which delays repair and increases overall costs, as highlighted by another participant. The analysis of this theme indicates that the limited number and uneven geographical distribution of charging stations and maintenance services are significant obstacles to the adoption of EVs among Omani citizens. Furthermore, the findings demonstrate a high level of awareness among participants regarding the need to enhance infrastructure.

4.1.5. Driving Enjoyment, Comfort, and Lifestyle Appeal

Qualitative analysis reveals that there is an agreement among participants that EV offer a more enjoyable driving experience compared to gasoline vehicles. One of them notes, ‘the acceleration is more exciting, and the response is instant, making driving more thrilling.’ Another participant says, ‘EVs teach drivers discipline and road rules,’ highlighting enhanced control and precision during driving. Participants emphasize the important technological advantages that EVs offer in terms of luxury. One participant states, ‘sound insulation and ease of entry and exit make the driving experience more comfortable and luxurious.’ Another participant adds, ‘the luxury of EVs surpasses that of traditional cars because of their advanced control technologies.’ Overall, the above discussion suggests that EVs are not only an economical and environmentally option but also an element of enjoyment, comfort, and luxury. This additional appeal is expected to encourage more users to adopt EVs in the upcoming years.

4.1.6. Cost Considerations and Long-Term Financial Benefits

The high cost of EVs is a major challenge for many Omani consumers. An analysis of participants responses shows various opinions on the price of EVs and their potential for long-term economic savings. Some participants acknowledge the high costs; others emphasize that EVs become more cost-effective over time compared to traditional vehicles. One participant says, ‘EVs are cheaper than conventional cars in the long run due to their lower fuel and maintenance costs.’ Another participant adds, ‘over time, EVs save money on charging and maintenance since they do not require oil changes or expensive spare parts.’ On the other hand, some participants have concerns about the high initial purchase cost of EVs. Thus, one of them states, ‘It is expensive, which makes some people hesitant to buy it despite its future benefits.’ From a different perspective, the car brand plays a significant role in this aspect. As one participant notes, “the extent of economic savings depends on the type of the car and the manufacturer: some models offer real cost savings, while others do not.” From these findings, it can be concluded that EVs offer significant long-term economic savings. However, their high initial cost is a challenge for some consumers, especially those with limited income.

4.1.7. Confidence in EV Technology, Safety, and Market Reliability

The qualitative analysis shows that the majority of respondents have a high level of confidence in EVs safety and protective features. One participant emphasizes, ‘The safety level of EVs, considering advanced safety systems, is much higher and safer than traditional vehicles, especially with car-to-car collision scenarios.’ Another participant says, ‘EVs are extremely safe, and I highly recommend that everyone own one.’ Moreover, some respondents stress the significance of having a safe driving style in order to get the most benefits from EVs. As one participant observes, ‘The level of safety is high but depends on driving habits and compliance with safety guidelines.’ Most surveyed participants confirm their trust in EVs, and one of them claims, ‘I have really high trust in EVs, and I strongly recommend other people try them.’ Overall, most participants believe that EVs are safe and secure. Hence, EVs have become a necessity for those seeking a very safe and secure form of transport.

4.2. Quantitative Analysis Results

4.2.1. Descriptive Analysis of Participants’ Intention to Adopt Electric Vehicles

Table 4. Descriptive statistics for the dependent variable dimensions and the composite score.

Dimension	Min.	Max.	Mean	SD	Composite Score Categories	N	%
Adoption intentions	2	9	6.04	1.420	Low intention to adopt EVs	21	4.7
Future interest	5	10	8.61	1.254	Moderate intention	133	29.7
Purchase likelihood	3	9	6.94	1.324	High intention	294	65.6
Excitement and consideration	4	10	7.84	1.678
Primary choice preference	3	10	7.02	1.591
Composite score	3.6	9.0	7.289	1.179

shows descriptive statistics for the five measurement dimensions that measure the intention to adopt EVs and the ranks of the respondents based on their tendency toward adoption. The results indicate obvious differences in consumer attitudes toward EV adoption in Oman. Descriptive analysis of the composite score reveals that 65.6% of respondents belong to the high adoption group, showing a strong desire to adopt EVs; most of them consider them a possible choice for their future car. Approximately 29.7% of the participants are in the medium intention group to adopt EVs, showing interest in EV technology but expressing some concerns about its immediate adoption. On the other hand, 4.7% of the participants are in the low intention category, indicating a low tendency of turning to electric vehicles in the near future.

