Interpretive Structural Modeling of Influential Factors Affecting Electric Vehicle Adoption in Saudi Arabia

Abstract

Electric vehicle (EV) adoption is a critical step toward achieving sustainable transportation and reducing carbon emissions, especially in regions like Saudi Arabia that are undergoing rapid urban development and energy diversification. However, the widespread adoption of EVs is hindered by a variety of interrelated economic, infrastructural, and policy-related factors. This study aims to systematically identify and structure these influencing factors using Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on a thorough literature review and expert consultation, 17 key factors affecting EV adoption in Saudi Arabia were identified. The ISM results reveal that purchase price, long-term savings, resale value, urban planning, and accessibility are among the most influential drivers of adoption. The MICMAC analysis complements these insights by categorizing the variables based on their driving and dependence power. The developed hierarchical model provides insights into the complex interdependencies among these factors and offers a strategic framework to support policymakers and stakeholders in accelerating EV uptake. The study contributes to a deeper understanding of the dynamics influencing EV adoption in emerging markets.

1. Introduction

In recent decades, the development of a sustainable transportation system has become a critical area of research, largely driven by escalating environmental and economic concerns. Due to its heavy dependence on fossil fuels, the transportation sector is the second-largest contributor of greenhouse gases (GHGs), specifically carbon dioxide (CO₂). Petroleum-powered vehicles are estimated to consume approximately 23% of all carbon dioxide emissions and 17% of the world’s hydrocarbon fuel [1]. In this context, to promote sustainable urban mobility, electric vehicles (EVs) are increasingly viewed as a promising alternative. EVs offer significant public health and environmental benefits by reducing oil dependency and mitigating pollution [2]. Their widespread adoption is expected to drive substantial shifts in stimulating economic growth and personal transportation technologies while also enhancing environmental sustainability. Furthermore, EVs typically provide quieter, smoother, and vibration-free driving, and require less maintenance compared to traditional internal combustion engines (ICEs).

EVs utilize rechargeable batteries that are powered by external electrical sources, and their widespread adoption has significant potential to improve air quality and reduce carbon dioxide emissions [3]. The emergence of EVs has transformed the automobile sector during the recent decades. This transformation is driven by a range of interrelated factors, including technological innovations, climate change, energy security, consumer attitudes, and sustainability objectives. Their adoption is noticed as instrumental in improving national and international efforts to fulfill climate adaptation commitments. In addition to environmental advantages, EVs offer several benefits over vehicles powered by ICEs, including reduced cabin noise and vibration, improved performance at low speeds, accessible charging infrastructure, zero tailpipe emissions, and lower operational costs [4]. The broader integration of EVs also has significant potential for enhancing industrial and economic growth by attracting capital investment in developing and developed countries. Accordingly, the large-scale deployment of EVs has been endorsed by several governments as a key strategic approach to achieving global climate objectives, limiting the temperature rise to below 2.0 degrees Celsius [5]. In a noteworthy development, eight leading nations, China, Canada, France, Japan, Sweden, the United States, Norway, and the United Kingdom, publicly committed to expanding the proportion of EVs in their government fleets, as announced at the Marrakech Climate Change Conference [6]. This collective action represents a significant transition towards reducing greenhouse gas (GHG) Emissions in the logistics and transportation sector, reflecting a broader shift towards sustainable, clean, and energy-efficient mobility solutions. Encouraging widespread EVs is a key and strategic pillar that is critical for promoting smart and sustainable urban transportation systems. On the economic front, the affordability of EVs can be improved by continued technological progress which is projected to drive down battery costs [7,8]. It is estimated that by 2030, EVs will be more cost-effective than conventional gasoline-based vehicles, accelerating their market penetration [9]. Considering the infrastructure perspectives, the expansion of charging stations is essential for alleviating range anxiety and encouraging EVs’ adoption, particularly in rural and suburban areas [10,11]. Large-scale deployment of EVs is significantly hindered by the lack of adequate charging’s infrastructure [12,13]. Consequently, identifying the optimal location for the Electric Vehicle Charging Stations (EVCSs) has become a key priority research area for the growth of the EV sector in urban areas [14].

Major urban cities across various economies face significant critical noise and air pollution issues, primarily caused by the extensive reliance on fossil fuels in ICEs. Over the past thirty years, Saudi Arabia has also witnessed a significant rise in pollution levels, driven by accelerated urbanization, a growing urban population, and rapid industrial development. Reducing transportation-related emissions through the adoption of reliable, sustainable, and clean mobility solutions can play a significant role in helping policymakers implement sustainable development strategies that address associated impacts and mitigate climate change. The Kingdom’s policymakers can mitigate air pollution, enhance human well-being, and promote sustainable urban development by embracing renewable energy sources with EVs. Consequently, the deployment of EVs as a sustainable mode of transportation has the potential to reduce environmental degradation while supporting the Kingdom’s vision of sustainability. The Kingdom’s growing population and rising purchasing power, alongside the government’s focus on a sustainable environment, are accelerating the widespread adoption of EVs in the country. However, the successful implementation of EVs presents significant challenges, including a critical step: identifying the key factors that influence the consumer’s willingness to adopt EVs over conventional vehicles. Consumers’ willingness to pay and their attitudes are vital factors in EV adoption; other factors, such as the availability of technology, charging station availability, the single-charge range, and grid preparedness, can also impact EV adoption. Effectively addressing these factors through the development of charging infrastructure, technological innovation, and the implementation of legislative initiatives can significantly accelerate adoption in the Kingdom.

Under Vision 2030, the Kingdom aims for 30% of vehicles in Riyadh to be electric by 2030, supported by over USD 50 billion in investments for EV infrastructure and manufacturing [15]. The creation of Ceer, the nation’s inaugural domestic EV manufacturer, marks a significant advance in Saudi Arabia’s automotive and industrial evolution. This initiative is boosted by significant foreign investments, particularly from Lucid Motors’ first international manufacturing site in King Abdullah Economic City [16]. Additionally, it aligns with the Saudi Green Initiative (SGI), which seeks to decrease carbon emissions by 278 million tons annually by 2030 [17]. A PIF-backed entity, EVIQ is tasked with deploying over 5000 fast chargers across 1000 locations by 2030 [18]. In Saudi Arabia’s social context, where community and family opinions hold substantial weight, social influence acts as a vital driver by normalizing EV adoption and reducing uncertainties associated with new technology. In a study based on the Theory of Planned Behavior, Habib et al. (2024) suggests that favorable attitudes among peers, friends, and family members toward EV adoption increase the likelihood of consumers developing positive attitudes, and enhances their willingness to consider and potentially purchase EVs [19]. In terms of cultural aspects, Maher et al. (2024) highlights that consumer attitudes are influenced by local socio-economic factors, with consumers in Saudi Arabia prioritizing economic benefits such as fuel savings and government incentives over environmental considerations [20]. In the KSA context, conventional internal combustion (ICE) vehicles still dominate (~93% of sales as of February 2025) because of the cheap fuel (USD 0.50/L) and plentiful options to keep ICE vehicles highly attractive [21]. Another important aspect is the development of charging infrastructures across the Kingdom to support the deployment of electric vehicles, particularly given its vast geographic area and the challenges of reaching rural and remote regions. Saleh et al. (2022) stressed that developing sufficient charging networks and strengthening grid infrastructure are therefore critical to facilitate widespread EV adoption in the country [22].

