Factor Analysis of EV Purchase Behavior in Medium-sized Cities in China:

Case Study of Luoyang City

Abstract

Electric vehicles (EVs) are the most popular method in the transportation sector to decarbonize and reduce local air pollution. Due to urbanization and policy support, small and medium-sized cities have become favourable markets for promoting EVs. However, current research focuses on large cities and ignores the potential of small and medium-sized cities. This study aims to fill the existing knowledge gap by focusing on the medium-sized urban area of Luoyang. Using exploratory factor analysis, the study attempts to identify and examine the key factors that drive consumer motivation to purchase EVs in this specific city. The results show that the strongest influencing factor on purchase intention is the economic aspect, followed by EV attributes, satisfaction, and environmental awareness. Furthermore, the analysis of individual characteristics shows that the younger population and consumers with previous experience of driving electric cars have higher purchase intention. This study emphasizes the need to consider the safety and convenience of electric vehicles in addition to their economic attributes. It underscores the need to create a level playing field for EVs and to develop strategies that address the characteristics and interests of mainstream EV customers, ultimately promoting their widespread adoption.

Introduction

According to the International Energy Agency (IEA), transportation is the second largest source of global carbon emissions after electricity, accounting for 37% (International Energy Agency, 2023). Specifically for China, a major CO2 emitter, organized transportation accounted for about 11% of total CO2 emissions in 2019 (Li, X., Tan et al., 2021), especially road transportation, which accounted for 87% and was the largest transportation mode in terms of CO2 emissions (Jiang, Ye et al., 2019) With the growth of GDP per capita, China's transportation demand will continue to grow in the future, and reducing carbon emissions from transportation is the key to reducing carbon emissions from transportation. Research has shown that new energy vehicles (NEVs) are of great importance in managing transportation pollution and reducing energy consumption, as evidenced by the findings of Shrestha, Zuidgeest et al. (2013) and Li, D., Guo et al. (2016). Despite the growing interest in EVs, their widespread adoption faces significant barriers due to consumer resistance (Bigerna and Micheli, 2018). Consumers' EV purchase behavior is influenced by multiple factors, including product attributes, policy attributes, urban infrastructure (Li, L., Wang et al., 2020), and psychological factors such as personal attitudes and environmental values (Choo and Mokhtarian, 2004; Laidley, 2013). The factors influencing consumers' purchase of pure EVs, and traditional fuel vehicles are different. Therefore, in order to promote EVs, it is necessary to identify the factors that influence consumers' EV purchase behavior.

To promote the development of green and low-carbon transportation, the Chinese government has introduced several policies (Sun, Chen et al., 2023). In 2010, the "New Energy Vehicle Purchase Subsidy Policy" was implemented, followed by the "Vehicle Purchase Tax Exemption Policy for New Energy Vehicles" in 2014. These initiatives were aimed at reducing the purchase cost of new energy vehicles (NEVs) and promoting their widespread adoption. Furthering these efforts, the "Parallel Management Measures for Average Fuel Consumption of Passenger Vehicle Enterprises and New Energy Vehicle Points" was established in 2018. This introduced two key indicators, "Average Fuel Consumption Points" (CAFC) and "New Energy Vehicle Points" (NEV) and integrated them into the policy framework. This parallel management approach clarifies the obligations of enterprises in the production of new energy vehicles, replaces the subsidy policy, and further promotes the application of new energy vehicles. In addition, the government tends to pilot and promote relevant policies for NEVs in medium and small cities, providing them with more opportunities and incentives. Local governments have also implemented different policies based on their own characteristics. For example, in Henan Province, 10 cities have jointly promoted the development of the low-speed EV industry. In 2018, Luoyang City issued the "Notice on Implementing Measures for Motor Vehicle Traffic Restriction," which introduced a classification management system for road traffic, restricted the use of fuel vehicles, and prioritized the passage of NEVs to promote the adoption of new energy vehicles. However, according to data released by the Ministry of Public Security, as of 2022, there were a total of 11.49 million NEVs in China, accounting for only 3.65% of the market share (Song, Y., 2022). Among them, sales of NEVs in medium and small cities reached 1.68 million units, with a penetration rate of 20.5%, which is lower than the national average of 25.64% for NEVs in rural areas (China EV100, 2020).

