The Influence of Psychological Factors on Consumer Purchase Intention for Electric Vehicles: Case Study from China: Integrating the Necessary Condition Analysis Methodology from the Perspective of Self-Determination Theory

Abstract

This paper examines the impact of psychological factors on consumer purchase intention for electric vehicles (EVs) through the lens of Self-Determination Theory (SDT). By integrating the three dimensions of autonomy, relatedness, and competence, this study addresses a research gap in consumer innovative consumption, offering a deeper understanding of green transportation. The research reveals that psychological factors significantly influence innovative consumption and the purchase intention of EVs, aligning with the existing literature. In sustainable transportation, psychological factors such as motivation, attitude, and inner activities increasingly drive purchase decisions. This study examines the direct and indirect effects of psychological factors on purchase intention by employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). It also considers the moderating role of driving experience in the relationship between psychological factors and innovative consumption. This combined data analysis approach provides a comprehensive understanding of the mechanisms influencing purchase intention, highlighting the intricate interplay between psychological determinants and consumer behavior in the adoption of electric vehicles.

1. Introduction

The automotive industry stands as a vital pillar within China’s national economy, experiencing rapid growth since the start of the 21st century.

Table 1. EV sales and gasoline vehicle sales comparison.

Year	EV Sales (Units)	Gasoline Vehicle Sales (Millions)
2014	74,800	19.70
2015	331,000	21.15
2016	507,000	22.98
2017	777,000	24.72
2018	1,200,000	23.71
2019	1,210,000	21.44
2020	1,330,000	19.29
2021	3,520,000	20.15
2022	6,500,000	21.35
2023	7,800,000	22.45

Source: Good Car Bad Car (IEA).

highlights significant trends in China’s electric vehicle (EV) market, showing robust growth over the past five years. Concurrently, sales of gasoline vehicles have exhibited fluctuating patterns, peaking in 2017, declining thereafter, and showing recent signs of recovery. China has consistently held the position of the world’s largest vehicle producer and seller for six consecutive years. However, traditional automobiles remain substantial contributors to carbon dioxide emissions. As vehicle numbers rise, issues concerning energy supply and environmental pollution have become increasingly prominent. In 2022, global carbon dioxide emissions increased by 0.9%, adding 321 million tons compared to 2021, totaling 36.8 billion tons for the year [1]. Vehicle exhaust emissions have become increasingly severe, with total motor vehicle pollutant emissions in 2022 reaching 11.73 billion tons, accounting for over 80% of total air pollutants (with nitrogen oxides and particulate matter exceeding 90% and hydrocarbons and carbon dioxide exceeding 80%). Vehicle exhaust emissions are a critical source of carbon dioxide, accounting for about 10% to 15% of total emissions. In particular, for PM2.5, motor vehicles contribute 25% to 40% of the emissions [2].

To address the challenges of resource scarcity and environmental pollution, there is global advocacy for constructing an ecological civilization and developing green transportation [1]. Consequently, research on consumer purchase behavior for electric vehicles (EVs) has gained increasing attention in the academic community. As a pivotal product for developing sustainable supply chains, the industrialization and marketization of EVs are urgently needed [3]. Firstly, developing EVs helps reduce environmental pollution and promote sustainable development. The impact of the adoption of electric vehicles (EVs) on CO₂ emissions has been assessed using spatial econometric models, with three findings. First, there are spatial spillover effects of EV adoption on CO₂ emissions, implying that the CO₂ mitigation of a city depends on local sales of EVs and sales of EVs in neighboring cities. A 1% increase in the sale of EVs in a city can reduce CO₂ emissions locally by 0.096% and by 0.087% in a nearby city. EVs indirectly impact CO₂ emissions through the substitution effect, energy consumption effect, and technological effect. The overall impact of EV adoption on CO₂ emissions is negative. Finally, we demonstrate the moderating effect of the urban energy structure on EVs’ CO₂ emissions mitigation. A 1% increase in the proportion of renewable energy generation increases the decarbonization of EVs by 0.036%. These findings have policy implications for the coordinated development of the EV market and energy system. Secondly, electric vehicles (EVs) align with the goals of energy conservation, environmental protection, and sustainable development, making them a pivotal industry for the future [4]. The development of China’s EV industry can promote the structural optimization and consumption upgrade of the traditional automobile industry and further advance the growth of China’s vehicle industry. To accelerate the development of the EV industry, the Chinese government has introduced a series of supportive policies [5]. Simultaneously, EV manufacturers are intensifying their efforts in technological innovation. For instance, they are focusing on advancements in battery technology, motor efficiency, and the charging infrastructure [6]. However, consumer enthusiasm for purchasing EVs remains low, and there are still many shortcomings in the market’s development level. In this context, the promotion and popularization of EVs are particularly important. By refining these elements, this paper aims to provide a more comprehensive understanding of the factors influencing consumer purchase intentions for EVs and the critical role of government policy (see

Table 2. China’s electric vehicle subsidy statistics.

Year	Number of Approved Subsidized Vehicles	Approved Subsidy Amount (100 Million CNY)
2016	50,208	123.33
2017	94,170	66.41
2018	202,070	141.35
2019	417,530	136.96
2020	194,516	46.03
2021	1,569,366	222.03
2022	3,168,813	299.36

Source: Ministry of Industry and Information Technology of China.

) and technological innovation in fostering a sustainable transportation ecosystem [7].

Previous studies have demonstrated that electric vehicles (EVs) can significantly reduce greenhouse gas emissions and decrease the concentration of air pollutants [8]. This is crucial for improving urban air quality and enhancing residents’ health. Furthermore, the widespread adoption of EVs helps to reduce dependence on traditional energy sources, such as petroleum, and promotes the transformation and optimization of the energy structure [4]. This study aimed to investigate the key factors influencing the purchase behavior of EVs, particularly in the context of the Chinese market. By analyzing consumers’ attitudes, perceptions, and behavioral patterns towards EVs [9], this research was intended to provide valuable insights for policymakers, enterprises, and research institutions, to foster the healthy development of the EV industry.

Previous literature has predominantly focused on the impact of green consumption benefit appeals on EV consumption behavior, considering individual differences such as environmental concern and self-responsibility awareness [10]. However, there is a paucity of research examining the relationship between consumer psychological factors and EV purchase intention and behavior. According to Leonov et al.’s research, consumer psychological factors play a pivotal role in the consumer decision-making process [11]. Motivation is the internal driving force that prompts consumers to take action. Consumer purchasing motivation can be functional, addressing needs such as safety and comfort, or emotional, involving the pursuit of pleasure, status symbols, and self-realization. Perception is the cognitive and interpretive process through which consumers understand products or brands, influenced by various factors such as advertising, word-of-mouth, brand image, and packaging. Consumers’ perceptions of products directly affect their purchase decisions [12,13]. For instance, high-quality packaging and a positive brand image can enhance consumers’ trust and favoring of products, thereby increasing their purchase intention.