This study investigates the critical factors influencing the adoption of EVs in Saudi Arabia. By employing Interpretive Structural Modeling (ISM) and MICMAC analysis, it offers a systematic and quantitative evaluation of the interrelationships among these factors. The integrated approach is designed to support policymakers, industry leaders, and stakeholders in formulating data-driven strategies to accelerate EV adoption and support long-term economic and environmental objectives. The key contributions of this research are as follows:

• It identifies a comprehensive set of factors impacting EV adoption, including the Total Cost of Ownership (TCO), Charging Infrastructure Availability, Government Incentives and Policies, Technological Advancements and Range, and Environmental Sustainability. This inclusive perspective captures the multi-faceted nature of adoption decisions.
• It assesses the driving power and dependence power of each factor, providing insights into which elements act as primary drivers and which are influenced by others. This distinction helps establish strategic priorities for effective intervention.
• It develops a hierarchical structure using ISM and MICMAC methodologies, uncovering dependencies and influence pathways among the factors. This structure serves as a decision-making tool for removing barriers and designing targeted initiatives to foster widespread EV adoption.

2. Background

This section addresses the study’s objectives by exploring the current state of electric EV adoption, identifying research gaps and contributions, and outlining the significant key drivers for EV implementation.

2.1. EV Adoption and Present Scenario

The transportation systems of emerging economies remain heavily dependent on fossil fuels [23], with ICE vehicles dominating the roads and consuming large quantities of hydrocarbons, including compressed natural gas (CNG), petrol, and diesel [24]. These vehicles include public buses, trucks, private cars, motorcycles, taxis, and auto-rickshaws, contributing significantly to GHGs. For instance, rapid economic growth in Saudi Arabia has led to an increase in personal vehicle and motorcycle ownership, particularly in major cities. Additionally, ridesharing services have expanded the use of motor vehicles, further elevating the demand for fossil fuel consumption [25].

The reliance on these vehicles exacerbates air pollution and contributes to climate change. Saudi Arabia, like many other nations, faces rising CO₂ emissions due to vehicle exhaust, industrial activity, and increased energy consumption, including the use of air conditioning [22,26,27]. The country is grappling with high levels of air pollution, which poses serious public health and environmental risks. The transport sector alone is responsible for a significant portion of national NO₂ and CO₂ emissions.

In recent years, Saudi Arabia has begun to adopt EVs as part of a broader strategy to reduce GHGs and promote sustainable transportation [28]. The government has set ambitious targets for reducing emissions by 2030 and is promoting the use of electric and hybrid vehicles, developing charging infrastructure, and encouraging investment in electric public transport [22,29]. Furthermore, Saudi Arabia is currently witnessing the initial phases of domestic EV production, as multiple companies are developing strategies to manufacture electric cars, buses, and essential components such as batteries. Public initiatives also focus on electrifying major transport networks [30]. For example, the introduction of electric buses for long-distance routes is underway, and the government is facilitating research and development in EV technology. These efforts are aligned with national sustainability goals and the global push toward low-emission transport systems.

Smart cities, founded on information and communication technology, present a great opportunity for advancing EVs and their incorporation into sustainable urban development [31]. These cities highlight the efficient use of resources to meet sustainability objectives. In the context of smart cities, EVCSs are crucial for facilitating the adoption and implementation of EVs, thereby enhancing the sustainability of urban settings [32]. With the proliferation of EVs, the strategic location of EVCSs become a vital concern for researchers and policymakers. Multiple studies have investigated different approaches, including coverage problems, to determine the ideal sites for charging stations [33,34]. This strategy emphasizes the equitable distribution to optimize regional coverage, hence reducing the distance drivers must travel to reach charging stations. The heuristic placement of EVCSs utilizes computational techniques to determine optimal sites based on population density, road networks, and power grid capacity [35,36]. Using traffic flow data, the flow-capturing method identifies regions with significant EV demand and positions EVCSs to meet these particular demands [37]. Considering the dynamic character of traffic flows, the traffic network equilibrium method maximizes EVCS location to reduce congestion and guarantee effective charging operations [38,39]. These studies offer significant insights and methodologies for tackling the EVCS location challenge. However, the methodologies employed in the aforementioned studies exhibit certain limitations regarding EVCS locations. For instance, the coverage problem may result in an uneven distribution of charging stations; the heuristic approach does not guarantee a globally optimal solution; the flow-capturing method may inadequately account for dynamic traffic changes; and the traffic network equilibrium approach may fail to represent real-time traffic fluctuations accurately.

In many countries, including China, where EV adoption is still in its early stages, the planning of EVCSs primarily aims to optimize net social benefits [40]. Additional waiting periods at charging stations can notably disrupt drivers and increase overall societal costs [36]. The widespread availability of the Internet has facilitated the emergence of online reservation services for EV charging as a practical approach to addressing these challenges. These services enable drivers to secure charging spots in advance, thereby eliminating or substantially reducing waiting periods and improving consumer convenience. Reservation services enhance system efficiency and enhance consumer satisfaction, promoting a more favorable perception of EVs and facilitating their broader adoption.

EV adoption in Middle East and North Africa (MENA) has experienced significant growth in recent years. Prior research has indicated an expected rise in the EV market from USD 2.7 billion to USD 7.65 billion from 2023 to 2028, respectively [41]. Barakat et al. conducted a comparative study across the MENA region. The research divides the region into three sub-regions, “Comprehensive Ecosystem Builder (e.g., UAE and KSA), Policy-Driven Outlier (e.g., Jordon) and Emerging Industrial Hubs (e.g., Egypt)”. The study also recommended strategies and policies for each region, for example, prioritizing public–private funds in battery technology, increased grid interoperability, and collaboration across different regions based on charging connector, payment, and charging standards [42]. Similarly, Qadir et al. (2024) [43] investigated the current and future EV polices across the Gulf Cooperation Council (GCC), emphasizing mitigating environmental challenges by reducing oil dependency. Considering the geopolitics and economic context of the region, the researcher recommended an exemption from import duties, the development of public charging infrastructure, and the provision of public awareness and a priority access complimentary card [43].

Similarly, a recent study from Alanazi et al. (2025) [44] discovered the potential challenges, opportunities, and requirements for EV development in the GCC. The findings show that there is a significant requirement of investment in Electric Road System (ERS) infrastructure and in battery technology [44]. Similarly, Ottesen et al. (2025) [45] investigated consumers’ experience in Kuwait, utilizing a questionnaire survey. The research evaluated challenges including the lack of charging infrastructure, a higher charging time, and maintenance including difficulties finding spare parts [45]. Meanwhile, Jayabalan et al. (2024) found that major concerns to adopt EVs are the higher purchasing price, a lack of infrastructure, and weather conditions [46]. However, Alyamani et al., in Riyadh, determined the willingness of consumers, utilizing a mixed logit model. The results indicate that nearly sixty percent of participants intended to purchase an EV within the next 3 years; moreover, expatriates, females, and consumers in their 40s were more interested. At the end, the researcher offered policy insights for the effective implementation of EVs in Riyadh [15].

2.2. Related Works, Research Gaps, and Contributions

In recent years, EVs have garnered significant global attention due to their potential to serve as viable and sustainable alternatives to conventional ICE vehicles, thus reducing the dependence on fossil fuels [47]. The widespread adoption of EVs is expected to foster economic and social growth, improve energy security, and reduce GHGs, contributing to climate-saving activities and supporting policymakers and global leaders in meeting the Sustainable Development Goals (SDGs) [5]. This has made EVs a primary focus in the transportation sector, prompting researchers to examine various aspects such as key technologies [4,48,49], adoption models [50,51], battery management systems [52,53], energy consumption [54,55,56,57], and barriers to EV adoption [9,58].

