Eco-label knowledge versus environmental concern toward consumer's switching intentions for electric vehicles: A roadmap toward green innovation and environmental sustainability

Abstract

The Chinese authorities firmly encourage the use of electric cars to decrease global pollution and fossil fuel consumption (electric vehicles). Nevertheless, research aimed at determining customers’ switching intentions toward electric vehicles and pro-environmental behavior are scarce in the country. The purpose of this study is to fill the knowledge gap by scrutinizing the linkage among general environmental knowledge, environmental concern, eco-label knowledge, eco-label attitude (ATT), switching intentions(SIN), and pro-environmental behavior that might affect customers’ SIN and PEB in the Chinese context. The present research has additionally improved the theoretical foundation of the Theory of Planned Behavior by adding three new aspects in along with the ongoing customers’ motive elements (eco-label knowledge, general environmental knowledge, and ATT). In a survey, 549 electric vehicle users in China were polled and their data were analyzed. The structural equation modeling method is used to assess theories that have been put forth. According to empirical evidence, general environmental knowledge, environmental concern, and eco-label knowledge have a favorable and substantial impact on ATT. In a similar vein, ATT has a positive impact on consumers’ SIN and PEB. The research adds to the field of study on renewable buying behavior and offers crucial and practical future paths for researchers and professionals.

Keywords

General environmental knowledge renewable consumption eco-label knowledge switching intentions pro-environmental behavior C12 C31 D22

Introduction

As a crucial element of the United Nations Sustainable Development Goals, reducing carbon emissions from transportation requires the use of electricity.^1,2 In recent years, government policies and technological advancements have enabled the electric vehicle (EV) industry to grow rapidly.³ Annual EV sale is increasing by 60% every year over the past 4 years.⁴ Globally, EVs account for 90% of all sales in China, Europe, and the United States. It is estimated that there are over 8 million EVs in the world, such as both small cars and bigger/heavier automobiles, with over half of these vehicles occurring in China.⁵ According to reports, the decline in the global auto industry and the removal of EV tax breaks in the majority of the key regions have caused a slowdown in EV sales, particularly in the final half of the year.⁶ Due to the relatively low share of EVs in the global car market (2.6%), it is unlikely that mobility electrification will be achieved in the near future. In the period 2012–2020, China's consumption of diesel fuel and petroleum increased by 256.56 million tons.⁷

As EVs are currently more expensive, there are fewer charging infrastructures available, and they require a longer charging time, consumers are less likely to buy them.⁸ There have been a number of fiscal and non-fiscal policies introduced by the Chinese government to facilitate the acceptance of EVs in the country. The federal government has carried out a number of economic policies, involving tax cuts, investment in infrastructure financing, compensation on expenditures, and electricity price subsidies. The majority of policies are non-financial benefits that include waivers from paying tolls and free community recharge at charging stations.⁹ The performance of EVs is of greater significance to the Chinese authorities compared to how well they are marketed in the business community. In the private sector, the most critical factor is the consumer's preference for EVs. In light of this, it is imperative to conduct a substantial study of the crucial elements influencing the adoption of EVs by consumers.

A number of studies have investigated how customers feel about environmentally conscious goods; however, most of these studies have not been focused on green automobiles. Despite being higher in general environmental knowledge (GEK) and eco-label knowledge (ELK), emerging market consumers tend to be less environmentally aware, indicating the need for further research. People are conscious of regional environmental concerns (ECs) but Bu et al.¹⁰ claim that they do not know how to engage with environmentally friendly purchasing behaviors. This study aims to address the lack of knowledge concerning customer switching intentions (SIN) and pro-environmental behavior (PEB) toward green cars in developing countries. The objective is to gain a better understanding of how and why customers in these regions decide to switch to green cars and how their environmental attitudes and actions impact these decisions. By conducting this research, we hope to lead to an enhanced knowledge of the design and marketing of green cars in developing countries. The market for green cars is predicted to rise, but not much is known about buyers’ views on green cars, particularly those of younger consumers. It is crucial to consider customers’ attitudes, beliefs, and procedures as well as their SIN for these goods when creating promotional approaches for environmentally friendly automobiles. This research investigates the relationship between customers’ SIN and PEB for EVs and GEK, ELK, EC, and ATT. Despite the increasing demand for EVs as a means of reducing carbon emissions and promoting environmental sustainability, many consumers still hesitate to make the switch due to a lack of knowledge about eco-labels and ECs. There is a need to investigate the relationship between ELK, EC, and consumer switching intentions for EVs, in order to develop a roadmap for promoting green innovation and sustainability in the automotive industry. This study aims to fill the knowledge gap in this area by exploring the factors that influence consumer behavior and developing strategies for increasing ELK and EC among consumers.

The current investigation makes three main contributions. First off, by examining all the elements, our inquiry fills a void in the knowledge that could affect consumers’ SIN and PEB of EVs, in contrast to earlier research. As far as the author is aware, this is the first study to analyze the significance of PEB for EVs in the Chinese context. The nation faces significant energy-related issues as a result of recent financial progress and a large populace, requiring an extensive study on how EVs can be implemented. Second, three distinct characteristics (e.g., ELK, GEK, and ATT) that could affect consumers’ SIN and PEB for EVs are added to the Theory of Planned Behavior (TPB). Finally, the present investigation broadens on previous study outcomes. For example, ELK played an important role in EV adoption.

The following research questions—within an EV context—can be derived from the arguments above: (a) How much customer's environmental knowledge, EC contributes to eco-label attitude? (b) To what extent ELK impact eco-label attitude of customers? (c) How does eco-label attitude influence behavioral responses (i.e., Switching intentions and PEB)?