Among the five dimensions of the dependent variable, the “future interest” dimension is the most significant, with a mean of 8.61 (SD = 1.25), reflecting high levels of curiosity and long-term interest in buying an EV. The results of the “excitement and consideration” dimension (M = 7.84, SD = 1.68) reveal that the majority of the participants are not only aware of EVs but also are considering adopting them. The results of the “preference as a primary option” (M = 7.02, SD = 1.59) and purchase likelihood (M = 6.94, SD = 1.324) dimensions indicate that EVs are considered a moderate to strong option for future purchases. The lowest mean is also recorded in the “adoption intentions” dimension (M = 6.04, SD = 1.42), meaning that although many participants express interest in EVs, factors such as infrastructure availability and cost may affect their purchase decisions.

There is no statistically significant difference between male and female respondents regarding their intentions and preferences to adopt electric vehicles, as illustrated in Figure 1. This finding indicates that gender does not play a decisive role in shaping EV adoption attitudes, with both genders demonstrating comparable levels of enthusiasm toward adopting electric mobility.

This finding may reflect evolving social norms in Oman, where interest in sustainable technologies is increasingly widespread across both male and female consumers. It is also possible that EV-related decisions are framed more around practical and economic considerations—such as cost; reliability; and infrastructure availability—rather than traditional automotive preferences; which may vary more by gender. Future studies could further explore whether this gender neutrality extends across broader transport attitudes or remains unique to electric vehicle contexts.

4.2.2. Descriptive Analysis of the Independent Variables

Table 5. Descriptive statistics for independent variables.

Variables	Mean			SD	Min	Max
	Possible Range	Value	(%)
1. Performance Expectancy	(5–50)	41.64	83.3	3.135	25	45
2. Effort Expectancy	(5–50)	37.78	75.6	5.367	19	45
3. Social Influence	(5–50)	39.67	79.3	3.022	29	43
4. Facilitating Conditions	(5–50)	34.97	69.9	4.457	18	40
5. Hedonic Motivation	(5–50)	33.75	67.5	3.026	17	37
6. Perceived Price Value	(5–50)	36.31	72.6	2.653	23	39
7. Trust	(5–50)	40.30	80.6	2.947	21	45

shows that the descriptive analysis of the independent variables are important factors in the intention to adopt EVs, and “performance expectancy” is the most important one, with the highest mean of 41.64 (83.3%). This indicates that the participants are highly aware of the effectiveness and benefits of EVs. “Trust” in EV Technology ranks second as the most important factor influencing individuals’ decision to adopt EVs, with an average score of 40.30 (80.6%), indicating that participants have a very high level of trust in the reliability and the safety of EVs. “Social Influence” comes in third place among the independent variables, with 39.67 (79.3%). This shows how social recommendations and social norms influence individuals’ decisions to adopt EVs.

Two medium values are found: Effort expectancy (37.78, 75.6%) and perceived price value (36.31, 72.6%), indicating that participants agree on the simplicity of use and possible long-term cost savings. However, cost and accessibility still influence the purchase of vehicles in some way. On the other hand, facilitating conditions (34.97, 69.9%) and hedonic motivation (33.75, 67.5%) have the lowest mean scores. This means that participants have some concerns about the limited infrastructure and the enjoyment that comes from driving an EV.

Figure 2 shows gender differences in the main independent variables that affect EV adoption. Both males and females show similar perceptions regarding all dimensions. This suggests that gender is not a significant factor influencing the factors of EV adoption in the current study.

