Psychological Drivers of Electric Vehicle Adoption in India’s Sustainable Mobility Transition

Psychological Drivers of Electric Vehicle Adoption in India’s Sustainable Mobility Transition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychological Drivers of Electric Vehicle Adoption in India’s Sustainable Mobility Transition Subojit Debnath, Sudip Kumar Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8723902/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract The shift to electric vehicles (EVs) is a key pillar in achieving sustainable mobility solutions worldwide, but progress is uneven, especially in emerging urban settings. Beyond technological readiness and infrastructure availability, consumer perceptions, social influence, and psychological dispositions play important roles in determining the willingness to adopt EVs. This study investigates the psychological and perceptual factors that determine the willingness to purchase EVs in the urban Indian context and adds to a broader understanding of the socio-technical aspects of sustainable mobility transition. Using survey data from commuters in Kolkata, 20 perception-based indicators were synthesised into latent behavioural constructs via Principal Component Analysis. These constructs, along with socio-demographic attributes, were incorporated into a non-linear machine learning framework to forecast EV purchase willingness. Model transparency was ensured through explainable AI techniques, enabling interpretation of the relative impact and direction of the behavioural factors. Results show that trust in technology and social influence are the strongest factors in motivating the EV purchase willingness, while perceived financial risk is the biggest barrier. Environmental attitudes and perceived usability have positive but secondary roles. These findings reveal the multidimensional and social nature of EV adoption decisions, and the importance of trust-building, social visibility, and targeted financial interventions to accelerate adoption. By combining behavioural science with data-driven modelling, this research advances interdisciplinary thinking about EV adoption and offers actionable insights for policy-makers, planners, and industry stakeholders to enable a just and sustainable transition to EV in rapidly urbanising regions. Electric Vehicle Adoption Sustainable Mobility Transition Social Acceptance of EVs Consumer behaviour Psychological Determinants Explainable Artificial Intelligence Urban Transport Planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Rising concerns about climate change, energy security, and air pollution are fundamentally transforming the global transport system. Internal combustion engine (ICE) powered vehicles, which have been the main form of transportation for a century and a half, are increasingly spotlighted in their contribution to greenhouse gas production, urban atmosphere pollution and dependence on non-renewable fossil fuels. While the transport sector contributes almost 37% of the total end-use emissions, road transport alone accounts for approximately 23% of the total CO₂ emissions [ 1 ]. The issues of rapid urbanisation, increasing travel demand, and oil market instability due to geopolitical factors have led to the need to transition towards cleaner and more sustainable forms of transport. Electric vehicles (EVs) have become a key component in green transport initiatives. Vehicles powered by electricity, which have zero tailpipe emissions, present a viable pathway to decarbonization of all transport on the road. Also, with more and more national power grids becoming greener with clean energy like solar and wind power, EVs’ life-cycle emissions decreased, making these vehicles progressively greener each day. The sales of EVs are booming all over the world, with over 10 million sales in 2022. Estimates show that by 2030, EVs will form over 60% of all vehicle sales in major markets [ 1 ]. The surge in EV sales is driven by policy support, financial support (e.g., subsidies, tax breaks), increased consumer awareness, technological advances, and cost reductions in battery production. Despite the promises of such advancements, it is to be noted that there are some concerns about the environmental sustainability of EVs. It is mainly the production, mining and end-of-life disposal of EV batteries, specifically lithium-ion batteries. They require large quantities of essential minerals such as lithium, cobalt and nickel to manufacture and tend to be produced in regions with political instability or governance challenges. The effects of mining can lead to deforestation, water pollution, disturbance to the ecosystem and misuse of human rights in the cases of child labour, among others [ 2 ]. Moreover, although EVs produce fewer emissions than their ICE counterpart, there is a lack of sufficient recycling of end-of-life batteries to reduce the probability of long-term effects on the environment [ 3 ]. When there are no proper ecosystemic measures to dispose of used batteries, there will be contamination of the soil and water, as well as a safety hazard. However, these challenges do not undermine the need to switch to electric mobility. Instead, they call for a sustainable, responsible and all-encompassing transition that has maintained a balance between environmental, technological and societal aspects. EVs still play a central role in solving the problems of city air pollution, climate change and the overall dependence on fossil fuels. Trade-offs of battery-producing products to the environment are slowly being solved by innovations in the chemicals used in production, like the solid-state batteries, enhancements in the efficiency of resources and extensions in closed-loop reprocessing structures [ 4 ]. Moreover, some car companies and countries have gone as far as announcing that they will cease the production of ICE vehicles in the next 20 years, which is another indication of the coming of electrification. While much is being done to enhance the technological and infrastructural aspects of EV adoption, human behaviour is one of the key bottlenecks [ 5 ]. Despite increased availability, many potential users are hesitant to purchase EVs due to psychological and perceived factors like range anxiety, inexperience with the technology, a lack of confidence in charging infrastructure, and concerns about purchasing and servicing costs [ 6 , 7 ]. Environmental values and other determinants like social influence and personal innovativeness also significantly affect consumer intentions [ 8 , 9 ]. For this purpose, it is vital to identify the psychological and perceptual drivers of EV adoption to implement effective public policies, marketing, and incentive programs. In the context of the electric mobility transition, the consumers’ willingness to adopt EVs is a crucial socio-technical interface between vehicle technologies, charging infrastructure, market diffusion, and policy effectiveness. Understanding behavioural acceptance is thus crucial not only for predicting individual purchase decisions, but also for planning at the system levels in terms of infrastructure deployment, incentives and long-term sustainability of electric mobility ecosystems. By placing consumer psychology in a data-driven and interpretable framework, this study adds to interdisciplinary work attempting to bridge technological innovation and social readiness for EVs. 2. Literature Review EVs have come out as a critical solution to transport decarbonization and energy security issues in the context of the worldwide climate crisis. In the last ten years, research on EV uptake has increased extensively, with studies focusing on factors ranging from the availability of infrastructure and economic incentives to psychological dispositions and behavioural intention. While early studies of EV adoption centred primarily on cost-benefit analysis and policy measures, increasing consideration is given to the understanding that non-economic, perceptual, and psychological determinants critically influence EV buying decisions [ 10 ]. 2.1 Determinants of EV Adoption Several studies have recognised major predictors of EV adoption, classified under instrumental and psychological determinants. Instrumental predictors are income, car price, fuel saving, subsidies from the government, and charging infrastructure [ 7 ]. Though very important, they often do not explain much of the variations in EV adoption, particularly when financial and infrastructural scenarios are kept constant. Scholars have resorted to psychological concepts based on behavioural theories to bridge this gap. Ajzen's Theory of Planned Behaviour (TPB) has been widely used to study EV adoption by conceptualising behavioural intention as a function of attitude, subjective norms, and perceived behavioural control [ 11 ]. Rogers' Diffusion of Innovations (DOI) theory has also emphasised the role of these individuals' personal innovativeness and social influence in adopting the latest technologies like EV [ 12 ]. All within the underlying framework, vehicle range perceptions, environmental awareness, perceptions of technology trust, perceived cost, and social influence have been persistent predictors. For example, Rezvani et al. (2015) found that symbolic value and environmental motivation increased the probability of EV adoption, while unfamiliarity with charging infrastructure and range anxiety were psychological barriers [ 5 ]. 2.2 Role of Perceptual and Latent Constructs Due to the multidimensional nature of decision-making in EV adoption, researchers have increasingly employed latent variable models to reduce high-dimensional survey data into interpretable constructs. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and Principal Component Analysis (PCA) methods have been used to reveal dimensions like "eco-consciousness," "cost anxiety," and "technological optimism" [ 6 ]. These latent constructs provide an interpretable and parsimonious basis for modelling behavioural intention, mainly when employed alongside predictive modelling tools [ 13 , 14 ]. However, most of these models are based on linear regression methods or structural equation modelling (SEM), which are built on assumptions of linearity and do not have predictive accuracy compared to contemporary machine learning approaches [ 15 , 16 ]. Moreover, the complexity of psychological constructs makes it problematic to describe richer individual differences only with fixed, theory-based frameworks [ 5 , 9 ]. 2.3 Application of Machine Learning and Explainable AI Machine learning (ML) methods have become increasingly popular for modelling transportation behaviour in recent years. Decision trees, random forests, and gradient boosting machines can classify mode choice and vehicle adoption with greater prediction accuracy compared to conventional models [ 16 ]. One major criticism of ML models is their "black box" nature, which hinders interpretability and undermines their policy relevance. To tackle this, researchers have started incorporating explainable artificial intelligence (XAI) techniques, namely SHapley Additive exPlanations (SHAP), to reveal the features' relative contribution and directional effect in the predictive models. SHAP offers global and individual-level insights, enabling analysts to see which variables are important and how they affect the prediction [ 17 ]. While XAI techniques are increasingly being explored across finance and medicine [ 17 ], their use in transport behaviour modelling and EV adoption modelling is still nascent. Current research primarily uses descriptive or correlation-driven methods instead of explainable predictive models incorporating psychological and perceptual factors and XAI [ 15 , 16 , 18 ]. 2.4 Research Gaps Based on the literature, several gaps remain: Challenges in identifying psychological and perceptual determinants of EV purchase willingness : Prior studies recognise range anxiety, cost sensitivity, social influence, and environmental concern as important factors, but the relative importance, interactions and their stability across situations are not yet determined. Current methods usually use self-reported survey measures that are subject to bias, and small sample sizes limit generalizability. This disjuncture necessitates more stringent procedures to reliably uncover and validate the latent psycho-perceptual dimensions of EV adoption behaviour. Limited integration of perception-based constructs with advanced ML techniques : Although psychological and perceptual variables were modelled through factor analysis or SEM, hardly any studies integrate them with sophisticated classifiers such as Extreme Gradient Boost (XGBoost) for enhanced predictive performance. Advanced ML techniques are required since they can model non-linear relationships between psychological, perceptual and demographic variables and are more predictive than traditional regression models. It is especially relevant in behavioural research, in which decision-making is hardly linear and interacting perceptual factors often influence it. Limited application of explainability tools in EV adoption modelling : Most ML applications in this domain are accuracy-oriented at the expense of transparency. The unavailability of interpretable ML negates the policy and behavioural understanding necessary for well-targeted interventions. Lack of contextualization in emerging markets : Most behavioural studies on EV adoption are focused on Europe and North America. There is a requirement to conduct analyses based on users' views in fast-moving urbanising markets such as India, where the levels of infrastructure, affordability and awareness vary widely. Addressing such gaps is especially important for interdisciplinary mobility research, where behavioural insights are required to ensure that technological and policy interventions translate into real-world adoption outcomes. 