Literature review and hypotheses development

Underpinning theory

The TPB is a widely used social psychology theory that explains human behavior in terms of the attitudes, beliefs, and intentions that underlie it.¹¹ According to the TPB, an individual's intention to engage in a particular behavior is the best predictor of whether they will actually perform that behavior. The TPB suggests that three factors influence an individual's intention to engage in a particular behavior: Attitude, subjective norm, perceived behavior control.¹² According to the TPB, these three factors interact to shape an individual's intention to perform a behavior, which in turn influences their actual behavior.¹³ Therefore, the TPB has been widely used in fields such as health psychology, social psychology, and marketing to understand and predict human behavior. The TPB has been applied in many contexts, including the adoption and use of EVs. In the context of EVs, the TPB can be used to understand and predict the factors that influence an individual's intention to adopt an EV and use it on a regular basis.

Environmental knowledge and eco-label attitude

In any situation where we encounter an attitude object, knowledge is considered a subset of attitude.^14–16 The amount of beliefs important to attitudes and experiences encountered during a given period of time can be defined as attitude-specific knowledge.^17–19 The prevalent thought is that more information will lead to better outcomes and influence attitudes and behaviors more effectively.²⁰ In the same vein, more knowledge of the environment enhances attitudes and behaviors related to the environment.^21,22 There has been some contention among researchers such as Kollmuss and Agyeman²³ that only a minor ratio of the PECB is directly associated with GEK, suggesting that knowledge, attitude, and behavior are mutually dependent and the relationship between GEK and PEB can be mediated by environmental attitudes.

There are several ways in which GEK can be defined. For example, the ability to identify, understand, and solve environmental problems.^24,25 Similarly, a knowledge of the major ecosystems of the natural environment, including facts, concepts, and relationships within them.²⁶ It is still possible to have a general understanding of environmental issues,^27,28 and environmental issues can also be discussed by consumers.^29–31 It has been studied in the past how consumers’ attitude toward the environment are affected by both GEK and information. Thus, depending on the preceding argument, we put forward the following hypothesis.

H₁:

GEK will have a positive influence on ATT

ECs and eco-label attitude

It has been found that many scholars have used the extended TPB model to test the effectiveness of the use of TPB, but the degree of variation appears to be increasing as well.^13,32,33 In recent years, EC has grown in importance due to a growing awareness of how nature contributes to human wellbeing. Accordingly, the construct of EC has been used by many scholars in their research. Accordingly, the construct of EC has been used by many scholars in their research. In order to determine whether individuals modify their attitudes to be more environmentally friendly, a broad perspective of saving the ecosystem is necessary.^34–36 A study conducted by Barber et al.¹⁵ using the extended TPB model concluded that customer attitudes toward green hotels are positively affected by EC. Hartmann and Apaolaza-Ibanez³⁷ found that EC was successfully influencing attitudes toward the brand. To understand the behaviors of shoppers and employees in the Netherlands who intend to use the transformation, De Groot and Steg³⁸ combined an EC construct with the TPB. The researchers found that EC significantly affect attitude and behaviors

H₂:

EC will have a positive impact on ATT

ELK and eco-label attitude

The purpose of an eco-label from a marketing policy perspective is to communicate environmental information about a specific product to consumers.³⁹ According to the ISO⁴⁰ a set of standards has been developed for claims relating to the environment and labels in order to ensure consumers can access accurate and relevant information as they evaluate products. In order to be effective, eco-labels must be understood by consumers and their information must be trusted.⁴¹

There has been substantial research on the connection between attitude and knowledge outside the environmental field.^42,43 Typically, PECB models include attitude as a mediator ecological understanding and environmental behavior.^15,44 It has been shown that incorporating specialized ecological knowledge is essential since customers depend on both generic and situational information when determining their behavior. Despite the fact that most research on the connection between knowledge and attitude have only focused an GEK.^27,45 A study by Polonosky et al.²¹ provides evidence that consumers’ knowledge about carbon emission reductions influences their attitudes and therefore their behaviors with respect to environmental issues. To identify how environmental attitudes and underlying environmental behavior are influenced by these eco-labels, this study integrated GEK with eco-label-specific knowledge. Hence, the supplementary hypothesis is put forward:

H₃:

ELK will have a positive impact on ATT

Eco-label attitude and switching intentions

As defined by Spears and Singh,⁴⁶ consumer attitudes toward brands are “a comparatively long-term, linear judgment that is presumably a source of motivation. The attitude of a brand is a reflection of both its popularity and favorable reputation. Numerous studies have shown that private label brand attitudes influence the behaviors of consumers that are important to companies, such as purchase and loyalty to private label brands.^47,48 A consumer's attitude is their assessment of the brand, which impacts their purchase intention and behavior in the future.³⁷ As a result, attitudes are viewed as being crucial to the success of marketing campaigns.

As a result of purchasing branded vehicles, consumers are more likely to switch from traditional to EVs.⁴⁹ There are many instances in which brand switching can be influenced by extrinsic cues (i.e., special offers) offered by competitive brands.⁵⁰ Previous researchers suggest that consumers are more likely to switch if they do not hold a positive attitude toward EVs.⁵¹ Researchers found that consumer attitudes positively influenced intentions to use energy-efficient products.^50,52 As a result of this exposure, their attitudes toward switching between these two EV brands were shaped, thereby influencing SIN. We thus put out the subsequent hypothesis.