Figure 3 shows the differences in the main factors that influence EV adoption regarding age groups. Performance Expectancy, Effort Expectancy, and Trust are higher among participants aged 20–29, which indicates that this age group is more likely to adopt an EV. The pursuit of fun and engagement is also higher in younger groups, making enthusiasm for new technologies another factor influencing their adoption. On the other hand, the older age group (50+) shows significantly lower scores in all dimensions, indicating resistance or skepticism toward EV adoption. Facilitating Conditions and Perceived Price Value remained relatively constant across age groups, showing the importance of improving accessibility and affordability for adoption across all demographic groups.

4.3. Regression Analysis Results

4.3.1. Assessment of the OLR Relevance

This study conducts a diagnostic assessment to determine the suitability of OLR for analyzing the factors that affect EV adoption in the Sultanate of Oman. The first step is using a test of parallel lines to evaluate the model fit and validation metrics. It shows a Chi-square of 7.509 and a non-significant p-value (0.378). These results confirm the assumption and validate the suitability of the OLR model for the data of the study. The OLR analysis evaluates the goodness and explanatory power of the model. The goodness-of-fit for the model is highly statistically significant (overall model Chi-square = 350.344, p < 0.001). This suggests that the model’s predictors provide objective predictive power. In addition, Nagelkerke Pseudo R-square (0.687) and McFadden Pseudo R-square (0.501) show significant explanatory power of the model at 68.7% and 50.1%, respectively, for the variation in adoption of EVs in Oman. The model fit and validation results are presented in

Table 6. Model fit and validation metrics.

Model Fit	Value
Chi-square (df = 7)	350.344 (p-value = 0.000)
Nagelkerke Pseudo R-square	0.687
McFadden Pseudo R-square	0.501
Test of Parallel Lines	Chi-Square = 7.509 (df = 7), p-value = 0.378

.

4.3.2. Multicollinearity Diagnostics

Before conducting ordinal logistic regression, multicollinearity diagnostics were performed using a linear regression model to assess collinearity among the independent variables. The results, as presented in

Table 7. Multicollinearity diagnostics for independent variables.

Predictor Variable	Tolerance	VIF
Performance Expectancy	0.393	2.546
Effort Expectancy	0.610	1.639
Social Influence	0.412	2.430
Facilitating Conditions	0.898	1.114
Hedonic Motivation	0.986	1.014
Perceived Price Value	0.655	1.528
Trust	0.904	1.106

, indicated that all Variance Inflation Factor (VIF) values were below 2.55 and all Tolerance values were above 0.39. These values fall within the acceptable thresholds (VIF < 5.0, Tolerance > 0.2), confirming that multicollinearity is not a concern in our estimated model.

4.3.3. Hypotheses Testing Results

Table 8. Hypothesis testing summary.

Hypothesis	Result
H1: Performance expectancy significantly influences individuals’ intentions to adopt and use EVs.	Supported
H2: Effort expectancy significantly influences individuals’ intentions to adopt and use EVs.	Supported
H3: Social influence significantly influences individuals’ intentions to adopt and use EVs.	Supported
H4: Facilitating conditions significantly influence individuals’ intentions to adopt and use EVs.	Supported
H5: Hedonic motivation significantly influences individuals’ intentions to adopt and use EVs.	Not Supported
H6: Perceived price value significantly influences individuals’ intentions to adopt and use EVs.	Supported
H7: Trust significantly influences individuals’ intentions to adopt and use EVs.	Supported

shows the results of the study’s hypothesis regarding the statistical relationship between the independent variables and participants’ EV adoption. The results confirm that Performance Expectancy, Trust, and Social Influence are the strongest factors affecting the EV adoption decision. On the other hand, Hedonic Motivation is not supported, suggesting that entertainment motivation may not play a significant role in determining adoption decisions.

4.3.4. OLR Results on EV Adoption in Oman

Table 9. Coefficients and odds ratios of the ordinal logistic regression model.

