Introduction

Greenhouse gas (GHG) emissions are rising, leading to growing worries about global ecosystems. Burning fossil fuels is the leading contributor to CO2 emissions (IEA 2019b), followed closely by the transportation industry (IEA 2019a). The transportation industry has set pollution reduction goals, and governments across the globe have implemented rules on internal combustion engine vehicles (ICEVs) (ICCT 2019). For this reason, companies are investing more in cutting-edge CO2 reduction technologies for internal combustion engines and alternative fuel cars.

A recent study predicts that China’s transport CO2 emissions will reach their maximum in 2039, whereas the sustainable development and rapid growth scenarios see emissions plateauing in 2035 and 2043, respectively. China’s transportation carbon emissions abatement is under a lot of pressure, but regulatory changes may help push the peak year forward (Wang and Wang 2021).

The future generation of cars will be electric-powered, which will solve these issues. Automobile manufacturers have started selling EVs since they have none of the traditional ICEV drawbacks, such as dust emission, vibration, or noise. EV promotion is a critical milestone for nations dealing with power generation, global warming, ecological sustainability, and the restructuring of the automotive sector (Cornell 2019). Because of this, most car-producing countries have developed national EV promotion strategies to address issues like energy scarcity and pollution from growing populations of motor vehicles.

EV adoption rates in different nations remain low regardless of these benefits, and the anticipated consequences of reducing energy shortages and pollution cannot be completely realized. Due to the reasons such as high purchasing costs, limited driving range, lengthy charging periods, and a lack of charging station infrastructure, electric vehicles have not been extensively accepted in the early market (Tarei et al., 2021). China is also one of those countries where the expected adoption rate has not been achieved so far (Huang et al., 2022).

Over four million Electronic vehicles are already on the roads in China by the end of 2020. However, China’s global CO2 emissions and population share exceed this ratio. In the future years, they will need an increase in the number of electric vehicles. China plans to capture 20% of the global electric vehicle market by 2025 (Huang et al., 2022). It is also anticipated that the sale of new energy vehicles will account for 25 to 30% of total new car sales by 2025 and 50% by 2030 (Jinzhao 2021).

China in 2018 has slashed half of its purchase subsidies policy, which will be phased out permanently in the next few years (Lu et al., 2020). According to experts, reducing subsidies would discourage low-income people from buying electric vehicles in the future (Lu et al., 2020). According to Wen et al. (2021) Sales of electric cars in China fell when the government substantially decreased subsidies for them. Furthermore, researchers suggest that the Chinese government must reconsider its eco-friendly practices and encourage international investors to play a significant role in environmental quality (Xu et al., 2020). The EV market’s growth may prove problematic due to these factors.

Environmental awareness is one of the influential factors that may lead people to buy electric vehicles. Policymakers often distinguish between technical and societal challenges (Sovacool 2009). Although the “technical” barriers to EV development may be significant, the “social” ones maybe even more. Besides these concerns, educating citizens and ascertaining whether they have adequate knowledge and awareness about environmental change is necessary.

Researchers revealed that factors like self-identification could influence consumers’ decision-making (McLeay et al., 2022; Wallace et al., 2021). Personality traits such as self-identification also influence consumers’ decision-making, and they try to reflect their behavior through actions. Furthermore, environmentally aware consumers try to participate in better ecological system building and improvement (Mustafa et al., 2023b; Mustafa et al. 2022c).

There is an urgent need to reduce CO2 emissions and their impacts on the ecological system and increase the comparatively low sale of EVs in different countries, specifically in China. These are the two important issues to study. To our knowledge, researchers have rarely studied consumer environmental awareness and self-identification expressiveness that influence EV adoption intention. Neither the adoption intentions have been explored in the context of EVs’ sacrifice and benefits values.

In the light of the literature mentioned above, this study aims to evaluate,

  • Do people’s awareness of environmental issues influence their decision to purchase an electronic vehicle?

  • How much environmental awareness contributes to a consumer’s decision to purchase an electric vehicle?

  • How do self-identification traits influence consumers’ EV buying decisions?

We utilized a value-based adoption model to examine the influence of environmental awareness on the adoption intentions of Chinese EV buyers. Kim et al. (2007) recommended identifying and incorporating more variables into their model to understand better customers’ attitudes about adopting new technologies. The presented hypotheses were checked using SEM, and the relevance of variables influencing users’ adoption intentions was ranked using ANN. According to the study results, consumer knowledge of the environment is essential in purchasing an electric vehicle, but it is not the most important. Based on sensitivity analysis, we have concluded that a lot of grey areas need to be improved and will be vital for increasing EV sales and reducing CO2 emissions.

Furthermore, a perceived value significantly mediates the relationship between understudy variables and adoption intention. There is an urgent need to educate people about ecological issues, and EV use benefits must be properly advertised by stakeholders (Govt and ev manufacturers). We also recommend that researchers use two-step SEM-ANN to understand factors related to the human psyche.