H₄:

ATT will have a positive influence on SIN

Eco-label attitude and PEB

As attitudes are crucial to consumer behavior, researchers can gain an in-depth knowledge of these attitudes in order to predict future behavior. Thus, it is more likely that changing consumers’ attitudes will result in desired behavior. The TPB and knowledge–attitude behavior models suggest that attitudes toward the environment have a direct impact on PECB as well as indirect impact. Environmental attitudes are defined as an assessment of the object of preserving the environment from a cognitive and emotional perspective.⁵³ Research shows that ecological attitude is one of the most powerful antecedents of environmental behavior.⁵⁴ Moreover, Ballantyne and Packer⁵⁴ argued that knowledge of the environment changes attitudes toward the environment, whereas understanding and attitudes regarding the environment enhance PECB. Researchers have proposed the knowledge-attitude-behavior model in light of the importance of attitudes.^14,45,55 Therefore, we suggest the underlying hypothesis (See Figure 1).

Figure 1.

Conceptual framework.

H₅:

ATT will have a positive impact on PEB

Methods

Sampling and data collection

This experiment's goal was to ascertain how customers in Shanghai, China, behave toward EVs in a pro-environmental context. In this survey, the focus was mainly on Shanghai, since this city introduced EVs and has always been the most successful in EV sales. As part of the survey, we conducted it between August and September 2022. We disseminated the questionnaires in both online and offline formats. The survey was conducted on Wenjuanxing, a popular online survey site in China. Our study was primarily designed to identify potential EV buyers in Shanghai, so we sent the questionnaire primarily to our teachers, colleagues, and relatives. We dispatched 398 invitations, and 301 people answered online, for a 72.6% return ratio. After removing 40 surveys with identical replies or conceptual mistakes, our research yielded 261 valid replies, obtaining an 86.7% accurate score.

During the interactive research, we visited Boyuan Rd, Jiading Qu, Shanghai in the 18th Shanghai International Automobile industry exhibition. It is vital to emphasize that the car displays considered for this research are large vehicle events in Beijing. This automotive exhibition brings a large number of qualified customers. In each of the three locations mentioned above, three young adults were surveyed using a convenience sample technique. In summary, the aforementioned three sites disseminated and collected 350 survey questionnaires. After deleting 62 items with the same replies for many items or with obvious problems, 288 valid replies were recovered, yielding an 82.2 percent valid rate. The questionnaire yielded 549 valid responses. The demographic factors are described statistically in Table 1.

Table 1.

Sample properties.

Participants’ characteristics	Frequency	Percentage
Gender
Male	356	64.8
Female	193	35.2
Age
18–22	98	17.9
23–27	173	31.5
28–30	278	50.6
Education
Higher School or below	10	1.8
Intermediate	52	9.5
Bachelors	240	43.7
Masters	198	36.1
PhD or above	49	8.9
Number of vehicles owned by the family
0	65	11.8
1	287	52.3
>2	197	35.9
Household monthly income (CNY)
<50,000	35	6.4
50,001–100,000	238	43.4
100,001–150,000	178	32.4
150,001>	98	17.9

Measures

We developed a survey questionnaire based on our earlier research. The survey questions, which were derived from earlier work, were evaluated using a 5-point Likert scale. A research by Taufique et al.³⁹ offered the framework for analyzing the four questions being used to measure perceived GEK. A sample item is, “ I know very well what the term ‘global warming’ means.” The three items of EC have been taken from the study of Bamberg⁵³. According to Taufique et al.,³⁹ we assessed ELK by utilizing four items. In the study by Arcury,⁵⁶ four items were taken to assess ATT. In the work of Sang and Bekhet,⁵⁷ four items constitute the SIN. We measured PEB using four items from the study of Taufique et al.³⁹

Results

Measurement model validation

To examine the interrelationships among the factors, we employed correlation research. The findings revealed a strong link between the factors (see Table 2). The average regression weight for each component in Table 3 is larger than 0.75, suggesting that the values are more reliable.⁵⁸ The discriminant validity of the derived average variance was examined using the square root of the derived average variance. The findings show evidence for discriminant validity, due to AVE's greater square root value than its connections to multiple variables.⁵⁹ The discriminant validity of AVE can also be determined by evaluating the MSV value in conjunction with all factors. When AVE exceeds MSV, discriminant validity is attained.⁶⁰ The findings suggest that AVE numbers for factors should be higher than MSV estimates. After that, we checked the possible link among these items using a convergent validity analysis using AVE and item loadings.⁶¹ The findings show that each variable's AVE number is greater than 0.5, indicating that it has met the standard and has 50% more variation. To determine whether the results at 0.917 were larger than 0.6, we used the Kaiser-Meyer-Olkin test. Due to the availability of this sample, all factorial analyses were possible. As a result, this sample allowed for the execution of all factorial analyses. BTS also produced a remarkable result of 9299.87, which satisfies the EFA criteria. All indicators in Table 6's data for communalities are higher than the recommended threshold value of 0.4 (Figure 2).

Figure 2.

Measurement model.

Table 2.

Discriminant validity.

Constructs	CR	ELK	ELA	EC	GEK	PEB	SIN
ELK	0.913	0.851
ELA	0.934	0.553	0.882
EC	0.939	0.537	0.447	0.914
GEK	0.936	0.558	0.542	0.462	0.884
PEB	0.909	0.623	0.504	0.472	0.442	0.845
SIN	0.902	0.564	0.527	0.602	0.525	0.542	0.834

Note: Bold values represent square root of AVEs. N = 549; Items are measured on 5-point Likert scale, where 1 = strongly disagree and 5 = strongly agree. ELK: eco-label knowledge; ELA: eco-label attitude; EC: environmental concern; GEK: general environmental knowledge; PEB: pro-environmental behavior; SIN: switching intentions.