Predictor Variables	β	S.E (β)	Wald	OR	95% CI for OR	p-Value
1. Performance Expectancy	0.367	0.075	24.082	1.444	1.247–1.672	0.000
2. Effort Expectancy	0.186	0.038	24.014	1.204	1.118–1.297	0.000
3. Social Influence	0.264	0.072	13.677	1.303	1.132–1.499	0.000
4. Facilitating Conditions	0.075	0.028	7.119	1.078	1.020–1.140	0.008
5. Hedonic Motivation	−0.007	0.051	0.019	0.993	0.899–1.097	0.891
6. Perceived Price Value	0.136	0.060	5.131	1.146	1.018–1.289	0.024
7. Trust	0.283	0.051	31.366	1.327	1.202–1.466	0.000

shows the OLR results illustrating the different levels of significance of the explanatory variables in EV adoption in Oman. Some variables have a strong impact on adoption, while others have a medium or low impact. Performance Expectancy, Trust, and Social Influence are the most significant predictors, while Hedonic Motivation is not statistically significant.

4.3.5. The Strongest Predictors of EV Adoption

OLR analysis indicates that Performance Expectancy is the most important factor in individuals’ intention to adopt EVs. This suggests that individuals who consider EVs efficient and high-performance are more likely to adopt them. The results also show that a one-unit change in the Performance Expectancy index is associated with a 44.4% increase in the likelihood of adopting EVs (OR = 1.444).

Trust in EV technology has a significant influence on adoption (β = 0.283, p < 0.001). The odds ratio of 1.327 demonstrates that with an increase in the trust index (consumers’ beliefs on EV reliability, EV safety, and EV long-term benefits), the probability of adoption increases by 32.7%. Similarly, social influence has a significant impact (β = 0.264, OR = 1.303, p < 0.001), highlighting the influence that peer suggestions, familial recommendations, and cultural acceptance can have on their decisions. The 1.303 odds ratio indicates that those who see positive social reinforcement for EV adoption are 30.3% more likely to adopt EVs. It is also important to check the trust interval for each of these three predictors, and all of them are greater than one, which supports their strong positive effect on the probability of EV adoption.

4.3.6. Moderate Predictors of EV Adoption

The results show that Effort Expectancy is a significant predictor, with an odds ratio (OR) of 1.204. This means that a one-unit increase in perceived ease of use leads to a 20.4% increase in the probability of adopting EVs. This indicates the essential role of consumers’ perceptions of how easy it is to integrate EVs into their daily lives. The results also confirm the positive influence of facilitating conditions, including infrastructure and support services (β = 0.075, OR = 1.078, p = 0.008). Accordingly, every one-unit increase in perceived infrastructure support leads to a 7.8% increase in the probability of adopting EVs. Similarly, Perceived Price Value has a significant effect (β = 0.136, OR = 1.146, p = 0.024). The odds ratio of 1.146 indicates that consumers who perceive EVs to offer good value for money due to long-term cost savings are 14.6% more likely to adopt EVs. The confidence intervals for these three factors include 1, supporting their significant effect on EV adoption.

4.3.7. Non-Significant Predictor: Hedonic Motivation

OLR analysis shows that Hedonic Motivation is not a significant predictor for the intention to purchase EVs, with values of (β = −0.007, OR = 0.993, and p = 0.891), meaning that individuals’ expectations of enjoyment and satisfaction when using EVs do not significantly influence their plans to adopt them in the future. Thus, the study participants are evaluating the possibility of owning an EV rather than actually owning it, which makes practical factors such as price, driving range, and infrastructure availability more influential in their decisions than the sense of enjoyment while driving. According to the results, when making a decision to switch to EVs, consumers are more likely to consider practical advantages such as efficiency, costs, and accessibility rather than entertainment factors. The research team opines that, unlike many prior studies, the entertainment motive did not impact participants’ intentions in any way. The results seem to suggest that the participants’ concentration on economic availability surpassed the inclination towards entertainment. This could further be explained by the fact that the penetration of electric cars in Omani society is still low, meaning the sorts of luxuries they enable are largely unfamiliar to the general populace who would want to use them. Thus, expectations of pleasure did not significantly impact participants’ intentions to adopt them.