Drawing on a comprehensive review of the existing literature, this study proposes a conceptual model based on Self-Determination Theory (SDT) to explore the relationships among autonomy, competence, relatedness, and consumer purchase intention of electric vehicles (EVs). The objective is to further validate the connections between psychological factors and the intention to purchase EVs, detailing the specific effects, underlying mechanisms, and formation processes of these psychological factors. This research aims to offer robust evidence to support governmental policy-making efforts aimed at encouraging EV consumption.

This paper integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) to analyze consumer intentions, exploring the primary factors influencing consumer purchase intentions. The findings will assist EV companies in forecasting market demand, investment, production, and planning marketing strategies. By formulating strategies based on different consumer needs and intentions, this research provides critical references for promoting the industrialization and marketization of EVs in China.

2. Theoretical Foundation and Hypotheses Development

2.1. Self-Determination Theory

Self-Determination Theory (SDT) is a seminal framework proposed by American psychologists Deci et al. in the 1980s to explain the motivation behind individual behavior [13]. The theory comprises four branches: basic psychological needs, organismic integration theory, cognitive evaluation theory, and attribution orientation theory. SDT elucidates the initiation and driving processes of individual behavior from a motivational perspective, positing that behavior is influenced by both intrinsic and extrinsic motivations. It emphasizes the degree to which behavior is voluntary or self-determined, highlighting the interplay between self-determination and external situational interventions in shaping motivation [14]. Unlike Maslow’s hierarchy of needs, SDT views individuals as proactive organisms with inherent psychological growth and developmental potential. This potential propels individuals to engage in activities that are both interesting and developmentally beneficial, thereby achieving an organic alignment between the individual and society. SDT posits that individuals generate basic psychological needs essential for self-development, which are categorized into autonomy, competence, and relatedness needs [15].

Psychological needs constitute the theoretical core of SDT. When individuals’ needs for autonomy, competence, and relatedness are fulfilled, their intrinsic motivation is effectively activated. Autonomy refers to the desire to autonomously determine one’s behavior rather than being controlled by external factors. Individuals whose autonomy needs are satisfied tend to perceive autonomy (PEA) when faced with challenges, exhibiting greater initiative and creativity [16]. Competence needs, also known as ability needs, pertaining to the desire for control over tasks and issues, representing a universal experience of utility. When competence needs are met, individuals perceive competence (PER) [17], believing they can effectively manage situations and make sound decisions in dynamic environments. Relatedness needs to involve seeking attention, recognition, and support from others within relational contexts. When these needs are satisfied, individuals perceive relatedness (PER), feel integrated into a group [18], and are more inclined to have altruistic intentions for the group’s benefit.

SDT asserts that individuals whose basic psychological needs are met can autonomously determine their behavior and perceive a high degree of control over their actions, with a desire for widespread support and recognition [19]. This results in robust intrinsic motivation. Such intrinsic motivation significantly impacts consumers’ purchase intentions, including their intention to purchase electric vehicles. Consequently, understanding and fulfilling these psychological needs is crucial in influencing consumer behavior towards adopting sustainable technologies like electric vehicles.

2.2. Hypothesis Development

2.2.1. Perceived Autonomy (PEA)

Perceived autonomy, a fundamental element of Self-Determination Theory (SDT), signifies the degree to which individuals believe their actions are self-initiated and personally endorsed [20]. In the context of adopting electric vehicles (EVs), perceived autonomy significantly influences consumer attitudes and behaviors. Autonomy pertains to the sense of control individuals feel over their actions and decisions. When individuals perceive a high level of autonomy, they are more inclined to pursue activities that resonate with their personal values and interests.

Deci and Ryan (2000) argue that when individuals perceive a high level of autonomy, they are more likely to engage in behaviors that resonate with their personal values and interests. In the context of EVs, this means that consumers who feel they have control over their decision to purchase an EV are more likely to experience satisfaction and commitment to their choice. Gagné and Deci (2005) highlight that perceived autonomy enhances intrinsic motivation, leading to greater engagement and persistence in activities [21]. Applied to EVs, this suggests that when consumers feel autonomous in their decision-making, they are more likely to be intrinsically motivated to choose and advocate for EVs, driven by personal satisfaction and alignment with environmental values. Pelletier et al. (1998) demonstrate that perceived autonomy in environmental decision-making leads to more sustainable behaviors [22]. This finding is particularly relevant to EV adoption, as it suggests that consumers who perceive their decision to adopt EVs as autonomous are more likely to engage in pro-environmental behaviors and support sustainable transportation solutions. Barth, Jugert, and Fritsche (2016) explore the role of autonomy in green consumerism, finding that consumers who perceive high autonomy in their purchasing decisions are more likely to choose environmentally friendly products [23].

Previous research indicates that autonomy, or the desire for self-direction, significantly influences consumer decisions in various contexts, including technology adoption. Consumers who feel autonomous are more likely to engage in behaviors that align with their personal values and interests [24]. It is evident that basic psychological needs promote consumers’ intention to purchase electric vehicles. Specifically, individuals with higher levels of perceived autonomy indicate a higher degree of self-need satisfaction, stronger self-direction, and greater freedom of choice. This reflects a robust “autonomous form,” where individuals can decide their behavior based on self-choice. When facing the decision to purchase an electric vehicle, consumers with high perceived autonomy will exhibit a positive and proactive green purchasing tendency, driven by strong autonomous motivation, and will follow their own will to implement the purchase behavior. Therefore, this study proposes the following hypothesis:

H1: 

Perceived autonomy (PEA) significantly impacts innovative consumption.

2.2.2. Perceived Relatedness (PER)

Perceived relatedness, another core component of Self-Determination Theory (SDT), refers to the need to feel connected to others, to care for and be cared for by others, and to have a sense of belonging both with other individuals and within one’s community. Deci and Ryan (2000) discuss how the need for relatedness drives individuals to engage in behaviors that foster connections with others. In the context of electric vehicle (EV) adoption, this suggests that consumers may be motivated to adopt EVs if it enhances their sense of belonging to a community of environmentally conscious individuals [14].

Baumeister and Leary (2022) propose that the need to belong is a fundamental human motivation that influences a wide range of behaviors [25]. Their research implies that EV adoption can be driven by the desire to belong to social groups that value sustainability and green technology. Heffner, Kurani, and Turrentine (2007) explore how social identity influences EV adoption. They find that consumers who identify with social groups that prioritize environmental sustainability are more likely to adopt EVs [26], underscoring the importance of perceived relatedness in motivating EV purchases. Noppers et al. (2014) examine the role of social norms in the adoption of sustainable innovations, including EVs [27]. Their study shows that perceived social support and belonging to pro-environmental communities significantly impact the likelihood of adopting EVs, highlighting the role of perceived relatedness.