Theoretical background and hypothesis development

Kim et al. propose a Value-based adoption model to examine how new technologies are adopted (Kim et al., 2007). Their findings suggest new technology adoption choices may be understood using VAM. More precisely, the willingness to adopt is evaluated by a comprehensive comparison of estimated worth, advantages, and sacrifices. The primary focus of rational technology users is maximizing value, which is determined by weighing the advantages and disadvantages of various options (Kuo et al., 2009). This concept may be used in our study environment since a customer considers value while making continuing choices in addition to the original adoption decision. Several recent studies have investigated how environmental awareness affects consumer buying decisions (Paul et al., 2016). A study by (Chen and Hung 2010) found that raising people’s knowledge of green hotels had a beneficial effect on their attitudes, subjective norms, and sense of behavioral control over such hotels. According to Mustafa et al. (2022), Environmentally aware people are more likely to pay extra for ecologically friendly products. According to Paul et al. (2016), customers are more likely to purchase eco-friendly goods if they are aware of the benefits. People’s ecological behavior has been studied using various variables, including environmental understanding. According to researchers, people’s decision-making may be significantly affected by their knowledge of an issue (Kaplan 1991). As a result, increasing environmental consciousness and understanding equates to increasing environmentally responsible conduct.

Furthermore, people may use products to reflect who they want to be rather than who they are (Markus and Kunda 1986), and people use products to represent themselves (Aaker 1999). Self-expressive products represent the social self of consumers, or at least consumers perceive that it adds some reflection of their action to their image or portrays their social ‘role’ and positively influences how society sees them (Carroll and Ahuvia 2006). Wallace et al. (2014) recommended further study into self-expressive brands and products that can express the social self, so we have incorporated this factor in our model to study the impact on EV adoption.

The study’s objective is to include environmental awareness into the original value-based adoption model (VAM) to create an integrated research model for examining consumers’ intentions to adopt electric vehicles. We have integrated self-identification as one of the EV purchasing benefits in our model that a consumer can get by using eco-friendly vehicles and portraying himself as a responsible citizen. Based on the literature discussed above, the proposed research model is presented below (Fig. 1).

Fig. 1
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Conceptual framework

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Hypotheses Development

To anticipate and explain new technology adoption behavior, VAM is the most often used model (Kim et al., 2007). Key concepts are defined here: perceived gains (benefits), perceived losses (costs), and perceived value (value). The following are our definitions of these constructs in this study:

Perceived usefulness

An important factor in whether or not customers would accept new technology or innovative products is the degree to which they see the adoption of a technology or system as advantageous and expediting the performance of goods and services (Venkatesh et al., 2003). Recently Jaiswal et al. (2021) revealed that perceived usefulness positively correlates with the attitude toward EV adoption and the intention to adopt EVs. In adopting EVs as environmentally friendly cars, the psychological concept of perceived utility encompasses the following fundamental characteristics. EVs are beneficial for decreasing CO2 emissions and gasoline consumption, decreasing users’ domestic transportation costs, and improving health quality by protecting users from air pollution. Another study revealed that the general perception of EVs is linked with the usefulness that leads to the adoption intention (Bühler et al., 2014). We believe that perceived usefulness significantly affects users’ perceived value of EVs. Perceived usefulness of EVs will boost the perceived value of EVs, and users adopt EVs; hence we hypothesized that,

H1

Ease of use has a positive influence on EV users’ perceived value.

H1a

Perceived value of Electric Vehicles acts as a mediator between perceived usefulness and intention to adopt Electric Vehicles.

Self-identification expressiveness

There are self-expressive products that consumers think improve their image, depict their social “role,” and positively impact how society views them. Using these products makes people feel that society will accept them as responsible citizens. A study based on extended planned behavior explored that one’s social identity’s expressiveness favorably affects one’s inclination to utilize MMS messaging (Thorbjørnsen et al., 2007). Another study on autonomous vehicles revealed that Self-identification expressiveness has a considerable role in adopting autonomous vehicles (McLeay et al., 2022). Researchers have also explored that self-expression positively affects warmth and competence stereotypes (Japutra et al., 2018). In the context of EV adoption, people will perceive that if they use EVs instead of traditional automobiles, society will consider them responsible citizens who control CO2 emissions. Secondly, they feel inner peace by playing their role for the good of society and the world. Hence we believe that Self-identification Expressiveness will influence consumers on the perceived value of EVs, and this perceived value will influence them to adopt EVs, so we have hypothesized that,

H2

Self-identification Expressiveness of a consumer will influence EV value.

H2a

Perceive value mediates the relationship between self-identification expressiveness and EV adoption intentions.

Technicality

A technicality in this study means the operating system, EV technology, and change in an electric vehicle’s engine, range, durability, and driving style compared to other traditional vehicles. Electronic vehicles are known for competitive advantages over traditional automobiles (dust emission, vibration, or noise). These factors play an important role in consumers’ perceived value of electronic vehicles. Researchers revealed that technicalities such as EV technology, performance, range, and durability, development of alternate fuel technology are some of the technical barriers that affect the consumer adoption of EVs (Tarei et al., 2021). Researchers found an insignificant relationship between technological performance risk with perceived value (Kim et al., 2018).

On the other hand, researchers have also found that Perceived technicality positively affects the purchase intention of EV users (Pradeep et al., 2021). In another study, scholars revealed that the psychological view of users about EVs is influenced by the perception of technical efficiency and advancement (Huang and Qian 2021). Researchers have also found that technological reliability will positively impact EV adoption after considering other considerations (Chen et al., 2020). So, we have hypotheses that,

H3

The technical sophistication of electric cars will contribute positively to the development of customers’ perceived value of EVs.