5. Discussion

This study adopts a mixed-method approach relying on the expanded unified theory of acceptance and use of technology (UTAUT2). Among the seven discussed factors, Performance Expectations, Trust in EVs Technology, and Social Influence are the most important ones in adoption intention. These findings are consistent with technology acceptance theories, especially UTAUT2, that emphasize that perceived usefulness, peer influence, and trust are very important factors in determining the consumers’ attitudes toward adopting new technologies [63,64,65]. Overall, the results show that participants prefer functional efficiency and long-term reliability of the product over entertainment when making decisions to choose EVs.

In addition to informing survey design, the qualitative findings in this study served to interpret and support the ordinal logistic regression results, thereby enhancing the study’s overall explanatory power. The narratives provided by participants regarding the inadequate charging infrastructure, high costs of spare parts, and scarce maintenance services directly support the statistical significance of facilitating conditions in shaping adoption decisions. In a similar manner, owners’ consistent expressions of confidence in EV safety, reliability, and performance align with the strong predictive role of the trust factor. Conversely, the lack of emphasis on enjoyment or lifestyle appeal in the interviews reflects the statistical insignificance of hedonic motivation, suggesting that Omani consumers currently prioritize functionality, cost-efficiency, and practicality over entertainment value. This triangulation between qualitative and quantitative findings underscores the robustness of the results and highlights the contextual relevance of the adoption predictors.

Regarding the role of performance expectations in EV adoption, the results show that performance expectations are the most important factor. The results also show that participants tend to have EVs if they have positive perceptions about their efficiency, reliability, and technological advancement compared to conventional cars. Participants also identify lower operating and maintenance costs and attractive features such as autonomous driving and smart connectivity as main factors for adopting EVs. According to previous studies and global trends, adopters focus on performance, fuel economy, and environmental efficiency, which is consistent with the findings of Chanda et al. [66], Jain et al. [37], and, and Singh et al. [44]. Therefore, policymakers, car agents, and manufacturers should strengthen public awareness of the benefits of EVs through targeted campaigns that focus on the technological advancements and long-term cost savings of EVs.

Regarding the influence of social networks on EV adoption decisions, the results indicate their crucial role in shaping participants’ attitudes. Some of them indicate that family, friends, and colleagues are the main influencers in their purchase decisions. Thus, personal recommendations and social norms are really important. This is very important in an emerging market such as Oman, where adoption depends more on trust in the social network than on the media or experts. This is consistent with the previous studies that have shown that social networks influence market adoption with direct and indirect effects [67,68]. The existence of early adopters in a consumer’s social network can increase the spread of EVs due to the positive experiences from people they trust [17]. Accordingly, social influence is crucial in encouraging individuals to buy EVs. It should be taken into account that the growth of user groups and peer-to-peer interaction platforms increases promotion and establishes social credibility for EVs, that accelerates their adoption.

In addition, general trust in technology is also a significant factor that affects the consumer’s acceptance of EVs. Individuals are more likely to buy EVs when they consider them as safe and a new generation of transportation [69]. This study identifies trust as the third most important factor in EV adoption in Oman. Trust is determined by battery life, vehicle safety, and charging system reliability. This finding is consistent with previous studies that have shown trust to be a critical element in technology acceptance [33,53]. Moreover, general trust in manufacturers and regulatory frameworks affects autonomous vehicle adoption [70], but trust in EVs extends to institutional support, warranties, and safety standards. This means that manufacturers and policymakers must address existing doubts. The team of this study believes that this can be achieved by providing more information about battery efficiency, expanding the geographical range of charging stations, extending the warranty period, and conducting independent safety audits. Accordingly, the consumer’s uncertainty can be reduced, and their acceptance of EVs can increase.

Regarding perceived price value, the results prove that it is an important factor in forming the participants’ intention to adopt EVs, which is consistent with several previous studies using the theoretical UTAUT2 model [47,66,71,72]. Although many participants state that EVs need lower operating costs in the long run, the high initial cost remains a real barrier. This suggests that while lower operating costs increase the likelihood of adoption, high initial costs and limited charging infrastructure remain important issues. Therefore, reducing import tariffs, providing financial incentives, and increasing public awareness of the long-term benefits should be highlighted to overcome these price barriers and accelerate adoption rates.