Perceived competence, another critical component of SDT, refers to an individual’s belief in their capability to handle challenges and solve problems [25,27]. Individuals with a strong sense of perceived competence believe they can effectively address the issues they encounter. When faced with the decision to purchase an electric vehicle, those with strong perceived competence are confident that their efforts can contribute positively to addressing environmental issues. Driven by relatedness motivation, consumers actively seek information about EV products, compare and evaluate the attributes of electric vehicles, assess the associated risks, and ultimately make the decision to purchase an electric vehicle. The need for relatedness and belonging can also impact purchase intentions. Studies have shown that consumers are influenced by the opinions and behaviors of their social networks, which can affect their attitudes toward new technologies like EVs.

Thus, perceived relatedness and perceived competence both play vital roles in influencing consumer behavior towards EV adoption. By understanding and leveraging these psychological needs, policymakers and marketers can devise strategies that enhance the appeal of electric vehicles, thereby fostering a more sustainable future. Therefore, this study proposes the following hypothesis:

H2: 

Perceived relatedness significantly impacts innovative consumption.

2.2.3. Perceived Competence (PEM)

Perceived competence significantly influences the adoption of electric vehicles (EVs) as it enhances consumers’ confidence in understanding and using new technology, which is critical for the acceptance of innovative products.

Heffner, Kurani, and Turrentine (2007) found that consumers who perceive themselves as knowledgeable about EV technology are more likely to adopt EVs [28]. This suggests that increasing consumer competence through education can positively impact EV adoption. Ziegler (2012) suggested that enhancing consumer competence via educational initiatives and informational campaigns can drive the adoption of sustainable technologies, including EVs [29]. This indicates that when consumers feel competent in their ability to evaluate and use new technologies, their likelihood of engaging in innovative consumption behaviors increases. Noppers et al. (2014) investigated the role of perceived competence in the adoption of sustainable innovations, including EVs [27]. Their findings indicate that perceived competence significantly impacts the likelihood of adopting these technologies, reinforcing the importance of consumer education and awareness. A study by Coca-Stefaniak and Liébana-Cabanillas (2019) suggested that consumers’ attitudes towards EVs are significantly influenced by their perceived competence in understanding the technical performance and value of these vehicles [30]. Kaufmann, Kumar, and Carter (2020) discuss how consumer competence affects the adoption of innovative products [31]. They suggest that consumers who feel competent in their ability to evaluate and use new technologies are more likely to engage in innovative consumption behaviors, such as adopting EVs. Dutta and Hwang (2021) explored how perceived behavioral control, which is closely related to perceived competence, influences sustainable consumption intentions [32].

Research consistently highlights the pivotal role of perceived competence in adopting electric vehicles (EVs). The feeling of being capable and effective in one’s actions is another crucial factor. Research suggests that consumers who perceive themselves as competent in understanding and using EV technology are more likely to consider purchasing EVs [33]. Empirical studies suggest that when consumers feel more knowledgeable and capable of understanding EV technology, their likelihood of purchasing and using EVs increases. Educational programs and campaigns that demystify the technology, highlight the benefits, and providing practical usage scenarios can make a significant difference. Furthermore, creating supportive environments where consumers can engage with EV technology, directly and indirectly, enhances their perceived competence. By ensuring that consumers have ample opportunities to learn and experience the functionality and benefits of EVs firsthand, stakeholders can drive higher adoption rates and support the transition towards a more sustainable transportation system. Therefore, this study proposes the following hypotheses:

H3: 

Perceived competence (PEM) significantly impacts innovative consumption.

2.2.4. The Mediating Effects of Innovative Consumption

Consumers with strong innovativeness have a greater propensity to accept new ideas and are more inclined to try, purchase, and use products with innovative attributes. Mishra suggest that consumer innovativeness plays a crucial role in the decision-making process [34]. Consumers with high levels of innovativeness focus more on the “novel” attributes of products during the pre-purchase information-gathering process, enhancing the influence of emotional information and forming stronger purchase intentions [32]. Li et al. found that highly innovative consumers prefer new experiences and are more willing to use new products to satisfy their pursuit of a high-quality life [35]. Therefore, electric vehicles (EVs), which embody sustainable innovation and provide a sense of achievement, security, and happiness through their green, environmentally friendly, energy-saving, safe, and healthy characteristics, will become the preferred choice for innovative consumers. Chao et al. pointed out that the continued use of green innovative products by consumers depends significantly on their level of innovativeness [36].

Electric vehicles’ environmental attributes are often realized through innovations in green principles, technologies, processes, structures, and materials. Their novelty and advanced nature can better meet the consumption needs of innovative consumers, effectively stimulating consumer innovativeness and promoting purchase intentions. The continuous innovation of electric vehicle products can cater to consumers’ tendencies to pursue new experiences, adopt new lifestyles, and accept new products, thereby encouraging the purchase of electric vehicles.

Huang and Qian investigated the factors influencing the adoption of new energy vehicles, including EVs, in China [37]. Their study suggests that innovative consumption behavior, such as openness to trying new technologies, mediates the relationship between individual characteristics and EV adoption. Sharmina et al. (2016) examine the role of innovation in sustainable energy transitions. While not specific to EVs, their study discusses how innovative consumption practices, such as adopting energy-efficient technologies, can mediate the transition to sustainable energy systems [38]. Sundararajan and González-Pernía (2020) explore the adoption of electric mobility in European cities. Their study suggests that innovative consumption behavior, such as the intention to try new transportation modes like EVs, can mediate the adoption of sustainable mobility solutions [39]. Chen et al. (2017) investigate the adoption of electric vehicles in China, finding that innovative consumption behavior, such as the intention to adopt new technologies, mediates the relationship between individual characteristics and EV adoption intentions [40].

According to Self-Determination Theory (SDT), when consumers exhibit strong perceived autonomy (PEA), perceived competence (PER), and perceived relatedness (PER), they show a higher interest in new experiences and products. This heightened interest translates into a greater intention to accept and try new innovations, demonstrating a stronger consumer innovativeness. Thus, fostering these psychological needs can enhance consumer engagement with innovative products such as electric vehicles, driving their adoption and supporting sustainable consumption patterns. Therefore, this study proposes the following hypotheses:

H4: 

Innovative consumption significantly impact electric vehicle purchase intention.

H5: 

Innovative consumption plays a mediating role in the impact of PEA on EV purchase intention.

H6: 

Innovative consumption plays a mediating role in the impact of PEM on EV purchase intention.

H7: 

Innovative consumption plays a mediating role in the impact of PER on EV purchase intention.

2.2.5. The Moderate Effects of Driving Experience

Experience primarily refers to the myriad interactions and engagements individuals have in their daily lives. Holbrook et al. first introduced experience as a central concept in understanding consumer behavior within the marketing realm. Building upon this, Schmitt proposed the concept of experiential marketing, defining it as providing consumers with sensory, emotional, cognitive, associative, and action experiences through various media. Consumers’ product experiences typically occur in consumption contexts consciously orchestrated by companies, where products and services deliver memorable experiences to consumers.