Located in China's Henan Province, Luoyang is easily accessible to major urban centers. In addition, Luoyang has a booming economy and a diversified industrial landscape, making it an important market for EV manufacturers and suppliers. Therefore, an in-depth understanding of the dynamics and factors influencing the adoption of electric vehicles by Luoyang residents is important for promoting the development of the electric vehicle market in the region. This study focuses on Luoyang, a medium-sized city in China, and uses questionnaires and exploratory factor analysis to explore the factors influencing consumers' purchase intention. Based on the analysis results, policy recommendations are proposed to promote the popularity of new energy electric vehicles.

Literature Review

Factors from psychological factors

In the field of psychological research, various factors such as cognition, attitudes and norms are used to elucidate behavior, with the results helping to infer the factors that matter to consumers. Klöckner (2014) research showed that a number of elements, including perceived need, sense of responsibility, personal norms, attitudes, perceived behavioral control, knowledge, planning ability, and intention, influence the consumer's decision to purchase EVs.

Environmental concern: According to (Goh and Wahid, 2015), "social and environmental friendliness" is an important factor in consumers' purchase or consideration behavior when purchasing an environmentally friendly vehicle. Khaola, Potiane et al. (2014) and Peters and Dütschke (2014) found that consumers' level of concern for environmental issues and awareness of environmentally friendly behaviors played an important role in their decision-making process regarding the purchase of new energy vehicles. (Mostafa, 2007) further supported this view by revealing that environmental knowledge, awareness of environmental issues, and other assumptions can be considered to have a positive relationship with the final purchase intention. Schuitema, Anable et al. (2013) and Liao, Wu et al. (2020) found that people's environmental self-identity had a positive effect on their perceptions of electric vehicles. Kahn (2007) found that environmentalists choose greener modes of transportation. Tan and Goh (2018) confirmed that environmental awareness has the strongest effect on people's purchase intention. Gallagher and Muehlegger (2011) studied the purchase of hybrid EVs by US consumers and found that groups with strong preferences for environmentalism and energy security preferred EVs.

Attitudes: Schuitema, Anable et al. (2013) found that attitudes towards hydrogen fuel cells were the determinants of eventual purchase. An empirical study by Lashari, Ko et al. (2021) on the drivers of consumer EV purchase intention found that attitudes were directly related to purchase intention. A study by Bühler, Cocron et al. (2014) found that the biggest factor influencing purchase behavior was consumer attitudes, and consumer awareness (understanding of automotive and environmental issues) was shown to directly influence consumer attitudes. In several other studies, EV adoption has been shown to be driven by environmental attitudes. Studies have shown that attitudes influence people's behavioral intentions (Yu, Lu et al., 2018).

Norms: According to , social norms influence consumers' purchase of electric vehicles. Axsen and Kurani (2012) examined the influence of interpersonal relationships on perceptions of hybrid electric vehicles (HEVs). Their results showed that potential buyers' interpersonal connections significantly influenced their evaluation of HEV technology, with closer connections having a greater impact. In a related study, Bennett and Vijaygopal (2018) found that the decision to choose new energy vehicles was predominantly influenced by recommendations from friends. Afroz, Rahman et al. (2015) found that UK consumers were not particularly aware of the cost, performance, and environmental impact of clean cars; instead, recent news stories about clean cars had the greatest influence on their purchase decisions.

Perceptions: Kang and Park (2011) conducted a study to explore the determinants that influence Korean consumers' acceptance of hydrogen fuel cell vehicles. Their findings revealed that both perceived risk and perceived benefit play a significant role in shaping the acceptance of EVs. Song, M. R., Chu et al. (2022) found that expectation and perceived risk influence the purchase decision of EVs. Bühler, Cocron et al. (2014) found that perceived benefit and perceived ease of use influenced consumers' purchase intention.

Value: De Luca, Di Pace et al. (2020) argue that there is an image-enhancing value in choosing a hybrid vehicle. Some consumers purchase a hybrid vehicle because the message of "conserving natural resources and maintaining natural ecosystems" and "being socially responsible and caring for others" is communicated to those around them. Yeğin and Ikram (2022) analyzed interview transcripts and found that the image enhancement value of hybrid EVs was the most important factor influencing the decision to purchase an HEV. Mahamuni and Subramanian (2021) conducted a study on the symbolic significance of BEVs. They found that in the United States, individuals who attribute high symbolic value to their vehicles are more likely to purchase an EV, suggesting that EVs are a symbol of higher social status. In China, however, this trend appears to be reversed.

In addition to environmental considerations, attitudes, norms, perceptions, and values, other psychological constructs are expected to influence EV adoption. Research by Tu and Yang (2019) found that instrumental, hedonic, and symbolic motivations are influential in the purchase and use of automobiles. In addition, the importance of emotions in this context has been underscored in various exploratory studies, as highlighted by Ivanova and Moreira (2023).