H3a

Perceived value plays a role as a mediator between perceived technicality and EVs’ adoption intention.

Cost value

Many studies measure the relationship between cost and EV adoption in a different context. In our research, “cost value” refers to the money an electronic vehicle owner spends on the vehicle’s purchase and maintenance. The cost factor is crucial to measuring consumers’ decision-making. According to scholars (Zhang et al., 2018), cost value significantly influences perceived value in EV adoption. Recent studies have also revealed that social and economic factors are vital for the adoption and use of EVs (Ashraf Javid et al., 2021). Kim et al. (2018) revealed that price and battery cost has a negative impact on perceived value regarding EV adoption. The higher cost of EVs is one of the leading barriers to EV adoption (Chhikara et al., 2021).

Furthermore, researchers have shown that the price of EVs is a greater barrier to their adoption than the available range of EVs (Tarei et al., 2021). Cost value plays a vital role in the consumers’ psyche. It affects the perceived value of electric vehicles, leading to the final decision to adopt or reject an electric vehicle. Hence, we hypothesize that,

H4

Consumers’ perceptions of electric cars’ value are influenced by their cost.

H4a

Perceived value mediates the cost value of EVs and their adoption intention.

Environmental awareness

Environmental awareness is a way of thinking that includes environmental concerns, solutions, techniques, and ideas for dealing with environmental issues (Wang et al., 2016). The world’s top priority is reducing CO2 emissions and other factors contributing to global warming. Every nation is trying its best to cope with this challenge. Individuals can play a vital role at an individual level if they are much aware of the consequences of this issue. Researchers have found that individuals with good knowledge of the environment make decisions to reduce global warming (Paul et al., 2016). Kim et al. (2018) found that Environmental concerns are positively associated with adoption intention. They also discovered that environmental concerns effects influence the perceived value of consumers. The extended TPB model (Chen and Tung 2014) was used to discover that environmental variables significantly impact consumer intent. Research on the adoption of EVs revealed that Environmental concerns are positively linked to consumers’ adoption intention, personal moral norms, personal behavioral control, and subjective norms (Wang et al., 2016). Another study revealed a strong correlation between environmental concerns and the desire to adopt AEVs (Wu et al., 2019). We believe that environmentally aware consumers will buy EVs because they know the consequences of driving a traditional vehicle. We believe that environmental awareness will enhance the perceived value of EVs in consumers’ eyes and help them adopt EVs. With this discussion, we hypothesize that,

H5a

Consumers’ Perceived Value of Electronic Vehicles is significantly influenced by Environmental Awareness.

H5b

Environmental awareness significantly influences electronic vehicle adoption intention.

H5c

EVs’ perceived value mediates the relationship between environmental awareness and EV adoption intentions.

Perceived Value

When discussing perceived benefits, we discuss the advantages of a new product, service, or technology (Shin 2009). According to Mustafa et al. 2022a person’s impression of the benefits that various products or activities may offer is their perceived value. Studies on the adoption of EVs indicate that the best advantages include faster speeds, charging capacity, Ease of use, autonomous driving, purchasing price, and a greater level of quality as well as pleasure (Jang and Choi 2021; Jaiswal et al., 2021; McLeay et al., 2022). Perceived value refers to the benefits that consumers believe EVs will bring. Perceived value is consistent with the study’s notion of an EV as long as the user thinks it has the potential to produce value, even if that value has not yet been realized. Zhang et al. (2018) found that the perceived value of EVs is significant for EV adoption. In another study in Malaysia, researchers found that perceived value is significantly related to the intention to use EVs (Asadi et al., 2021). The findings of Kim et al. (2018) revealed that perceived value positively influenced the adoption intention of EVs. In the light of the literature mentioned in this paragraph, we proposed that,

H6

Intention to adopt an Electric Vehicle is positively influenced by consumers’ perceived value of electric vehicles.

Control variables

Demographics is a critical determinant of whether or not customers will embrace a new product or technology, whether for good or negative reasons (Laukkanen 2016). The bulk of demographic study has focused on whether or not people embrace EVs. Demographic research is critical to the success of the EVs’ deployment process. Researchers have studied the role of age, gender, income, and education on EV adoption and found that education level significantly impacts EV adoption (Tiwari et al., 2020). Huang and Qian (2021) found that the demographic control variable gender significantly affects EV adoption, whereas household income does not. Researchers (Chen et al., 2020) also used socio-demographic factors to assess EV adoption. They found that age is negatively associated, whereas gender and income positively impact consumer EV adoption intentions. They also found that men are more influenced to buy EVs than their counter gender. Based on the above literature, we have decided to incorporate demographic variables such as age, gender, and education level of consumers as a control variable in our study.

Methodology

Research Context

In light of our study’s aim, we chose to test our model in China (Fig. 2). The rationale for this choice is that China was one of the first nations to deploy EV technology and is the world’s most populated country, making it the largest market for any goods. To ensure the validity and applicability of our findings, we disseminated our questionnaire across China’s main cities (Fig. 2). Choosing these locations is based on the fact that the consumers questioned came from diverse backgrounds. Furthermore, these areas have a high economic development pace, demonstrating the country’s distinct economic characteristics.