The experience of electric vehicle (EV) products underscores the consumers’ interactions with the energy-saving, environmentally friendly, low-carbon, safe, healthy, and user-friendly attributes of the products, as well as the feelings of natural beauty, vitality, tranquility, and harmony these attributes evoke. The driving experience of EV products can not only stimulate consumers’ sense of environmental responsibility and mission but also effectively enhance their intention to purchase EVs.

Rauh et al. investigate the impact of driving range and infrastructure on consumer preferences for electric vehicles (EVs). Their findings indicate that the overall driving experience, including aspects such as comfort and performance, plays a moderating role in alleviating range anxiety and subsequently influencing EV adoption [41]. Similarly, Graham-Rowe et al. (2012) investigate the impact of driving experience on the adoption of electric vehicles, suggesting that positive experiences, such as enjoying the driving experience of an EV [42], can alleviate concerns about range and charging infrastructure. Hardman et al. (2014), investigating the elements influencing the adoption of electric vehicles in the UK, discovered that driving experience, including aspects like vehicle performance and comfort, moderates the relationship between individual characteristics and EV adoption intentions [43]. Ho (2017) examine factors influencing consumer attitudes towards electric vehicles in Hong Kong, finding that driving experience, including vehicle performance and handling, moderates the relationship between environmental concerns and EV adoption intentions [44], thereby highlighting the importance of Self-Determination Theory (SDT) in understanding EV adoption. Wang et al. (2018) investigate the influence of driving experience on the adoption of electric vehicles, concluding that driving experience [45], encompassing factors like comfort and performance, moderates the relationship between perceived benefits and EV adoption intentions, supporting the role of SDT in understanding EV adoption. Similarly, N. Wang et al. suggests that positive driving experiences, such as enjoying the driving performance of an EV, can moderate the impact of perceived barriers on EV adoption [46], further reinforcing the application of SDT. Li et al. (2020) explore the factors influencing the intention to purchase electric vehicles, and they find that driving experience, including aspects like vehicle comfort and performance, moderates the relationship between perceived benefits and EV adoption intentions [47], underscoring the relevance of SDT in understanding EV adoption.

In summary, when individuals’ basic psychological needs are satisfied, they develop perceived autonomy (PEA), perceived competence (PEM), and perceived relatedness (PER), which stimulate consumer innovativeness, thereby eliciting internal motivation for purchasing innovative products. The driving experience of electric vehicles (EVs) can serve as an external environmental cue. Consumers form a comprehensive evaluation of product value through their functional and hedonic experiences with EV products. When consumers’ internal and external motivations are highly consistent, they experience higher psychological coherence, which fosters consumer innovativeness and drives them to make EV purchase decisions. Conversely, when there is a discrepancy between consumers’ external environment and their internal inclinations, the inconsistency between internal and external motivations leads to psychological resistance, which restricts the generation of consumer innovativeness and limits green purchasing behavior. Therefore, this study proposes the following hypotheses:

H8: 

Driving experience plays a moderating role in the effect of PEA on innovative consumption.

H9: 

Driving experience plays a moderating role in the effect of PEM on innovative consumption.

H10: 

Driving experience plays a moderating role in the effect of PER on innovative consumption.

Based on the above theoretical analysis, this study constructs the research model shown in Figure 1.

Figure 1 illustrates the proposed research model that investigates the influence of psychological factors on consumers’ purchase intentions toward electric vehicles (EVs), with innovative consumption as a mediating variable and driving experience as a moderating variable.

Solid lines indicate direct relationships between variables.

Psychological factors (PEA, PER, PEM) have direct effects on innovative consumption (INC).

Innovative consumption (INC) has a direct effect on EV purchase intention (EPI).

Dotted lines indicate moderating relationships.

Driving experience (DRE) moderates the relationships.

The proposed research model (Figure 1) integrates psychological factors derived from Self-Determination Theory (SDT) to understand their influence on consumers’ purchase intentions toward electric vehicles (EVs). Specifically, it examines how perceived autonomy (PEA), perceived relatedness (PER), and perceived competence (PEM) contribute to innovative consumption (INC) behaviors, which in turn affect EV purchase intentions (EPIs). Furthermore, the model incorporates driving experience (DRE) as a moderating variable, which influences the strength of the relationships between psychological factors and innovative consumption. This comprehensive model aims to fill the research gap by highlighting the importance of psychological needs and their interaction with past driving experiences in shaping consumer behavior in the context of EV adoption.

2.3. Contribution

This study draws on Self-Determination Theory (SDT) to explore the psychological factors influencing consumers’ purchase intentions toward electric vehicles (EVs). SDT posits that human motivation is driven by the need to fulfill three psychological needs: autonomy, relatedness, and competence (see

Table 3. Author contribution table.

Research Focus	Key Findings	Research Gap	Novelty of Current Study
Value (PGV) and EV purchase intention	Environmental concerns and green benefits positively influence EV purchase intentions.	Lack of focus on psychological factors beyond environmental values.	Investigates psychological needs (autonomy, relatedness, competence) and their influence on EV purchase intention.
Technology adoption and psychological factors	Autonomy and competence influence technology adoption.	Limited application to the EV context, especially in China.	Applies SDT to the EV context, exploring how psychological needs drive purchase intentions.
Social influence on consumer behavior	Social networks and relatedness impact consumer decisions.	Insufficient exploration of relatedness in EV adoption.	Examines the role of relatedness in shaping EV purchase intentions.

).

This study addresses the research gap by integrating SDT to explore how the fulfillment of autonomy, relatedness, and competence needs influences consumers’ purchase intentions toward EVs. Unlike previous research that primarily focuses on perceived green value, this study provides a novel perspective by emphasizing psychological factors, offering deeper insights into consumer behavior in the context of EV adoption.

3. Methodology

3.1. Data Collection

A carefully structured survey questionnaire was designed to gather quantitative data on consumers’ green purchasing intentions in live-streaming contexts. The questionnaire consisted of three distinct sections. The first section included screening questions meticulously crafted to ensure participants met the study’s eligibility criteria.

Participants were required to not own electric vehicle products to qualify. The survey questionnaire was structured into three sections. The middle section consisted of 30 carefully crafted items, each rated on a 5-point Likert scale, designed to assess participants’ responses across ten key constructs outlined in the theoretical framework. The final section included demographic questions aimed at gathering information about participants’ personal profiles and social characteristics.

Originally developed in English, the questionnaire underwent translation into Chinese using back-translation methods to ensure accuracy and accessibility for online respondents. Both the English and Chinese versions of the questionnaire were rigorously reviewed by two experienced researchers specializing in consumer behavior in live-streaming environments, ensuring their reliability and precision.