Preferences for EV attributes

In addition to psychological factors, various attributes of EVs may also influence consumers' purchase behavior. In this paper, we focus on four aspects of the literature review: financial, technical, infrastructure, and policy attributes.

Financial attributes

Financial attributes refer to the various types of monetary costs of vehicle purchase and use.

Purchase price: According to the 2019 China New Energy Vehicle Market Insight Report, in terms of consumer price orientation, 42% of consumers believe that the purchase budget should be between 200,000 and 300,000 RMB. This price has some limitations because the survey was conducted in tier 1 and tier 2 cities in China (Wang, 2019).

Costs: Wen and Noor (2015) found that consumers perceive the potential increase in electricity costs as one of the main barriers to EV purchase. The results of Gan and Wang (2017) on customer preferences and EV adoption suggest that cost is one of the main factors influencing purchase behavior. Bockarjova and Steg (2014) argue that the most important barrier to EV adoption is the perceived high monetary and non-monetary costs of EVs. Dagher, Itani et al. (2015) found similar results in China, where consumers consider vehicle and operating costs when deciding whether to purchase a hybrid vehicle.

Technical attributes

Technical attributes describe the technical characteristics of the vehicle itself:

Range: The relatively short driving range is widely regarded as one of the major barriers to the purchase of an EV. The findings of Dutta and Hwang (2021) on customer preference and acceptance of EVs show that charging inconvenience and short battery life are the main factors influencing the purchase line. Ouyang, Ou et al. (2020) suggest that the propensity to choose a certain range could be influenced by the density of charging stations and the duration of charging. For PHEVs, an extended range of all-electric driving (distance powered only by the battery) is also hypothesized to increase the likelihood of purchase, as shown by Hu, Wang et al. (2023). He, Hu et al. (2023) argue that the power and driving performance of future EVs may partially offset the lower utility of low mileage, long charging time, and high cost.

Performance: It is expressed in terms of engine horsepower, acceleration time, or top speed. Consumers generally prefer higher performance.

Brands and diversity: People prefer brands from a particular country, but not in the same order of preference across countries, according to (Lee, Baig et al., 2021).

Warranties: Warranties have been found to have a positive effect on EV purchases Peters and Dütschke (2014). Because there is so much uncertainty about battery life, consumers may prefer certainty in these areas. The significance of the impact of warranties remains unclear based on the available results. De Luca, Di Pace et al. (2020) found that the impact of battery life became more pronounced after a three-month trial period with EVs among participants, although both effects were not considered statistically significant. Therefore, further research is needed to verify the effect of warranty.

Infrastructure attributes

Infrastructure characteristics primarily emphasize the accessibility of charging facilities. Numerous studies have shown that this aspect significantly increases the likelihood that consumers will purchase electric vehicles, probably because it reduces the time and effort users have to spend searching for them, as well as their concerns about range. As electric vehicles are mainly dependent on long charging times, the lack of a charging station at home or at work makes their regular use almost impossible. Potdar, Batool et al. (2018) analyzed the factors hindering the development of EVs, and the mismatched charging infrastructure is one factor. EPRI found that the lack of EV infrastructure is one of the barriers for consumers to purchase EVs (Electric Power Research Institute, 2017).

Policy attributes

Policy attributes include various policy instruments to facilitate the purchase of EVs. Wu, Yang et al. (2018) analyzed the factors hindering the development of EVs and found that these factors included the shortcomings of the EV subsidy policy and the embarrassment of the EV market. De Haan, Peters et al. (2007), in investigating the factors affecting the willingness of Swiss residents to purchase Toyota hybrid vehicles, found that the purchase registration tax was one of the factors affecting the purchase of Toyota hybrid vehicles. Gallagher and Muehlegger (2011) examined sales data of new energy vehicles in the United States over a six-year period. The results show that both sales tax cuts and income tax cuts have a positive impact on sales, but sales tax cuts have a larger impact. Similarly, Urrutia-Mosquera and Fábrega (2021) provide evidence that purchase tax exemption and tax refund policies provide significant incentives for new energy vehicle purchasers. Alternatively, Diamond (2009) conducted a survey of American residents and found that government incentive policies did not stimulate consumer demand for hybrid vehicles; instead, the price of gasoline was the most important factor.