Fig. 2
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Study area

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Questionnaire Design and Sampling

Data about technology use may be acquired in several ways depending on the study’s objectives. To get high-quality data, we used several procedures to ensure the authenticity of the data. We slightly changed each item to fit our study setting by adapting tools from previous studies. We have adopted the construct for perceived value, usefulness, cost value, enjoyment, adoption intention, and technicality (Kim et al., 2007). Whereas the construct for environmental awareness from (Zahedi et al., 2019) and Self-Identification Expressiveness from (Wallace et al., 2021; McLeay et al., 2022). (Appendix 1)

The questionnaire’s initial draft was based on existing literature. Further adjustments were made as a result of the input obtained from the academics. Items were constructed in English and then translated into Chinese after revisions based on the research’s goals and the participants’ specific requirements. A seven-point Likert scale was used to calculate the difference between the extremes of agreement (7 = strongly agree) and disagreement (1 = strongly disagree) for all variables.

We have collected responses from EV users to validate the hypotheses. In order to choose responders, the following characteristics were taken into account: People who live in these cities permanently and are at least 18 years old are eligible to participate in this survey. Every tenth person was randomly selected and asked to participate in the survey in order to minimize sampling bias. Respondents were told that their answers would remain anonymous and only be used for academic reasons via the Introduction of the questionnaire survey. Two types of information were collected: demographic data and measuring items used to build our study model. We began by conducting a pilot survey. After receiving feedback from respondents to a pilot survey, we made revisions to the questionnaire. Afterward, a formal survey was performed online using the Chinese professional online questionnaire (www.wjx.cn). We have requested the respondents to provide their phone numbers so that we would be able to contact them in case of further information or cross-validation and to identify double attempts of response for data cleaning purposes. Seven hundred-four complete responses from the eight major cities of China have been obtained in an online survey. Our sample size is much larger than the given threshold of 10 responses to each construct item, assuring that the present sample may be used for empirical research.

Analysis and results

Respondents’ demographic Profile

Respondents’ demographic information, including their age, gender, education level, marital status, and vehicle drive in kilometers per year, is presented in Table 1.

Table 1 Demographic of Sample
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CMB & non-response bias

To assess the potential presence of common method biases (CMB) in the data, given its collection at a single time point and from a single source, the methodology incorporates several measures to examine and mitigate CMB. As part of our procedural measures, the purpose of the study was clearly stated at the beginning of each questionnaire. Additionally, respondents were assured that their identity and personal information would remain confidential. Considerable attention was dedicated to the design phase of our survey’s structure. The scales utilized in this study were selected based on their established reliability and validity, ensuring accurate and unambiguous measurement (Podsakoff et al., 2012). The participants were instructed to provide their responses to the questions with complete honesty, emphasizing that there was no definitive correct or incorrect answer. Utilizing established scales that were meticulously designed to minimize any potential ambiguity was the initial phase of the study. In addition, Harman’s single-factor test was performed through exploratory factor analysis. The results indicated that when considering only one factor, the data variance accounted for a mere 36.4%, falling below the established threshold of 50% (Appendix 4). Consequently, the presence of Common Method Bias (CMB) in the data was not substantiated. Furthermore, following a span of 20 days, a subsequent survey was administered to assess the presence of CMB. In the subsequent survey, a condensed version of the initial questionnaire was employed, wherein a solitary proxy item was selected to encapsulate each section (Ashraf et al., 2021). The initial and follow-up collected dataset exhibited strong positive correlations.

In addition, the methodology employed in this study involved the utilization of the approach established by Armstrong and Overton (1977) to assess the potential presence of non-response bias. The study employed independent sample t-tests and chi-square analyses to examine and compare the demographic characteristics of the initial 38 respondents with the final 38 respondents. These characteristics encompassed factors such as age and gender. There were no notable disparities observed between the two groups, with no statistical significance found (p < 0.05).

Multivariate assumptions

Following Mustafa and Zhang (2023b), before doing any multivariate tests, it is necessary to examine the multivariate assumptions such as linearity, multicollinearity, and homoscedasticity; the K-S test was used to determine the distribution of data normality, but the results reveal that the data is not normally distributed. According to Appendix 5, there are linear and nonlinear interactions between the dependent and independent Constructs. This research looked at the VIF (variance inflation factor) scores to see any model collinearity problems. There are no problems with collinearity if the VIF values are less than 5 (Table 2), as stated by (Hair Jr et al., 2016). VIF values for all variables were less than 5 in this study. It demonstrates that the present research data are not collinear and strengthens the robustness of the model. Afterward, this assumption is confirmed by the scatter plot of the regression normalized predicted and residual values (Appendix 5). Mustafa et al. (2023b) state that PLS-SEM is superior to factor-based SEM when the data has a non-normal distribution. A two-step analysis is recommended due to the model’s nonlinear interactions. There’s no way to deal with nonlinear linkages between constructs using approaches like composite-based SEM and factor-based SEM, so we turn instead to the ANN.