The survey was conducted online in January 2024 using the WJX platform (www.wjx.cn). This platform is widely acknowledged as a premier online survey data collection platform in China, catering specifically to surveys, assessments, and voting. It boasts a substantial user base of nearly 50 million users nationwide [12,48,49]. The dependability of the data obtained from this platform has been validated by previous studies [6,50,51,52]. Data were amassed at disparate intervals and via diverse conduits to mitigate the potential for standard method variance. The online survey was disseminated across fan groups dedicated to purchasing sessions spotlighting electric vehicles. These assemblies consisted of viewers who participated in electric vehicle sessions revolving around green product acquisitions. The survey was allocated to each assembly on three separate instances throughout January 2024. Purposive sampling was utilized to ensure that respondents were mainland Chinese viewers engaged in electric vehicle driving experience sessions centered around green products within the past year. A total of 505 responses were garnered, and after the exclusion of invalid entries, a total of 478 responses were deemed valid.

Table 4. Respondent profiles.

Variable	Category	Frequency	Percent	Cumulative Percent
Gender	M	247	51.7	51.7
	F	231	48.3	100
Age	18–25	63	13.2	13.2
	26–35	157	32.8	46
	36–45	143	29.9	75.9
	45–55	91	19	95
	55 above	24	5	100
Education level	College diploma or below	193	40.4	40.4
	Bachelor’s degree	245	51.3	91.6
	Master’s degree or above	40	8.4	100
Income	3000–5000	244	51	51
	5001–10,000	194	40.6	91.6
	10,001–15,000	33	6.9	98.5
	Above 15,000	7	1.5	100
Total		478	100

Respondent profiles (n = 478).

presents a synopsis of the demographic attributes of the respondents.

3.2. Measures and Data Analysis

In this investigation, all employed items were sourced and adapted from prior research. The measures for perceived autonomy (PEA) were modeled after Charng, Piliavin, and Callero (1988) and encompassed 5 items. The scales for perceived relatedness (PER) were inspired by Hsiao, Chuan-Chuan Lin, Wang, Lu, and Yu (2010) and consisted of 5 items. The metric for perceived competence (PEM) was formulated based on the scale delineated by [51] and included 5 items. Perceived competence (PEM) in regard to innovative consumption (INC) had three moderating variables. Perceived autonomy (PEA) in innovative consumption (INC) had 5 items, as did perceived relatedness (PER) to innovative consumption (INC) [53,54]. Perceived competence (PEM) in innovative consumption (INC) was gauged through the scale articulated by [55,56,57], once again incorporating 5 items. Finally, the assessment for electric vehicle intention (EPI) was adapted from the methodology of [11,58] and included 5 items. Each item was assessed utilizing a five-point Likert scale, spanning from “strongly disagree” to “strongly agree”. The detailed measurements are available in

Table 5. Measurement items.

	Variables	Measurement Items
Perceived autonomy	PEA 1	When making a car purchase decision, I am more inclined to base it on my personal needs rather than external pressure.
	PEA 2	I feel that purchasing an electric vehicle is an autonomous decision, rather than a trend-following behavior.
	PEA 3	When purchasing a car, I am more concerned about whether the electric vehicle can meet my personal travel needs.
	PEA 4	When purchasing a car, I am not influenced by social opinion about electric vehicles.
	PEA 5	I feel that purchasing an electric vehicle is my own contribution to environmental protection.
Perceived relatedness	PER 1	I believe that purchasing an electric vehicle is an important contribution to environmental protection and sustainable development.
	PER 2	My intention to buy an electric vehicle is influenced by my love for technology.
	PER 3	I would consider purchasing an electric vehicle because I see it as an investment in future technology.
	PER 4	I more intend to buy from car brands that are committed to social and environmental responsibility.
	PER 5	I believe that purchasing an electric vehicle is a way to participate in social sustainable development.
Perceived competence	PEM 1	When purchasing a car, I will prioritize electric vehicles with advanced technology and high performance.
	PEM 2	I intend to buy an electric vehicle because I believe I can fully master its advanced driving technology.
	PEM 3	I feel confident and not troubled about the maintenance and use of electric vehicles.
	PEM 4	I am confident in purchasing an electric vehicle because I believe I have the ability to handle technical challenges.
	PEM 5	When considering purchasing an electric vehicle, I will thoroughly research its technological performance.
Innovative consumption	INC 1	I am interested in trying novel products that I have not experienced before.
	INC 2	I like to try products that are relatively new on the market and used by few people.
	INC 3	I tend to purchase products that integrate technology and innovation.
	INC 4	I have a strong interest in products that adopt the latest technology and design.
	INC 5	Purchasing an electric vehicle is a reflection of my appreciation for technological innovation.
Driving experience	DRE 1	I am very interested in the driving experience of electric vehicles.
	DRE 2	I have high expectations for the handling performance of electric vehicles.
	DRE 3	When purchasing a car, I will prioritize electric vehicle brands that offer a good driving experience.
	DRE 4	I believe that the driving feel of an electric vehicle is a key factor in the car purchase decision.
	DRE 5	I believe that electric vehicles can provide a more unique and satisfying driving experience.
EV purchase intention	EPI 1	Considering environmental factors, I am willing to purchase an electric vehicle.
	EPI 2	I believe that electric vehicles have a positive effect on reducing exhaust emissions.
	EPI 3	I will consider purchasing an electric vehicle in the future.
	EPI 4	Considering the uncertainty of fuel prices, I am more interested in purchasing an electric vehicle.
	EPI 5	I believe that the maintenance costs of electric vehicles are relatively low, and I am willing to purchase one.

.

This study identified both the sufficient and necessary conditions for consumers’ electric vehicle purchasing intentions in the context of psychological factors in electric vehicle purchasing. It utilized two methodologies, PLS-SEM and NCA, adhering to academic standards. The PLS-SEM comprises a measurement and structural model; the former illustrates the relationship between latent and observed variables, while the latter depicts the associations among latent variables.

3.3. Survey Items’ Selection

Relevance to constructs: The primary criterion for selecting these studies was their alignment with our research objectives. Each construct in our model—perceived autonomy (PEA), perceived relatedness (PER), perceived competence (PEM), innovative consumption (INC), driving experience (DRE), and EV purchase intention (EPI)—has been extensively examined in the fields of consumer behavior, environmental psychology, and sustainable consumption. The items adapted from previous studies effectively represent these constructs in the context of electric vehicle purchases. Empirical validation: The items selected from prior research have undergone rigorous empirical testing and validation, ensuring their reliability and accuracy in measuring the intended constructs. Utilizing established items minimizes the measurement error risk and enhances our findings’ credibility. For instance, the items measuring PEA, PER, and PEM are derived from studies that have successfully linked electric vehicles to consumer behavior, confirming their appropriateness for evaluating how green values influence electric vehicle purchase intention. The studies selected for item adaptation were chosen based on their alignment with the theoretical framework of our study. Constructs such as perceived autonomy (PEA), perceived relatedness (PER), and perceived competence (PEM) are rooted in psychological factors and Self-Determination Theory (SDT). By utilizing items from studies that employ these theories, we maintained theoretical consistency and coherence in our measurement approach.