Previous studies have employed different methodological approaches for identifying the factors influencing EV adoption, recognizing that the proper identification and prioritization of these factors are critical for the successful implementation of EVs. For example, Liu et al. investigated the key drivers for EV diffusion from a multi-stakeholder perspective in China, utilizing the 2-tuple linguistic method and the decision-making trial and evaluation laboratory (DEMATEL) to prioritize critical factors [59]. Similarly, Liang et al. examined the economic factors influencing the establishment of EV charging stations using the DEMATEL-ISM method [60]. Other researchers, such as Verma et al., have explored the drivers of EV purchase decisions based on innovation diffusion theory [61]. Meanwhile, Suman et al. (2020) employed SWOT analysis to evaluate the potential of the EV market and business strategies [62]. Ahmed and Karmaker addressed the challenges of EV adoption [63], while others have explored the integration of renewable energy into EVs [64,65] and charging station infrastructure [65,66]. Aungkulanon et al. (2023) applied a Fuzzy AHP to rank obstacles to EV adoption in Thailand [67], identifying infrastructure policy barriers as most critical. Such AHP approaches yield priority weights for factors but, as noted by Sonar et al. (2023), do not consider the interrelation between criteria [68]. Sonar et al. (2023) used the DEMATEL to uncover causal links among EV adoption criteria in India [68], and Kuo et al. (2022) combined ANP and DEMATEL to analyze interdependent EV transition barriers [69]. These methods identify influential factors but do not structure them into a clear hierarchy of dependence from root causes to final outcomes. Pamidimukkala et al. (2023) developed a structural equation model of EV adoption barriers in the United States (using survey data from 733 respondents) [70]. Their SEM quantified how different barrier categories affect adoption, showing that all but environmental barriers were significant. This method requires a pre-existing, well-defined theoretical model to test. In a study context, a large sample of observational data is not available in Saudi Arabia. Alyamani et al. (2024) used a stated-preference survey and mixed logit in Riyadh (Saudi Arabia) to identify the importance of charging infrastructure and incentives for EV adoption [15]. These quantitative approaches provide valuable insights, but they focus on statistical relationships rather than explicitly structuring the factors. While methods like AHP and SEM calculate numerical weights and the DEMATEL identifies cause-and-effect links, ISM uses expert knowledge to create a hierarchical structure. This structure reveals which factors are key drivers and which are dependent outcomes.

A key strength of the proposed ISM/MICMAC approach is its ability to unpack context-specific complex problems by offering an explicit understanding of the hierarchical structure of interrelationships among the considered factors. Unlike SEM which relies on large sample sizes, strict statistical assumptions or AHP which deliver numerical weights, ISM/MICMAC commonly relies on small expert panels, allowing researchers to map driving dependence powers and causal linkages even when empirical data are fragmented. Further the ISM interpretation also facilities the integration of tacit knowledge from diverse domain experts, offering a systematic model for visualizing how enablers and barriers interact. MICMAC complements this by quantifying dependence powers and driving, providing crucial insights into which factors are the key drivers, autonomous, dependent, or linkage variables. Together, the ISM/MICMAC approaches offer a balanced blend of flexibility, transparency, and explanatory depth, making them suitable to be considered for policy-oriented studies where structural relationship understanding among the barriers and enablers is important.

Despite the growing body of research, several gaps remain. Current studies have often focused on specific factors or perspectives, such as those in industrialized countries, and have not fully explored the multi-dimensional nature of EV adoption. There is a lack of comprehensive research that simultaneously considers behavioral, social, infrastructural, and strategic factors in emerging economies [71]. Additionally, studies have not adequately addressed the interrelationships among the various factors influencing EV adoption.

This study selected a comprehensive set of 17 variables based on an extensive review of the existing literature across these primary domains: consumer adoption of EVs, charging infrastructure optimization, and grid capacity and policy integration. Previous studies predominantly focus on isolated factors within these domains, often overlooking the interdependencies between them. For example, many studies on EV adoption emphasize consumer willingness to pay or range anxiety without considering the impact of infrastructure and grid readiness. Others focus on infrastructure placement but neglect the broader socioeconomic and policy factors that drive consumer demand. By synthesizing these disparate streams, this study selected variables that cover not only consumer behavior and infrastructure but also technological, policy, and economic factors that are specific to Saudi Arabia’s unique context. To enhance analytical clarity, these 17 variables were consolidated into five over-arching categories: Charging Infrastructure Availability, Government Incentives and Policies, Technological Advancements and Range, Total Cost of Ownership, and Environmental Sustainability. These categories ensure a more structured approach to understanding the multi-dimensional aspects of EV adoption. The variables were selected based on their relevance to both the environmental and the economic goals of the Kingdom’s Vision 2030, ensuring that they address the identified research gaps and provide a more holistic framework for understanding EV adoption and infrastructure deployment.

Further this research aims to address these gaps by integrating multiple dimensions of EV adoption and hierarchically modeling the drivers of EV diffusion. Specifically, this study will employ robust Interpretive Structural Modeling-Matrix Impact of Cross-Multiplication Applied to Classification (ISM-MICMAC) approaches to evaluate the drivers of sustainable mobility in emerging economies. In doing so, the study intends to contribute to achieving specific SDGs and provide a novel framework for understanding the adoption of sustainable mobility practices in these contexts.

2.3. Identification of the Key Drivers for Successful Adoption of EVs

Haddadian et al. [5] have briefly discussed various drivers and barriers to accelerating EV adoption and suggested the development of a suitable regulatory framework to facilitate this process. Guno et al. [72] focused on how different drivers influence EV adoption, specifically within the public transportation sector. Several researchers have utilized the Theory of Planned Behavior (TPB) to predict potential customers’ intentions toward EVs. For instance, Moons and De Pelsmacker [73] explored the factors affecting an EV’s usage intention using the TPB and identified several correlated elements.

Several studies have highlighted the significance of individual TPB factors or their combinations in relation to EV adoption in developing countries, including India [74,75], Pakistan [76], Indonesia [77], and Malaysia [78]. The TPB posits that attitudes, subjective norms, and perceived behavioral control influence behavioral intentions, with the potential to broaden the scope of analysis [74,76,77]. Additionally, Wu et al. [47] examined the public acceptability of EVs, focusing on customers’ environmental concerns, and found it to exert a substantial indirect influence on adoption intentions. Ng et al. [79] conducted a psychological study on environmental behaviors to assess customer purchasing intentions for EVs, while Xu et al. [80] investigated how consumers’ driving experiences affect EV adoption intentions from an emotional perspective. Jenn et al. [81] emphasized the necessity of various financial and non-financial incentives and their impact on EV adoption.

To identify a preliminary list of key drivers for EV adoption, Boolean operators were utilized in the search for relevant articles using the following key terms: “drivers for EVs adoption” OR “factors for EVs introduction” OR “critical success factors for EV acceptance” AND “EVs in emerging economies” OR “achieving SDGs by identifying the key factors for EV implementation in emerging economies.” The study involved examining articles from databases such as Google Scholar, Science Direct, and Scopus, considering only research published after 2010.

2.4. Summary of Existing Studies

Recent studies have systematically examined the existing literature concerning difficulties, categorizing them into several distinct classifications: financial implications associated with EV ownership, the influencing factors affecting EV manufacturers, the technological characteristics of electric vehicles, infrastructural limitations, and the psychological traits of road users [82]. Since this paper focuses on Saudi Arabia, our discussion is limited to studies about the Saudi market. As there are very few papers focusing on Saudi Arabia, we have extended our discussion to include studies of not only the GCC region, but other emerging economies as well.

In Riyadh, consumer acceptance of EVs is significantly influenced by monetary incentives, accessible charging infrastructure, and non-financial benefits like designated parking spaces. However, cultural factors, such as a lower likelihood of Saudi nationals purchasing EVs compared to expatriates, also play a role [15]. A study aims to utilize a Multi-Level Perspective (MLP) framework to examine the current state of EV adoption in Turkey and interpret the challenges and prospects for a transition to EVs. The transition to EVs faces socio-technical challenges, including underdeveloped policies and market dynamics, which are common in less-developed transport settings [83]. Similarly, in Jordan, while government incentives like tax exemptions are crucial, challenges such as insufficient charging infrastructure and high initial costs persist, necessitating public–private collaboration to enhance infrastructure and consumer education [84].