Many academics have realized that these analyses can be balanced and offer a more thorough study of the data (Mustafa and Zhang 2023a). According to researchers, combining SEM and ANN investigations yields the best results since it allows researchers to use the strengths of both approaches. PLS-SEM was utilized first to discover the significant exogenous constructions and then used as input neurons in the ANN study to fully comprehend the nonlinearity across the predictors following conventional techniques for completing a dual analysis. This study used IBM SPSS-25 to conduct ANN research.

Table 2 Measurement model results
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PLS-SEM

Measurement Model Assessment

Table 2 shows the research model based on 26 items drawn from the seven 7 variables. The instrument’s reliability and validity were determined using the measurement model. The authors (Hair Jr et al., 2016) believe that measurement model evaluation should be based on indicators’ and constructs’ reliability and convergent and discriminant validity. The instrument’s reliability was assessed using Cronbach’s Alpha (α) and indicator loadings. Convergent validity measures whether constructs properly assess the research variables or not. Variable composite reliability (CR) refers to how well an indicator’s variance matches the latent construct’s, representing the overall variance. Indicator reliability is established by loadings greater than the 0.6 criteria (Hair Jr et al., 2016). (Fig. 3) Factor loading was greater than 0.6 for all items. As a result, all latent variables are internally consistent (CR > 0.7) (Hair Jr et al., 2016). When the average variance extracted (AVE) findings for all constructs are more than 0.50, and convergent validity is demonstrated. Table 2 demonstrates that all the criteria for reliability and validity are fulfilled.

Additionally, discriminant validity is used to assess whether the correlation score between an indicator and its measure is greater than the correlation score between the indicator and other measures. The discriminant validity of this study was determined using Fornell and Larcker’s criteria, which stipulated that the square root of each construct’s AVE must be larger than its greatest correlation with any other construct (Hair Jr et al., 2016). The diagonal line values are obtained by multiplying each AVE value by its square root. All diagonal AVE values in the construct correlation matrix are greater than the other correlation values, as shown in Table 3, ensuring discriminant validity. The mean score of all variables and the standard deviation of all variables are also reported in Table 3.

Fig. 3
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SEM model evaluation

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Table 3 Discriminant Validity
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Structural model

The path analysis result of the structural model’s bootstrapping 5,000 resamples indicates that all hypotheses are significant (Table 4). We determined the existence of a significant association using the Path coefficient, t-statistics, and 95% confidence intervals. If the 95% confidence interval’s lower and upper bounds do not include a zero, the link between the latent construct is significant. Findings revealed that all hypotheses are statistically significant and accepted. Table 4 summarizes direct relationship results (beta, SD. Dev, T, and P values), mediation effect, and control variables assessment. (Appendix 2)

Measurement and structural model fitness

Kaynak (2003) proposed several indicators to assess the accuracy of a measurement model. These include the chi-square to a degree of freedom ratio (χ²/df or CMIN/df), the root mean square error of approximation (RMSEA), the normative fit index (NFI), the goodness of fit index (GFI), the standardized root mean squared residual (SRMR), and the comparative fit index (CFI). The results of the study revealed that the χ²/df values for both the measurement model and structural model were found to be 1.704 and 2.092, respectively. These values meet the requirement set by Bagozzi and Yi (1988), which states that the χ²/df should be less than 3. The RMSEA values of 0.052 and 0.061 in this study meet the recommended maximum value of 0.08 as suggested by Browne and Cudeck (1992). In addition, the SRMR values were 0.043 and 0.036, satisfying the 0.1 cut-off criterion established by Hu and Bentler (1998). Based on the findings, it can be concluded that the measurement and structural models exhibit a strong fit with the data collected. The Model fit measures for the measurement and structural models are outlined in Table 4.

Table 4 Model fit indices
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Hypothesis tests after evaluating the control variables of Age, Gender, and Education in Table 5 indicate that none of the three control factors significantly influences adoption intention.

Table 5 Hypothesis test
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ANN Analysis

With the help of an artificial neural network (ANN) and a multi-layer perceptron, we created a massively parallel computer processor with a neural tendency to retain and make available experimental data following Mustafa et al. (2022e). An ANN’s basic processing units are known as neurons or nodes. A nonlinear activation feature that the distant neuron nodes use routes data from the input neurons to the outgoing neurons during the learning phase (Mustafa and Zhang 2023b; Mustafa et al., 2023a). According to Mustafa et al. (2022d), synaptic weights would be modulated throughout learning to accomplish the desired result. Neuron nodes in the hidden layer practice predictably address the input nodes to identify the output node. ANN visible architectures allow for shallow learning, whereas ANN hidden architectures allow for more in-depth training (Mustafa and Zhang 2023b).

We created a two-layer deep-ANN architecture for each output neuron node to allow for more learning by following (Mustafa et al., 2022c). In this study, the ANN model was built for the adoption intention. It was decided to use Sigmoid as the activation mechanism and automatically create the numbers of hidden neuron nodes (Mustafa et al. 2022b). To prevent the over-fitting problem, a 10-fold cross-validation technique was utilized in accordance with the advice of Mustafa and Zhang (2022), with 90% of the data being used for training and the remaining 10% for testing.