4. Results

4.1. PLS-SEM Results

4.1.1. Assessing the Outer Measurement Model

The robustness of measurement models is conventionally assessed through evaluations of reliability and validity. The core objective of reliability analysis is to scrutinize the internal consistency and stability of questionnaire scales, evaluated by Cronbach’s alpha coefficient and composite reliability (CR); Cronbach’s alpha and a CR value exceeding 0.7 are indicative of substantial reliability [59,60,61,62]. As delineated in

Table 6. Reliability and convergent validity.

Construct	Item	Loading	VIF	Cronbach’s Alpha	rho_A	CR	AVE
DRE	DRE1	0.864	2.407	0.855	0.86	0.897	0.635
	DRE2	0.815	2.027
	DRE3	0.784	1.738
	DRE4	0.743	1.598
	DRE5	0.773	1.731
EPI	EPI1	0.895	2.961	0.871	0.877	0.907	0.662
	EPI2	0.80	2.031
	EPI3	0.788	1.852
	EPI4	0.771	1.809
	EPI5	0.809	1.969
INS	INS1	0.853	2.421	0.852	0.854	0.895	0.63
	INS2	0.816	2.141
	INS3	0.753	1.758
	INS4	0.758	1.626
	INS5	0.783	1.784
PEA	PEA1	0.822	2.063	0.868	0.871	0.904	0.654
	PEA2	0.814	1.982
	PEA3	0.797	1.979
	PEA4	0.785	1.792
	PEA5	0.824	1.961
PER	PER1	0.829	2.057	0.859	0.864	0.899	0.64
	PER2	0.822	2.016
	PER3	0.773	1.874
	PER4	0.844	2.258
	PER5	0.727	1.495
PEM	PEM1	0.82	2.022	0.883	0.886	0.915	0.682
	PEM2	0.803	1.947
	PEM3	0.815	2.116
	PEM4	0.819	1.977
	PEM5	0.872	2.617

, Cronbach’s α coefficients for the utilized questionnaires in this study all surpassed 0.7, with CR values also exceeding this threshold, denoting the high reliability of the questionnaire data.

From Figure 2, it can be seen that PEA, PER, PEM, DRE, INS, and EPI all had a factor loading greater than 0.7. The proposed research model (Figure 2) investigates the impact of psychological factors—perceived autonomy (PEA), perceived competence (PEC), and perceived relatedness (PEM)—on innovative consumption (INS) and their subsequent influence on EV purchase intention (EPI). Driving experience (DRE) is introduced as a moderating variable to examine how past driving experiences influence these relationships. The model demonstrates that psychological factors significantly contribute to innovative consumption, which in turn strongly predicts purchase intention for EVs. The variance explained by innovative consumption (R² = 0.505) and purchase intention (R² = 0.305) indicates substantial explanatory power, validating the importance of these constructs in understanding consumer behavior towards EVs. The path coefficients in Figure 3 illustrate both direct and indirect relationships among perceived autonomy (PEA), perceived relatedness (PER), perceived competence (PEM), and electric vehicle purchase intention (EPI) among consumers. The arrows represent the hypothesized paths, with standardized path coefficients and their significance levels indicated in parentheses. Key relationships between latent variables include perceived autonomy (PEA) in innovative consumption (INS): β = 0.153, p = 0.032; perceived relatedness (PER) to innovative consumption (INS): β = 0.278, p = 0.000; perceived competence (PEM) in innovative consumption (INS): β = 0.186, p = 0.004; and innovative consumption (INS) following on from electric vehicle purchase intention (EPI): β = 0.553, p = 0.000. Additionally, the R-squared values indicate the proportion of variance explained in each endogenous construct: R-squared for innovative consumption (INS): 0.505; R-squared for electric vehicle purchase intention (EPI): 0.305.

The assessment of the measurement model’s validity focuses on two key aspects: convergent validity and discriminant validity. As shown in Table 6, all indicators exhibit Average Variance Extracted (AVE) values exceeding 0.5, demonstrating robust convergent validity of the model [63]. Concerning discriminant validity, the results from the Fornell–Larcker criterion and HTMT ratio analysis conducted using PLS-SEM 4.0 software are presented in

Table 7. Fornell–Larcker criterion.

	DRE	EPI	INS	PEA	PER	PEM	DRE × PEA	DRE × PER	DRE × PEM
DRE
EPI	0.409
INS	0.585	0.639
PEA	0.317	0.55	0.543
PER	0.353	0.613	0.652	0.635
PEM	0.545	0.623	0.63	0.64	0.638
DRE × PEA	0.06	0.045	0.133	0.126	0.033	0.128
DRE × PER	0.065	0.114	0.02	0.016	0.118	0.032	0.399
DRE × PEM	0.186	0.148	0.04	0.125	0.044	0.293	0.598	0.443

Note: The off-diagonal values in the above matrix are the square correlations between the latent constructs and the diagonals (bold) are AVEs.

and

Table 8. HTMT.

	DRE	EPI	INS	PEA	PER	PEM
DRE	0.797
EPI	0.354	0.814
INS	0.499	0.553	0.794
PEA	0.275	0.481	0.47	0.809
PER	0.305	0.532	0.561	0.553	0.8
PEM	0.475	0.548	0.548	0.563	0.558	0.826

Note: The off-diagonal values in the above matrix are the square correlations between the latent constructs and the diagonals (bold) are AVEs.

, as well as Figure 3. According to these analyses, the following is clear:

The square root of the Average Variance Extracted (AVE) for each variable exceeds its correlation coefficients with other variables, as indicated by the Fornell–Larcker criterion. The values of the Heterotrait–Monotrait (HTMT) ratio, which assesses the ratio of correlations between variables relative to their internal correlations, are below 0.85. These findings confirm satisfactory discriminant validity among the variables studied [64]. As such, the measurement model exhibits notable discriminant validity.

4.1.2. Inspecting the Inner Structural Model

Initially, we evaluated collinearity by examining variance inflation factors (VIFs) for all predictive constructs in the structural model. As shown in Table 6, VIF values ranged from 1.069 to 2.479, all well below the threshold of 3, indicating no significant collinearity issues. This indicates that collinearity was not a significant issue in the model [65,66,67,68]. Subsequently, we conducted bootstrapping with 5000 subsamples to assess the significance of the hypotheses [65,69].