Lin and Wu (2018) classified policy as an external condition factor, arguing that the better the policy is publicized, the greater the financial subsidy, the greater the benefits obtained, and the more likely consumers are to purchase EVs. According to "China's New Energy Vehicle Policy Trends," after the Chinese government started subsidizing new energy vehicles in 2013, the sales of EVs increased year by year. However, with the reduction of car purchase subsidies, the total sales of new cars in the country in 2019 decreased year on year, and consumer behavior was significantly affected by economic policies. In addition, we summarize the influencing factors related to various EV attributes of interest to researchers, as shown in Table 1 for illustrative purposes.

Table 1. Factors related to various EV attributes

Source	Properties of EVs
Axsen and Kurani (2012)	Power, energy, lifetime, cost, safety
Kimura and Sugimura (2012)	Safety, fuel replenishment, and maintenance costs
Adepetu and Keshav (2017)	Driving range, battery capacity, purchasing cost
Dumortier, Siddiki et al. (2015)	Battery cost, driving range
Barth, Jugert et al. (2016)	driving range, charging time
Ferguson, Mohamed et al. (2018)	Maintenance costs
Huang and Qian (2018)	Monetary attributes, charging service, driving range, coverage of charging stations
Zhang, Li et al. (2018)	Performance, ease of charging, range, battery Life expectancy
Chu, Im et al. (2019)	fuel costs, operation cost, charging convenience
Dorcec, Pevec et al. (2019)	Battery innovation, network of chargers, charging tariffs
Li, L., Wang et al. (2020)	Battery warranty, battery depreciation rate, operation cost

Socio-economic and demographic characteristics

Demographic characteristics make up the lifestyles of different households, and as with individual consumers, changes in lifestyles can affect the choice of goods. Therefore, we believe that basic consumer information will also have an impact on the purchase of new energy vehicles. This mainly includes gender, age including income, occupation, education and family composition. Ling, Cherry et al. (2021) Studies have shown that factors such as gender, education level, salary, and marital status significantly affect consumers' willingness to purchase HEVs. Power (2008) conducted a large-scale survey of 44,931 drivers in the United States (US) and found that consumers with higher levels of education and income were more likely to purchase new energy vehicles. Potoglou and Kanaroglou (2007) claim that consumers with middle income are the most likely segment to purchase new energy vehicles. Conversely, Gao and Kitirattragarn (2008) found through interviews with taxi owners in New York that younger drivers, those with shorter-term ownership, and drivers with higher incomes are more likely to consider purchasing a hybrid vehicle. Tal, Kurani et al. (2020), using data on hybrid vehicle purchases by California residents, show that most hybrid vehicle purchases are concentrated in more affluent areas. In addition, by analyzing the behavior of EV consumers and the characteristics of potential customers, we found that most potential EV customers are between 20 and 40 years old, with education mostly above college, annual income above $150,000, and the characteristic of already owning a fuel car at home.

Through previous studies, it can be found that many factors influence consumers' EV purchase behavior, and the influencing factors vary from region to region. The current research mainly focuses on either developed countries or first-tier developed cities in China, and there are almost no empirical studies in medium-sized cities. Considering the huge population and car ownership in medium-sized cities, it is undoubtedly very necessary to take them into account. In this study, we take Luoyang as the research area and investigate the influencing factors of consumers' EV purchase behavior in medium-sized cities.

Methodology

A questionnaire survey was designed and conducted to collect samples for the study. The questionnaire was divided into two parts. The first section included questions on demographics such as gender, age, education level, income, occupation, and family structure. In addition, respondents were asked about their willingness to purchase an electric car and their willingness to recommend such a purchase to others. Our empirical analysis used these two yes/no questions to indicate potential EV purchases. The second part focuses on the psychological factors of the respondents and their preferences for various attributes of EVs. A total of 30 questions were asked using a 5-point Likert scale, where 1 is strongly disagree and 7 is strongly agree.

Questionnaire design

The questionnaire was divided into two parts. Questions in the first part relate to demographic characteristics, including gender, age, education level, income, occupation, and family composition. Respondents were also asked if they would buy an electric car and if they would recommend others to buy one. Our empirical analysis used these two yes/no questions to indicate potential EV purchases. The second part focuses on the psychological factors of the respondents and their preferences for various attributes of EVs. A total of 30 questions, using a 5-point Likert scale, where 1 is strongly disagree and 7 is strongly agree.

Data collection

The questionnaire survey was conducted for two weeks at the end of September 2021. Random sampling was used, and all respondents were asked to complete the questionnaire through a web link sent by the authors. Then, principal component analysis and regression analysis were performed on the collected data, independent samples t-tests were performed on the extracted factors, and one-way ANOVA was performed on the demographic variables.