2.5 Objectives and Scope of Work The primary objective of the paper is to identify the psychological and perceptual determinants of the willingness to purchase EVs in the Indian urban context. To achieve this aim, the study is going to propose a methodology by combining dimensionality reduction and explainable AI. The scope of this paper is (i) to use PCA for finding psychological and perceptual dimensions, (ii) to use advanced ML model for accurate prediction, (iii) to utilize SHAP for offering interpretable, class-wise explanations of the role of each psychological and perceptual factors in encouraging or deterring EV adoption to overcome the lack of interpretability in existing EV adoption models, and (iv) to address the contextualisation gap in new markets. 3. Methodology 3.1 Study Area This study was conducted in Kolkata, the capital city of West Bengal, located in eastern India. It is one of the largest metropolitan cities of India with a population of over 14 million people in the metropolitan region and an urban structure that is highly densely motorised and travelled by. Within this context, the specific selected study area was Sector V, Salt Lake (Bidhannagar) and the surrounding neighbourhoods in northeastern Kolkata, geographically located in the region between 22.5804 °N and 88.4378 °E. The ideal information technology (IT) and business district of Kolkata is Sector V, which is part of Salt Lake (Bidhannagar), also known as the IT district of eastern India. It is home to many IT firms, business headquarters, educational facilities, startups, and commercial areas, and it receives many commuters daily citywide. Greater Bidhannagar Municipal Corporation, with Sector V, contained a population of more than 215000 according to the 2011 Census, and the region has experienced an accelerated development over the last ten years because of the continued urbanisation and enhanced infrastructure. The map of the area of study is presented in Fig. 1 . Sector V and the vicinity receive a lot of daily commuter traffic, primarily salaried workers, students, and service workers. These commuters are based on a multimodal combination of the mode of transport (bus, auto-rickshaw, metro) and the personal vehicle, so the area is a vital point in studying mobility behaviour in Kolkata. Sector V, with its sector-majority segment of middle- and higher-income residents, the development of a healthy road network, an increase in metro connectivity, and a further focus on smart mobility and green transportation, is a typical urban Indian setting where the willingness to embrace EVs can be examined in combination with the ongoing process of rapid urbanisation and the altering mobility needs. 3.2 Data Collection and Sampling Data were gathered through a standard in-person and online survey questionnaire from June to August 2025. A non-probabilistic, purposive sampling strategy was used to reach a demographically varied group of participants who often travel to or live in Sector V and the surrounding areas. After excluding incomplete or inconsistent entries, the final dataset included complete responses from 300 people out of the total sample of 362. Although this sample size is a little smaller than the threshold for population estimates based on Cochran’s formula, it is adequate for multivariate analysis. With a total of 25 survey items, this study has a respondent-to-item ratio of 12:1, which is higher than the recommended ratio of 10:1 for obtaining stable parameter estimates [ 13 ]. Study participation was voluntary. All respondents gave informed consent, and data anonymity was ensured during the analysis. The research was carried out according to the ethical practice of behavioural study and data privacy norms. 3.3 Survey Measures The questionnaire included Demographic variables (Age, Gender, Income, Education, and Trip purpose), as shown in Table 1 . Table 1 Demographic characteristics of commuters Sl. No. Demographics Encoding No. of response 1 Gender Female = 1 Male = 2 101 199 2 Age 25–40 years = 2 40–50 years = 3 50–60 years = 4 203 46 51 3 Education Undergraduate = 1 Postgraduate = 2 224 76 4 Per Month Income 18-20K = 1 20-40K = 2 40K-50K = 3 50K + = 4 11 68 191 30 5 Trip Purpose Home based work = 1 Home based education = 2 Home based others = 3 211 63 26 Responses on twenty perception-based items, each measured on a five point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree), were also collected (Table 2 ) followed by the target variable “willingness to purchase an EV”, measured on a three point ordinal scale: “1 = Not Willing,” “2 = Neutral,” and “3 = Willing”, to tap into core psychological drivers found in EV adoption literature. Table 2 Perception Variables Variable Encoding Variable Name Survey Statement Variable Description V1 Pro‑EV Environmental Commitment I support the use of electric vehicles as a way to reduce environmental harm. Five point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree) V2 Perceived EV Technological Reliability Electric vehicles are based on reliable and proven technology. V3 General Technology Optimism I am generally enthusiastic about adopting new technologies. V4 Charging‑Infrastructure Availability Charging stations for electric vehicles are sufficiently available near where I live or travel. V5 Belief in EV Environmental Benefits Electric vehicles significantly reduce air pollution and carbon emissions. V6 Support for Clean‑Energy Technology I support the transition from fossil fuels to clean-energy transportation technologies like EVs. V7 Perceived Societal Benefit of EVs Society as a whole would benefit from widespread electric vehicle adoption. V8 EV Maintenance Convenience Maintaining an electric vehicle is easier and more convenient than maintaining a conventional vehicle. V9 Perceived Recharging Time Acceptability The time it takes to recharge an electric vehicle is acceptable to me. V10 Initial Purchase‑Cost Concern Electric vehicles are too expensive to purchase initially. V11 Long‑term Ownership‑Cost Concern I am concerned about the long-term costs of owning and operating an electric vehicle. V12 Peer Adoption Influence People around me are considering or already using electric vehicles, which influences my opinion. V13 Climate‑Change Worry I am worried about climate change and want to take personal actions to help. V14 Driving‑Range Confidence I am confident that electric vehicles have sufficient driving range for my daily needs. V15 Cost–Benefit Value Appraisal Electric vehicles provide good overall value for their cost. V16 Social Approval of EV Ownership Owning an electric vehicle is socially respected in my peer group. V17 Suitability for Long‑Distance Travel Electric vehicles are suitable for long-distance travel. V18 Perceived Performance Electric vehicles offer good driving performance, such as acceleration and handling. V19 Personal Innovativeness I enjoy being among the first to try new technologies like electric vehicles. V20 Personal Pro‑Environmental Identity Protecting the environment is an important part of who I am. The study sample includes a diverse number of respondents who had to be 18 years or older, aware of the concept of electric EVs and capable of owning a private vehicle (now or in the future). All questions were pre-tested and adjusted according to pilot comments. Participants were informed of the possible environmental impacts associated with EV battery production and recycling so that their answers represented a balanced view of both positives and negatives on EV uptake. A reliability test provided a Cronbach's alpha of 0.808, signifying high internal consistency between perception variables. 3.4 Data Analysis The obtained survey data were processed by a structured pipeline consisting of dimensionality reduction, machine learning classification and XAI technique using SPSS Statistics and Python 3.13.3. The combined outcome enabled the prediction of willingness to buy EVs while providing interpretable results about this choice's psychological and demographic drivers. First, PCA was employed to reduce the dimensionality of the 20 Likert-scale psychological and perceptual variables. An oblimin (oblique) rotation allowed minimal correlation between latent factors to reflect a real-world scenario. This eased the identification of interpretable components, indicating important adoption factors. The resulting four principal components were kept and used as engineered features for developing the ML model. These PCA-created component scores were merged with five demographic measures, namely age, gender, income, education, and trip purpose, to form the final set of predictors. The response variable, “Willingness to purchase an EV”, was defined as a multi-class categorical response with three classes: “Not Willing”, “Neutral” and “Willing”. Then the outcome had been modelled using the XGBoost algorithm, a computationally efficient tree-based ensemble learning algorithm for modelling non-linear relationships and interactions in structured data [ 19 ]. The classifier was used in a pipeline on scikit-learn, and hyperparameter optimisation was done using grid search and with 5-fold cross-validation. The best model was selected based on performance in terms of accuracy and generalisation on a stratified 80/20 train-test split. The best hyperparameters were: learning_rate 0.1, max_depth 3, n_estimators 100. These hyperparameters are conservative, generalising, and they offer a balance between predictive power and model complexity. To assess the classifier's performance beyond overall accuracy, a set of metrics right for binary classification tasks, particularly in the case of class imbalance, was utilised. They included sensitivity, specificity, precision, F1-score, and Youden's Index as outlined by equations (1–5) below. All the measures were calculated concerning the confusion matrix, which is characterised by four components: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). A representation of a binary confusion matrix consisting of TP, FP, TN, and FN is shown in Table 3 . Table 3 Binary confusion matrix Predicted Actual Positive (1) Negative (0) Positive (1) TP FN Negative (0) FP TN Sensitivity = \(\:\frac{\text{T}\text{P}}{\text{T}\text{P}+\text{F}\text{N}}\) (1) Specificity = \(\:\frac{\text{T}\text{N}}{\text{T}\text{N}+\text{F}\text{P}}\) (2) Precision = \(\:\frac{\text{T}\text{P}}{\text{T}\text{P}+\text{F}\text{P}}\) (3) F1-score = \(\:\frac{2\times\:\text{P}\text{r}\text{e}\text{c}\text{i}\text{s}\text{i}\text{o}\text{n}\times\:\text{S}\text{e}\text{n}\text{s}\text{i}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}}{\text{P}\text{r}\text{e}\text{c}\text{i}\text{s}\text{i}\text{o}\text{n}+\text{S}\text{e}\text{n}\text{s}\text{i}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}}\) (4) Youden’s Index = Sensitivity+Specificity-1 (5) For interpretation of the model predictions, SHAP, an explainable AI method based on cooperative game theory, was applied [ 17 ]. SHAP values measure how much each input feature contributes to specific predictions, enabling us to analyse the model's global and local reasoning. Mainly, two types of SHAP plots were plotted to aid interpretability: Global importance revealing feature importance throughout the sample, Class-specific SHAP beeswarm summaries plotting how feature importance varies over the three levels of willingness. These explainable tools were used to ensure that the findings were not only statistically conclusive but also behaviorally valid, which gives policy or mobility planners concrete suggestions on how to promote the adoption of EVs. This integrated methodological approach ensures that predictive accuracy is complemented by behavioural interpretability, so that results are meaningful for both technical modelling and decision making in a policy-oriented context. 4. Results and Discussion 4.1 Principal Component Analysis PCA is a standard behavioural and transport research method to capture meaningfully interpretable factors from extensive collections of correlated variables [ 13 ]. The aim is to identify fewer coherent constructs that correspond to underlying patterns in perception and attitude towards EVs. 4.1.1 Adequacy of Sampling and Correlation Structure Two major tests initially confirmed the adequacy of the data for PCA. Bartlett's test for sphericity was highly significant (χ² = 3147.06, df = 190, p < 0.001), showing that the variables are sufficiently correlated for dimensionality reduction. Furthermore, Kaiser Meyer Olkin (KMO) measure of sampling adequacy was 0.806, which is rated as "meritorious" in Kaiser's threshold classification [ 20 ]. The above findings justify the appropriateness of using PCA on the dataset. 