In Oman, consumer concerns about battery reliability in extreme weather, high purchase prices, and insufficient charging stations are significant barriers, although there is moderate consumer interest in EVs [46]. Further, a study suggests in UAE Infrastructure development, particularly the establishment of a comprehensive charging network, is a critical barrier, as the current infrastructure is insufficient to support widespread EV adoption [85]. The comprehensive investigations have pinpointed complex factors that significantly influence the adoption of electric vehicles (EVs) in the context of Saudi Arabia.

3. Identification of Influence Factors

The effective adoption of EVs depends significantly on a range of influencing factors. To identify and structure these factors, this study engaged 13 domain experts, comprising 6 industry professionals and 7 academic researchers with direct involvement in EV-related initiatives (

Table 1. Summary Statistics of Experts Interviewed.

Expert ID	Designation	Sector	Qualification	Experience (Years)	Expertise
E1	Professor	Academia	PhD	25	Environment and sustainability
E2	Professor	Academia	PhD	10	Environment and sustainability
E3	Professor	Academia	PhD	35	Sustainable transportation
E4	Associate Professor	Academia	PhD	15	Intelligent Transportation System
E5	Assistant Professor	Academia	PhD	14	Road Asset Management
E6	Assistant Professor	Academia	PhD	8	Transportation and safety
E7	Research Associate	Industry	Master	4	Smart Mobility Solutions
E8	Research Associate	Academia	Master	4	Integration of autonomy in urban environment
E9	Research Associate	Industry	Mater	5	Urban Transportation Management
E10	Research Associate	Industry	Master	10	Urba Planning
E11	Research Analyst	Industry	Master	4	Design Engineer
E12	Research Analyst	Industry	Master	3	Urban Planning
E13	Research Analyst	Academia	Master	4	Intelligent transportation system

). The selection of expert participants aligns with existing ISM and MICMAC research, where sample sizes typically range between 5 and 10 [86], and as noted in prior studies, the minimum acceptable expert input for structural modeling is at least 2 [87]. Moreover, while the sample size of 13 experts may appear to be relatively small, it is consistent with the practice in expert-based studies, where panels of 10–15 experts are often employed to ensure both the depth of knowledge and the feasibility of consensus-building. Recent studies have also demonstrated the adequacy of similar sample sizes in structured modeling and expert judgment research [88,89]. Therefore, the sample used in this study is considered appropriate and sufficient to generate reliable insights into the interrelationships among EV adoption factors.

All experts in this study were actively engaged in EV development programs in Saudi Arabia. To ensure informed contributions, a collection of the relevant literature on EV adoption was provided to the participants. Following a 15-day review period, semi-structured interviews and a structured brainstorming session were conducted to extract expert insights. Through this collaborative process, 17 key variables influencing EV adoption were identified.

To enhance analytical clarity, these 17 variables were consolidated into five overarching categories: Charging Infrastructure Availability, Government Incentives and Policies, Technological Advancements and Range, Total Cost of Ownership, and Environmental Sustainability. Redundant or overlapping items were merged, and shared causal roots were grouped, resulting in a refined, comprehensive framework for evaluating EV adoption drivers. The hierarchical structure of these factors, as developed through the ISM approach, is illustrated in Figure 1.

4. Materials and Methods

This section outlines the research approach, including its design, methodology, data collection techniques, sampling strategy, expert selection criteria, and analytical tools. The study adopts an interpretivist philosophical stance, emphasizing the importance of expert interpretation and contextual understanding. The research design integrates a comprehensive literature review, expert input, and structured analysis. Data was gathered through a two-stage process: a systematic review of the relevant academic literature and in-depth discussions with subject matter experts. The ISM technique was used to identify, organize, and map the interrelationships among the key factors influencing EV adoption. To support and validate the ISM results, MICMAC analysis was conducted, offering insights into the driving and dependent nature of the identified factors.

4.1. ISM Methodology

ISM is a widely recognized methodology for identifying hierarchical relationships and dependencies among influencing factors. The ISM process begins by selecting a set of variables related to the research problem, followed by establishing contextual relationships between them through expert input. Initially, a Structural Self-Interaction Matrix (SSIM) is developed based on pairwise comparisons of the variables. This SSIM is then converted into a Reachability Matrix, with a careful examination for transitivity to ensure the logical consistency of the identified relationships. Through this refinement, the Final Reachability Matrix (FRM) is obtained.

Subsequently, the factors are organized into different hierarchical levels, and the Reachability Matrix is structured into a conical form. To verify conceptual soundness, MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis is conducted, highlighting the driving and dependence powers of each factor, and any inconsistencies are addressed accordingly.

Broadly, the ISM procedure includes generating the SSIM, developing the Reachability Matrix, conducting level partitioning, and constructing the final hierarchical model. ISM is particularly valuable for analyzing complex systems where little prior structural knowledge exists. Although it relies on expert judgment introducing some subjectivity, it remains advantageous due to its lower expert number requirements and fewer methodological constraints compared to techniques like Delphi or Structural Equation Modeling (SEM).

Two core principles underpin ISM: transitivity and reachability. In this context, if element “i” is linked to “j,” and “j” is linked to “k,” transitivity suggests that “i” is indirectly linked to “k.” The Reachability Matrix captures such direct and indirect relationships, enabling the partitioning of elements into a structured hierarchy. The full ISM process followed in this study is illustrated in Figure 2.

ISM is a qualitative technique used to identify, structure, and analyze complex systems. It provides a systematic framework for mapping the interrelations among system essentials, thereby offering a clearer recognition of the system’s framework and response. The ISM process involves several key steps: initially, identifying the relevant factors influencing the system, followed by establishing contextual relationships among them, and developing an SSIM.

These interrelationships are then represented through an adjacency matrix, which visually captures direct links between the factors. The development of these matrices is guided by expert assessments and professional judgments, ensuring that the model reflects real-world insights. In the adjacency matrix, direct relationships between factors are denoted by the symbol “A”.

$$A={\left({\mathrm{a}}_{\mathrm{i}\mathrm{j}}\right)}_{\mathrm{n}\times \mathrm{n}}$$

(1)

$$a_{ij}=\left\{\begin{array}{c}1,V_{i}M{V}_j\\ 0,V_i\overline{M}{V}_j\end{array}\right.$$

(2)

In Equation (2), $\overline{M}$ reveals that there is no such relation between $V_j$ and $V_i$. Although it has a direct link with $V_j$. Following this, to demonstrate the direct and indirect relationship between variables Reachability Matrix (R) is utilized. Moreover, this assessed the transitivity in system by recognizing the indirect effects, articulated in Equation (3).

$$A^1\ne A^2\ne A^3\ne \dots \dots ..\ne A^{\lambda }=A^{\lambda +1 } = R$$

(3)

The symbol $\lambda$ indicates the number of computations. To establish the hierarchical framework among various factors, level partitioning is employed, applying R. Three factors, RS ($V_i)$,$AS \left(V_i\right),$ and $IS \left(V_i\right)$, reachability, the antecedent, and the intersection set, respectively, employed in level division are presented in the following equations:

$$\begin{array}{c}RS\left(V_i\right)=\left\{V_j∣ V_j\in V, r_{ij}=1\right\}\\ AS\left(V_i\right)=\left\{V_j\mid { V}_j\in V, \hspace{1em}{r}_{ji}=1\right\}\\ IS\left(V_i\right)=RS\left(V_i\right)\cap AS\left(V_i\right)\end{array}$$

(4)

where V is the set of whole elements, “A” is the given element, and A and B are direct elements (respectively, in V), we say that RS ($V_i$), reachability set $V_i$ is of the set including the elements $V_J$ that can be reached from the $V_i$ (where $V_i$∈ V, $V_j$ ∈ V). The $r_{ij}$ is an element of the Reachability Matrix that represents the direct relation between $V_i$ and $V_j$.