In the formal test, 413 samples were collected in this survey, of which 365 were valid samples and 48 were invalid samples. Among them, 57.26% were male and 42.74% were female; 44.11% were under 35 years old, 46.3% were 36-55 years old, and 9.59% were over 55 years old. In addition, respondents with a bachelor's degree or higher accounted for 73% of the total number of respondents. Those with an annual income of 60-100,000 were the most numerous, accounting for 36.16%, and those with an annual income of over 100,000 accounted for 43.29% (respondent profiles are in Appendix 1).

Data analysis

In the research conducted, the techniques of Principal Component Analysis (PCA) and Maximum Variance Analysis (MAA) were used within the framework of exploratory factor analysis to identify the determinants influencing the adoption of pure electric vehicles by consumers. Principal component analysis offers two different methods for factor extraction: the first involves isolating factors with eigenvalues greater than 1, while the second depends on a predetermined number of factors. A comparative analysis of the results derived from both methods was performed to determine the more appropriate approach. The identified factors were then labeled according to the items they contained. Methods such as Independent Samples T-test and One-Way ANOVA were used to test for the presence of statistically significant differences between various demographic categories of users with respect to each factor. These demographic categories included gender, age, education level, income level, and previous experience with electric vehicle purchases.

Results

Demographic analysis

According to the results of the descriptive statistical analysis, there are significant differences in EV purchasing behavior among different age and education levels of respondents in Luoyang. Young people aged 26-35 have a more positive attitude toward purchasing EVs, while the higher the education level, the more likely they are to be interested in purchasing EVs. According to Figure 1, 55% of those who already own a car are willing to buy an EV. And Figure 2 shows that people with driving experience are more likely to buy an EV.

Figure 1. Sample analysis of the impact of owning a car on the purchase of EVs.

Figure 2. Sample analysis of the impact of car driving experience on the purchase of EVs

Validity and reliability

The results of the KMO and Bartlett's sphericity tests revealed a KMO measure of 0.928, an approximate chi-square value of 10349.974 in Bartlett's sphericity test, 435 degrees of freedom, and a p-value of 0.000, indicating statistical significance. These results indicated that the correlation coefficients and partial correlation coefficients of all variables in this study showed sufficient sampling fit, making them suitable for factor analysis, as noted by Taber (2018). In addition, the results of this study indicated that the overall scale had a Cronbach's alpha coefficient of 0.964, indicating robust internal consistency.

Factor analysis

Principal component analysis (PCA) was used to extract the factors that promote consumers' EV purchase behavior. Specifically, factors with eigenvalues greater than 1 were extracted, and then the extracted factors were named according to the included items. As shown in Table 2, five main factors were extracted from the 30 items in this study, explaining 73.84% of the total variance.

Table 2. The result of the total amount of variance.

	Initial intrinsic value			Sum of squares of load after extraction			Sum of squares of load after rotation
Composition	Total	Percentage of dispersion	Cumulative percentage	Total	Percentage of dispersion	Cumulative percentage	Total	Percentage of dispersion	Cumulative percentage
1	12.865	42.885	42.885	12.87	42.885	42.885	5.727	19.09	19.09
2	4.058	13.526	56.41	4.058	13.526	56.41	5.171	17.236	36.326
3	2.398	7.994	64.405	2.398	7.994	64.405	4.247	14.156	50.482
4	1.705	5.683	70.088	1.705	5.683	70.088	3.505	11.683	62.165
5	1.126	3.754	73.842	1.126	3.754	73.842	3.503	11.677	73.842
6	0.931	3.105	76.947

As shown in Table 3, Factor 1 had 6 items, Factor 2 had 8 items, Factor 3 had 6 items, Factor 4 had 5 items, and Factor 5 had 5 items.

Table 3. The result of The rotated loading matrix.