Reliability analysis

To rate the accuracy of each design, we employed the Cronbach-alpha method. According to Table 3's results, all categories’ Cronbach values were higher than the suggested cutoff point of 0.70,⁶² confirming the accuracy of the data.^63,64 All variables’ elements were checked for consistency using a composite reliability calculation (CR). The study's findings show that the CR values are higher than the limit value of 0.70.⁶⁵

Table 3.

Results of factor loadings and reliability of latent constructs.

Constructs	Items	SFL	AVE	α
Eco-label knowledge			0.722	0.871
	ELK1	0.814
	ELK2	0.793
	ELK3	0.781
	ELK4	0.794
Eco-label attitude			0.774	0.902
	ATT1	0.834
	ATT2	0.835
	ATT3	0.883
	ATT4	0.811
Environmental concern			0.831	0.909
	EC1	0.855
	EC2	0.915
	EC3	0.843
General environmental knowledge			0.783	0.904
	GEK1	0.834
	GEK2	0.828
	GEK3	0.921
	GEK4	0.791
Pro-environmental behavior			0.715	0.864
	PEB1	0.924
	PEB2	0.685
	PEB3	0.734
	PEB4	0.822
Switching intentions			0.696	0.856
	SI1	0.662
	SI2	0.801
	SI3	0.873
	SI4	0.871

Multicollinearity

To check for multicollinearity problems and determine the appropriate levels of variance inflation factor (VIF), a regression analysis is conducted. The VIF number must not be greater than 3.0.⁶⁶ The findings show that this approach has no multicollinearity problems because the numbers of VIF and Tolerance are within the variable's recommended limit and are in accordance with.^67,68 Table 4 presents the findings.

Table 4.

Collinearity diagnostics.

Constructs	Collinearity Statistics
Constructs	Tolerance	VIF
GEK	0.745	1.458
EC	0.865	1.654
ELK	0.745	1.845
ATT	0.922	1.665
SIN	0.890	1.525

Notes: Dependent variable: PEB. EC: environmental concern; GEK: general environmental knowledge; PEB: pro-environmental behavior; SIN: switching intentions; ELK: eco-label knowledge.

Common method variance

A variety of scientific and empirical methods have been used to measure common method variation (CMV). First, a trial was conducted as preliminary research to assess the applicability of the tools after the items were created to stay straightforward, precise, and brief.⁶⁹ Furthermore, Harman's model demonstrates that CMV effects exist when a single component accounts for at least 50% of the total variation.^64,69–71 The results of this research show that the most important component only accounted for 25.89% of the variance, which is below the 50% criterion and confirms that there was no CMV in the data. Lastly, in order to evaluate the CMV,⁷² looked into the association between unobserved variables. All inter-variable relationships are lower than 0.90. As a result, according to our mathematical analysis, there was no CMV (Tables 5 and 6).

Table 5.

Kaiser–Meyer–Olkin (KMO) and Bartlett's test.

KMO and Bartlett's test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy		0.917
Bartlett's Test of Sphericity	Approx. Chi-Square	9299.87
	Df	426
	Sig.	0.000

Notes: df: degree of freedom, Sig: significance.

Table 6.

Communalities findings.

Constructs	Communalities
Constructs	Initial	Extraction
GEK	1	0.544
EC	1	0.645
ELK	1	0.965
ATT	1	0.577
SIN	1	0.625
PEB	1	0.765

Notes: Extraction Method: Maximum likelihood. EC: environmental concern; GEK: general environmental knowledge; PEB: pro-environmental behavior; SIN: switching intentions; ELK: eco-label knowledge.

Structural path model

Furthermore, we employed the classificatory treatment of goodness-of-fit indices provided by.⁷³ Initially, absolute fit measures are presented; second, incremental fit measures are explored; and third, parsimonious adjusted measures are discussed. In IBM's Amos 24.0 output, good model fitness was indicated as follows: X² = 1142.22, Df = 672, RMSEA = 0.055, GFI = 0.865, and AGFI = 0.806, indicating that absolute fit indices are less than the threshold as CMIN/DF below 3.0, RMSEA below the value of 0.08,⁷⁴ GFI and AGFI greater than 0.90 as recommended by.⁷⁵ It is recommended that the incremental fit indices must be greater than 0.90, and our analysis validated this cutoff point as NFI = 0.901, RFI = 0.922, TLI = 0.912, CFI = 0.926.⁷⁴ Lastly, parsimonious fit indices need to be higher than 0.50 to demonstrate model fit, and results indicate PCFI = 0.861, PNFI = 0.801 and PGFI = 0.706.⁷⁴ Overall, the results confirmed the efficiency of the developed model.

Structural model and hypothesis outcomes

In order to test the hypothetical relationships, we applied a structural equation modeling technique (SEM).⁷⁶ In Figure 3, our initial analysis of the multi-collinearity issue showed a variance inflation factor (VIF) between 1.052 and 2.932, which was considerably below the threshold 3.0 criterion. In Table 7 and Figure 2, a review of the findings revealed that GEK had a substantial favorable effect on ATT (H1–β_GEK→ATT= 0.306, p < 0.001). In addition, EC is also significantly and positively related to ATT (H2–β_EC→ATT = 0.140, p < 0.001). As a result, it supports both the first and second hypotheses. Additionally, the direct result of the third hypothesis showed that ELK and ATT have a favorable and substantial association (H3–β_ELK→ATT = 0.305, p < 0.01); therefore, the third hypothesis is supported. Further, findings indicated that ATT had a positive and significant relationship with SIN (H4–β_ATT→SIN = 0.527, p < 0.001). According to our results, ATT is positively related to PEB (H5–β_ATT→PEB = 0.504, p < 0.001).