Level partition is denoted as follows:

$$L_m=\left\{V_j∣V_{j }\in V-L_0-L_1-\dots -L_{m-1},IS\left(V_i\right)=RS\left(F_i\right)\right\}$$

(5)

where $L_m$ indicates the levels $m : m= \mathrm{1,2}$ ≤ n; $L_0$ = Ø.

The findings reveal the identity of five levels following five iterations.

4.2. MICMAC Methodology

In MICMAC analysis, all factors of a system are classified in terms of driving power (ability to influence) and dependence power (helplessness to be influenced). This grouping leads to 4 different clusters [90,91]:

• Step 1: Autonomous Factors (Cluster I):

These quantities are independent and separate from the other part of the system. They suffer from poor driving and dependence power, and behave as independent islands with negligible influence on the system dynamic as a whole.

• Step 2: Dependents (Cluster II):

As suggested by their names, these are dominated by other considerations in the system. They have negative driving power and positive dependence power, that is, they are less powerful in driving change, but more powerful as those who are driven.

• Step 3: Clustering Factors (Cluster III):

These intermediaries can influence other factors and be influenced themselves. They acquire medium driving and dependence power that is essentially necessary for propagating alterations through the system and for bulletproofing.

• Step 4: Motivating factors (Cluster IV):

They are the elements with high driving power and low dependence power. They, in turn, have a large effect on other features but are not very affected by a change. Because they play a dominant role for the state of the system, driving force should be primarily considered when considering and controlling the system.

The driving force and the dependence force are shown in the parameters in Equation (6).

$$\begin{array}{c}DF\left(V_i\right)={\displaystyle \sum _{j=1}^n} f_{ij} (i=\mathrm{1,2},\dots \cdot n)\\ DP\left(V_i\right)={\displaystyle \sum _{j=1}^n}{ f}_{ji }(i= \mathrm{1,2},\dots \cdot n)\end{array}$$

(6)

5. Results and Discussion

5.1. ISM Analysis

We carry out ISM analysis systematically, step by step.

• Step 1: Identification of influcence Factors

A comprehensive review of the relevant literature, along with expert consultations, resulted in the identification of 17 key factors influencing EV adoption. These factors are thoroughly discussed in Section 3, and a summarized overview is provided in

Table 2. Identifying Influence Factors for EVCS Location.

Factor ID	Factor Name	Dimension	Grouping Criteria	Rationale
1.1	Purchase price		Related to the initial investment in EVs	Purchase price is a key determinant of the total cost and is critical in the consumer’s decision-making.
1.2	Operational cost		Recurring costs for maintenance and energy	Operational cost impacts long-term affordability and EV ownership.
1.3	Long-term savings		Economic benefit over the vehicle’s lifespan	Long-term savings affect the financial attractiveness of EVs compared to conventional vehicles.
1.4	Resale value		Residual value and depreciation rate	Resale value is essential for assessing the long-term value proposition of an EV.
2.1	Accessibility	Charging Infrastructure Availability	Availability and proximity of charging stations	Accessibility is critical for reducing range anxiety and enhancing EV adoption.
2.2	Urban planning		Design and layout of cities to support EV infrastructure	Urban planning impacts how well EV charging can be integrated into existing city infrastructure.
2.3	Home charging options		Availability of home-based charging infrastructure	Home charging is an important factor for EV adoption, particularly for consumers in residential areas.
3.1	Financial incentives	Government Incentives and Policies	Government support to reduce purchase cost	Financial incentives help make EVs more affordable and encourage consumer adoption.
3.2	Regulatory support		Government regulations supporting EV adoption	Regulations like tax rebates or EV mandates influence the market for EVs.
3.3	Infrastructure investments		Public sector investment in charging infrastructure	Investment in infrastructure is necessary to support the scaling of EV adoption.
4.1	Battery technology		Advances in battery life, efficiency, and cost reduction	Battery technology is a core enabler for improving EV range and reducing operational costs.
4.2	Vehicle range	Technological Advancement and Range	Vehicle performance related to distance per charge	EV range is a critical factor influencing adoption, especially for long-distance drivers.
4.3	Performance and features		Vehicle specifications, features, and user experience	Technological advancements in performance can make EVs more appealing compared to traditional vehicles.
5.1	Air pollution control		Impact on environmental quality	EVs’ role in reducing air pollution is a key environmental benefit.
5.2	GHG Minimization		Contribution to lowering GHGs	EVs help reduce carbon footprints, contributing to the fight against climate change.
5.3	Noise level reduction	Environmental Sustainability	Impact on reducing noise pollution from vehicles	EVs are quieter than traditional vehicles, reducing noise pollution in urban areas.
5.4	Battery recycling potential	Environmental Sustainability	Potential for recycling EV batteries	Recycling reduces environmental impact and supports sustainability efforts.

.

• Step 2: Create a Contextual Relationship and SSIM

An extensive review of the literature, along with expert consultations, was carried out to examine the complex dynamics amongst identified predictors. These ties were defined by a normative panel of judges through a pairwise comparison and encoded with an arrow, with four distinct symbols for the direction of the relationship.

• V indicates that variable i positively influences variable j;
• X denotes a mutual positive influence between variables i and j;
• O signifies no significant relationship between the variables.

To examine the interrelationships among the 17 factors influencing EV adoption, an SSIM was constructed based on expert evaluations. This matrix, shown in

Table 3. Structural Self-Interaction Matrix (SSIM).

No. “i”		Factors “j”	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1				V	V	V	V	V	V	V	V	V	V	V	V	V	V	V	V
2					A	A	O	O	O	O	A	X	A	O	A	O	O	O	O
3						V	V	O	O	O	O	O	V	V	V	O	O	O	O
4							V	O	O	O	O	O	O	O	O	O	O	O	O
5								A	V	O	V	V	V	V	V	O	O	O	O
6									V	V	V	V	V	V	V	O	O	O	O
7										X	X	V	V	V	O	O	O	O	O
8											A	V	V	V	O	O	O	O	O
9												V	V	V	O	O	O	O	O
10													X	V	O	O	O	O	O
11														X	X	V	V	V	V
12															X	O	O	O	O
13																O	O	O	O
14																	X	X	X
15																		X	X
16																			X
17

, was developed using pairwise comparisons between the factors, with each relationship represented by one of four standard notations:

For example, in Table 3, Purchase Price (Factor 1) strongly influences all other factors, which is evident from the presence of V in every corresponding cell across the row. Conversely, Operational Costs (Factor 2) show A in the cell with Purchase Price (Factor 1), suggesting that Purchase Price is influenced by Operational Costs in this instance.

The matrix also highlights mutual dependencies such as the X relationships observed between Home Charging Options (Factor 7) and both Financial Incentives (Factor 8) and Regulatory Support (Factor 9), indicating a reciprocal influence among these mid-tier variables. Meanwhile, the presence of O in cells such as those between Operational Costs (Factor 2) and Air Pollution Control (Factor 14) indicates no significant interaction between those two factors.

This SSIM provides a structured foundation for constructing the Reachability Matrix and further hierarchical modeling. It enables a clear understanding of which factors act as drivers and which operate as outcomes or intermediaries in the EV adoption ecosystem.