In Table 6, we can see how well the ANN model predicts outcomes. It’s safe to say that the ANN model has excellent prediction ability because of its minimal root mean square error (Mustafa and Zhang 2023b). We also calculated a goodness-of-fit coefficient equivalent to the R2 in the PLS-SEM research to assess the ANN models’ effectiveness (Mustafa et al., 2022a). The ANN model’s R2 values are significantly higher than the PLS-SEM model’s (69.5%), suggesting that the predictor variables are more clearly indicated in the ANN analysis than in the PLS-SEM model. ANN’s ability to capture nonlinear connections, along with two-deep learning architectures, is what largely accounts for this finding. (Appendix 3)

The guideline (Mustafa and Zhang 2022) quantifies the prediction capacities of each input neuron (Table 7). To get the normalized value of these neurons, we divide their relative importance by the maximum potential significance and represent the result as a percentage. The results indicate that perceived value is the most significant predictor, with normalized importance of 100% followed by perceived usefulness (95.94%), Self-identification expressiveness (27.32%), and cost value (26.6%), and Environmental awareness (18.96%). Among all the factors that significantly influence consumers’ adoption intention in the first stage of analysis (PLS-SEM), Environmental awareness is the second least influential factor in the sensitivity analysis (ANN).

Table 6 RMSE values
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Table 7 Sensitivity analysis
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Discussion & implications

The study aims to create an integrated research model to examine consumers’ intentions of adopting electric vehicles and determine the extent to which environmental awareness and self-identification expressiveness are important for adopting electric vehicles. Secondly, as suggested by Kim et al. (2007), environmental awareness and self-identification expressiveness are integrated into the original value-based adoption model (VAM) for technology adoption to enhance the canvas of VAM in understanding consumers’ intentions to adopt electronic vehicles. Furthermore, to get more precise and accurate results, we have applied SEM-ANN dual-stage methodology recommended by several scholars for technology adoption models (Mustafa et al., 2022e; Mustafa et al. 2022c).

Study results have revealed that the newly incorporated variable (environmental awareness) in VAM is statistically significant and plays a vital role in electronic vehicle adoption in China. It enhances the perceived value of EVs and plays a significant role in EV adoption. It means people are aware of environmental change and its effects on the ecological system. Before buying a vehicle, they consider this factor and probably go for an EV to adopt when they buy a new vehicle. It also supports the claim that the environmentally friendly characteristics of a vehicle improve its value in consumers’ perception, and they at least think about the environmentally friendly vehicle before making a final decision to buy. These results are consistent with previous studies that environmental-related concerns play a significant role in the adoption intention of EVs (Wang et al., 2016). However, this assertion contradicts the conclusions drawn by Kim et al. (2018), who argued that environmental innovativeness does not significantly influence the adoption intention of EV consumers in Korea. It also contradicts the findings of Austmann and Vigne (2021), who found that EA did not affect the electric car industry throughout the analyzed era.

We further discovered that perceived usefulness and technicality contribute positively to customers’ perceptions of EV value (from a psychological point of view). Self-identification, a human personality trait, also influences their decision-making to adopt eco-friendly products. Consumers want to express by the actual behavior that they are actively participating in social responsibilities.

Consumers think about the technicality and usefulness of EVs before making their adoption decision. Consumers decide to buy EVs and become permanent users based on their perception and experience of each variable. In other words, we can say EV benefits lead to a perception of value that an EV can provide to a customer, leading to the intention and, finally, a permeant adoption or usage.

The fact that improvements in EV technology, performance, range, and durability solve typical problems associated with conventional cars, including pollution, vibrations, and noise, is a strong argument in favor of technicalities favorably affecting the perceived value of EVs. These technological advancements benefit EV ownership in general and help promote favorable opinions of the vehicles. Consumers’ perception of EVs as dependable, efficient, and environmentally friendly will rise as they get more familiar with these technological breakthroughs. Our results are consistent with the earlier findings (Pradeep et al., 2021, Huang and Qian 2021) but contradict Tarei et al. (2021), who found it a barrier to EV adoption in India.

Individuals recognize EVs’ practical benefits and advantages over conventional cars, contributing to a favorable perception of their worth. Lower monthly expenses, less dependence on oil and gas, and possible incentives all add to the value proposition of electric vehicles. The perceived value of EVs rises when customers see them as a feasible choice for addressing their mobility demands while also being in line with their environmental values. It is consistent with the findings of scholars (Asadi et al., 2021; Kim et al., 2018; Jaiswal et al., 2021; Chen et al., 2020).

Study results also revealed that cost value is negatively linked with the perceived value of EVs. Although environmentally aware consumers are concerned about the ecological system and global warming and want to play their role in reducing CO2 emissions, the high prices of Electric vehicles are still a concern to consumers. High prices could be a potential barrier to achieving EV diffusion targets in various countries, including China (Lu et al., 2020). We can conclude that consumers being environmentally friendly and knowledgeable about the ecological system compare the benefits and sacrifices before adopting a new product. After a detailed analysis, they inclined towards environment-friendly vehicles. It is consistent with prior studies where researchers found that consumers are price-conscious (McLeay et al., 2022; Jang and Choi 2021). Contrary to the claims made by Mustafa et al. (2022f) regarding the positive impact, the present analysis also presents contradictory evidence with the findings of Kim et al. (2018), who claimed that EVs’ operational economic advantage boosts their perceived worth in Korean consumers.