4.1.2 Component Extraction and Explained Variance A PCA with oblimin rotation allows minimal correlations between components, which is an essential consideration in psychological modelling where constructs often overlap [ 21 ]. Four components were kept based on eigenvalues greater than one and scree plot examination, shown in Fig. 3 . As shown in Table 4 , the rotated component solution explained a cumulative 66.3% of the total variance, which exceeds the conventional 60% threshold recommended for behavioural survey research [ 14 ]. Table 4 Component Characteristics Unrotated solution Rotated solution Eigenvalue Proportion var. Cumulative SumSq. Loadings Proportion var. Cumulative Component 1 4.354 0.218 0.218 3.637 0.182 0.182 Component 2 3.583 0.179 0.397 3.586 0.179 0.361 Component 3 2.904 0.145 0.542 3.275 0.164 0.525 Component 4 2.410 0.120 0.663 2.752 0.138 0.663 4.1.3 Component Loadings and Interpretation The rotated component (RC) matrix (Table 5 ) showed a neat and interpretable pattern, with all the factors loading very strongly on one component (> 0.5) and minimal cross-loadings (< 0.5). Each component was named according to the thematic similarity of its highest-loading items: RC1: Environmental & Pro-EV Attitude : The factors that have the highest loadings are Personal Pro-EV Identity (0.872), Pro-EV Environmental Commitment (0.869), Belief in EV Environmental Benefits (0.726), Support for Clean Energy Technology (0.613), Climate Change Worry (0.579) and Perceived Societal Benefit of EVs (0.560). The component captures normative environmental concern and supportive attitudes towards sustainable mobility. RC2: Tech Trust & Social Influence : Includes factors like General Technology Optimism (0.829), Social Approval of EV Ownership (0.779), Perceived EV Technological Reliability (0.733), Personal Innovativeness (0.628) and Peer Adoption Influence (0.540), which capture trust in innovation and influence of peer and societal signals on EV preference. RC3: Perceived Usability & Range Assurance : Defined by loadings for Driving Range Confidence (0.755), Perceived Performance (0.700), Charging Infrastructure Availability (0.687), Perceived Recharging Time Acceptability (0.615), Suitability for Long Distance Travel (0.590), and EV Maintenance Convenience (0.554). This component shows pragmatic judgments of the satisfaction of daily mobility requirements by EVs. RC4: Cost Sensitivity & Financial Risk : Consists of Initial Purchase Cost Concern (0.779), Cost–Benefit Value Appraisal (0.776) and Long-Term Ownership Cost Concerns (0.626), emphasising price-related hindrance and economic risk perception. Table 5 Component Loadings RC1 RC2 RC3 RC4 Uniqueness Pro EV Environmental Commitment 0.869 -0.120 -0.065 -0.039 0.244 Perceived EV Technological Reliability -0.073 0.733 0.156 -0.134 0.400 General Technology Optimism 0.059 0.829 0.217 -0.001 0.207 Charging Infrastructure Availability -0.279 0.094 0.687 -0.289 0.314 Belief in EV Environmental Benefits 0.726 -0.454 0.053 0.020 0.356 Support for Clean Energy Technology 0.613 0.338 0.037 -0.461 0.245 Perceived Societal Benefit of EVs 0.560 0.330 -0.213 0.473 0.255 EV Maintenance Convenience 0.215 -0.201 0.554 -0.377 0.489 Perceived Recharging Time Acceptability 0.035 0.408 0.615 -0.276 0.300 Initial Purchase Cost Concern 0.046 -0.062 -0.199 0.779 0.329 Long term Ownership Cost Concern -0.316 -0.102 0.256 0.626 0.442 Peer Adoption Influence -0.254 0.540 0.467 0.270 0.325 Climate Change Worry 0.579 0.333 0.183 -0.310 0.366 Driving Range Confidence -0.103 -0.063 0.755 0.221 0.388 Cost Benefit Value Appraisal 0.099 0.153 0.272 0.776 0.297 Social Approval of EV Ownership -0.035 0.779 -0.230 0.037 0.388 Suitability for Long Distance Travel 0.126 -0.456 0.590 0.160 0.501 Perceived Performance 0.335 0.146 0.700 0.246 0.315 Personal Innovativeness 0.248 0.628 -0.276 0.275 0.378 Personal Pro Environmental Identity 0.872 0.052 0.056 0.123 0.212 Note. Applied rotation method is oblimin. Low uniqueness values, which indicate high communalities, for most items, prove that the retained factors account for a significant percentage of variance within items. The structure is also consistent with earlier studies on EV adoption, whereby environmental concern, cost perception, usability, and trust in society are usually reported as dominant behavioural dimensions [ 5 , 6 ]. These four elements were preserved as composite attributes (component scores) and applied during the following predictive modelling phase. Their internal consistency and theoretical fit underpin construct validity and model simplicity, calling for their inclusion in machine learning. 4.2 Predictive Modelling As indicated in the classification report (Table 11 ), the model showed excellent predictive ability for all three classes with an overall accuracy of 86.67% on the test set. Overall, the macro-averaged F1-score of 0.86 and weighted average of 0.87 stand for balanced and consistent performance across categories, with no significant class imbalance in prediction quality. Table 11 Classification Report of XGBoost model Sensitivity Specificity Precision F1-score Youden’s Index Not Willing 0.90 0.897 0.90 0.90 0.80 Neutral 0.84 0.86 0.95 0.89 0.697 Willing 0.86 0.85 0.71 0.77 0.70 Accuracy 0.87 Macro-average 0.86 Weighted-average 0.87 The model did an excellent job classifying the "Neutral" and "Not Willing" classes with high precision and sensitivity. Interestingly, the "Willing" class was less precise (0.71) but more sensitive (0.86), meaning that the model was able to find most truly willing people, though it also labelled some who were not willing. This is a common trade-off in behavioural prediction models where motivational predictors may overlap [ 16 ]. The high classification accuracy is proof of the effectiveness of PCA-based feature engineering combined with a robust non-linear classifier such as XGBoost. By dropping redundancy and easing interpretability, the psychological and perceptual features enabled the model to discover meaningful behavioural patterns without overfitting [ 9 ]. Adding demographic variables provided further explanatory richness. The confusion matrix (Table 12 ) shows that there were only two total misclassifications for the "Not Willing" group and two for the "Willing" group, showing that the classifier had learned the boundaries of behaviour well. However, the "Neutral" class was more confused with four misclassifications, which might be because perceptions in that category are ambivalent or overlapping. Table 12 Confusion Matrix of XGBoost Classifier Predicted Not Willing (21) Neutral (22) Willing (17) Actual Not Willing (21) 19 0 2 Neutral (25) 1 21 3 Willing (14) 1 1 12 From a policy perspective, this model can target awareness and incentive programs by focusing on perceptual profiles with a greater likelihood of translating into EV adopters. For example, respondents who teeter between "Neutral" and "Willing" would be most sensitive to interventions that minimise range anxiety or highlight cost savings. These findings are consistent with earlier studies proving that combining psychological constructs and socio-demographic variables improves EV adoption modelling [ 5 , 6 ]. Moreover, the success of XGBoost in this context aligns with its known strengths in handling tabular, heterogeneous behavioural data [ 19 ]. 4.3 Model Explainability Using SHAP 4.3.1 Global Feature Importance Figure 5 shows the global SHAP bar chart, which shows features by their mean absolute SHAP values throughout all predictions. The most influential feature for predicting EV purchase willingness was: RC2: Tech Trust & Social Influence RC1: Environmental & Pro-EV Attitude RC4: Cost Sensitivity & Financial Risk RC3: Perceived Usability & Range Assurance The prevalence of RC2 emphasises the crucial importance of technological optimism, belief in EV systems, and peer sanction in deciding behaviour intention. Participants who showed greater technology confidence and increased social exposure to EVs were more inclined to be called "Willing." This corroborates research which identifies social norms and perceived reliability in tech as key facilitators of EV adoption, particularly for early adopters in metropolitan technologically advanced locations [ 8 ]. The second most influential factor, RC1, says that individuals with high environmental identity, concern for climate, and faith in EVs' environmental contribution are more likely to adopt. Though ranking second, its strong influence verifies that ecological values are still key motivational drivers, as recorded by green mobility transitions literature [ 5 ]. RC4 appeared as the third most significant feature, highlighting that financial risks and perceptions of expense are still relevant deterrents. Individuals self-reporting EVs as pricey or financially risky were more likely to be predicted as "Not Willing." Unexpectedly, RC3, which measures charging ease, performance, and maintenance expectations, placed fourth. This suggests that, while significant, usability factors related to practicality were less potent than attitudinal and social perception-based factors, perhaps because the sample had less exposure to real-world EV use. These results suggest that the pace of EV adoption is influenced more by social trust and perceived legitimacy of the technology than by the technical performance alone, reinforcing the importance of behavioural and social dimensions in the broader mobility transition. 4.3.2 Directional Influence by Class Class-wise SHAP Beeswarm plots were analysed to understand how every psychological and perceptual component affected individual behavioural outcomes (Figs. 6 – 8 ). The plots show each class label’s size and direction of feature contributions. For the "Not Willing" class (Fig. 7 ), the most potent positive factor was “RC4: Cost Sensitivity & Financial Risk”, suggesting that those viewing EVs as costly or financially risky were most likely to be unwilling to adopt. Low values of “RC2: Tech Trust & Social Influence” additionally elevated unwillingness, showing the impact of techno scepticism and poor peer influence. Contrarily, high scores for “RC1: Environmental & Pro-EV Attitude” decreased the probability of being labelled as “Not Willing”. Therefore, financial concern and technology distrust are major deterrents and environmental concern and usability confidence are protective factors. For the "Neutral" class (Fig. 8 ), high scores of “RC2: Tech Trust & Social Influence” and “RC4: Cost Sensitivity & Financial Risk” were most responsible for neutrality, suggesting ambivalence based on both fiscal restraint and some techno scepticism. “RC1: Environmental & Pro-EV Attitude” and “RC3: Perceived Usability & Range Assurance” had minimal directional impact in this group, as there was little strong ideological or practical commitment. This implies that neutrality arises from a mix of uncertainty, lack of exposure, and lack of polarising opinions. Those respondents might be most susceptible to change through awareness campaigns or enhanced EV visibility in their social environment. For the "Willing" class (Fig. 9 ), high values of “RC2: Tech Trust & Social Influence” exerted a substantial positive impact, suggesting that people who trusted EV technology and were socially exposed to its uptake were most likely to be willing. “RC1: Environmental & Pro-EV Attitude” also had a positive impact, highlighting the influence of ecological values. “RC3: Perceived Usability & Range Assurance”, in addition to supportive influence, and low “RC4: Cost Sensitivity & Financial Risk”, reducing financial concern. Overall, this profile shows tech-savvy, environmentally friendly consumers who see EVs as usable and socially supported with little cost-related doubt. To explore interaction effects, a 3D Partial Dependence Plot (Fig. 9 ) was developed in terms of the normalised values of two major psychological and perceptual factors: "RC4: Cost Sensitivity & Financial Risk" and "RC1: Environmental & Pro-EV Attitude". The surface shows a steep increase in the predicted probability of EV purchase willingness in regions with high environmental attitude and low financial risk. In comparison, high-cost sensitivity suppresses willingness across most levels of environmental attitude. The interaction suggests that good environmental attitudes alone are insufficient unless complemented by reduced perceived financial risk, highlighting the necessity of economic incentives and awareness to stimulate EV adoption. 5. Conclusions and Future Scope This research created a unified modelling framework to gain insights into psychological and perceptual determinants of EV purchase willingness in an urban Indian setting. Four latent constructs were determined using PCA: ' Environmental & Pro-EV Attitude’, ‘Tech Trust & Social Influence’, ‘Perceived Usability and Range Assurance’ and ‘Cost Sensitivity and Financial Risk’. Predictive modelling was based on these elements and additional socio-demographic factors. An overall accuracy of 86.7% indicated the usefulness of non-linear modelling, such as the XGBoost classifier. SHAP-based explainability verified that the most significant positive influence was on ‘Tech Trust and Social Influence’, whereas ‘Cost Sensitivity and Financial Risk’ was the most significant negative driver. Such results imply a lot. To begin with, policy interventions can focus not just on environmental advantages but also on creating trust in technology and increasing social visibility of EVs (e.g., awareness campaigns, peer adoption programs). Second, the most powerful obstacle is the perception of financial risk, which may directly be overcome by specific subsidies, warranty guarantees, or new ownership strategies. Third, usability and range issues were less decisive, but to empower doubtful consumers, enhancing the presence of charging infrastructure is to be addressed. From a system perspective, these behavioural insights have direct implications for the planning of electric mobility. Infrastructure investments, charging network visibility and incentive schemes are likely to be more effective if they are combined with trust-building measures and social diffusion dynamics identified in this study. Without parallel attention to user perception and social acceptance, technological and regulatory advances risk underperforming in accelerating the sustainable mobility transition. To the stakeholders in the industry, the findings imply that market acceptance can be enhanced by positioning EVs as technologically trustworthy, socially approved, and environmentally advantageous, and decreasing the risk perceptions, among others. Future studies need to increase the number of participants, who are actual EV users and early adopters, consider objective infrastructure measures, and consider a longitudinal approach to determine the changes in willingness depending on new policy and market changes. The framework could be extended to other green modes of mobility (e.g., e-bikes, car-sharing) and tested on its generalisability through policy simulations. Declarations Author Contributions: S.D: Conceptualization, Methodology, Investigation, Visualization, Writing – original draft. S.K.R: Supervision, Review and editing – Final draft. All authors contributed for the manuscript and agreed with the final version of manuscript. Clinical Trial Number: Not applicable Ethics approval: This study protocol was reviewed and approved by the Department of Research Ethics Committee, Department of Civil Engineering, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India. The research was conducted in accordance with institutional ethical guidelines and principles of the Declaration of Helsinki. Consent to participate: Informed consent was obtained from all individual participants included in the study. Participation was voluntary, and all responses were collected anonymously. Consent to Publish: Not applicable Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability statement: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References International Energy Agency. World Energy Outlook 2023. OECD; 2023. Amnesty International. This is what we die for. Democratic Republic of Congo; 2016. Harper G, Sommerville R, Kendrick E, Driscoll L, Slater P, Stolkin R, Walton A, Christensen P, Heidrich O, Lambert S, Abbott A, Ryder K, Gaines L, Anderson P. Recycling lithium-ion batteries from electric vehicles. Nature. 