• Step 3: Develop Reachability Matrix

The Reachability Matrix is developed from the SSIM and is used to identify both direct and indirect relationships among variables. It helps illustrate the extent to which one factor influences another through chains of influence. To build this matrix, a systematic conversion is performed, transforming the SSIM into the Initial Reachability Matrix (IRM) by applying a set of predefined rules. These rules define how the symbolic relationships in the SSIM (V, A, X, O) are translated into binary form (1 or 0):

If the SSIM entry at position (i, j) is ‘V’, then IRM(i, j) is assigned 1 and IRM(j, i) is assigned 0, indicating a one-way influence from variable i to j.

If the entry is ‘A’, IRM(i, j) is set to 0 and IRM(j, i) is set to 1, indicating that variable j influences variable i.

If the symbol is ‘X’, both IRM(i, j) and IRM(j, i) are set to 1, representing mutual influence.

For ‘O’, no influence exists between the variables, so both IRM(i, j) and IRM(j, i) are set to 0.

Using this conversion logic, the IRM is constructed and presented in

Table 4. IRM of influence factors of EVCS location.

Variables	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
Purchase Price	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Operational Costs	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
Long-Term Savings	0	1	1	1	1	0	0	0	0	0	1	1	1	0	0	0	0
Resale Value	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0
Accessibility	0	0	0	0	1	0	1	0	1	1	1	1	1	0	0	0	0
Urban Planning	0	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0
Home Charging Options	0	0	0	0	0	0	1	1	1	1	1	1	0	0	0	0	0
Financial Incentives	0	0	0	0	0	0	1	1	0	1	1	1	0	0	0	0	0
Regulatory Support	0	1	0	0	0	0	1	1	1	1	1	1	0	0	0	0	0
Infrastructure Investments	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0
Battery Technology	0	1	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1
Vehicle Range	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0
Performance and Features	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0
Air pollution control	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1
GHG Emission Minimization	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1
Noise level reduction	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1
Batteries recycling potential	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1

. To capture indirect relationships, the FRM is then generated by applying transitivity principles, ensuring the inclusion of all influence paths within the system. The FRM is presented in

Table 5. FRM of influence factors of EVCS location.

Variables	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	Driving Power
Purchase Price	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	17
Operational Costs	0	1	0	0	0	0	0	0	0	1	1 *	1 *	1 *	1 *	1 *	1 *	1 *	9
Long-Term Savings	0	1	1	1	1	0	1 *	1 *	1 *	1 *	1	1	1	1 *	1 *	1 *	1 *	15
Resale Value	0	1	0	1	1	0	1 *	1 *	1 *	1 *	1 *	1 *	1 *	1 *	1 *	1 *	1 *	14
Accessibility	0	1 *	0	0	1	0	1	1 *	1	1	1	1	1	1	1 *	1 *	1 *	13
Urban Planning	0	1 *	0	0	1	1	1	1	1	1	1	1	1	1 *	1 *	1 *	1 *	14
Home Charging Options	0	1 *	0	0	0	0	1	1	1	1	1	1	1 *	1 *	1 *	1 *	1 *	12
Financial Incentives	0	1 *	0	0	0	0	1	1	1 *	1	1	1	1 *	1 *	1 *	1 *	1 *	12
Regulatory Support	0	1	0	0	0	0	1	1	1	1	1	1	1 *	1 *	1 *	1 *	1 *	12
Infrastructure Investments	0	1	0	0	0	0	0	0	0	1	1	1	1 *	1 *	1 *	1 *	1 *	9
Battery Technology	0	1	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	9
Vehicle Range	0	1 *	0	0	0	0	0	0	0	1 *	1	1	1	1 *	1 *	1 *	1 *	9
Performance and Features	0	1	0	0	0	0	0	0	0	1 *	1	1	1	1 *	1 *	1 *	1 *	9
Air pollution control	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	4
GHG Minimization	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	4
Noise levels reduction	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	4
Batteries recycling potential	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	4
Dependence Power	1	13	2	3	5	2	8	8	8	13	13	13	13	17	17	17	17

Note: In the FRM, “1” denotes a direct relationship, “0” indicates no relationship, and “*” denotes a transitive relationship derived through intermediate links.

.

Following the development of the FRM, the driving power (number of variables a factor influences) and dependence power (number of variables that influence a factor) are calculated. These values serve as the foundation for the MICMAC analysis, which categorizes the variables into four groups, autonomous, dependent, linkage, and driving factors, providing deeper insights into their roles within the system.

• Step 4: Partition level

Following the construction of the FRM, each factor is analyzed through two key sets: the reachability set, which includes all factors that a given variable can influence either directly or indirectly, and the antecedent set, which includes all factors that influence the given variable. The intersection set comprising the common elements shared between the reachability and antecedent sets is then determined for each variable.

A factor is considered to be at a specific hierarchical level if its reachability set and intersection set are identical. These variables are then assigned to the current level and excluded from subsequent iterations. The process is repeated iteratively, removing one level’s factors at a time, until all variables have been placed into their appropriate hierarchical levels.

As shown in the Supplementary Materials, Table S1, for example, Factor 14–17 has matching reachability and intersection sets in the first iteration, placing it at the lowest level of the hierarchy. Once assigned, it is removed from the matrix, and the process is repeated for the remaining factors. This continues until each variable is appropriately ranked.

The final result, presented in the Supplementary Materials, Table S2, reflects the multi-level structure that is necessary for developing the ISM digraph and the comprehensive hierarchical model. This iterative partitioning method reveals complex dependencies and relationships among factors, providing a deeper understanding of the system and supporting the creation of a structured and reliable ISM framework.

• Step 5: Obtaining the directed graph

The final step in the ISM methodology involves constructing a digraph (Figure 3) to visually represent the hierarchical relationships among the 17 factors influencing EV adoption. This structured model was derived from the FRM, with transitive links removed to reduce redundancy and highlight direct dependencies.

At the top of the hierarchy (Level 7) is the Purchase Price (Factor 1), identified as the most influential driver in the adoption of EVs. It impacts nearly every other factor in the system, making it a critical determinant for policymakers and manufacturers.

Moving down to Level 6, Long-Term Savings (Factor 3) is positioned as the next most impactful factor. It reflects the long-term economic considerations that influence the purchase decision beyond the upfront cost.

Level 5 includes Resale Value (Factor 4) and Urban Planning (Factor 6). These factors link financial and spatial planning dimensions, suggesting that EV adoption is shaped not only by personal finances but also by how urban infrastructure supports the use of EVs.

In Level 4, Accessibility (Factor 5) stands alone. It emphasizes the need for convenient and user-friendly access to EV-related services, which can either encourage or deter adoption.

Level 3 encompasses a cluster of enablers, including Home Charging Options (Factor 7), Financial Incentives (Factor 8), and Regulatory Support (Factor 9). These factors serve as key facilitators in translating policy and infrastructure readiness into practical adoption opportunities for consumers.

Level 2 encompasses technical and cost-operational factors, including Operational Costs (Factor 2), Infrastructure Investments (Factor 10), Battery Technology (Factor 11), Vehicle Range (Factor 12), and Performance and Features (Factor 13). These factors directly affect the user experience and cost-efficiency of EV ownership.

At the base of the hierarchy, in Level 1, are outcome-related sustainability factors: Air Pollution Control (Factor 14), Greenhouse Gas Emission Minimization (Factor 15), Noise Level Reduction (Factor 16), and Battery Recycling Potential (Factor 17). These are the most dependent variables, reflecting environmental and long-term effects that result from the successful adoption of EV strategies.

This ISM-based hierarchical model offers a clear understanding of how various economic, technical, infrastructural, and environmental factors interact. It helps decision-makers prioritize interventions, starting from the most influential drivers at the top and progressing toward dependent outcomes at the base, thereby guiding the development of comprehensive and effective EV adoption strategies in Saudi Arabia. To avoid potential bias arising from the expert panel, a sensitivity analysis focused on the running of the ISM hierarchy after randomly excluding one expert at a time was performed. Modeling results revealed minor structural variations, demonstrating the stability of the relationships.