One compelling argument for the positive impact of self-identification expressiveness on the perceived value of EVs is that individuals who strongly identify themselves as environmentally conscious and prioritize sustainability are more inclined to recognize their environmental advantages as valuable. The congruence between individuals’ personal values and the environmentally friendly attributes of EV results in a heightened perception of value, as they view EVs as a medium for expressing and maintaining their ecological principles.

Our study results revealed that the perceived value of EVs is a significant mediator between the predictors and the adoption intention of EVs. The EV’s perceived value better explains every considered benefit and sacrifice of adopting an EV. Consumers calculate the benefits an EV provides and their sacrifices to adopt EVs. These factors conclude a final value of EV that leads to the adoption intention.

Even though several studies have shown that age, gender, and level of education all play a role in EV adoption, we discovered that these parameters (as control variables) has little impact on EV adoption when combined with environmental awareness. The results of this research run counter to those of numerous others (Tiwari et al., 2020; Chen et al., 2020; Huang and Qian 2021).

In the second stage of our analysis, we applied the Artificial Neural network approach to identify the importance of each factor in our model. Results revealed that perceived value is the most important factor behind EV adoption, followed by Perceived usefulness. Surprisingly, environmental awareness, which was found significant in SEM results, was one of the least important factors when comparing all the normalized factors’ importance for a consumer to adopt EV. It means although Environmental awareness is a significant factor, and consumers think about it before buying EVs, this is not the most important factor behind their intention to adopt or reject. They are much more concerned about perceived value and usefulness compared to environmental awareness. The second stage results also support perceived value’s role as a mediator as the most important factor (100% relative value).

Furthermore, sensitivity analysis results revealed that TEC, EA, SIE, and CV are less valuable in the consumers’ minds when adopting an EV (grey areas need to improve). Although these are statistically significant factors, Government and EV manufacturing companies need to work on these areas and educate consumers to attain the less CO2 emission goal and high EV sales.

Implication of the study

As a result of the study’s findings, many theoretical and practical implications have been identified. Firstly, we have applied the VAM model in EV adoption and integrated environmental awareness and self-identification expressiveness. We found that Environmental awareness is a significant predictor of electric vehicle adoption. VAM is based on two pillars: a product’s perceived value is an outcome of some perceived benefits and sacrifices. We have revealed that apart from EVs’ perceived benefits and sacrifices, the environmental awareness of consumer also plays a significant role in EV value building and its adoption. Results have also revealed that self-identification expressiveness is a benefit consumers achieve due to their traits. Secondly, we found that EV perceived value is inversely correlated with cost. Although environmentally conscious customers care about the environment and want to help reduce CO2 emissions, the expensive cost of electric cars remains a worry. Prices may be a deterrent to EV adoption in several countries, notably China. Customers’ intentions to use electric vehicles may be predicted using this study, which shows that perceived value influences adoption intention. As a result, our research sheds light on how customers’ environmental awareness may improve their desire to purchase an electric vehicle. Thirdly our integrated model and hybrid approach explained better variance as compared to the VAM base model. Our model explained 63.5% variance at the first stage of analysis, i.e., SEM, and 69.5% at the final stage of analysis (ANN). In contrast, the original VAM model explained only a 35.9% variance (Kim et al., 2007) for technology adoption. We propose SEM-ANN hybrid methodology to cope with nonlinear issues in a dataset and for better results for TAMs. Furthermore, our empirically tested model is valuable to the literature for comprehending consumer EV adoption. This model can also be used for future studies to investigate the adoption intentions of new technologies based on their value.

Policy implications

The findings of this research also have some practical implications. Consumers aware of the negative consequences of driving a conventional vehicle will feel more responsible and committed to doing something about them. This study revealed that Environmental awareness is a significant factor behind EV adoption and plays a vital role in creating perceived value for EVs. Still, it is not the most important factor when we compare all the factors in our study. Our study identified that EA, CV, and TEC are the factors that need immediate attention and can be helpful to reduce CO2 emissions and achieve high sales of EVs if people are properly educated about environmental issues, the cost compared with traditional vehicles, the technology used in EVs and operational technicality of EVs. There is a need for the government to play a critical role and educate people on nonrenewable energy, especially about how much energy individuals may save and decrease CO2 emissions via electric cars. As a result, individuals will be more conscious of their role in combating environmental problems via the adoption of electric vehicles.

To achieve its target of reducing CO2 emissions and shifting from conventional transportation to EV, The Chinese government should promote environmental awareness by implementing targeted campaigns and educational initiatives aimed at the general public. The environmental advantages of electric vehicles can be emphasized, including mitigating emissions and enhancing air quality.

Enact measures and incentives aimed at mitigating the perceived cost burden associated with EVs, including subsidies, tax incentives, and the establishment of charging infrastructure. These measures are expected to incentivize prospective buyers to perceive a greater value in the adoption of EVs.

The government needs to encourage and facilitate research and development endeavors to improve EVs’ technical aspects. Promote partnerships between academic institutions and EV manufacturers with the aim of enhancing the performance, range, and charging infrastructure of EVs, consequently augmenting their perceived utility.

The government may broadcast to customers the benefits of utilizing electric vehicles on resources and the environment and raise consumer awareness. Through cultural education, the government should promote consumers’ social responsibility and green behavior for pro-environmental behavior to fulfill their moral obligations to preserve the environment voluntarily and actively.