2019;575:75–86. https://doi.org/10.1038/s41586-019-1682-5 . World Economic Forum. A Vision for a Sustainable Battery Value Chain in 2030 Unlocking the Full Potential. to Power Sustainable Development and Climate Change Mitigation; 2019. Rezvani Z, Jansson J, Bodin J. Advances in consumer electric vehicle adoption research: A review and research agenda. Transp Res D Transp Environ. 2015;34:122–36. https://doi.org/10.1016/j.trd.2014.10.010 . Franke T, Krems JF. Understanding charging behaviour of electric vehicle users. Transp Res Part F Traffic Psychol Behav. 2013;21:75–89. https://doi.org/10.1016/j.trf.2013.09.002 . Sierzchula W, Bakker S, Maat K, van Wee B. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy. 2014;68:183–94. https://doi.org/10.1016/j.enpol.2014.01.043 . Axsen J, Kurani KS. Social Influence, Consumer Behavior, and Low-Carbon Energy Transitions. Annu Rev Environ Resour. 2012;37:311–40. https://doi.org/10.1146/annurev-environ-062111-145049 . Jansson J, Nordlund A, Westin K. Examining drivers of sustainable consumption: The influence of norms and opinion leadership on electric vehicle adoption in Sweden. J Clean Prod. 2017;154:176–87. https://doi.org/10.1016/j.jclepro.2017.03.186 . Axsen J, Kurani KS. Developing sustainability-oriented values: Insights from households in a trial of plug-in hybrid electric vehicles. Glob Environ Change. 2013;23:70–80. https://doi.org/10.1016/j.gloenvcha.2012.08.002 . Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211. https://doi.org/10.1016/0749-5978(91)90020-T . Rogers EM. Diffusion of innovations. 5th ed. Free; 2003. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. Pearson Education Limited; 2014. Tabachnick BG, Fidell LS. (2013) Using multivariate statistics. Pearson Education. Feng F, Ding X, Zou J, Gao L, Sun Q. Intention to adopt battery electric vehicles among the early-to-late majority: Policy and behavioral insights from post-subsidy China. Res Transp Econ. 2025;114:101649. https://doi.org/10.1016/j.retrec.2025.101649 . de Luca S, Pace R, Di, Bruno F. Accounting for attitudes and perceptions influencing users’ willingness to purchase Electric Vehicles through a Hybrid Choice Modeling approach based on Analytic Hierarchy Process. Transp Res Procedia. 2020;45:467–74. https://doi.org/10.1016/j.trpro.2020.03.040 . Lundberg S, Lee S-I. (2017) A Unified Approach to Interpreting Model Predictions. Ribeiro MT, Singh S, Guestrin C. (2016) Why Should I Trust You? In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, pp 1135–1144. Chen T, Guestrin C. (2016) XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, pp 785–794. Kaiser HF. An Index of Factorial Simplicity. Psychometrika. 1974;39:31–6. https://doi.org/10.1007/BF02291575 . Costello AB, Osborne JW. Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assess Res Evaluation. 2005. https://doi.org/https://doi.org/10.7275/jyj1-4868 . 10:. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8723902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590226321,"identity":"6b548777-ae9f-4271-bc6e-9556b21cdca7","order_by":0,"name":"Subojit 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17:03:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 6\u003c/strong\u003e SHAP Beeswarm plot for Not Willing class\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8723902/v1/827ba51fea4c843ac28a6291.png"},{"id":102748530,"identity":"e92bd49a-80fa-456e-a7e0-048c636e7bb8","added_by":"auto","created_at":"2026-02-16 09:11:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 7\u003c/strong\u003e SHAP Beeswarm plot for Neutral class\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8723902/v1/ef6403ac08571a6bdae83ae2.png"},{"id":102747651,"identity":"a59f0209-46b1-419a-a7f4-8c6358e332c5","added_by":"auto","created_at":"2026-02-16 09:05:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 8\u003c/strong\u003e SHAP Beeswarm plot for Willing class\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8723902/v1/c8d9ea3d21be5d11dbcf6337.png"},{"id":102622715,"identity":"380936bc-16ce-4de6-b549-6650a4fb6ca2","added_by":"auto","created_at":"2026-02-13 17:03:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 9 \u003c/strong\u003e\u0026nbsp;3D Partial Dependence plot on EV purchase willingness\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8723902/v1/a746a638d8452c428053da2a.png"},{"id":104397275,"identity":"490ed7b0-36b8-447e-bc89-634e5867f7bd","added_by":"auto","created_at":"2026-03-11 11:45:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2578086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8723902/v1/74a15d89-5e1a-4d9b-bd53-444222e02dc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychological Drivers of Electric Vehicle Adoption in India’s Sustainable Mobility Transition","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRising concerns about climate change, energy security, and air pollution are fundamentally transforming the global transport system. Internal combustion engine (ICE) powered vehicles, which have been the main form of transportation for a century and a half, are increasingly spotlighted in their contribution to greenhouse gas production, urban atmosphere pollution and dependence on non-renewable fossil fuels. While the transport sector contributes almost 37% of the total end-use emissions, road transport alone accounts for approximately 23% of the total CO₂ emissions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The issues of rapid urbanisation, increasing travel demand, and oil market instability due to geopolitical factors have led to the need to transition towards cleaner and more sustainable forms of transport.\u003c/p\u003e \u003cp\u003eElectric vehicles (EVs) have become a key component in green transport initiatives. Vehicles powered by electricity, which have zero tailpipe emissions, present a viable pathway to decarbonization of all transport on the road. Also, with more and more national power grids becoming greener with clean energy like solar and wind power, EVs\u0026rsquo; life-cycle emissions decreased, making these vehicles progressively greener each day. The sales of EVs are booming all over the world, with over 10\u0026nbsp;million sales in 2022. Estimates show that by 2030, EVs will form over 60% of all vehicle sales in major markets [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The surge in EV sales is driven by policy support, financial support (e.g., subsidies, tax breaks), increased consumer awareness, technological advances, and cost reductions in battery production.\u003c/p\u003e \u003cp\u003eDespite the promises of such advancements, it is to be noted that there are some concerns about the environmental sustainability of EVs. It is mainly the production, mining and end-of-life disposal of EV batteries, specifically lithium-ion batteries. They require large quantities of essential minerals such as lithium, cobalt and nickel to manufacture and tend to be produced in regions with political instability or governance challenges. The effects of mining can lead to deforestation, water pollution, disturbance to the ecosystem and misuse of human rights in the cases of child labour, among others [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, although EVs produce fewer emissions than their ICE counterpart, there is a lack of sufficient recycling of end-of-life batteries to reduce the probability of long-term effects on the environment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. When there are no proper ecosystemic measures to dispose of used batteries, there will be contamination of the soil and water, as well as a safety hazard.\u003c/p\u003e \u003cp\u003eHowever, these challenges do not undermine the need to switch to electric mobility. Instead, they call for a sustainable, responsible and all-encompassing transition that has maintained a balance between environmental, technological and societal aspects. EVs still play a central role in solving the problems of city air pollution, climate change and the overall dependence on fossil fuels. Trade-offs of battery-producing products to the environment are slowly being solved by innovations in the chemicals used in production, like the solid-state batteries, enhancements in the efficiency of resources and extensions in closed-loop reprocessing structures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, some car companies and countries have gone as far as announcing that they will cease the production of ICE vehicles in the next 20 years, which is another indication of the coming of electrification.\u003c/p\u003e \u003cp\u003eWhile much is being done to enhance the technological and infrastructural aspects of EV adoption, human behaviour is one of the key bottlenecks [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite increased availability, many potential users are hesitant to purchase EVs due to psychological and perceived factors like range anxiety, inexperience with the technology, a lack of confidence in charging infrastructure, and concerns about purchasing and servicing costs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Environmental values and other determinants like social influence and personal innovativeness also significantly affect consumer intentions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For this purpose, it is vital to identify the psychological and perceptual drivers of EV adoption to implement effective public policies, marketing, and incentive programs.\u003c/p\u003e \u003cp\u003eIn the context of the electric mobility transition, the consumers\u0026rsquo; willingness to adopt EVs is a crucial socio-technical interface between vehicle technologies, charging infrastructure, market diffusion, and policy effectiveness. Understanding behavioural acceptance is thus crucial not only for predicting individual purchase decisions, but also for planning at the system levels in terms of infrastructure deployment, incentives and long-term sustainability of electric mobility ecosystems. By placing consumer psychology in a data-driven and interpretable framework, this study adds to interdisciplinary work attempting to bridge technological innovation and social readiness for EVs.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eEVs have come out as a critical solution to transport decarbonization and energy security issues in the context of the worldwide climate crisis. In the last ten years, research on EV uptake has increased extensively, with studies focusing on factors ranging from the availability of infrastructure and economic incentives to psychological dispositions and behavioural intention. While early studies of EV adoption centred primarily on cost-benefit analysis and policy measures, increasing consideration is given to the understanding that non-economic, perceptual, and psychological determinants critically influence EV buying decisions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Determinants of EV Adoption\u003c/h2\u003e \u003cp\u003eSeveral studies have recognised major predictors of EV adoption, classified under instrumental and psychological determinants. Instrumental predictors are income, car price, fuel saving, subsidies from the government, and charging infrastructure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Though very important, they often do not explain much of the variations in EV adoption, particularly when financial and infrastructural scenarios are kept constant.\u003c/p\u003e \u003cp\u003eScholars have resorted to psychological concepts based on behavioural theories to bridge this gap. Ajzen's Theory of Planned Behaviour (TPB) has been widely used to study EV adoption by conceptualising behavioural intention as a function of attitude, subjective norms, and perceived behavioural control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Rogers' Diffusion of Innovations (DOI) theory has also emphasised the role of these individuals' personal innovativeness and social influence in adopting the latest technologies like EV [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll within the underlying framework, vehicle range perceptions, environmental awareness, perceptions of technology trust, perceived cost, and social influence have been persistent predictors. For example, Rezvani et al. (2015) found that symbolic value and environmental motivation increased the probability of EV adoption, while unfamiliarity with charging infrastructure and range anxiety were psychological barriers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Role of Perceptual and Latent Constructs\u003c/h2\u003e \u003cp\u003eDue to the multidimensional nature of decision-making in EV adoption, researchers have increasingly employed latent variable models to reduce high-dimensional survey data into interpretable constructs. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and Principal Component Analysis (PCA) methods have been used to reveal dimensions like \"eco-consciousness,\" \"cost anxiety,\" and \"technological optimism\" [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These latent constructs provide an interpretable and parsimonious basis for modelling behavioural intention, mainly when employed alongside predictive modelling tools [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, most of these models are based on linear regression methods or structural equation modelling (SEM), which are built on assumptions of linearity and do not have predictive accuracy compared to contemporary machine learning approaches [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, the complexity of psychological constructs makes it problematic to describe richer individual differences only with fixed, theory-based frameworks [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Application of Machine Learning and Explainable AI\u003c/h2\u003e \u003cp\u003eMachine learning (ML) methods have become increasingly popular for modelling transportation behaviour in recent years. Decision trees, random forests, and gradient boosting machines can classify mode choice and vehicle adoption with greater prediction accuracy compared to conventional models [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. One major criticism of ML models is their \"black box\" nature, which hinders interpretability and undermines their policy relevance.\u003c/p\u003e \u003cp\u003eTo tackle this, researchers have started incorporating explainable artificial intelligence (XAI) techniques, namely SHapley Additive exPlanations (SHAP), to reveal the features' relative contribution and directional effect in the predictive models. SHAP offers global and individual-level insights, enabling analysts to see which variables are important and how they affect the prediction [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile XAI techniques are increasingly being explored across finance and medicine [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], their use in transport behaviour modelling and EV adoption modelling is still nascent. Current research primarily uses descriptive or correlation-driven methods instead of explainable predictive models incorporating psychological and perceptual factors and XAI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research Gaps\u003c/h2\u003e \u003cp\u003eBased on the literature, several gaps remain:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eChallenges in identifying psychological and perceptual determinants of EV purchase willingness\u003c/em\u003e: Prior studies recognise range anxiety, cost sensitivity, social influence, and environmental concern as important factors, but the relative importance, interactions and their stability across situations are not yet determined. Current methods usually use self-reported survey measures that are subject to bias, and small sample sizes limit generalizability. This disjuncture necessitates more stringent procedures to reliably uncover and validate the latent psycho-perceptual dimensions of EV adoption behaviour.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLimited integration of perception-based constructs with advanced ML techniques\u003c/em\u003e: Although psychological and perceptual variables were modelled through factor analysis or SEM, hardly any studies integrate them with sophisticated classifiers such as Extreme Gradient Boost (XGBoost) for enhanced predictive performance. Advanced ML techniques are required since they can model non-linear relationships between psychological, perceptual and demographic variables and are more predictive than traditional regression models. It is especially relevant in behavioural research, in which decision-making is hardly linear and interacting perceptual factors often influence it.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLimited application of explainability tools in EV adoption modelling\u003c/em\u003e: Most ML applications in this domain are accuracy-oriented at the expense of transparency. The unavailability of interpretable ML negates the policy and behavioural understanding necessary for well-targeted interventions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eLack of contextualization in emerging markets\u003c/em\u003e: Most behavioural studies on EV adoption are focused on Europe and North America. There is a requirement to conduct analyses based on users' views in fast-moving urbanising markets such as India, where the levels of infrastructure, affordability and awareness vary widely.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAddressing such gaps is especially important for interdisciplinary mobility research, where behavioural insights are required to ensure that technological and policy interventions translate into real-world adoption outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Objectives and Scope of Work\u003c/h2\u003e \u003cp\u003eThe primary objective of the paper is to identify the psychological and perceptual determinants of the willingness to purchase EVs in the Indian urban context. To achieve this aim, the study is going to propose a methodology by combining dimensionality reduction and explainable AI.\u003c/p\u003e \u003cp\u003eThe scope of this paper is (i) to use PCA for finding psychological and perceptual dimensions, (ii) to use advanced ML model for accurate prediction, (iii) to utilize SHAP for offering interpretable, class-wise explanations of the role of each psychological and perceptual factors in encouraging or deterring EV adoption to overcome the lack of interpretability in existing EV adoption models, and (iv) to address the contextualisation gap in new markets.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in Kolkata, the capital city of West Bengal, located in eastern India. It is one of the largest metropolitan cities of India with a population of over 14\u0026nbsp;million people in the metropolitan region and an urban structure that is highly densely motorised and travelled by.\u003c/p\u003e \u003cp\u003eWithin this context, the specific selected study area was Sector V, Salt Lake (Bidhannagar) and the surrounding neighbourhoods in northeastern Kolkata, geographically located in the region between 22.5804 \u0026deg;N and 88.4378 \u0026deg;E. The ideal information technology (IT) and business district of Kolkata is Sector V, which is part of Salt Lake (Bidhannagar), also known as the IT district of eastern India. It is home to many IT firms, business headquarters, educational facilities, startups, and commercial areas, and it receives many commuters daily citywide. Greater Bidhannagar Municipal Corporation, with Sector V, contained a population of more than 215000 according to the 2011 Census, and the region has experienced an accelerated development over the last ten years because of the continued urbanisation and enhanced infrastructure. The map of the area of study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSector V and the vicinity receive a lot of daily commuter traffic, primarily salaried workers, students, and service workers. These commuters are based on a multimodal combination of the mode of transport (bus, auto-rickshaw, metro) and the personal vehicle, so the area is a vital point in studying mobility behaviour in Kolkata. Sector V, with its sector-majority segment of middle- and higher-income residents, the development of a healthy road network, an increase in metro connectivity, and a further focus on smart mobility and green transportation, is a typical urban Indian setting where the willingness to embrace EVs can be examined in combination with the ongoing process of rapid urbanisation and the altering mobility needs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Collection and Sampling\u003c/h2\u003e \u003cp\u003eData were gathered through a standard in-person and online survey questionnaire from June to August 2025. A non-probabilistic, purposive sampling strategy was used to reach a demographically varied group of participants who often travel to or live in Sector V and the surrounding areas. After excluding incomplete or inconsistent entries, the final dataset included complete responses from 300 people out of the total sample of 362. Although this sample size is a little smaller than the threshold for population estimates based on Cochran\u0026rsquo;s formula, it is adequate for multivariate analysis. With a total of 25 survey items, this study has a respondent-to-item ratio of 12:1, which is higher than the recommended ratio of 10:1 for obtaining stable parameter estimates [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Study participation was voluntary. All respondents gave informed consent, and data anonymity was ensured during the analysis. The research was carried out according to the ethical practice of behavioural study and data privacy norms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survey Measures\u003c/h2\u003e \u003cp\u003eThe questionnaire included Demographic variables (Age, Gender, Income, Education, and Trip purpose), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of commuters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncoding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. of response\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;40 years\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003cp\u003e40\u0026ndash;50 years\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003cp\u003e50\u0026ndash;60 years\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203\u003c/p\u003e \u003cp\u003e46\u003c/p\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUndergraduate\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003ePostgraduate\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224\u003c/p\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer Month Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18-20K\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003e20-40K\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003cp\u003e40K-50K\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003cp\u003e50K\u0026thinsp;+\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e68\u003c/p\u003e \u003cp\u003e191\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrip Purpose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHome based work\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eHome based education\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003cp\u003eHome based others\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003cp\u003e63\u003c/p\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResponses on twenty perception-based items, each measured on a five point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 5\u0026thinsp;=\u0026thinsp;Strongly Agree), were also collected (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) followed by the target variable \u0026ldquo;willingness to purchase an EV\u0026rdquo;, measured on a three point ordinal scale: \u0026ldquo;1\u0026thinsp;=\u0026thinsp;Not Willing,\u0026rdquo; \u0026ldquo;2\u0026thinsp;=\u0026thinsp;Neutral,\u0026rdquo; and \u0026ldquo;3\u0026thinsp;=\u0026thinsp;Willing\u0026rdquo;, to tap into core psychological drivers found in EV adoption literature.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerception Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Encoding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvey Statement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable Description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePro‑EV Environmental Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI support the use of electric vehicles as a way to reduce environmental harm.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"19\" rowspan=\"20\"\u003e \u003cp\u003eFive point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 5\u0026thinsp;=\u0026thinsp;Strongly Agree)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived EV Technological Reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectric vehicles are based on reliable and proven technology.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Technology Optimism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am generally enthusiastic about adopting new technologies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharging‑Infrastructure Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharging stations for electric vehicles are sufficiently available near where I live or travel.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelief in EV Environmental Benefits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectric vehicles significantly reduce air pollution and carbon emissions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupport for Clean‑Energy Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI support the transition from fossil fuels to clean-energy transportation technologies like EVs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Societal Benefit of EVs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSociety as a whole would benefit from widespread electric vehicle adoption.