Figure 3.

Results of hypothesis.

Table 7.

Results of hypotheses.

Hypothetical paths		β	S.E.	t-value	p-value	Results
H1	General Environmental Knowledge -> Eco-label Attitude	0.306	0.098	3.111	0.002	Supported
H2	Environmental Concern -> Eco-label Attitude	0.140	0.080	1.730	0.014	Supported
H3	Eco-Label Knowledge -> Eco-label Attitude	0.305	0.113	2.690	0.007	Supported
H4	Eco-label Attitude -> Switching Intentions	0.527	0.064	8.126	0.000	Supported
H5	Eco-label Attitude -> Pro-environmental Behavior	0.504	0.078	6.356	0.000	Supported

Discussion

This study aimed to investigate the factors influencing the SIN of individuals from Shanghai, China, regarding the purchase of EVs. The results generally corroborate⁷⁷ assertion that Beijing, Chinese participants are deeply devoted to protecting their ecosystem. In this study, we investigated and analyzed the factors that influence consumers’ SIN and PEBs toward EVs. In this study, consumers’ GEK, EC, ELK and ATT are employed to assess customers’ SIN and PEBs of EVs in Shanghai, China.

According to our research, all variables have favorable effects on customers’ SIN and PEB for EVs. The results suggest that consumer attitudes toward EVs are significantly influenced by GEK. Researchers have found that attitude-specific knowledge can be defined as the amount of attitudes-related beliefs and experiences encountered during a particular period of time.¹⁷ It is generally believed that broader understanding will result in more effective attitudes and behaviors.¹⁴ Furthermore, greater knowledge of the environment impacts attitudes and behavior in relation to the environment.

Our experiment's findings also show a favorable correlation between EC and ATT. Several researchers believe that EC have gained prominence because humans are becoming increasingly aware of how nature contributes to their wellbeing.^13,32,33 It is important to examine people's attitudes toward environmental protection in order to determine whether they will change their behavior toward becoming more environmentally friendly.^34,35,38 Combined the TPB with an EC construct to argue that EC significantly affects attitudes and behaviors.

Additionally, customer knowledge of eco-labels is a substantial influencer on their ELK. The importance of incorporating specialized ecological knowledge has been demonstrated since consumers rely both on general information and situational information to make decisions. There have been very few studies that examine the relationship between knowledge, attitudes, and behavior in the context of the environment.^27,45 A study by²¹ provides evidence that consumers’ knowledge about carbon emission reductions influences their attitudes and therefore their behaviors with respect to environmental issues. It has been observed that consumers tend to be skeptical of eco-label information when the source is not regarded as trustworthy. Moreover, green transportation can include options such as electric or hybrid vehicles, public transit systems, cycling, and walking. One of the main benefits of green transportation is that it helps to reduce air pollution, which can have negative impacts on human health and the environment. In a similar vein, renewable energy plays an important role in the development of green transportation, as it provides a sustainable source of power. Renewable energy sources such as solar, wind, and hydroelectric power can be used to charge EVs and can also power public transit systems such as trains and busses.

Finally, we have found that the ATT toward EVs is strongly associated with SIN and PEB. Many studies have demonstrated the influence of private label brand attitudes on consumers’ behaviors that are important to companies, such as their purchase and loyalty to private label brands.^47,48 It is more likely for consumers to switch from traditional to EVs when they purchase branded vehicles.⁴⁹ There are numerous instances in which competitive brands’ extrinsic cues (e.g., special offers) can influence brand switching.⁵⁰ Additionally, research indicates that environmental attitudes are powerful antecedents of environmental behavior.⁵⁴ Moreover,⁵⁴ argued that knowledge of the environment changes attitudes toward the environment, whereas understanding and attitudes regarding the environment enhance pro-environmental consumer behavior.

Theoretical implications

This study provides theoretical implications which contribute to the existing literature on sustainable consumption in order to enhance the TPB. The purpose of this research is to provide a theoretic framework that takes into account consumers’ SIN, their PEB, and the importance of attributes for EVs. In this article, the focus is on GEK, ELK, and ATT and how they relate to EVs, SINs, and PEBs as a whole. By projecting a favorable image, environmental development seeks to encourage consumers to buy environmentally friendly goods.^39,78 The theoretical model provided here is also distinctive in that it was created and validated in a stable industry (e.g., China). In addition, we featured that ATT plays a crucial part in comprehending consumer behavior in terms of sustainable consumption. As attitudes are crucial to consumer behavior, researchers can gain an in-depth knowledge of these attitudes in order to predict future behavior. Thus, it is more likely that changing consumers’ attitudes will result in a desired behavior.

Policy recommendations

Our results suggest that in order to boost Chinese EV purchasing desire and support EV growth, some significant policy suggestions may be required. First, China aims to put more effort into developing rapid charging facilities and enhancing EV safety. It is expected that this study will significantly affect customer views and understanding of EVs. Customers of EVs may be prepared to decide on a buy as a consequence.

Second, it's important for businesses and agencies to collaborate to raise customer knowledge of EVs. It would be essential to spread knowledge about EVs and show how they can be used to restore the ecosystem while lowering running costs in order to achieve this. The use of exhibits and experience sites can help educate the public about EVs. Customers can gain greater awareness of important policy choices by watching public service announcements, such as TV advertisements and expert discussions.