	1	2	3	4	5
Q4:The purchase and use of EVs can alleviate the problem of global warming	0.883
Q6:The purchasQ6: The purchase and use of EVs can reduce vehicles emissions	0.882
Q 2:I think it is necessary to use EVs on a large scale	0.878
Q1:The development of EVs contributes to environmental protection	0.856
Q3: I believe that purchasing and using EVs is a responsibility to the environment and society.	0.847
Q5: I believe that purchasing and using EVs will alleviate the problem of oil consumption.	0.842
Q30:Insufficient after-sales service and warranty		0.815
Q26:Anxiety about safety		0.794
Q27:Low comfort level		0.777
Q25:Power shortage		0.771
Q28:Expensive		0.77
Q29:Lack of design and style		0.727
Q24:Few charging stations		0.722
Q23: Short sailing time		0.705
Q21:Subsidy policy			0.815
Q19:Tax abatement policy			0.804
Q20:Increase in gasoline prices			0.756
Q22:Restrict the passage of gasoline vehicle license plates			0.685
Q9:Low daily maintenance costs			0.664
Q10:The price of recharging is lower than the price of gasoline.			0.646
Q16:Get information about EVs from friends and family				0.763
Q17:Get information about EVs from salesperson				0.736
Q18:Get information about EVs from Car ads and websites				0.733
Q15:Professional Influence				0.701
Q14:Friends and Family Influences				0.683
Q11:Name recognition within the group					0.791
Q13:Appreciation and respect from others					0.79
Q12:Satisfaction or elation					0.789
Q8:For the same performance, even if depreciation is higher					0.542
Q7:For the same performance, even if the price is equal or higher					0.519

The items included in factor 1 are: Q1, Q2, Q3, Q4, Q5, Q6. Electricity is considered to be cleaner than fossil fuels such as oil and gasoline in use. Consumers' awareness of environmental protection is reflected in the importance they attach to reducing carbon dioxide emissions and mitigating global warming, as well as their expectation to improve the problem of oil depletion. Therefore, the name of factor 1 is "environmental protection".

The items included in Factor 2 are: Q23, Q24, Q25, Q26, Q27, Q28, Q29, Q30. EVs have several characteristics that distinguish them from traditional gasoline vehicles, including driving time, charging infrastructure, battery life, purchase cost, and after-sales service and warranty. Therefore, Factor 2 is called "EV Attributes".

The items included in Factor 3 are: Q9, Q10, Q19, Q20, Q21, Q22. In Q19-Q22, the economic burden of consumers is reduced by tax rebates and subsidies, while in Q9 and Q10, the daily maintenance cost and fuel consumption of EVs are cheaper than those of regular cars, which is also a reason for consumers to choose EVs. All these factors show the influence on consumers' purchase behavior in an economic way, so it is named as the third main factor "economy".

The items included in Factor 1 are Q1, Q2, Q3, Q4, Q5, Q6. Electricity is considered cleaner than fossil fuels such as oil and gasoline. Consumers' awareness of environmental protection is reflected in the importance they attach to reducing carbon dioxide emissions and mitigating global warming, as well as their expectation to improve the problem of oil depletion. Therefore, the name of factor 1 is "environmental protection".

The items included in Factor 2 are: Q23, Q24, Q25, Q26, Q27, Q28, Q29, Q30. EVs have several characteristics that distinguish them from traditional gasoline vehicles, including driving time, charging infrastructure, battery life, purchase cost, and after-sales service and warranty. Therefore, Factor 2 is called "EV Attributes".

The items included in Factor 3 are: Q9, Q10, Q19, Q20, Q21, Q22. In Q19-Q22, the economic burden of consumers is reduced by tax rebates and subsidies, while in Q9 and Q10, the daily maintenance cost and fuel consumption of EVs are cheaper than those of regular cars, which is also a reason for consumers to choose EVs. All these factors show the influence on consumers' purchase behavior in an economic way, so it is named as the third main factor "Economy".

The items included in factor 4 are: Q14, Q15, Q16, Q17, Q18. When purchasing an EV, consumers are influenced by the information and reviews they gather from family, friends, and professionals. Consumers' purchase behavior reflects their reputation in their environment, so the fourth main factor is called "Reputation".

The items included in Factor 5 are: Q7, Q8, Q11, Q12, Q13. We believe that buying an electric car is a choice that is in line with environmental protection and sustainability, so we want to increase our awareness and recognition within the group, feel respected by others, and bring satisfaction. On the other hand, it is a choice to buy an electric car even though it is more expensive and depreciates more than a regular car with the same performance. This proves that for this category of consumers, price is no longer the primary consideration, but a reflection of their own intrinsic evaluation and satisfaction. Therefore, the fifth main factor is called "satisfaction".

Regarding the environmental consideration (F1), EV attributes (F2), economy (F3), reputation (F4) and satisfaction (F5) of each sample, multiple regression analysis was then conducted to find the correlation between the above five main factors and the final purchase behavior, and to identify the most influential main factors.

Regression analysis

The value of R2 split is 0.590, which means that the regression equation can be obtained with high accuracy, since the presumed regression equation can account for about 60% of the purchase actions (Table 4).