The results showed that the majority of the paths were statistically significant at the 0.05 level, thereby confirming most of the proposed hypotheses. Specifically: The path from driving experience (DRE) to innovative consumption (INS) was significant (β = 0.296, t-value = 5.163). Perceived autonomy (PEA) in innovative consumption (INS) showed significance (β = 0.153, t-value = 2.145). Perceived relatedness (PER) to innovative consumption (INS) was statistically significant (β = 0.278, t-value = 4.745). Perceived competence (PEM) in innovative consumption (INS) demonstrated significance (β = 0.186, t-value = 2.887). Innovative consumption (INS) significantly influenced electric vehicle purchase intention (EPI) (β = 0.553, t-value = 11.204). Both the roles of driving experience (DRE) and perceived autonomy (PEA) in innovative consumption (INS) were supported (β = 0.158, t-value = 2.212). All variables in this study exhibited statistically significant relationships.

However, the paths from DRE and PER to INS (β = −0.042, t-value = 0.757) and from DRE and PER to INS (β = 0.011, t-value = 0.195) were not significant.

Moreover, the model explained a substantial amount of variance in the dependent variables, with R² values ranging from 0.186 for PEM to 0.296 for DRE. The predictive relevance was further confirmed by Q² values, all of which were above zero, ranging from 0.442 to 0.49 for INS. In terms of effect size (f²), the model revealed that several predictor constructs had a substantial impact on the dependent variables. For example, INS had a large effect on DRE (f² = 0.1324) and PEA (f² = 0.028), while PER (f² = 0.086) and PEM had a large effect on INS (f² = 0.033).

Furthermore, Smart-PLS 4.0 was employed to investigate the moderation effect through a two-stage approach (Henseler & Fassott, 2010) [70]. The analysis results, depicted in

Table 9. Assessment of structural model.

Hypothesis and Path	Coefficient	Standard Deviation	T Statistic	p-Value	f-Square	VIF	Result
DRE -> INS	0.296	0.057	5.163	***	0.134	1.324	Support
PEA -> INS	0.153	0.071	2.145	0.032	0.028	1.697	Support
PER -> INS	0.278	0.059	4.745	***	0.086	1.808	Support
PEM -> INS	0.186	0.064	2.887	0.004	0.033	2.125	Support
INS; R2 = 0.505; Q2 predict = 0.442
INS -> EPI	0.553	0.049	11.204	***	0.44	1	Support
EPI; R2 = 0.305; Q2 predict = 0.49
DRE × PEA -> INS	0.158	0.071	2.212	0.027	0.056	1.941	Support
DRE × PER -> INS	−0.042	0.055	0.757	0.449	0.004	1.64	Not Support
DRE × PEM -> INS	−0.011	0.059	0.195	0.845	0	1.401	Not Support

Note. *** p < 0.001.

and Figure 4, demonstrated a statistically significant and positive influence of the interaction between DRE and PEA on INS (β = 0.158, t-value = 2.212), supporting the hypothesis. However, the interaction between DRE and PER impacting INS (β = −0.042, t-value = 0.757) and DRE and PEM impacting INS (β = −0.011, t-value = 0.195) did not significantly impact INS, thus not supporting the hypothesis.

4.2. Necessary Condition Analysis

Necessary Condition Analysis (NCA) provides a fresh approach to analyzing complex causal relationships by identifying indispensable conditions that impact outcome variables. Unlike traditional methods, NCA not only identifies these critical conditions but also quantifies their effects and limitations. This method is particularly effective in highlighting scenarios where specific factors are necessary but not sufficient on their own to determine outcomes between dependent and independent variables [71,72]. In contrast to traditional sufficiency-based analyses, Necessary Condition Analysis (NCA) offers a quantitative assessment of the essential conditions required to achieve a specific outcome level. It provides valuable insights into the magnitude of these conditions’ effects and identifies potential bottlenecks in achieving desired outcomes.

Initially, we utilized the PLS-SEM technique to derive scores for the latent variables [64,72]. Following the initial step using PLS-SEM to obtain latent variable scores, we proceeded to apply the NCA package within the PLS-SEM software to conduct the NCA analysis, adhering to established guidelines [72]. NCA involves plotting a ceiling line that intersects the upper-left data points on an x–y graph. This process is depicted in Figure 4, which showcases scatter plots for all pertinent relationships.

Next, an analysis was performed to evaluate the statistical significance of the effect sizes (d) related to the latent variable scores, using a random sample size of 10,000 cases [43,71,72]. Since the CE-FDH method is particularly suitable for survey data gathered using a five-point Likert scale, the interpretation of the NCA results followed this approach. As indicated in

Table 10. Necessary Condition Analysis results (Method: CR-FDH).

	Effect Size	Obs. above Ceiling	Accuracy	Slope	Intercept	Condition Inefficiency	Outcome Inefficiency	Rel. Inefficiency	Abs. Inefficiency	p-Value
INC
PEA	0.115	1	99.791	3.404	10.141	75.179	7.064	76.932	22.354	0.001
PER	0.166	10	97.908	2.786	6.061	65.233	4.225	66.702	17.427	***
PEM	0.171	13	97.28	2.666	6.127	64.397	4.026	65.83	17.588	***
EPI
INC	0.072	5	98.954	1.824	5.24	71.302	49.776	85.587	23.571	***

Note: *** p < 0.001.

, the findings revealed that perceived autonomy (PEA) (d = 0.11, p = 0.001), perceived relatedness (PER) (d = 0.166, p < 0.001), and perceived competence (PEM) (d = 0.171, p < 0.001) are critical conditions influencing innovative consumption (INC). Similarly, innovative consumption (INC) (d = 0.072, p < 0.001) was identified as a critical condition impacting electric vehicle purchase intention (EPI).

The bottleneck approach in EPI further clarified the critical thresholds needed to achieve specific performance levels. According to

Table 11. Bottleneck table (CE-FDH).

EPI	INC	PEA	PER	PEM
0.00%	N	N	N	N
10.00%	N	1	1	5
20.00%	N	1	1	5
30.00%	N	2	1	5
40.00%	N	2	2	5
50.00%	3	2	17	5
60.00%	7	2	19	5
70.00%	8	3	25	6
80.00%	8	3	28	31
90.00%	10	3	34	34
100.00%	31	3	41	36

, achieving different levels of EPI requires specific thresholds: For a 50% EPI level: At least 3% INC, 2% PEA, 17% PER, and 5% PEM are needed. For a 60% EPI level: At least 7% INC, 2% PEA, 19% PER, and 5% PEM are necessary. To achieve 100% EPI: The exact conditions are specified in the table, at no less than 31% for INC, PEA no less than 3%, PER at no less than 41%, and PEM at no less than 36%.

5. Conclusions and Discussion

This study empirically investigated the relationships between perceived autonomy (PEA), perceived competence (PEM), perceived relatedness (PER), electric vehicle (EV) purchase intention, and their interactions, leading to several key conclusions. Firstly, perceived autonomy (PEA) significantly enhances consumers’ intention to purchase electric vehicles (EVs). Furthermore, the model identifies three indirect pathways mediated by innovative consumption (INC) that influence electric vehicle purchase intention (EPI). The findings underscore that perceived autonomy (PEA), perceived competence (PEM), and perceived relatedness (PER) positively impact innovative consumption (INC), suggesting that individuals with stronger psychological factors are more likely to intend to purchase environmentally friendly products.