At the same time, EV manufacturing companies can increase their sales by being a partner in such campaigns. These campaigns must focus on providing proper knowledge about our ecological system, CO2 emission, and its consequences so that consumers take it seriously and pay more attention to environmentally friendly vehicles when buying new vehicles. When customers see benefits in using electric vehicles, it is anticipated that they will be more inclined to adopt this technology since their attitudes about accepting electric vehicles are more favorable. Manufacturers of EVs would do well to create and promote campaigns that highlight the vehicles’ positive impact on the environment while simultaneously addressing the vehicles’ negative reputation for technical shortcomings. Educating the public about EVs, showcasing their advantages, and providing a satisfying ownership experience may all help. Vehicle manufacturers can show consumers the value of EVs by demonstrating the high quality of their products, the high quality of their services, their reliability, and their low cost compared to the feature and benefits.

Spend more on R&D to enhance electric vehicles’ features, including their batteries, charging stations, and overall efficiency. If these aspects of EVs keep improving, more people will see how beneficial they are and buy one. Align product development goals with the Chinese government and take advantage of government incentives and policies via tight collaboration. Manufacturers may get a deeper appreciation for policy aims, increase their knowledge of EVs, and assist the country in meeting its greenhouse gas reduction and EV sales goals.

In addition to the primary stakeholders, some other individuals or groups have an interest or are affected. For instance, enhance the charging infrastructure network to augment the accessibility and convenience for owners of EVs. This will effectively address the technical aspects and enhance the perceived utility of EVs.

Financial institutions should engage in partnerships with EV manufacturers and governmental entities to establish appealing financing alternatives and loan initiatives aimed at alleviating the financial constraints associated with the acquisition of EVs. The aforementioned factor is expected to favorably impact the perceived value of EVs.

NGOs and environmental advocates should persist in their efforts to enhance public consciousness regarding the environmental advantages associated with EVs and emphasize the significance of sustainable transportation. Enhanced collaboration between governmental entities and EV manufacturers has the potential to augment their endeavors and facilitate a more widespread acceptance and utilization of EVs.

Through the implementation of these policy implications, various stakeholders, including the Chinese government and EV manufacturers, can collaboratively expedite the widespread adoption of EVs within China. This concerted effort would yield multiple benefits, such as the reduction of greenhouse gas emissions and the cultivation of a transportation ecosystem that is both sustainable and environmentally conscious.

Limitations and Future Work avenues

This study has some limitations, but they may be used to guide future research on EV adoption. First, we did not examine the relationship between the level of income and the likelihood of adopting an electric vehicle. Consumer income may be studied in the future to see how it affects electronic vehicle adoption. Future research may employ various theoretical models, such as TAM or UTAUT, to explain consumer electric vehicle adoption intentions. Third, since the research was done in China, there’s a chance that people’s desire to adopt electric vehicles differs depending on where they live and their use of a vehicle. As a consequence, no generalizations can be made from the findings. We also recommend identifying casual combinations and configurations of factors influencing human behavior toward adopting EVs. This will help to understand the cognitive factors’ role in EV adoption and new strategies to achieve maximum EVs sale.

Conclusion

To sum up, the results emphasize the significance of perceived usefulness, self-identification expressiveness, technicality, and environmental awareness in influencing individuals’ assessment of the value linked to electric vehicles. Furthermore, the findings underscore the significance of perceived value and environmental awareness in stimulating the propensity to adopt.

The research findings indicate that the perceived value of electric vehicles is positively influenced by perceived usefulness, self-identification expressiveness, technicality, and environmental awareness. The aforementioned proposition implies that individuals who self-identify as environmentally conscious and place importance on electric vehicles’ practical and technological aspects are more inclined to perceive them as possessing value. Conversely, the cost value exhibits an adverse influence on the perceived value, signifying that individuals perceive the cost of electric vehicles as a hindrance to their acceptance.

Moreover, the findings suggest that the perception of value functions as an intermediary variable between the aforementioned determinants and the inclination to embrace electric vehicles. The study suggests that the indirect influence of adoption intentions can be observed through the mediating role of perceived value, which is positively associated with perceived usefulness, self-identification expressiveness, technicality, and environmental awareness. Nonetheless, it exerts an adverse influence on the association between cost value and the intention to adopt, suggesting that the perceived value of electric vehicles decreases with an increase in the cost factor.

The findings of the sensitivity analysis highlight the importance of perceived value and perceived usefulness as key drivers of electric vehicle adoption. The aforementioned factors have been identified as the primary determinants with significant influence, underscoring the imperative to augment individuals’ perception of electric vehicles’ value proposition and tangible advantages. Furthermore, the research emphasises the need for enhanced focus and development on environmental consciousness and electric vehicles’ technological components to effectively attain sales objectives and mitigate the impact of greenhouse gas emissions.

The study’s empirical results offer significant insights into how environmental awareness and self-identification expressiveness impact the adoption of electric vehicles in China. The findings of this study can be leveraged by policymakers, industry stakeholders, and environmental advocates to devise focused tactics that tackle the identified factors and facilitate the extensive uptake of electric vehicles. This, in turn, can aid in promoting the sustainability of China’s transportation sector and mitigating environmental issues.