Following the removal of transitive links, the refined digraph was used to construct the final Interpretive Structural Model (ISM), presented in Figure 4. This model illustrates the hierarchical structure of 17 key factors influencing EV adoption. The factors are organized into seven distinct levels, with the most influential driver (Level 7) at the top and the most dependent outcomes (Level 1) at the bottom.

At Level 7, Purchase Price (Factor 1) emerges as the most critical driver. It serves as the primary consideration for potential EV buyers, heavily shaping their decision to adopt electric mobility. Its influence cascades down to nearly all other variables in the model.

Level 6 includes Long-Term Savings (Factor 3), which reflects the financial benefits gained over time from reduced fuel and maintenance costs. While it is not as dominant as the purchase price, this factor plays a significant role in justifying the upfront investment in EVs.

In Level 5, we find Resale Value (Factor 4) and Urban Planning (Factor 6). Resale Value influences perceptions of long-term economic return, while urban planning affects infrastructure readiness and the integration of EVs into city layouts.

Accessibility (Factor 5) is rated at Level 4, indicating the physical and logistical ease of using an EV. This includes access to roads, parking, and local infrastructure that supports EV functionality.

Level 3 contains Home Charging Options (Factor 7), Financial Incentives (Factor 8), and Regulatory Support (Factor 9). These policy- and infrastructure-related enablers significantly shape public willingness and ability to adopt EVs by reducing barriers and improving convenience.

Level 2 encompasses a range of operational and technical considerations, including Operational Costs (Factor 2), Infrastructure Investments (Factor 10), Battery Technology (Factor 11), Vehicle Range (Factor 12), and Performance and Features (Factor 13). These factors collectively determine the practicality, reliability, and cost-effectiveness of owning and operating.

At the base of the hierarchy (Level 1) are the environmental impact outcomes: Air Pollution Control (Factor 14), Greenhouse Gas Emission Minimization (Factor 15), Noise Level Reduction (Factor 16), and Battery Recycling Potential (Factor 17). These are highly dependent on the upstream adoption and development of EV technology, policy, and infrastructure. While essential to long-term sustainability, they are shaped by the decisions and factors placed higher in the ISM structure.

Previous studies on EV adoption in Saudi Arabia have identified several key factors influencing the transition to electric mobility. Previous studies highlighted economic factors such as purchase price, operational cost, long-term savings, and resale value as critical barriers to EV adoption, emphasizing the challenges posed by high upfront costs and limited resale value [19,20,22]. Charging infrastructure, particularly accessibility, urban planning, and home charging options, was also found to be a significant enabler of EV adoption [92]. Furthermore, government incentives, including financial incentives, regulatory support, and infrastructure investments, play a pivotal role in encouraging EV adoption [22]. Technological advancements in battery technology, vehicle range, and performance features have been found to directly influence consumer perceptions of EVs [19]. Additionally, the environmental benefits of EVs, including air pollution control, GHG minimization, noise level reduction, and battery recycling potential, have been highlighted as motivating factors in studies conducted by [20]. These factors, together with the government’s focus on sustainability under Vision 2030, shape the dynamics of EV adoption in Saudi Arabia.

The factors influencing EV adoption in Saudi Arabia exhibit both similarities and differences when compared to other countries, particularly those in the Gulf region, China, and other emerging economies. In Norway, the government’s role in EV adoption is critical, with generous financial incentives and a well-developed charging infrastructure [93]. This is also seen in the United Arab Emirates (UAE), where government policies provide significant incentives, including subsidies and tax exemptions, to promote EV adoption [94]. In China, technological advancements in battery technology and vehicle range have been pivotal, with government support focused on reducing the cost of EVs and improving charging networks [95]. Meanwhile, in India, while government incentives and financial support have spurred initial growth, infrastructure challenges remain significant, and range anxiety is a more prominent barrier compared to other countries [96]. In Saudi Arabia, the primary drivers for EV adoption are infrastructure development, particularly charging stations, and financial incentives, closely tied to Vision 2030 goals. While environmental sustainability factors, such as air pollution control and GHG emissions’ reduction, are relevant, they are somewhat less emphasized compared to countries like Norway. These comparisons highlight the diverse strategies that have been adopted by different countries, reflecting their unique socio-economic, policy, and infrastructure landscapes. Recent ISM and MICMAC studies in the context of emerging economies have found a consistent set of factors to EV adoption and can provide useful benchmarks for understanding the structural barriers to their adoption in another jurisdiction. For instance, a recent study identified thirteen influential barriers to EV adoption in India [97]. These include factors such as high cost, limited consumer awareness and inadequate charging infrastructure. In another study, researchers reported that policy, financial barriers, uncertainty, and infrastructure gaps were the main constraints to EVs adoption [70]. Similarly, prior research indicated factors like supportive ecosystems, consumer perceptions, and affordability issues were the main barriers [98]. Across the aforementioned studies, it is revealed that charging limitations, cost-related barriers, and government policy consistently ranked as the main drivers of resistance to EVs adoption in these as ISM/MICMAC frameworks [99,100,101]. While few of these findings align with the results of the study which as conducted in the context of the KSA, the presence of unique aspects such as strong fiscal incentives, large-scale infrastructure investments, and ambitious Vision 2030 goals can may accelerate EV adoption more rapidly than in other emerging economies.

As Saudi Arabia’s EV market matures, we expect the hierarchy of adoption drivers to evolve in response to advancements in infrastructure, increasing consumer awareness, and the growing availability of EV models. Initially, financial considerations, such as purchase price and long-term savings, are likely to be the dominant factors influencing adoption, as affordability remains a significant barrier. However, as the market grows and infrastructure improves, the relative importance of charging accessibility and urban planning will likely increase. Over time, technological advancements in battery efficiency and vehicle range, coupled with enhanced consumer awareness of the environmental and economic benefits of EVs, will likely shift the focus towards sustainability factors such as air pollution control and greenhouse gas emission reductions. As the market reaches greater maturity and the benefits of EV adoption become more tangible, the policy-driven incentives and financial support that are currently seen as crucial enablers may become less pronounced, while the broader environmental and technological factors could take a more central role. While the ISM analysis results reveal the general levers (Purchase Price, Long-Term Savings, Resale Value, Accessibility) for EVs’ adoption, grounding these findings in KSA-specific realities clarifies the essential policy implications. The national Vision 2030 proposes a nascent transport electrification plan on a large scale, implying that purchase-price interventions are expected to interact strongly with national planning and infrastructure rollout. For instance, the lower fuel prices reduce the short-term economic attractiveness of EVs, raising the relative importance of other non-price levers like home charging, accessibility, and targeted incentives for EV users. Likewise, major changes in the public transport sector including the development and expansion of national charging networks create windows of opportunity to leverage linkage variables shown in the results. Similarly, simultaneous action on charging infrastructure, incentives, and regulatory and policy support can yield cascading effects that ISM analysis suggests. Additionally, local cultural preferences of factors such as batter technology and resale perceptions explain why these factors appear as important dependent nodes identified by the ISM.