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEV Maintenance Convenience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintaining an electric vehicle is easier and more convenient than maintaining a conventional vehicle.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Recharging Time Acceptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe time it takes to recharge an electric vehicle is acceptable to me.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial Purchase‑Cost Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectric vehicles are too expensive to purchase initially.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong‑term Ownership‑Cost Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am concerned about the long-term costs of owning and operating an electric vehicle.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeer Adoption Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople around me are considering or already using electric vehicles, which influences my opinion.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate‑Change Worry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am worried about climate change and want to take personal actions to help.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDriving‑Range Confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am confident that electric vehicles have sufficient driving range for my daily needs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCost\u0026ndash;Benefit Value Appraisal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectric vehicles provide good overall value for their cost.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Approval of EV Ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOwning an electric vehicle is socially respected in my peer group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuitability for Long‑Distance Travel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectric vehicles are suitable for long-distance travel.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectric vehicles offer good driving performance, such as acceleration and handling.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersonal Innovativeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI enjoy being among the first to try new technologies like electric vehicles.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersonal Pro‑Environmental Identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtecting the environment is an important part of who I am.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study sample includes a diverse number of respondents who had to be 18 years or older, aware of the concept of electric EVs and capable of owning a private vehicle (now or in the future).\u003c/p\u003e \u003cp\u003eAll questions were pre-tested and adjusted according to pilot comments. Participants were informed of the possible environmental impacts associated with EV battery production and recycling so that their answers represented a balanced view of both positives and negatives on EV uptake. A reliability test provided a Cronbach's alpha of 0.808, signifying high internal consistency between perception variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e \u003cp\u003eThe obtained survey data were processed by a structured pipeline consisting of dimensionality reduction, machine learning classification and XAI technique using SPSS Statistics and Python 3.13.3. The combined outcome enabled the prediction of willingness to buy EVs while providing interpretable results about this choice's psychological and demographic drivers.\u003c/p\u003e \u003cp\u003eFirst, PCA was employed to reduce the dimensionality of the 20 Likert-scale psychological and perceptual variables. An oblimin (oblique) rotation allowed minimal correlation between latent factors to reflect a real-world scenario. This eased the identification of interpretable components, indicating important adoption factors. The resulting four principal components were kept and used as engineered features for developing the ML model.\u003c/p\u003e \u003cp\u003eThese PCA-created component scores were merged with five demographic measures, namely age, gender, income, education, and trip purpose, to form the final set of predictors. The response variable, \u0026ldquo;Willingness to purchase an EV\u0026rdquo;, was defined as a multi-class categorical response with three classes: \u0026ldquo;Not Willing\u0026rdquo;, \u0026ldquo;Neutral\u0026rdquo; and \u0026ldquo;Willing\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThen the outcome had been modelled using the XGBoost algorithm, a computationally efficient tree-based ensemble learning algorithm for modelling non-linear relationships and interactions in structured data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The classifier was used in a pipeline on scikit-learn, and hyperparameter optimisation was done using grid search and with 5-fold cross-validation. The best model was selected based on performance in terms of accuracy and generalisation on a stratified 80/20 train-test split. The best hyperparameters were: learning_rate 0.1, max_depth 3, n_estimators 100. These hyperparameters are conservative, generalising, and they offer a balance between predictive power and model complexity.\u003c/p\u003e \u003cp\u003eTo assess the classifier's performance beyond overall accuracy, a set of metrics right for binary classification tasks, particularly in the case of class imbalance, was utilised. They included sensitivity, specificity, precision, F1-score, and Youden's Index as outlined by equations (1\u0026ndash;5) below. All the measures were calculated concerning the confusion matrix, which is characterised by four components: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). A representation of a binary confusion matrix consisting of TP, FP, TN, and FN is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Binary confusion matrix\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003c/br\u003e \u003cp\u003eSensitivity = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{P}}{\\text{T}\\text{P}+\\text{F}\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eSpecificity = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{N}}{\\text{T}\\text{N}+\\text{F}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003ePrecision = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{P}}{\\text{T}\\text{P}+\\text{F}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003eF1-score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{2\\times\\:\\text{P}\\text{r}\\text{e}\\text{c}\\text{i}\\text{s}\\text{i}\\text{o}\\text{n}\\times\\:\\text{S}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}}{\\text{P}\\text{r}\\text{e}\\text{c}\\text{i}\\text{s}\\text{i}\\text{o}\\text{n}+\\text{S}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003eYouden\u0026rsquo;s Index\u0026thinsp;=\u0026thinsp;Sensitivity+Specificity-1 (5)\u003c/p\u003e \u003cp\u003eFor interpretation of the model predictions, SHAP, an explainable AI method based on cooperative game theory, was applied [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. SHAP values measure how much each input feature contributes to specific predictions, enabling us to analyse the model's global and local reasoning.\u003c/p\u003e \u003cp\u003eMainly, two types of SHAP plots were plotted to aid interpretability:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGlobal importance revealing feature importance throughout the sample,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClass-specific SHAP beeswarm summaries plotting how feature importance varies over the three levels of willingness.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese explainable tools were used to ensure that the findings were not only statistically conclusive but also behaviorally valid, which gives policy or mobility planners concrete suggestions on how to promote the adoption of EVs.\u003c/p\u003e \u003cp\u003eThis integrated methodological approach ensures that predictive accuracy is complemented by behavioural interpretability, so that results are meaningful for both technical modelling and decision making in a policy-oriented context.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal Component Analysis\u003c/h2\u003e \u003cp\u003ePCA is a standard behavioural and transport research method to capture meaningfully interpretable factors from extensive collections of correlated variables [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The aim is to identify fewer coherent constructs that correspond to underlying patterns in perception and attitude towards EVs.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Adequacy of Sampling and Correlation Structure\u003c/h2\u003e \u003cp\u003eTwo major tests initially confirmed the adequacy of the data for PCA. Bartlett's test for sphericity was highly significant (χ\u0026sup2; = 3147.06, df\u0026thinsp;=\u0026thinsp;190, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), showing that the variables are sufficiently correlated for dimensionality reduction. Furthermore, Kaiser Meyer Olkin (KMO) measure of sampling adequacy was 0.806, which is rated as \"meritorious\" in Kaiser's threshold classification [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The above findings justify the appropriateness of using PCA on the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Component Extraction and Explained Variance\u003c/h2\u003e \u003cp\u003eA PCA with oblimin rotation allows minimal correlations between components, which is an essential consideration in psychological modelling where constructs often overlap [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Four components were kept based on eigenvalues greater than one and scree plot examination, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the rotated component solution explained a cumulative 66.3% of the total variance, which exceeds the conventional 60% threshold recommended for behavioural survey research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComponent Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eUnrotated solution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003eRotated solution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eProportion var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSumSq. Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eProportion var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Component Loadings and Interpretation\u003c/h2\u003e \u003cp\u003eThe rotated component (RC) matrix (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed a neat and interpretable pattern, with all the factors loading very strongly on one component (\u0026gt;\u0026thinsp;0.5) and minimal cross-loadings (\u0026lt;\u0026thinsp;0.5). Each component was named according to the thematic similarity of its highest-loading items:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRC1: Environmental \u0026amp; Pro-EV Attitude\u003c/b\u003e: The factors that have the highest loadings are Personal Pro-EV Identity (0.872), Pro-EV Environmental Commitment (0.869), Belief in EV Environmental Benefits (0.726), Support for Clean Energy Technology (0.613), Climate Change Worry (0.579) and Perceived Societal Benefit of EVs (0.560). The component captures normative environmental concern and supportive attitudes towards sustainable mobility.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRC2: Tech Trust \u0026amp; Social Influence\u003c/b\u003e: Includes factors like General Technology Optimism (0.829), Social Approval of EV Ownership (0.779), Perceived EV Technological Reliability (0.733), Personal Innovativeness (0.628) and Peer Adoption Influence (0.540), which capture trust in innovation and influence of peer and societal signals on EV preference.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRC3: Perceived Usability \u0026amp; Range Assurance\u003c/b\u003e: Defined by loadings for Driving Range Confidence (0.755), Perceived Performance (0.700), Charging Infrastructure Availability (0.687), Perceived Recharging Time Acceptability (0.615), Suitability for Long Distance Travel (0.590), and EV Maintenance Convenience (0.554). This component shows pragmatic judgments of the satisfaction of daily mobility requirements by EVs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRC4: Cost Sensitivity \u0026amp; Financial Risk\u003c/b\u003e: Consists of Initial Purchase Cost Concern (0.779), Cost\u0026ndash;Benefit Value Appraisal (0.776) and Long-Term Ownership Cost Concerns (0.626), emphasising price-related hindrance and economic risk perception.