Table 4．The results of regression model.

R	R-squared	Adjusted R-squared	RMSE	DW
0.768	0.590	0.584	1.269	2.067

Moreover, in the analysis of variance, the F value is 103.149 and the significance rate is 0.000, in order to observe the significance of the regression equation, 0.05 is smaller and the presumed regression equation is significant. Determining the significance of the coefficients and the degree of influence of each explanatory variable (Table 5).

Table 5. The results of ANOVA.

	SS	df	MS	F-value	significance
regression	830.847	5	166.169	103.149	.000
Residuals	578.338	359	1.611
total	1409.184	364

The relationship between purchase behavior (target parameter) and environmental awareness, EV attributes, affordability, reputation, and satisfaction (explanatory parameters) can be observed in Table 6. The significance rates of EV attributes, economy, and satisfaction are less than 0.000, and the significant rate of environmental awareness is less than 0.05, indicating that the explanatory variables are significant for the target variables. In other words, the four elements of "environmental awareness", "EV attributes", "economy" and "satisfaction" are significantly related to purchase behavior. In contrast, the significance of "word of mouth" is 0.750, which is greater than 0.1, indicating that word of mouth is not related to purchase behavior.

The t-value determines the magnitude of the influence of these four explanatory variables; the higher the absolute value of the t-value, the greater the influence. The absolute value of "Economy" is 8.443, indicating that economic factors are the most important factors influencing purchase behavior. It is followed by "EV Attributes"(t=5.556), "Satisfaction"(t=3.565) and "Environmental Awareness"(t=2.939). Factor 4, "Reputation", was not relevant because its significant rate was greater than 0.1 and was therefore removed. Based on this result, the next step is to develop an appropriate response.

Table 6. The results of regression.

	unstandardized coefficients		standardized coefficients	t-value	significance	The 95% confidence interval for B
	B	SE				Lower limit	Upper limit
constant	.047	.326		.145	.885	-0.595	0.689
environmental awareness	.044	.015	.131	2.939	.004	0.014	0.073
EV attributes	.075	.013	.216	5.556	.000	0.048	0.101
economy	.226	.027	.447	8.443	.000	.173	.279
reputation	-.010	.031	-.016	-.319	.750	-.070	.050
satisfaction	.101	.028	.176	3.565	.000	.045	.157

In summary, this study analyzed the factors influencing consumer purchasing behavior from two perspectives. The first is the correlation analysis based on demographic information. It shows that age and education have an influence on the purchase of EVs. The age is mainly from 26 to 35 years old, and this age group has certain consumption ability and is more receptive to new things. The education level is mainly above university, and the higher the education level, the higher the concern for EVs and environmental protection, and the higher the possibility of purchase. In addition, some consumers want to buy even if they have a car, which proves that there are great benefits of EVs and more potential consumers. In addition, people with EV driving experience have a higher willingness to buy, while people without driving experience are almost hesitant due to the poor information channels and probably the vague impression of EVs. The second aspect is based on the results of the factor analysis of 30 main factors. Four main factors are concluded, and through regression analysis, the main factors are ranked by "economy", "EV attributes", "satisfaction", and "environmental awareness" in order of influence on purchase behavior.

Discussion and Implication

The survey results show that "economy" is the most important factor influencing consumers' purchase behavior of EVs, followed by "EV attributes". Based on these findings, this study discusses the development and promotion of EVs from two perspectives: government and business.