The ISM results reveal that factors including Purchase Price, Long-Term Savings, Resale Value, Urban Planning, and Accessibility are identified as the most influential drivers for EV adoption in KSA. Targeted interventions such as tax incentives, subsidies, and cost–benefit optimized pricing strategies are suggested to lower financial barriers for potential consumers. Likewise, policy regulation that is focused on infrastructure development like implementing supportive urban zoning policies and the strategic placement of charging stations can facilitate and encourage EV adoption. Similarly, integration of EV-friendly infrastructure into city planning and public transportation systems could also encourage their wide-range adoption. in addition, other measures in the context of the KSA such as preferential electricity tariffs for home/public charging, low-interest loans for EV purchase, resale-value guarantees, and the integration of EVs into urban zoning can neutralize the barriers to EV adoption on a large scale. The highlighted recommendations could translate the hierarchical structure of influential factors into actionable strategies, providing practical guidance for urban planners and policymakers in the study area and other similar jurisdictions. Policy packages in the KSA should focus on adopting combined interventions such as price-reducing measures (tax break subsidies, differentiated registration fees, etc.) plus the rapid deployment of targeted charging infrastructure to promote a convenient transition to EVs that are aligned with Vision 2030 goals.

5.2. MICMAC Analysis

The MICMAC analysis categorizes the 17 variables influencing EV adoption into four strategic quadrants, helping to uncover their respective roles in shaping adoption outcomes. As illustrated in Figure 5, no variables fall into the autonomous quadrant, confirming that each factor is actively integrated within the EV adoption framework. The independent (driving) variables, which possess high driving power and low dependence, are the primary enablers of EV adoption. The Purchase Price (F1) is the most influential factor, as affordability remains a significant barrier for many consumers. Lower upfront costs can significantly accelerate market penetration. Long-Term Savings (F3) and Resale Value (F4) are also crucial financial motivators; consumers are more likely to adopt EVs when they perceive there to be cost efficiency over time and fair value retention. Accessibility (F5) ensures that EVs are a practical choice for daily mobility. At the same time, Urban Planning (F6) determines how well cities are structured to support infrastructure, such as dedicated lanes, parking, and charging zones.

The linkage variables—Home Charging Options (F7), Financial Incentives (F8), and Regulatory Support (F9)—are highly dynamic and central to the system. They not only influence other variables but are also influenced by them. These enablers form the core operational and policy infrastructure that shapes the overall EV ecosystem. Effective financial incentives reduce the financial burden of switching to EVs, regulatory support creates a favorable environment through mandates or tax exemptions, and the widespread availability of home charging makes EVs more convenient and user-friendly. These factors require collaborative efforts from the government and private sectors to maintain balance across the adoption system.

The intermediate zone includes Operational Costs (F2), Infrastructure Investments (F10), Battery Technology (F11), Vehicle Range (F12), and Performance and Features (F13). These variables represent the technological and economic feasibility of EVs in everyday use. For instance, high battery performance and a longer vehicle range help alleviate range anxiety, one of the key psychological barriers to adoption. Similarly, robust infrastructure investments ensure the consistent availability of charging stations, while advanced features increase consumer satisfaction and competitiveness compared to traditional vehicles. Operational costs, such as electricity prices and maintenance expenses, directly impact the long-term value proposition for users.

At the other end of the spectrum, dependent variables—Air Pollution Control (F14), Greenhouse Gas (GHG) Emission Minimization (F15), Noise Level Reduction (F16), and Battery Recycling Potential (F17)—are the end outcomes of successful EV adoption. While these factors do not directly drive the system, they are crucial for assessing the long-term impact of EVs on sustainability and public health. Cleaner air, reduced emissions, and lower noise pollution are key to achieving national environmental goals and meeting global climate commitments. Battery recycling, meanwhile, is crucial for mitigating concerns about the environmental impact of EV components, particularly as the adoption of EVs increases.

The classification of factors into distinct quadrants provides valuable insights for public policy and strategic planning. Independent (driving) variables, such as purchase price, long-term savings, and accessibility, represent the most pressing barriers and enablers of EV adoption. Policymakers should prioritize reducing the upfront cost of EVs through subsidies, tax incentives, and financial support programs. Additionally, enhancing accessibility through the development of EV-friendly urban infrastructure, such as dedicated charging stations and parking zones, is essential for facilitating adoption. On the other hand, linkage variables, including home charging options, financial incentives, and regulatory support, are critical for creating a supportive ecosystem. A collaborative effort between government and industry to ensure the consistent availability of charging infrastructure, alongside favorable regulations and incentives, will accelerate adoption. Intermediate variables, like battery technology, vehicle range, and infrastructure investments, should be addressed through continued investment in research and development, along with public–private partnerships to expand charging networks and improve vehicle performance. Lastly, dependent variables, such as air pollution control and battery recycling, highlight the long-term environmental benefits of EV adoption. Policymakers should focus on monitoring the sustainability outcomes of EV adoption, including the establishment of recycling programs and ensuring that the environmental gains are maximized. By targeting each factor based on its strategic quadrant, governments can implement differentiated policies that foster a balanced and sustainable transition to electric mobility.

6. Conclusions

This study presents a comprehensive structural assessment of the key factors influencing the adoption of EVs in Saudi Arabia, utilizing ISM and MICMAC analyses. By systematically identifying and analyzing 17 critical factors, the research reveals the complex interrelationships and hierarchies that govern the transition to EVs in the region.

The ISM-based hierarchical model highlights that economic considerations such as Purchase Price, Long-Term Savings, and Resale Value serve as the most influential drivers of adoption. These are further supported by infrastructural and planning-related elements, such as Urban Planning and Accessibility, which play essential roles in facilitating EV readiness within urban environments. Conversely, sustainability-related outcomes such as Air Pollution Control, GHG Emission Minimization, Noise Reduction, and Battery Recycling Potential are placed at the bottom of the hierarchy, emphasizing their status as dependent variables that reflect the long-term impact of successful adoption.

The MICMAC analysis complements these insights by categorizing the variables based on their driving and dependence power. Key financial and infrastructural enablers are identified as independent or driving factors, while regulatory instruments and technological options fall into the linkage or intermediate zones, requiring coordinated stakeholder actions. Notably, no autonomous factors were identified, underscoring the integrated and interdependent nature of the EV adoption ecosystem in Saudi Arabia.

Together, these findings offer valuable strategic guidance for policymakers, urban planners, and industry stakeholders. By prioritizing high-impact drivers such as affordability, charging infrastructure, and policy incentives, and by addressing the interlinked barriers identified in this study, a more targeted and effective roadmap for EV adoption can be developed. This systems-based approach is essential to achieving the Kingdom’s sustainability goals and transitioning toward a cleaner, more efficient transportation future.

While this study provides valuable insights into the key factors influencing EV adoption in Saudi Arabia, several avenues for future research remain. First, future studies could explore the role of consumer behavioral psychology in EV adoption, focusing on factors such as social influence, environmental awareness, and the perceived social status associated with EV ownership. Additionally, research could extend beyond the urban context to examine rural and remote areas, where charging infrastructure and socio-economic factors may present unique challenges. Another promising direction would be to investigate the long-term economic and environmental impacts of EV adoption, including the effectiveness of incentives and policies in fostering sustainable growth. Moreover, future work could incorporate a broader regional perspective by comparing EV adoption trends and strategies across different countries in the Middle East, taking into account cultural, economic, and policy variations. Further integrating advanced modeling techniques such as agent-based modeling or system dynamics could offer deeper insights into the complex interactions and feedback loops within the EV adoption ecosystem, providing more robust policy recommendations. Likewise, studies could focus on the potential cost–benefit analysis of various drivers under different EV adoption scenarios. This study is context-specific to Saudi Arabia; hence, future research could be undertaken to adapt and validate the proposed framework in different socioeconomic and policy contexts to ensure boarder applicability. Finally, future research conducting a sensitivity analysis using different weighting schemes such as equal weighting, experience-based weighting, and familiarity-based weighting would enhance the robustness of the results. This approach would provide valuable insights into how variations in criteria weights influence the outcomes of the ISM/MICMAC framework, allowing for a more nuanced understanding of the factors driving EV adoption.