Regarding effort expectations and ease of use, the study results show the importance of this factor, as EVs are easier to use compared to conventional vehicles. The simplicity of the electric drive system, ease of driving, and the lack of complex mechanical parts also support the superiority of EVs over conventional vehicles. However, there are still some concerns regarding the process of adopting EVs, especially charging and energy consumption. This strong emphasis on ease of use, even among participants who have not owned an EV, may reflect the general observability of usability features—such as smooth driving or reduced maintenance needs—which can be understood or witnessed without direct experience.

Unlike the other factors, entertainment motives for EV adoption are not a decisive factor in the adoption intention. The results indicate that consumers in the decision-making stage prefer practical and economic factors over personal enjoyment. This may be explained by the fact that many survey participants have never driven an EV themselves and thus lack first-hand experience of the driving pleasure or emotional satisfaction often associated with EVs. Consequently, entertainment-related features remain abstract or less relevant at the pre-adoption stage.

This is in contrast to the results of some previous studies conducted based on the UTAUT2 model that confirm that entertainment motives are an influential factor in EV adoption [73,74]. However, its unimportant role in the current study indicates that factors such as cost, practical benefits, and ease of use are more influential factors. This finding highlights that in early-stage EV markets like Oman, consumers prioritize observable utility over experiential appeal, which may shift as direct exposure to EV driving increases.

6. Conclusions and Policy Implications

6.1. Conclusions

This study aims to close part of the knowledge gap related to EV adoption by studying consumers’ thinking and attitudes in Oman. The results show that adopting EVs is connected to important factors such as factors related to practical and economic considerations, which are the most significant in the EV adoption decisions. Performance expectations are the most influential in the adoption decision, as the adoption decision of EVs is based on their functional efficiency, environmental benefits, and savings in fuel and maintenance costs. This study also indicates that consumer intentions are significantly influenced by their trust in EV technology; adoption is more likely among individuals who trust the long-term value of EVs, as well as their safety and reliability.

In addition, social influence is another important factor showing the influence of social norms and recommendations from friends, especially those with experience, on purchasing decisions. The study also indicates that individuals’ willingness to adopt EVs is greatly affected by their knowledge of the benefits of use, the availability of maintenance centers, highway charging stations, residential areas, and their geographical distribution. Conversely, entertainment motivation is not a very important factor in EV adoption intentions. This finding suggests that Omanis place greater importance on practicality and affordability factors than on entertainment and innovation ones.

It is also important to acknowledge a limitation regarding the representativeness of our sample. The study sample is concentrated among younger participants (70% aged 20–29) and urban residents (82%), which could limit the generalizability of the findings to the broader population in Oman, particularly those residing in rural areas or belonging to older age demographics who may encounter distinct infrastructural challenges or possess varying levels of awareness. While purposive sampling provided valid responses within the context of emerging electric vehicles, future research should contemplate the use of post-stratification weights or the implementation of stratified sampling to more effectively represent demographic diversity. Furthermore, it is recommended that additional research be conducted to investigate the variations in adoption intentions among rural and underrepresented populations, thereby offering a more comprehensive national perspective on electric vehicle adoption.

Another limitation we recognize in our study is the reliance on self-reported data, which can be influenced by social desirability bias. Participants may overestimate their willingness to adopt electric vehicles (EVs) based on perceived environmental or social criteria, which may not accurately represent their true behavior. Moreover, although the results offer significant implications for Oman, their applicability to other GCC nations may be restricted. Variations in infrastructure preparedness, policy environments, and socio-economic conditions throughout the Gulf region suggest that similar studies should be conducted in other GCC contexts to validate the applicability of these insights. Future research is also recommended to investigate possible interaction effects (for instance, between income and trust or age and social influence), which could uncover more profound insights into how various factors collectively affect EV adoption behavior.