Additionally, perceived autonomy (PEA), perceived competence (PEM), and perceived relatedness (PER) positively boost the intention to purchase electric vehicles (EPI) by way of innovative consumption (INC) serving as a mediator. This underscores the critical role of innovative consumption (INC) in facilitating the impact of psychological factors on the intention to purchase environmentally friendly electric vehicles (EPI). Reinforcing individuals’ confidence in achieving environmental objectives and their perception of the value of green products can enhance the electric vehicle purchase intention (EPI).

Thirdly, when comparing the three moderation paths, it becomes apparent that driving experience (DRE) enhances consumers’ intention to purchase electric vehicles (EPI) by bolstering perceived autonomy (PEA), perceived competence (PEM), and perceived relatedness (PER). These psychological mechanisms influence consumers’ perceptions of competence and relatedness. Previous research has suggested that perceived green value acts as a mediator between environmental responsibility and the intention to purchase green products. In contrast, this study emphasizes that innovative consumption (INC) plays a more significant mediating role between psychological factors (perceived autonomy, perceived relatedness, perceived competence) and electric vehicle purchase intention. This indicates that individuals prioritizing environmental concerns are more inclined to believe in achieving environmental goals, thereby fostering a green purchase intention and subsequent adoption of environmentally friendly purchasing behaviors.

5.1. Theoretical Implications

This study applied Self-Determination Theory (SDT) to examine how psychological factors—specifically autonomy, relatedness, and competence—affect consumer decision-making. It investigated these factors’ roles in shaping consumer purchase intentions and their mediation in consumer acceptance of electric vehicles (EVs) (Dutta & Hwang, 2021 [32]). There is limited research specifically on consumers’ innovative consumption within the framework of SDT. This study addressed that gap by highlighting autonomy, relatedness, and competence as pivotal dimensions underpinning psychological factors. It aimed to deepen the understanding of how consumer purchase intentions are formed in the context of sustainable transportation. Furthermore, this study pioneered exploration into the impact of innovative marketing strategies across various psychological backgrounds. Our findings underscore that psychological factors significantly influence both innovative consumption and EV purchase intentions, aligning with the existing literature.

In today’s era of sustainable transportation, consumer psychological factors have gained significant prominence. Consumer purchase decisions appear to be less driven by environmental responsibility and self-efficacy and more influenced by motivation, attitudes, and inner impulses. This shift reflects consumers’ growing emphasis on personal experiences, often resulting in spontaneous purchase decisions. In today’s societal culture, there is an increasing appreciation for individual perceptions among consumers. These insights offer new perspectives on the factors shaping purchase intentions, enriching the existing literature on consumer decision-making within psychological contexts.

This study positions the intention to purchase electric vehicles (EVs) as a natural outcome of autonomy, competence, and relatedness. Building on Sarkar’s (2011) [73] findings, which demonstrate that the structure, relationships, and cognitive dimensions of consumer psychological factors positively influence purchase decisions and foster consumer loyalty to products, our study contributes by focusing on consumer psychological dynamics. While previous research has explored the impact of psychological motivations and attitudes on sustainable purchase intentions, our study advances this discussion specifically within the context of green purchasing intentions and psychological factors. This not only extends our understanding of how psychological factors shape consumer behavior but also aligns with the growing emphasis on eco-conscious consumerism in contemporary markets.

Our research findings are consistent with prior studies, highlighting how consumer motivations and attitudes toward products influence environmentally conscious purchasing tendencies. Moreover, our study unveils the mechanisms through which innovative consumption influences sustainable consumer behavior. By emphasizing innovative consumption as a mediating variable, our study breaks new ground in exploring how psychological factors influence consumer decision-making processes. Specifically, it underscores that psychological factors positively impact innovative consumption and that innovative consumption plays a crucial mediating role between psychological factors and purchase intentions.

This study meticulously examines both the direct and indirect pathways through which psychological factors influence purchase intentions, while also considering how driving experience moderates these relationships. This not only underscores the complexity of these dynamics but also provides a comprehensive understanding of the mechanisms influencing purchase intentions, including when these influences are amplified or diminished.

5.2. Practical Implications

Based on this study’s findings, the electric vehicle industry can benefit significantly from focusing on several key practical implications.

Firstly, emphasizing the positive influence of perceived autonomy (PEA), perceived competence (PEM), and perceived relatedness (PER) on purchase intentions is crucial. Platforms should prioritize shaping consumer perceptions by conducting thorough market research to align with environmental concerns and values that resonate with target consumers. Collaborating with influential figures who champion sustainability can further strengthen this alignment, reflecting shared values that enhance intentions to purchase electric vehicles.

Secondly, psychological factors such as perceived autonomy, perceived competence, and perceived relatedness play foundational roles in shaping purchase intentions. To capitalize on this, platforms should highlight green sustainable development initiatives through strategies like real-time advertisements and shared electric vehicle programs. Leveraging endorsements from authentic influencers and fostering community engagement via forums or chat rooms focused on sustainability can also enhance consumer awareness and purchase intentions.

Lastly, recognizing the mediating role of innovative consumption (INC), alongside psychological factors, in influencing electric vehicle purchase intentions underscores the importance of promoting sustainable choices. By emphasizing long-term benefits, sharing personal stories, and integrating interactive elements that sustain consumer interest, platforms can foster a supportive community that encourages sustainable purchasing behaviors.

In conclusion, implementing these multifaceted strategies not only enhances consumer perceptions but also boosts motivation to adopt electric vehicles, thereby promoting sustainable consumer behavior in the market.

6. Limitations

Certainly, the potential impact of autonomous vehicles, electric vehicles, and autonomous electric vehicles on enhancing urban transportation sustainability is substantial. Autonomous vehicles, known for their conservative driving style, have the potential to significantly reduce gasoline and energy consumption compared to traditional manually driven cars.

However, this study has several limitations. Firstly, it focused on evaluating its framework using samples solely from Chinese consumers, potentially limiting the generalizability of the findings beyond the Chinese context. Future research could improve upon this by exploring the model with more diverse samples that encompass different geographical and ethnic backgrounds.

Secondly, being based on a cross-sectional survey, this study is susceptible to methodological biases that could affect the robustness of the data. Future research could mitigate these biases by employing longitudinal data collection methods across various time points and incorporating distractor items to enhance data accuracy.

Furthermore, while this study provides valuable insights, it is essential to note that the sample size poses certain limitations. The overall results derived from our sample may not fully represent the broader population of 50 million potential consumers.

Lastly, this study underscores the significance of perceived autonomy (PEA), perceived competence (PEM), and perceived relatedness (PER) in influencing consumer green purchasing behaviors. Future investigations could build upon this foundational understanding by employing complexity theory and the fsQCA method to explore different configurations of consumer green purchasing, offering more efficient sustainability solutions for decision makers.