Thirdly, the government should develop and improve financial reward programs that motivate people to buy EVs. EVs are still in the early stages of growth even though they are now available for purchase in some marketplaces. It requires a significant amount of government assistance. Instead of implementing a 1% sales tax, the authorities should instead embrace a five-year tax-free strategy for EVs. Additionally, the cost of energy at recharge stations needs to be reduced. Instead of subsidized EV purchases, rewards should be offered to enhance the efficiency of EVs. In order to encourage non-EV users to purchase EVs, incentives may be made available.

Finally, to assist individuals advance to the next level of development, it would be beneficial if lawmakers could offer more details on the advantages and disadvantages of EVs. If people are aware of their driving styles and the services they can provide, the gap among internal and extrinsic reasons for buying EVs (and their prices) may also reduce. As a result, people will not need incentives to switch to EVs. Despite this, as more people switch to EVs, the efficacy and efficiency of finance policies are likely to decline. The best way to change behavior is not by giving rewards to everyone, but by focusing on behavior change.

Conclusions

According to the study's findings, SIN and PEB are favorably and substantially correlated with customer GEK, EC, ELK, and ATT regarding EVs. The research's empirical findings also demonstrate that the overall surroundings and ELK have a favorable and substantial impact on ATT for EVs. According to the study's results, businesses need to educate customers about the environmental and safety laws mandating the growth of EVs. This study emphasizes the significance of GEK, ELK, and other characteristics as a factor in choosing environmentally friendly EVs. Lastly, the investigation suggests that future studies evaluate how customers use autonomous cars using questionnaire data or research methodologies.

In addition, there are some recommendations for future researchers, including: Exploring new technologies and innovations in the EV industry, such as battery technology, charging infrastructure, and autonomous driving, and their impact on consumers’ switching intentions and the adoption of EVs can help inform industry development and inform policy decisions aimed at promoting sustainable transportation. Similarly, conducting longitudinal studies that track consumers’ ELK, EC, and switching intentions over an extended period can provide valuable insights into the effectiveness of eco-labeling programs and the evolution of consumer behavior. Lastly, investigating the impact of eco-labeling on sustainable consumption behaviors beyond the purchase decision, such as product use and disposal, can help identify the potential for eco-labeling to promote more sustainable lifestyles.

Footnotes

Author note

Wani Fang is currently affiliated with Business School, Hohai University, Nanjing 210024, China.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Wanli Fang

Wanli Fang works at Business School of Hohai University, China.

Yongrong Xin currently works at Business School, Jiangsu Open University. She does research in Environmental Economics, International Economics, Macroeconomics, and Energy Economics

Zhenhua Zhang works at Business School, Jiangsu Open University.

References

Dogan

. Energy consumption and economic growth: evidence from low-income countries in Sub-Saharan Africa. Int J Energy Econ Policy 2014; 4: 154–162.

Dogan

Hodžić

Šikić

. Do energy and environmental taxes stimulate or inhibit renewable energy deployment in the European Union? Renew Energy 2023; 202: 1138–1145.

Gnann

Stephens

Lin

, et al. What drives the market for plug-in electric vehicles?-A review of international PEV market diffusion models. Renew Sustain Energy Rev 2018; 93: 158–164.

Wen

Yang

Zhou

, et al. Impacts of COVID-19 on the electric vehicle industry: Evidence from China. Renew Sustain Energy Rev 2021; 144: 111024.

Irle

. Global BEV & PHEV sales for 2019. EV Vol available http//www.ev-volumes.com/ (last accessed August 2020).

Sheldon

Dua

. Effectiveness of China’s plug-in electric vehicle subsidy. Energy Econ 2020; 88: 104773.

Huang

. Electric vehicle development in Beijing: An analysis of consumer purchase intention. J Clean Prod 2019; 216: 361–372.

Zhang

Luo

, et al. A review on thermal management of lithium-ion batteries for electric vehicles. Energy 2022; 238: 121652.

Long

Chen

. Consumers’ evaluation of national new energy vehicle policy in China: An analysis based on a four paradigm model. Energy Policy 2016; 99: 33–41.

10.

Dang

WVT

Wang

, et al. Environmental orientation, green supply chain management, and firm performance: empirical evidence from Chinese small and medium-sized enterprises. Int J Environ Res Public Health 17. Epub ahead of print 2020. DOI: https://doi.org/10.3390/ijerph17041199.

11.

Wang

Zhang

. Determinants of energy-saving behavioral intention among residents in Beijing: extending the theory of planned behavior. J Renew Sustain Energy 2014; 6: 53127.

12.

Ajzen

. From intentions to actions: a theory of planned behavior. In: Kuhl

Beckmann

(eds) Action control. Berlin, Heidelberg: Springer, 1985, pp.11–39.

13.

Macovei

O-I

. Applying the theory of planned behavior in predicting proenvironmental behaviour: The case of energy conservation. Acta Univ Danubius Œconomica 2015; 11: 15–32.

14.

Fabrigar

Petty

Smith

, et al. Understanding knowledge effects on attitude-behavior consistency: the role of relevance, complexity, and amount of knowledge. J Pers Soc Psychol 2006; 90: 556.

15.

Barber

Taylor

Strick

. Wine consumers’ environmental knowledge and attitudes: Influence on willingness to purchase. Int J Wine Res 2009; 1: 59–72.

16.