Government perspective: China's EV industry policy is currently at a critical stage of adjustment and improvement, especially on the issue of subsidy policy. For the next phase of the EV industry, it is essential to support policy formulation. However, small and medium-sized cities with lower overall incomes, such as Luoyang, are at risk of a significant drop in EV demand if the subsidy policy is reduced too much. Therefore, the government must pay attention to several critical points when formulating the policy. First, in order to stimulate the purchase of EVs, it is necessary to implement policies that encourage the purchase of EVs even if subsidies are reduced. The government can increase restrictions and emission standards on conventional fuel vehicles to stimulate market demand for EVs. In addition, urban transportation policies, such as providing free parking for EVs during off-peak hours and allowing them to use bus lanes, can increase consumers' EV attributes and satisfaction with EVs. This can promote the sustainable development of the EV industry without relying heavily on subsidies. In addition, promoting technological innovation and industrial development for EVs is crucial for their continuous improvement. The government should provide funding and industry support to reduce the research costs of EVs while improving their performance. Incorporating EV charging infrastructure into urban planning is crucial for the widespread adoption of EVs. Urban planners and policymakers should prioritize the integration of charging stations in residential areas, commercial districts, and public spaces. By strategically placing charging stations in convenient locations such as parking lots, shopping centers, and along major transportation routes, the accessibility and convenience of charging for electric vehicle owners can be greatly improved. The scalability and futureproofing of the charging infrastructure should also be considered. Planning for a scalable network of charging stations that can accommodate the growing number of electric vehicles is essential. This includes the deployment of fast charging stations capable of providing fast charging solutions, as well as ensuring compatibility with different charging standards to accommodate a wide range of electric vehicle models. The integration of smart charging solutions and grid integration should also be considered. Smart charging systems can optimize charging schedules based on electricity demand, renewable energy availability, and grid stability. This integration can facilitate effective management of energy resources and reduce strain on the electrical grid. By incorporating these considerations into urban planning, cities can foster a supportive environment for electric vehicle adoption, reduce range anxiety, and promote the overall sustainability of transportation systems. On the other hand, in order to increase consumer awareness and willingness to purchase EVs, it is crucial to strengthen environmental awareness promotion and education, and improve word-of-mouth and environmental awareness, two aspects that consumers value. EVs are durable green consumer goods with high social and environmental value. Therefore, to promote EVs, it is necessary to strengthen environmental public opinion guidance, emphasize their safety, environmental protection and health concepts, cultivate citizens' awareness of green consumption, and guide them to spontaneously purchase EVs.

Business perspective: The development of EVs depends on the improvement of companies' independent research and development capabilities. In order to dispel consumers' doubts about EVs and promote EV production and research, companies must focus on improving battery performance, software functionality, and convenience, while providing consumers with the most comfortable and convenient services. Sufficient improvements in reputation and satisfaction are crucial to shaping consumer attitudes toward EVs. Service quality is positively correlated with customer satisfaction, perceived value, and purchase behavior. This requires companies to improve the quality of pre-sales and after-sales services, establish convenient pre-sales consultation and after-sales service channels, provide comprehensive support services, and accurately meet customers' needs. In addition, the development of commercial models such as EV leasing and EV sharing can reduce the high production and manufacturing costs of EVs and even enable low-income people to experience EVs, while also contributing to companies' efforts to achieve environmental sustainability. This not only promotes the concept of green consumption, but also helps companies better understand consumer demand for EVs. By continuously improving brand awareness, product quality and service levels, establishing reasonable pricing and marketing strategies, strengthening cooperation with governments and other companies, and jointly promoting the development and design of the EV market, companies can promote the growth of the EV industry.

Conclusion

This study aims to investigate the factors influencing consumers' EV purchase behavior in Luoyang City, using an online questionnaire survey. The results will be used to provide policy recommendations for the government and enterprises, with the goal of promoting the development of EVs and raising awareness of their benefits. In future research, this study plans to evaluate the impact of policies on the EV market and examine production and marketing policies using data analysis software. The promotion of EVs, along with raising environmental awareness among consumers, can help reduce carbon emissions and air pollution, thereby contributing to China's dual-carbon goals. This study emphasizes the importance of linking environmental awareness with the expansion of the EV market, as it is critical to achieving sustainable development both in China and globally. However, it is important to acknowledge the limitations of this study. First, the study focuses only on Luoyang City, which may lead to limited generalizability due to the influence of factors such as urban development, master plan, and social style. Second, this study relies on survey variables obtained from previous research and reports, which may be influenced by subjective judgments and may not include all significant variables, leading to potential data reliability and validity problems. Third, there are only 64,000 EV owners in Luoyang at present, which makes it difficult to increase the sample size. This study mainly examines the factors that influence the intention to purchase electric vehicles, and EV ownership is only one of these factors. Based on previous research cases (Degirmenci, Breitner et al., 2017), the sample size of this study is sufficient to demonstrate its relevance. Nevertheless, this study hopes to collect a larger sample size of EV purchasers as research subjects in the future to more robustly measure the influence of actual purchases on facilitating purchase intention.

Author Contributions

Conceptualization, L.M., S.S.; methodology, S.S., L.M. and P.Z.; software, L.M. and Z.G.; investigation, P.Z.; resources, S.S.; data curation, L.M.; writing—original draft preparation, S.S., L.M. and P.Z.; writing—review and editing, S.S., X.Q., L.M. and Z.G.; supervision, S.S. and L.M. All authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

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Appendix 1. respondent profile