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\u003cp style='margin-top:12.0pt;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",serif;color:black;'\u003eTable 5\u003c/span\u003e\u003c/strong\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;Component Loadings\u003c/span\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\n \u003ctable style=\"border: none; border-collapse: collapse; width: 100%;\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:2.25pt 9.0pt 2.25pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:2.25pt 9.0pt 2.25pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eRC1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:2.25pt 9.0pt 2.25pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eRC2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:2.25pt 9.0pt 2.25pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eRC3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:2.25pt 9.0pt 2.25pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eRC4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid black 1.0pt;border-right:none;padding:2.25pt 9.0pt 2.25pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:center;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eUniqueness\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:8.1pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePro EV Environmental Commitment\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:8.1pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.869\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.120\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.065\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.039\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.244\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:8.1pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePerceived EV Technological Reliability\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.073\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.733\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.156\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.134\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.400\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eGeneral Technology Optimism\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.059\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.829\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.217\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.207\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eCharging Infrastructure Availability\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.279\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.094\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.687\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.289\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.314\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eBelief in EV Environmental Benefits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.726\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.454\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.053\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.356\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eSupport for Clean Energy Technology\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.613\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.338\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.037\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.461\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.245\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePerceived Societal Benefit of EVs\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.560\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.330\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.213\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.473\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.255\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eEV Maintenance Convenience\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.215\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.201\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.554\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.377\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.489\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePerceived Recharging Time Acceptability\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.035\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.408\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.615\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.276\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.300\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eInitial Purchase Cost Concern\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.046\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.062\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.199\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.779\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.329\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eLong term Ownership Cost Concern\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.316\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.102\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.256\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.626\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.442\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePeer Adoption Influence\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.254\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.540\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.467\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.270\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.325\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eClimate Change Worry\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.579\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.333\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.183\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.310\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.366\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eDriving Range Confidence\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.103\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.063\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.755\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.221\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.388\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eCost Benefit Value Appraisal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.099\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.153\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.272\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.776\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.297\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eSocial Approval of EV Ownership\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.035\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.779\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.230\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.037\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.388\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eSuitability for Long Distance Travel\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.126\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.456\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.590\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.160\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.501\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePerceived Performance\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.335\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.146\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.700\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.246\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.315\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePersonal Innovativeness\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.248\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.628\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e-0.276\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.275\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.378\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.1in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003ePersonal Pro Environmental Identity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;background:#D9D9D9;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.1in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.872\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.1in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.052\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.1in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.056\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.1in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:#A6A6A6;'\u003e0.123\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"border:none;border-bottom:solid windowtext 1.0pt;padding:.75pt 0in .75pt 9.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.1in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;text-align:right;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003e0.212\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"padding:.75pt 9.0pt .75pt 0in;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" style=\"padding:.75pt .75pt .75pt .75pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:115%;font-size:16px;font-family:\"Aptos\",sans-serif;'\u003e\u003cspan style='font-size:11px;line-height:115%;font-family:\"Times New Roman\",serif;color:black;'\u003eNote. \u0026nbsp;Applied rotation method is oblimin.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003cp\u003eLow uniqueness values, which indicate high communalities, for most items, prove that the retained factors account for a significant percentage of variance within items. The structure is also consistent with earlier studies on EV adoption, whereby environmental concern, cost perception, usability, and trust in society are usually reported as dominant behavioural dimensions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese four elements were preserved as composite attributes (component scores) and applied during the following predictive modelling phase. Their internal consistency and theoretical fit underpin construct validity and model simplicity, calling for their inclusion in machine learning.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Predictive Modelling\u003c/h2\u003e \u003cp\u003eAs indicated in the classification report (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e11\u003c/span\u003e), the model showed excellent predictive ability for all three classes with an overall accuracy of 86.67% on the test set. Overall, the macro-averaged F1-score of 0.86 and weighted average of 0.87 stand for balanced and consistent performance across categories, with no significant class imbalance in prediction quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification Report of XGBoost model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF1-score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden\u0026rsquo;s Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Willing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMacro-average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eWeighted-average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe model did an excellent job classifying the \"Neutral\" and \"Not Willing\" classes with high precision and sensitivity. Interestingly, the \"Willing\" class was less precise (0.71) but more sensitive (0.86), meaning that the model was able to find most truly willing people, though it also labelled some who were not willing. This is a common trade-off in behavioural prediction models where motivational predictors may overlap [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe high classification accuracy is proof of the effectiveness of PCA-based feature engineering combined with a robust non-linear classifier such as XGBoost. By dropping redundancy and easing interpretability, the psychological and perceptual features enabled the model to discover meaningful behavioural patterns without overfitting [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Adding demographic variables provided further explanatory richness.\u003c/p\u003e \u003cp\u003eThe confusion matrix (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e12\u003c/span\u003e) shows that there were only two total misclassifications for the \"Not Willing\" group and two for the \"Willing\" group, showing that the classifier had learned the boundaries of behaviour well. However, the \"Neutral\" class was more confused with four misclassifications, which might be because perceptions in that category are ambivalent or overlapping.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfusion Matrix of XGBoost Classifier\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Willing (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWilling (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eActual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Willing (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilling (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom a policy perspective, this model can target awareness and incentive programs by focusing on perceptual profiles with a greater likelihood of translating into EV adopters. For example, respondents who teeter between \"Neutral\" and \"Willing\" would be most sensitive to interventions that minimise range anxiety or highlight cost savings.\u003c/p\u003e \u003cp\u003eThese findings are consistent with earlier studies proving that combining psychological constructs and socio-demographic variables improves EV adoption modelling [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, the success of XGBoost in this context aligns with its known strengths in handling tabular, heterogeneous behavioural data [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Model Explainability Using SHAP\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Global Feature Importance\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the global SHAP bar chart, which shows features by their mean absolute SHAP values throughout all predictions. The most influential feature for predicting EV purchase willingness was:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRC2: Tech Trust \u0026amp; Social Influence\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRC1: Environmental \u0026amp; Pro-EV Attitude\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRC4: Cost Sensitivity \u0026amp; Financial Risk\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRC3: Perceived Usability \u0026amp; Range Assurance\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of RC2 emphasises the crucial importance of technological optimism, belief in EV systems, and peer sanction in deciding behaviour intention. Participants who showed greater technology confidence and increased social exposure to EVs were more inclined to be called \"Willing.