Dogan

Ulucak

Kocak

, et al. The use of ecological footprint in estimating the environmental kuznets curve hypothesis for BRICST by considering cross-section dependence and heterogeneity. Sci Total Environ 2020; 723: 138063.

17.

Wood

Rhodes

Biek

. Working knowledge and attitude strength: an information-processing analysis. Attitude Strength: Antecedents and Consequences 1995; 4: 189–202.

18.

Asif

Zhongfu

Irfan

, et al. Assessing eco-label knowledge and sustainable consumption behavior in energy sector of Pakistan: An environmental sustainability paradigm. Environ Sci Pollut Res 2023; 30: 41319–41332.

19.

Dogan

Madaleno

Taskin

. Which households are more energy vulnerable? Energy poverty and financial inclusion in Turkey. Energy Econ 2021; 99: 105306.

20.

Dogan

Madaleno

Taskin

, et al. Investigating the spillovers and connectedness between green finance and renewable energy sources. Renew Energy 2022; 197: 709–722.

21.

Polonsky

Vocino

Grau

, et al. The impact of general and carbon-related environmental knowledge on attitudes and behaviour of US consumers. J Mark Manag 2012; 28: 238–263.

22.

Ahmad

Irfan

Salem

, et al. Energy efficiency in the post-COVID-19 era: exploring the determinants of energy-saving intentions and behaviors. Front Energy Res 2022; 9: 824318.

23.

Kollmuss

Agyeman

. Mind the gap: why do people act environmentally and what are the barriers to pro-environmental behavior? Environ Educ Res 2002; 8: 239–260.

24.

Zsóka

Szerényi

Széchy

, et al. Greening due to environmental education? Environmental knowledge, attitudes, consumer behavior and everyday pro-environmental activities of Hungarian high school and university students. J Clean Prod 2013; 48: 126–138.

25.

Asif

Zhongfu

Ahmad

, et al. Influencing factors of consumers’ buying intention of solar energy: a structural equation modeling approach. Environ Sci Pollut Res 2022; 30: 30017–30032.

26.

Fryxell

CWH

. The influence of environmental knowledge and values on managerial behaviours on behalf of the environment: An empirical examination of managers in China. J Bus Ethics 2003; 46: 45–69.

27.

Jin

, et al. Influence of environmental concern and knowledge on households’ willingness to purchase energy-efficient appliances: A case study in Shanxi, China. Sustain 2019; 11: 1073.

28.

Rashid

NRNA

Khalid

Rahman

NIA

. Environmental corporate social responsibility (ECSR): Exploring its influence on customer loyalty. Procedia Econ Financ 2015; 31: 705–713.

29.

Khan

Johl

. Nexus of comprehensive green innovation, environmental management system-14001-2015 and firm performance. Cogent Bus Manag 6. Epub ahead of print January 2019. DOI: https://doi.org/10.1080/23311975.2019.1691833.

30.

Poortinga

Steg

Vlek

. Values, environmental concern, and environmental behavior: A study into household energy use. Environ Behav 2004; 36: 70–93.

31.

Jinru

Changbiao

Ahmad

, et al. How do green financing and green logistics affect the circular economy in the pandemic situation: Key mediating role of sustainable production. Econ Res Istraz 2021; 35: 3836–3856.

32.

Ahmad

Kim

Anwer

, et al.

Schwartz Personal values, theory of planned behavior and environmental consciousness: How tourists’ visiting intentions towards eco-friendly destinations are shaped?

J Bus Res 2020; 110: 228–236.

33.

Gao

Wang

, et al. Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resour Conserv Recycl 2017; 127: 107–113.

34.

Jabbour

CJC

. Environmental training and environmental management maturity of Brazilian companies with ISO14001: Empirical evidence. J Clean Prod 2015; 96: 331–338.

35.

Kaiser

Wölfing

Fuhrer

. Environmental attitude and ecological behaviour. J Environ Psychol 1999; 19: 1–19.

36.

Nureen

Liu

Ahmad

, et al. Exploring the technical and behavioral dimensions of green supply chain management : A roadmap toward environmental sustainability. Environ Sci Pollut Res 2022; 29: 63457–63457.

37.

Hartmann

Apaolaza-Ibáñez

. Consumer attitude and purchase intention toward green energy brands: The roles of psychological benefits and environmental concern. J Bus Res 2012; 65: 1254–1263.

38.

De Groot

Steg

. General beliefs and the theory of planned behavior: The role of environmental concerns in the TPB. J Appl Soc Psychol 2007; 37: 1817–1836.

39.

Taufique

KMR

Vocino

Polonsky

. The influence of eco-label knowledge and trust on pro-environmental consumer behaviour in an emerging market. J Strateg Mark 2017; 25: 511–529.

40.

ISO Standardization IO for Environmental labels and declarations: Type I environmental labelling: principles and procedures (ISO 14024: 1999). Geneva, Switzerland: ISO, 2000.

41.

Waris

Ahmed

. Empirical evaluation of the antecedents of energy-efficient home appliances: Application of extended theory of planned behavior. Manag Environ Qual An Int J 2020; 31: 915–930.

42.

Doney

Cannon

Mullen

. Understanding the influence of national culture on the development of trust. Acad Manag Rev 1998; 23: 601–620.

43.

Jiang

J-C

Chen

C-A

Wang

C-C.

Knowledge and trust in E-consumers’ online shopping behavior. In: 2008 International symposium on electronic commerce and security. IEEE, 2008, pp.652–656.

44.

Davies

Foxall

Pallister

. Beyond the intention–behaviour mythology: An integrated model of recycling. Mark Theory 2002; 2: 29–113.