In addition to the national context, the results of this study are consistent with broader trends observed in both the GCC countries and emerging economies. In Saudi Arabia and the UAE, performance expectancy and trust in technology have consistently emerged as dominant drivers of EV adoption, reflecting the dual priorities of environmental sustainability and technological innovation in oil-rich but increasingly climate-conscious societies [75]. Similarly, in emerging regions such as India, infrastructure readiness and perceived price value remain critical, given the persistent challenges related to affordability, accessibility, and service availability [72]. These regional parallels validate the relevance of the UTAUT2-based framework used in this study and highlight the potential for cross-country policy learning. Future research comparing EV adoption across the GCC and other emerging markets could yield valuable insights into shared enablers and barriers, helping to inform scalable and context-specific strategies for accelerating the shift toward sustainable mobility.

6.2. Policy and Industry Implications

The researchers believe that, in light of the study results, it can be recommended to implement several measures to support and accelerate the adoption of electric vehicles in the Sultanate of Oman as follows:

• Paying attention to the infrastructure and public charging network for EVs is essential, as the availability and accessibility of charging stations were shown to significantly influence adoption behavior. In addition, policymakers should give priority to expanding the charging stations in all areas, providing fair geographical distribution in order to enhance consumer confidence and encourage them to adopt EVs. This aligns with the statistically significant role of facilitating conditions, which affect consumers’ perceptions of ease of access and system support.
• Encouraging investments in the EV services sector, particularly in maintenance infrastructure and support systems, directly responds to consumer concerns about long-term usability and technical support. Facilitating the licensing procedures for maintenance centers and providing incentives for private sector investment in service facilities can address such concerns effectively. This recommendation is grounded in the significant impact of facilitating conditions identified in the regression model.
• Enhancing financial incentives, such as reducing tariffs on imported EVs and spare parts, and providing direct support through low- or no-interest loans, can increase affordability and adoption. This is especially critical for middle-income consumers, who may perceive upfront EV costs as a barrier. The significance of perceived price value in the regression analysis supports this recommendation as a core economic driver of EV adoption.
• Raising public awareness and promoting environmental sustainability are essential to strengthening consumer perceptions of EV performance and long-term benefits. Informing the community about the environmental advantages of EVs and their role in reducing carbon emissions contributes to more sustainable mobility choices. This approach is supported by the strong influence of performance expectancy, which was the most powerful predictor in the statistical model.
• Initiating consumer confidence in EVs through transparent marketing, highlighting safety, technological innovation, and reliable warranties, is crucial for building trust. Collaboration among government institutions, vehicle agencies, and industry stakeholders is recommended to ensure consistent and credible messaging. This reflects the statistically significant effect of trust in the regression findings, showing its direct link to adoption intentions.
• Investing in scientific research aims at stimulating positive trends toward EVs. Moreover, encouraging and funding multidisciplinary studies can highlight the social, technological, economic, and environmental factors that affect consumer decisions. The results of this research can provide valuable scientific insights that help policymakers, decision makers, and stakeholders develop effective strategies that accelerate the adoption process of sustainable mobility.

6.3. Limitations and Future Research

This study has some limitations that should be taken into consideration in future research, which are

• A limitation of this study is that the qualitative data were collected exclusively from current electric vehicle owners. While this approach provided direct empirical insights, it excluded potential users whose expectations and concerns may differ. Therefore, future research is encouraged to include current and potential electric vehicle users to provide a more comprehensive understanding of the motivations and barriers to electric vehicle adoption among different user groups.
• While conducting the current study, the researchers identified some limitations that could have a significant impact on the generalizability of the results. It became clear that the majority of participants in the qualitative phase were male, which may make it difficult to understand potential gender differences in electric vehicle adoption. Furthermore, the focus on early adopters of this technology may not necessarily reflect the views of potential future users as the market evolves. The results also highlight several challenges related to infrastructure, particularly the limited availability of charging stations and maintenance services, indicating the importance of conducting future, long-term studies to measure the impact of these factors on adoption decisions and consumer satisfaction. Since the study relied on a cross-sectional design, it does not allow for tracking shifts in attitudes and behaviors over time. Therefore, adopting longitudinal designs in subsequent studies would contribute to a deeper understanding of the dynamics of electric vehicle adoption in changing contexts.