\" This corroborates research which identifies social norms and perceived reliability in tech as key facilitators of EV adoption, particularly for early adopters in metropolitan technologically advanced locations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe second most influential factor, RC1, says that individuals with high environmental identity, concern for climate, and faith in EVs' environmental contribution are more likely to adopt. Though ranking second, its strong influence verifies that ecological values are still key motivational drivers, as recorded by green mobility transitions literature [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRC4 appeared as the third most significant feature, highlighting that financial risks and perceptions of expense are still relevant deterrents. Individuals self-reporting EVs as pricey or financially risky were more likely to be predicted as \"Not Willing.\"\u003c/p\u003e \u003cp\u003eUnexpectedly, RC3, which measures charging ease, performance, and maintenance expectations, placed fourth. This suggests that, while significant, usability factors related to practicality were less potent than attitudinal and social perception-based factors, perhaps because the sample had less exposure to real-world EV use.\u003c/p\u003e \u003cp\u003eThese results suggest that the pace of EV adoption is influenced more by social trust and perceived legitimacy of the technology than by the technical performance alone, reinforcing the importance of behavioural and social dimensions in the broader mobility transition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Directional Influence by Class\u003c/h2\u003e \u003cp\u003eClass-wise SHAP Beeswarm plots were analysed to understand how every psychological and perceptual component affected individual behavioural outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The plots show each class label\u0026rsquo;s size and direction of feature contributions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the \"Not Willing\" class (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the most potent positive factor was \u0026ldquo;RC4: Cost Sensitivity \u0026amp; Financial Risk\u0026rdquo;, suggesting that those viewing EVs as costly or financially risky were most likely to be unwilling to adopt. Low values of \u0026ldquo;RC2: Tech Trust \u0026amp; Social Influence\u0026rdquo; additionally elevated unwillingness, showing the impact of techno scepticism and poor peer influence. Contrarily, high scores for \u0026ldquo;RC1: Environmental \u0026amp; Pro-EV Attitude\u0026rdquo; decreased the probability of being labelled as \u0026ldquo;Not Willing\u0026rdquo;. Therefore, financial concern and technology distrust are major deterrents and environmental concern and usability confidence are protective factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the \"Neutral\" class (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e), high scores of \u0026ldquo;RC2: Tech Trust \u0026amp; Social Influence\u0026rdquo; and \u0026ldquo;RC4: Cost Sensitivity \u0026amp; Financial Risk\u0026rdquo; were most responsible for neutrality, suggesting ambivalence based on both fiscal restraint and some techno scepticism. \u0026ldquo;RC1: Environmental \u0026amp; Pro-EV Attitude\u0026rdquo; and \u0026ldquo;RC3: Perceived Usability \u0026amp; Range Assurance\u0026rdquo; had minimal directional impact in this group, as there was little strong ideological or practical commitment. This implies that neutrality arises from a mix of uncertainty, lack of exposure, and lack of polarising opinions. Those respondents might be most susceptible to change through awareness campaigns or enhanced EV visibility in their social environment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the \"Willing\" class (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e), high values of \u0026ldquo;RC2: Tech Trust \u0026amp; Social Influence\u0026rdquo; exerted a substantial positive impact, suggesting that people who trusted EV technology and were socially exposed to its uptake were most likely to be willing. \u0026ldquo;RC1: Environmental \u0026amp; Pro-EV Attitude\u0026rdquo; also had a positive impact, highlighting the influence of ecological values. \u0026ldquo;RC3: Perceived Usability \u0026amp; Range Assurance\u0026rdquo;, in addition to supportive influence, and low \u0026ldquo;RC4: Cost Sensitivity \u0026amp; Financial Risk\u0026rdquo;, reducing financial concern. Overall, this profile shows tech-savvy, environmentally friendly consumers who see EVs as usable and socially supported with little cost-related doubt.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore interaction effects, a 3D Partial Dependence Plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e) was developed in terms of the normalised values of two major psychological and perceptual factors: \"RC4: Cost Sensitivity \u0026amp; Financial Risk\" and \"RC1: Environmental \u0026amp; Pro-EV Attitude\". The surface shows a steep increase in the predicted probability of EV purchase willingness in regions with high environmental attitude and low financial risk. In comparison, high-cost sensitivity suppresses willingness across most levels of environmental attitude. The interaction suggests that good environmental attitudes alone are insufficient unless complemented by reduced perceived financial risk, highlighting the necessity of economic incentives and awareness to stimulate EV adoption.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Conclusions and Future Scope","content":"\u003cp\u003eThis research created a unified modelling framework to gain insights into psychological and perceptual determinants of EV purchase willingness in an urban Indian setting. Four latent constructs were determined using PCA: ' Environmental \u0026amp; Pro-EV Attitude\u0026rsquo;, \u0026lsquo;Tech Trust \u0026amp; Social Influence\u0026rsquo;, \u0026lsquo;Perceived Usability and Range Assurance\u0026rsquo; and \u0026lsquo;Cost Sensitivity and Financial Risk\u0026rsquo;. Predictive modelling was based on these elements and additional socio-demographic factors. An overall accuracy of 86.7% indicated the usefulness of non-linear modelling, such as the XGBoost classifier. SHAP-based explainability verified that the most significant positive influence was on \u0026lsquo;Tech Trust and Social Influence\u0026rsquo;, whereas \u0026lsquo;Cost Sensitivity and Financial Risk\u0026rsquo; was the most significant negative driver.\u003c/p\u003e \u003cp\u003eSuch results imply a lot. To begin with, policy interventions can focus not just on environmental advantages but also on creating trust in technology and increasing social visibility of EVs (e.g., awareness campaigns, peer adoption programs). Second, the most powerful obstacle is the perception of financial risk, which may directly be overcome by specific subsidies, warranty guarantees, or new ownership strategies. Third, usability and range issues were less decisive, but to empower doubtful consumers, enhancing the presence of charging infrastructure is to be addressed.\u003c/p\u003e \u003cp\u003eFrom a system perspective, these behavioural insights have direct implications for the planning of electric mobility. Infrastructure investments, charging network visibility and incentive schemes are likely to be more effective if they are combined with trust-building measures and social diffusion dynamics identified in this study. Without parallel attention to user perception and social acceptance, technological and regulatory advances risk underperforming in accelerating the sustainable mobility transition.\u003c/p\u003e \u003cp\u003eTo the stakeholders in the industry, the findings imply that market acceptance can be enhanced by positioning EVs as technologically trustworthy, socially approved, and environmentally advantageous, and decreasing the risk perceptions, among others.\u003c/p\u003e \u003cp\u003eFuture studies need to increase the number of participants, who are actual EV users and early adopters, consider objective infrastructure measures, and consider a longitudinal approach to determine the changes in willingness depending on new policy and market changes. The framework could be extended to other green modes of mobility (e.g., e-bikes, car-sharing) and tested on its generalisability through policy simulations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e S.D: Conceptualization, Methodology, Investigation, Visualization, Writing \u0026ndash; original draft. S.K.R: Supervision, Review and editing \u0026ndash; Final draft. All authors contributed for the manuscript and agreed with the final version of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study protocol was reviewed and approved by the Department of Research Ethics Committee, Department of Civil Engineering, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India. The research was conducted in accordance with institutional ethical guidelines and principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study. Participation was voluntary, and all responses were collected anonymously.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests:\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Energy Agency. 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Practical Assess Res Evaluation. 2005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.7275/jyj1-4868\u003c/span\u003e\u003cspan address=\"10.7275/jyj1-4868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 10:.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-vehicles","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Vehicles](https://link.springer.com/journal/44465)","snPcode":"44465","submissionUrl":"https://submission.springernature.com/new-submission/44465/3","title":"Discover Vehicles","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electric Vehicle Adoption, Sustainable Mobility Transition, Social Acceptance of EVs, Consumer behaviour, Psychological Determinants, Explainable Artificial Intelligence, Urban Transport Planning","lastPublishedDoi":"10.21203/rs.3.rs-8723902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8723902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe shift to electric vehicles (EVs) is a key pillar in achieving sustainable mobility solutions worldwide, but progress is uneven, especially in emerging urban settings. Beyond technological readiness and infrastructure availability, consumer perceptions, social influence, and psychological dispositions play important roles in determining the willingness to adopt EVs. This study investigates the psychological and perceptual factors that determine the willingness to purchase EVs in the urban Indian context and adds to a broader understanding of the socio-technical aspects of sustainable mobility transition. Using survey data from commuters in Kolkata, 20 perception-based indicators were synthesised into latent behavioural constructs via Principal Component Analysis. These constructs, along with socio-demographic attributes, were incorporated into a non-linear machine learning framework to forecast EV purchase willingness. Model transparency was ensured through explainable AI techniques, enabling interpretation of the relative impact and direction of the behavioural factors. Results show that trust in technology and social influence are the strongest factors in motivating the EV purchase willingness, while perceived financial risk is the biggest barrier. Environmental attitudes and perceived usability have positive but secondary roles. These findings reveal the multidimensional and social nature of EV adoption decisions, and the importance of trust-building, social visibility, and targeted financial interventions to accelerate adoption. By combining behavioural science with data-driven modelling, this research advances interdisciplinary thinking about EV adoption and offers actionable insights for policy-makers, planners, and industry stakeholders to enable a just and sustainable transition to EV in rapidly urbanising regions.\u003c/p\u003e","manuscriptTitle":"Psychological Drivers of Electric Vehicle Adoption in India’s Sustainable Mobility Transition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 17:03:12","doi":"10.21203/rs.3.rs-8723902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-12T11:55:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T01:36:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T08:36:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230019233060753118341260938322034674408","date":"2026-02-10T11:43:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266641952578976759788060736338364155167","date":"2026-02-10T11:30:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T11:01:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T16:49:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-07T15:39:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Vehicles","date":"2026-02-07T15:33:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-vehicles","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Vehicles](https://link.springer.com/journal/44465)","snPcode":"44465","submissionUrl":"https://submission.springernature.com/new-submission/44465/3","title":"Discover Vehicles","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1078ac7-5f7e-4bcc-be71-55548e9a525d","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:39:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 17:03:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8723902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8723902","identity":"rs-8723902","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}