45.

Flamm

. The impacts of environmental knowledge and attitudes on vehicle ownership and use. Transp Res Part D Transp Environ 2009; 14: 272–279.

46.

Spears

Singh

. Measuring attitude toward the brand and purchase intentions. J Curr Issues Res Advert 2004; 26: 53–66.

47.

Girard

Trapp

Pinar

, et al. Consumer-based brand equity of a private-label brand: Measuring and examining determinants. J Mark Theory Pract 2017; 25: 39–56.

48.

Kandampully

Zhang

Bilgihan

. Customer loyalty: A review and future directions with a special focus on the hospitality industry. Int J Contemp Hosp Manag 2015; 27: 379–414.

49.

Wang

Wei

. Social media peer communication and impacts on purchase intentions: A consumer socialization framework. J Interact Mark 2012; 26: 198–208.

50.

Bansal

Taylor

St. James

. “Migrating” to new service providers: toward a unifying framework of consumers’ switching behaviors. J Acad Mark Sci 2005; 33: 96–115.

51.

Augusto

Torres

. Effects of brand attitude and eWOM on consumers’ willingness to pay in the banking industry: Mediating role of consumer-brand identification and brand equity. J Retail Consum Serv 2018; 42: 1–10.

52.

Reiner

. Why consumers switch energy suppliers: The role of individual attitudes. Energy J 2017; 38: 25–53.

53.

Bamberg

. How does environmental concern influence specific environmentally related behaviors? A new answer to an old question. J Environ Psychol 2003; 23: 21–32.

54.

Ballantyne

Packer

. Promoting environmentally sustainable attitudes and behaviour through free-choice learning experiences: What is the state of the game? Environmental Education Research 2006; 11: 281–295.

55.

Salem

Elbaz

Al-alawi

, et al. Is eco-label hotel engagement the pathway to sustainability practices via entrepreneurial resilience and orientation in Oman? Findings from PLS-SEM and fsQCA. Int J Contemp Hosp Manag 2023; 35: 717–742.

56.

Arcury

. Environmental attitude and environmental knowledge. Hum Organ 1990; 49: 300–304.

57.

Sang

Y-N

Bekhet

. Modelling electric vehicle usage intentions: An empirical study in Malaysia. J Clean Prod 2015; 92: 75–83.

58.

Hair

Black

Babin

, et al. Multivariate data analysis. New Jersey: Prentice-Hall, Inc., 2006.

59.

Ahmad

Zhao

Irfan

, et al. Modeling heterogeneous dynamic interactions among energy investment, SO2 emissions and economic performance in regional China. Environ Sci Pollut Res 2020; 27: 2730–2744.

60.

Fornell

Larcker

. Structural equation models with unobuervable variables and measurement error: Algebra and statistics. J Mark Res 1981; 18: 382.

61.

Wong

KKK-K

. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark Bull 2013; 24: 1–32.

62.

Nunnally

. Psychometric theory 3E. New York, NY: Tata McGraw-hill education, 1994.

63.

Ahmad

Liu

Irfan

, et al. Unleashing the mechanism among salesforce control system, salesforce ambidexterity, and emotional exhaustion to enhance the competitive advantage of organizations. Front Psychol 2022; 13: 909656.

64.

Ahmad

Akbar

MuD

. Validating a multidimensional perspective of relationship marketing on brand attachment, customer loyalty and purchase intentions: a serial mediation model. J Strateg Mark 2021: 1–24.

65.

Hair

Jr Matthews

Matthews

, et al. PLS-SEM or CB-SEM: updated guidelines on which method to use. Int J Multivar Data Anal 2017; 1: 107.

66.

Field

. Discovering statistics using IBM SPSS statistics. Newcastle upon Tyne, UK: Sage, 2013.

67.

Strupeit

Palm

. Overcoming barriers to renewable energy diffusion: business models for customer-sited solar photovoltaics in Japan, Germany, and the United States. J Clean Prod 2016; 123: 124–136.

68.

Ahmad

Liu

Akhtar

, et al. Does service-sales ambidexterity matter in business-to-business service recovery? A perspective through salesforce control system. Ind Mark Manag 2022; 102: 351–363.

69.

Podsakoff

MacKenzie

Lee

, et al. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol 2003; 88: 879–903.

70.

Harman

HH.

Modern factor analysis. 3rd rev. ed. Chicago, IL: University of Chicago press., 1976.

71.

Ahmad

Liu

Asif

, et al. Ambidextrous leadership and service recovery performance under B2B selling context : An examination through service innovation capability. SAGE Open 2022; 12(2): 1–17.

72.

Bagozzi

Phillips

. Assessing construct validity in organizational research. Adm Sci Q 1991; 36(3): 421–458.

73.

Hooper

Coughlan

Mullen

. Structural equation modelling: Guidelines for determining model fit. Electron J Bus Res Methods 2008; 6: 53–60.

74.

Bentler

. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model 1999; 6: 1–55.

75.

MacCallum

Hong

. Power analysis in covariance structure modeling using GFI and AGFI. Multivariate Behav Res 1997; 32: 193–210.

76.

Dash

Paul

. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol Forecast Soc Change 2021; 173: 121092.

77.

Zhan

. Consumer purchase intention of electric vehicles in China: The roles of perception and personality. J Clean Prod 2018; 204: 1060–1069.

78.

Testa

Iraldo

Vaccari

, et al. Why eco-labels can be effective marketing tools: Evidence from a study on Italian consumers. Bus Strateg Environ 2015; 24: 252–265.